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1
Masouman, Ashkan, and Charles Harvie. "Forecasting, impact analysis and uncertainty propagation in regional integrated models: A case study of Australia." Environment and Planning B: Urban Analytics and City Science 47, no. 1 (April 16, 2018): 65–83. http://dx.doi.org/10.1177/2399808318767128.
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The integration of input–output and econometric models at regional level has gained popularity for its superior performance in forecasting employment and examining the impacts of policies. There are a number of approaches to integrate the two models. This paper examines the integration of input–output with econometric modelling using two merging methodologies, namely coupling and holistic embedding. Each methodology is analysed with respect to the accuracy of its results of total and sectoral employment forecasting. Both methodologies are applied to a regional economy in Australia. The methodology which shows superior forecasting accuracy is applied to examine the significance of sectors that generate the highest number of employments relative to other sectors.
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Zhu, Rong, Zuo Quan Zhang, Xiao Yue Li, Xuan Wu, and Su Zhang. "The Study on the Plasticity Theoretical Models of the Volatility of Stock Prices." Advanced Materials Research 518-523 (May 2012): 5963–67. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.5963.
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This paper analyzes the characteristics of the stock price fluctuation compared with elastic-plastic theory in mechanics and introduces the concept of stock equilibrium price, plasticity of stock price analogically. A basic model of the stock plasticity under the relationship between stock price fluctuation and trading volume changes is also built. Tested by 20 kinds of stocks from Shanghai and Shenzhen stock markets in China by using the econometric analysis software Eviews3.0 afterwards, the basic model is improved, and three developed models are built from it. Finally, this paper obtains more scientific and reasonable stock price plasticity model after the comparative analysis of the four previous models.
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Shi, Chao, and Xiaosheng Zhuang. "A Study Concerning Soft Computing Approaches for Stock Price Forecasting." Axioms 8, no. 4 (October 18, 2019): 116. http://dx.doi.org/10.3390/axioms8040116.
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Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors. The fast advancement of soft computing techniques provides an alternative approach for estimating and forecasting volatile stock prices. Soft computing approaches exploit tolerance for imprecision, uncertainty, and partial truth to progressively and adaptively solve practical problems. In this study, a comprehensive review of latest soft computing tools is given. Then, examples incorporating a series of machine learning models, including both single and hybrid models, to predict prices of two representative indexes and one stock in Hong Kong’s market are undertaken. The prediction performances of different models are evaluated and compared. The effects of the training sample size and stock patterns (viz. momentum and mean reversion) on model prediction are also investigated. Results indicate that artificial neural network (ANN)-based models yield the highest prediction accuracy. It was also found that the determination of optimal training sample size should take the pattern and volatility of stocks into consideration. Large prediction errors could be incurred when stocks exhibit a transition between mean reversion and momentum trend.
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Chlebus, Marcin, Michał Dyczko, and Michał Woźniak. "Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem." Central European Economic Journal 8, no. 55 (January 1, 2021): 44–62. http://dx.doi.org/10.2478/ceej-2021-0004.
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Abstract Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised by a wide portfolio of strategic branches influencing its valuation. We use Nvidia Corporation stock covering the period from 07/2012 to 12/2018 and apply various econometric and machine learning models, considering a diverse group of exogenous features, to analyse the research problem. The results suggest that it is possible to develop predictive machine learning models of Nvidia stock returns (based on many independent environmental variables) which outperform both simple naïve and econometric models. Our contribution to literature is twofold. First, we provide an added value to the strand of literature on the choice of model class to the stock returns prediction problem. Second, our study contributes to the thread of selecting exogenous variables and the need for their stationarity in the case of time series models.
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Akbulaev, Nurkhodzha, Basti Aliyeva, and Shehla Rzayeva. "Analysis of the Influence of the Price of Raw Oil and Natural Gas on the Prices of Indices and Shares of the Turkish Stock Exchange." Pénzügyi Szemle = Public Finance Quarterly 66, no. 1 (2021): 151–66. http://dx.doi.org/10.35551/pfq_2021_1_8.
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This article is a review on the impact of prices and their dependence on the cost of oil and natural gas on the world stock markets. The main studies and results achieved in the field of the impact of prices on both the stock index and industrial stocks and the dependence on the level of oil prices are presented. The paper presents an econometric study on the choice of offers on the securities market that allows us to identify the main specifics of changes in prices for the stock index and industrial shares in the daily period from 13. 05. 2012 to 01. 12. 2019. The article uses methods for estimating the impact of the price of natural gas and WTI crude oil using the Gretl statistical program, taking into account the selection of the main correlation features of the price matrix. Of the 13 proposed research models, only one model showed its statistical insignificance. A paired linear model of the CocaCola share price dependence and its dependence on NGFO prices was presented and analyzed in detail. Based on the results of econometric modeling, linear regression models were constructed for the dependence of stock prices on the NGFO and WTISPOT prices. The Gretl environment allows you to evaluate the situation in the econometric environment and make a forecast based on the obtained models of the dependence of stock prices and make appropriate conclusions.
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Milon, J. Walter. "Travel Cost Methods for Estimating the Recreational Use Benefits of Artificial Marine Habitat." Journal of Agricultural and Applied Economics 20, no. 1 (July 1988): 87–101. http://dx.doi.org/10.1017/s0081305200025681.
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AbstractThe growing popularity of marine recreational fishing has created considerable interest in artificial marine habitat development to maintain and enhance coastal fishery stocks. This paper provides a comparative evaluation of travel cost methods to estimate recreational use benefits for new habitat site planning. Theoretical concerns about price and quality effects of substitute sites, corner solutions in site choice, and econometric estimation are considered. Results from a case study indicate that benefit estimates are influenced by the way these concerns are addressed, but relatively simple single site models can provide defensible estimates. Practical limitations on data collection and model estimation are also considered.
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Nautiyal, Neeraj, and P. C. Kavidayal. "Analysis of Institutional Factors Affecting Share Prices: The Case of National Stock Exchange." Global Business Review 19, no. 3 (March 14, 2018): 707–21. http://dx.doi.org/10.1177/0972150917713865.
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This study offers empirical findings on the impact of institutional variables on firm’s stock market price performance. In order to identify the influence of companies financial on NIFTY 50 Index, our sample consists of balanced panel of 30 actively traded companies (that becomes the study’s index representative) over a massive transition period, 1995–2014. Attempts have been made with a wide range of econometric models and estimators, from the relatively straightforward to (static) more complex (dynamic panel analyses) to deal with the relevant econometric issues. Results indicate that increasing debt in capital structure does not establish any significant relation with the stock prices. Earnings per share (EPS) shows a poor explanation of price variation. Economic value added (EVA) indicates a positive relation with current as well as previous year’s stock price performances. However, dividend payout (DIVP) and dividend per share (DPS) achieve negative relationship at moderately significant level. The present study confirms that performance of companies fundamental ratios will be essential and immensely helpful to investors and analysts in assessing the better stocks that belong to different industry groups.
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Ndayisaba, Gilbert, and Abdullahi D. Ahmed. "CEO remuneration, board composition and firm performance: empirical evidence from Australian listed companies." Corporate Ownership and Control 13, no. 1 (2015): 534–52. http://dx.doi.org/10.22495/cocv13i1c5p2.
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Classical economic theories establishing a relationship between CEO remuneration and firm performance has paid particular attention to solve conflict of interest between managerial team and firm shareholders, by designing an optimum CEO remuneration that motivate executives to work in the best interest of shareholders. Many international and less Australian empirical researches suggest that there is overwhelming evidence that firm performance is strongly linked with CEO remuneration. In this paper, we reassess the association of firm performance and CEO remuneration variables using dynamic econometric models and comprehensive data from Australian Stock Exchange (ASX). We find a positive and strong association between CEO pay of top 200 Australian public listed companies and company performance. Obtained findings are similar to USA, UK and Canada studies results. We further test the effect of board and ownership features on CEO remuneration–performance sensitivity in the top 200 Australian public companies listed on ASX. Specifically, for the period of 2003-2007, our results highlight the importance of ownership structure in influencing remuneration–performance relationship. Monitoring block holders boost the responsiveness of long term incentives (LTI) remuneration to performance, thus straightening shareholder and manager welfares. However, based on a short term investment horizon strategy, insider block holders increase (decrease) the sensitivity of short-term incentives remuneration (long term incentives pay). Surprisingly, for the period 2008-2013, our findings suggest that ownership and board features did not influence significantly CEO pay-performance sensitivities. Finally, we find that larger boards increase (decrease) the responsiveness of CEO’s known remuneration (long term incentives) to performance.
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Provenzano, Davide. "The migration–tourism nexus in the EU28." Tourism Economics 26, no. 8 (March 10, 2020): 1374–93. http://dx.doi.org/10.1177/1354816620909994.
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This study explores the nexus between tourism and migration on an intra-European scale over the period 2000–2015. Complex-network analysis and gravity models were the investigation methods preferred. For each year under study, we built two country-to-country networks to map and reveal the connections between states as shaped by migration stocks and tourism flows, respectively. Then, the main determinants of the correlation patterns between the two networks were investigated by several econometric analysis. Results point to a quite similar topological structure for the tourism and migration networks as well as to a significant and reciprocal direct influence between tourism and migration movements inside the European Union. No relevant indirect causal relationship is present in the tourism–migration nexus instead.
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Ewers Lewis, Carolyn J., Mary A. Young, Daniel Ierodiaconou, Jeffrey A. Baldock, Bruce Hawke, Jonathan Sanderman, Paul E. Carnell, and Peter I. Macreadie. "Drivers and modelling of blue carbon stock variability in sediments of southeastern Australia." Biogeosciences 17, no. 7 (April 16, 2020): 2041–59. http://dx.doi.org/10.5194/bg-17-2041-2020.
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Abstract. Tidal marshes, mangrove forests, and seagrass meadows are important global carbon (C) sinks, commonly referred to as coastal “blue carbon”. However, these ecosystems are rapidly declining with little understanding of what drives the magnitude and variability of C associated with them, making strategic and effective management of blue C stocks challenging. In this study, our aims were threefold: (1) identify ecological, geomorphological, and anthropogenic variables associated with 30 cm deep sediment C stock variability in blue C ecosystems in southeastern Australia, (2) create a predictive model of 30 cm deep sediment blue C stocks in southeastern Australia, and (3) map regional 30 cm deep sediment blue C stock magnitude and variability. We had the unique opportunity to use a high-spatial-density C stock dataset of sediments to 30 cm deep from 96 blue C ecosystems across the state of Victoria, Australia, integrated with spatially explicit environmental data to reach these aims. We used an information theoretic approach to create, average, validate, and select the best averaged general linear mixed effects model for predicting C stocks across the state. Ecological drivers (i.e. ecosystem type or ecological vegetation class) best explained variability in C stocks, relative to geomorphological and anthropogenic drivers. Of the geomorphological variables, distance to coast, distance to freshwater, and slope best explained C stock variability. Anthropogenic variables were of least importance. Our model explained 46 % of the variability in 30 cm deep sediment C stocks, and we estimated over 2.31 million Mg C stored in the top 30 cm of sediments in coastal blue C ecosystems in Victoria, 88 % of which was contained within four major coastal areas due to the extent of blue C ecosystems (∼87 % of total blue C ecosystem area). Regionally, these data can inform conservation management, paired with assessment of other ecosystem services, by enabling identification of hotspots for protection and key locations for restoration efforts. We recommend these methods be tested for applicability to other regions of the globe for identifying drivers of sediment C stock variability and producing predictive C stock models at scales relevant for resource management.
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Davy, M. C., and T. B. Koen. "Variations in soil organic carbon for two soil types and six land uses in the Murray Catchment, New South Wales, Australia." Soil Research 51, no. 8 (2013): 631. http://dx.doi.org/10.1071/sr12353.
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The aim of this study was to investigate variations in soil organic carbon (SOC) for two soil types and six common land uses in the New South Wales Murray Catchment and to explore the factors influencing those variations. Samples were collected from 100 sites on duplex soils (Ustalfs) of the Slopes region, and 100 sites on red-brown earths (Xeralfs) of the Plains region. Stocks of SOC (0–30 cm) across the study area ranged between 22.3 and 86.0 t ha–1, with means (± s.e.) of 42.0 ± 1.3 and 37.9 ± 0.8 t ha–1 for the Slopes and Plains regions, respectively. Higher SOC stocks were present in pasture-dominated land uses compared with mixed cropping in the Slopes region, with particularly high stocks found in pastures at positions on a slope of 7–10%. No significant differences in SOC stocks were identified between land-use groups (pastures or cropping) in the Plains region (<500-mm rainfall zone). Significant correlations were found between SOC and a range of climatic, topographical, and soil physico-chemical variables at both the catchment and sub-regional scale. Soil physico-chemical and topographical factors play an important role in explaining SOC variation and should be incorporated into models that aim to predict SOC sequestration across agricultural landscapes.
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MISSAOUI, Sahbi, and Nizar RAISSI. "Underpricing Process of IPOs in Tunis Stock Exchange: An Agent-Based Modelling Approach." Accounting and Finance Research 10, no. 2 (April 7, 2021): 1. http://dx.doi.org/10.5430/afr.v10n2p1.
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The fundamental problematic treated in our study was an attempt to explain an anomaly in the issuance of new stocks in IPOs process. The objective of this research is to analyze the effect of certain variables on the level of undervaluation by presenting certain econometric models issued from Agent-based modelling approach. Certain variables can be predictive of the phenomenon of undervaluation such as: the Stock equity distributed to institutional investors, liquidity in the secondary market measured by the price range and the type of investor who can be insiders or outsiders, in addition to these variables we have introduced some control variables which in turn help explain the level of underpricing and which are the age of the company, its size and dimension, the volume of trade and the volatility. Empirically and based on a sample of 16 companies, we were able to respond to our problematic. In fact, according to the hypotheses tests, the prices of the newly introduced stocks on the stock exchange are mostly undervalued which were aligned with our study. Thereby, the methodology adopted based to Dynamic linear models (DLM) that allows offering a very generic framework to analyse time series data. The results of this research were, in part, consistent with work done in developed countries (especially in USA and Europe). Indeed, the undervaluation is in a positive relationship with certain explanatory variables such as the Institutional ownership (INST), Insiders ownership (INSID), Price range (FOUR), etc. On the other hand, we were able to identify significant negative relationships between the initial undervaluation and the basic variable Outsiders ownership (OUTSID), the size of companies listed on the Tunis Stock exchange (BVMT) and the volume of issued stocks.
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Jiang, Xiaoquan, and Qiang Kang. "Cross-Sectional PEG Ratios, Market Equity Premium, and Macroeconomic Activity." Journal of Accounting, Auditing & Finance 35, no. 3 (January 8, 2018): 471–500. http://dx.doi.org/10.1177/0148558x17748277.
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This article explores the information content of PEG ratios (price/earnings to growth ratios) for future aggregate returns and economic fundamentals. We first establish an analytic link between PEG ratios and time-varying expected returns of stocks. We then combine the link with empirical asset pricing models to extract market-wide information from cross-sectional PEG ratios. The resultant cross-section estimates of the risk premiums on stock betas serve as proxies for market-wide information. The proxies contain salient information about future market equity premiums and macroeconomic activity both in-sample and out-of-sample. Moreover, the proxies outperform aggregate PEG ratios and the cross-section beta-premium estimate based on conventional valuation ratios and retain incremental power in forecasting future market equity premiums. The results are robust to using various econometric methods for standard error adjustments.
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Gray, Jonathan M., Thomas F. A. Bishop, and Peter L. Smith. "Digital mapping of pre-European soil carbon stocks and decline since clearing over New South Wales, Australia." Soil Research 54, no. 1 (2016): 49. http://dx.doi.org/10.1071/sr14307.
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Digital soil models and maps have been developed for pre-European (pre-clearing) levels of soil organic carbon (SOC) over New South Wales, Australia. These provide a useful first estimate of natural, unaltered soil conditions before agricultural development, which are potentially important for many carbon-accounting schemes such as those prescribed by the Intergovernmental Panel on Climate Change, carbon-turnover models such as RothC, and soil-condition monitoring programs. The modelling approach adopted included multiple linear regression and Cubist piecewise linear decision trees. It used 1690 soil profiles from undisturbed or only lightly disturbed native vegetation sites across all of eastern Australia, together with a range of covariates representing key soil-forming factors. The digital soil maps of pre-clearing SOC (% and mass) over New South Wales provide a more sophisticated alternative to currently available, equivalent maps. Independent validation of the SOC mass predictions over the top 30 cm revealed a concordance correlation coefficient of 0.76, which was 13% higher than the currently used map. Total pre-clearing SOC stocks amount to 4.21 Gt in the top 30 cm, which compared with a current stock estimate of 3.68 Gt, suggesting a total SOC loss of ~0.53 Gt over the entire state. The extent of SOC decline in both absolute and relative terms was found to be highly dependent on the climate, parent material and land use regime, reaching a maximum decline of 44.3 t/ha or 50.0% relative loss in cooler (moist) conditions over mafic parent materials under regular cropping use. The models also provide valuable pedological insights into the factors controlling SOC levels under natural conditions.
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Hami, Mustapha El, and Ahmed Hefnaoui. "Analysis of Herding Behavior in Moroccan Stock Market." Journal of Economics and Behavioral Studies 11, no. 1(J) (March 10, 2019): 181–90. http://dx.doi.org/10.22610/jebs.v11i1(j).2758.
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Frontier markets, particularly the Moroccan financial market, are characterized by a narrowness of market, inability to absorb erratic price fluctuations and the low liquidity of securities that encourage investors to herd and imitate those who have all the information about the market. A quantitative research approach was used to analyze the existence of herding n Moroccan stock market. The daily data used in this study concerns the period from 04/01/2010 to 29/12/2017 and contains the daily returns of the MASI and a total of 43 traded stocks. Statistical and econometric methods such as multidimensional scaling and Cross-sectional absolute deviation were used. Subsequently, after the regression models were examined, findings indicated that the first stocks with the highest similarity to the index return are BMCE, BCP, IAM, ATW and CMSR, and the first stocks with the highest dissimilarity are PAP, IBC and SNP, This will have to allow investors to choose profitable alternatives and avoid those that present a possible risk. The results did also show the existence of herding in the Moroccan stock market both upward and downward. This finding was supported by the clear existence of a non-linearity between market performance and CSAD measurement, which confirms the prediction of a non-linear inversion relationship between CSAD and ð‘…ð‘š. This could be due to the low level of transparency that prevails in frontier stock exchanges and reduces the quality of their information environment, which leads investors not to react rationally and to draw information from the transactions of their peers.
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Guzmán, Alexander, Christian Pinto-Gutiérrez, and María-Andrea Trujillo. "Trading Cryptocurrencies as a Pandemic Pastime: COVID-19 Lockdowns and Bitcoin Volume." Mathematics 9, no. 15 (July 27, 2021): 1771. http://dx.doi.org/10.3390/math9151771.
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This paper examines the impact of COVID-19 lockdowns on Bitcoin trading volume. Using data from Apple mobility trends and several time-series econometric models, we find that investors became active participants during the COVID-19 pandemic period and traded more bitcoins on days with low mobility associated with lockdown mandates. These results remain robust after controlling for stocks and gold returns, the VIX index, and the level of attention and sentiment toward Bitcoin, as measured by Google search frequencies and the tone of Tweets discussing Bitcoin. These results suggest that when individual investors have ample free time on their hands, they trade cryptocurrencies as a pastime and use the Bitcoin market as a form of entertainment. Moreover, our results have important implications concerning investors’ herding behavior and overconfidence leading to noise trader risks and bubbles typically accompanied by high trading volume in cryptocurrency markets.
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Kirschbaum, Miko U. F., Guillaume Simioni, Belinda E. Medlyn, and Ross E. McMurtrie. "On the importance of including soil nutrient feedback effects for predicting ecosystem carbon exchange." Functional Plant Biology 30, no. 2 (2003): 223. http://dx.doi.org/10.1071/fp02152.
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To grow, plants need both carbon, which is fixed in photosynthesis, and inorganic nutrients, which are generally obtained from the soil. Much interest currently exists in trying to understand the uptake and storage of carbon by terrestrial ecosystems. This paper investigates to what extent carbon gain and storage are modified by soil nutrient availability. This issue is investigated in relation to both short-term carbon fluxes on the time scale of interannual variability and long-term ecosystem carbon stocks on time scales of several thousand years.We conclude from simulations with an ecosystem model (CenW) that interannual variations in carbon gain can be significantly affected by feedback effects through the nutrient cycle. This feedback effect operates principally through an imbalance between carbon and nutrient dynamics. In years that allow high carbon gain, nutrient supply typically does not match the increased carbon supply so that foliar nutrient concentrations are reduced. This lowers productivity below that which could be expected if foliar nutrient concentration remained the same. The importance of these feedback effects is shown to be greatest at intermediate levels of water availability and nutrient supply, and is relatively more important for net ecosystem carbon exchange than for net primary production.We conclude that the long-term build-up of carbon stocks in ecosystems is often controlled by the rate at which nutrients can be gained. This conclusion is based on data from published studies showing that the slow build-up of carbon matches the gain in nitrogen, phosphorus and sulfur, and on our simulations of system carbon stocks in response to fertiliser addition.The paper concludes with a discussion of the importance and feasibility of including these processes into models at different scales, including the broad continental scale. For modelling net ecosystem exchange for Australia, it is regarded as feasible and desirable to use models that are constrained by these system-internal feedback effects. Such models have already been used for large-scale simulations in Australia and other countries.
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Allen, D. E., M. J. Pringle, D. W. Butler, B. K. Henry, T. F. A. Bishop, S. G. Bray, T. G. Orton, and R. C. Dalal. "Effects of land-use change and management on soil carbon and nitrogen in the Brigalow Belt, Australia: I. Overview and inventory." Rangeland Journal 38, no. 5 (2016): 443. http://dx.doi.org/10.1071/rj16009.
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Soil and land-management interactions in Australian native-forest regrowth remain a major source of uncertainty in the context of the global carbon economy. We sampled soil total organic C (TOC) and soil total N (TN) stocks at 45 sites within the Brigalow ecological community of the Brigalow Belt bioregion, Queensland, Australia. The sites were matched as triplets representing three land uses, specifically: uncleared native brigalow forest (‘Remnant’); grassland pasture (‘Pasture’), derived by clearing native vegetation and maintained as pasture for a minimum of 10 years, and; regrowing native brigalow forest (‘Regrowth’, stand ages ranging from 10 to 58 years) that had developed spontaneously after past vegetation clearing for pasture establishment. Soil TOC fractions and natural abundance of soil C and N isotopes were examined to obtain insight into C and N dynamics. An updated above- and belowground carbon budget for the bioregions was generated. Average soil TOC stocks at 0–0.3-m depth ranged from 19 to 79 Mg ha–1 and soil TN stocks from 1.8 to 7.1 Mg ha–1 (2.5th and 97.5th percentiles, respectively). A trend in stocks was apparent with land use: Remnant > Regrowth ≅ Pasture sites. Soil δ13C ranged from –14 to –27‰, and soil δ15N ranged from 4‰ to 17‰, in general reflecting the difference between Pasture (C4-dominated) land use and N2-fixing (C3-dominated) Remnant and Regrowth. Mid-infrared spectroscopy predicted C fractions as a percentage of soil TOC stock, which ranged from 5% to 60% (particulate), 20–80% (humus) and 9–30% (resistant/inert). The geo-referenced soil and management information we collected is important for the calibration of C models, for the estimation of national C accounts, and to inform policy developments in relation to land-resource management undertaken within the Brigalow Belt bioregions of Australia.
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Punt, André E., Fred Pribac, Terence I. Walker, Bruce L. Taylor, and Jeremy D. Prince. "Stock assessment of school shark, Galeorhinus galeus, based on a spatially explicit population dynamics model." Marine and Freshwater Research 51, no. 3 (2000): 205. http://dx.doi.org/10.1071/mf99124.
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The school shark (Galeorhinus galeus) resource off southern Australia is assessed by use of an assessment approach that takes account of the spatial structure of the population. The population dynamics model underlying the assessment considers the spatial as well as the age-specific characteristics of school shark. It allows for a series of fisheries (each based on a different gear type), explicitly models the pupping/recruitment process, and allows for multiple stocks. The values for the parameters of this model are determined by fitting it to catch-rate data and information from tagging studies. The point estimates of the pup production at the start of 1997 range from 12% to 18% of the pre-exploitation equilibrium size, depending on the specifications of the assessment. Allowing for spatial structure and incorporating tag release–recapture data lead to reduced uncertainty compared with earlier assessments. The status of the resource, as reflected by the ratio of present to virgin pup production and total (1+) biomass, is sensitive to the assumed level of movement between the stocks in New Zealand and those in Australia, with lower values corresponding to higher levels of movement.
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Tirmizi, Syed Muhammad Ali, Haider Ali, and Sharif Ullah Jan. "Petroleum and Food Sectors Lost Stock Returns against Investments in PSX." Global Management Sciences Review VI, no. I (March 30, 2021): 99–111. http://dx.doi.org/10.31703/gmsr.2021(vi-i).10.
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The impact of exchange rate exposure and market return on stock returns of petroleum and food sectors PSX listed firms has been investigated empirically. Two econometric models formulated based on the Jorion approach of the two-factor model have been analyzed for petroleum and food sectors stock returns, market return and exchange rate (i.e., USD) for the study period 2005-2012, which represent an era of military regime proceeded by the democratic government of Pakistan Peoples Party. A sample of 37 petroleum and food sectors listed firms have been evaluated by applying the unit root test and OLS multiple regression. Further, the Quandt-Andrews test of unknown breakpoint has been applied, which showed an extended structural break during the period 2007 to 2010. Additionally, the results revealed that the coefficients of exchange rate and market return are negatively related to petroleum and food sectors stock returns. Therefore, investors must take precautions before investing funds in stocks of food and petroleum sector firms.
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Shazhdekeeva, N. K., and A. O. Chanpalova. "SOLUTION OF THE DUAL PROBLEM BY THE BARANKIN-DORFMAN METHOD FOR THE FORMATION OF THE INVESTMENT PORTFOLIO." BULLETIN Series of Physics & Mathematical Sciences 70, no. 2 (June 30, 2020): 130–34. http://dx.doi.org/10.51889/2020-2.1728-7901.20.
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The article focuses on the consideration of econometric models of stock quotes of large domestic companies based on modeling the securities portfolio and predicting its behavior using mathematical modeling using elements of probability theory and mathematical statistics. It is also shown how the problem of choosing the optimal portfolio can be reduced to the problem of convex quadratic programming. In this article, based on the Markowitz model, a model of an optimal investment portfolio with bilateral restrictions on variables associated with the requirements of the law is developed. An example is considered in which 10 types of stocks and bonds of large Kazakhstani companies are selected, which generates an optimal set of assets and calculates the risk of an optimal portfolio for a given level of expected return. Based on the results obtained, companies and entrepreneurs can build a strategy for investing and buying shares, knowing the probable income from a portfolio of certain types of securities.
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Pringle, M. J., D. E. Allen, T. G. Orton, T. F. A. Bishop, D. W. Butler, B. K. Henry, and R. C. Dalal. "Effects of land-use change and management on soil carbon and nitrogen in the Brigalow Belt, Australia: II. Statistical models to unravel the climate-soil-management interaction." Rangeland Journal 38, no. 5 (2016): 453. http://dx.doi.org/10.1071/rj16010.
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The impact of grazing on soil carbon (C) and nitrogen (N) cycles is complex, and across a large area it can be difficult to uncover the magnitude of the effects. Here, we have linked two common approaches to statistical modelling – regression trees and linear mixed models – in a novel way to explore various aspects of soil C and N dynamics for a large, semiarid bioregion where land use is dominated by grazing. The resulting models, which we term RT-LMM, have the pleasing visual appeal of regression trees, and they account for spatial autocorrelation as per a linear mixed model. Our RT-LMM were developed from explanatory variables that related information on climate, soil and past land management. Response variables of interest were: stocks of soil total organic carbon (TOC), soil total nitrogen (TN), and particulate organic C (POC); the ratio of TOC stock to TN stock; and the relative abundance of stable isotopes δ13C and δ15N in the soil. Each variable was sampled at the depth interval 0–0.3 m. The interactions of land use with, in particular, air temperature and soil phosphorus were strong, but three principal management-related effects emerged: (i) the use of fire to clear native vegetation reduced stocks of TOC and TN, and the TOC : TN ratio, by 25%, 19% and 9%, respectively, suggesting that TOC is more sensitive to fire than TN; (ii) conversion of native vegetation to pasture enriched soil with δ13C by 1.7 ‰; subsequent regrowth of the native vegetation among the pasture restored δ13C to its original level but there was no corresponding change in TOC stock; and, (iii) the time elapsed since clearing reduced POC stocks and the TOC : TN ratio.
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Gray, Jonathan M., and Thomas F. A. Bishop. "Mapping change in key soil properties due to climate change over south-eastern Australia." Soil Research 57, no. 5 (2019): 467. http://dx.doi.org/10.1071/sr18139.
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Climate change will lead to altered soil conditions that will impact on plant growth in both agricultural and native ecosystems. Additionally, changes in soil carbon storage will influence carbon accounting schemes that may play a role in climate change mitigation programs. We applied a digital soil mapping approach to examine and map (at 100-m resolution) potential changes in three important soil properties – soil organic carbon (SOC), pH and sum-of-bases (common macro-nutrients) – resulting from projected climate change over south-eastern Australia until ~2070. Four global climate models were downscaled with three regional models to give 12 climate models, which were used to derive changes for the three properties across the province, at 0–30 and 30–100 cm depth intervals. The SOC stocks were projected to decline over the province, while pH and sum-of-bases were projected to increase; however, the extent of change varied throughout the province and with different climate models. The average changes primarily reflected the complex interplay of changing temperatures and rainfall throughout the province. The changes were also influenced by the operating environmental conditions, with a uniform pattern of change particularly demonstrated for SOC over 36 combinations of current climate, parent material and land use. For example, the mean decline of SOC predicted for the upper depth interval was 15.6 Mg ha–1 for wet–mafic–native vegetation regimes but only 3.1 Mg ha–1 for dry–highly siliceous–cropping regimes. The predicted changes reflected only those attributable to the projected climate change and did not consider the influence of ongoing and changing land management practices.
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Gray, Jonathan M., and Thomas F. A. Bishop. "Corrigendum to: Mapping change in key soil properties due to climate change over south-eastern Australia." Soil Research 57, no. 7 (2019): 805. http://dx.doi.org/10.1071/sr18139_co.
Full textAbstract:
Climate change will lead to altered soil conditions that will impact on plant growth in both agricultural and native ecosystems. Additionally, changes in soil carbon storage will influence carbon accounting schemes that may play a role in climate change mitigation programs. We applied a digital soil mapping approach to examine and map (at 100-m resolution) potential changes in three important soil properties – soil organic carbon (SOC), pH and sum-of-bases (common macro-nutrients) – resulting from projected climate change over south-eastern Australia until ~2070. Four global climate models were downscaled with three regional models to give 12 climate models, which were used to derive changes for the three properties across the province, at 0–30 and 30–100 cm depth intervals. The SOC stocks were projected to decline over the province, while pH and sum-of-bases were projected to increase; however, the extent of change varied throughout the province and with different climate models. The average changes primarily reflected the complex interplay of changing temperatures and rainfall throughout the province. The changes were also influenced by the operating environmental conditions, with a uniform pattern of change particularly demonstrated for SOC over 36 combinations of current climate, parent material and land use. For example, the mean decline of SOC predicted for the upper depth interval was 15.6 Mg ha–1 for wet–mafic–native vegetation regimes but only 3.1 Mg ha–1 for dry–highly siliceous–cropping regimes. The predicted changes reflected only those attributable to the projected climate change and did not consider the influence of ongoing and changing land management practices.
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Page, K. L., R. C. Dalal, and Y. P. Dang. "How useful are MIR predictions of total, particulate, humus, and resistant organic carbon for examining changes in soil carbon stocks in response to different crop management? A case study." Soil Research 51, no. 8 (2013): 719. http://dx.doi.org/10.1071/sr13064.
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Measures of particulate organic carbon (POC), humus organic carbon (HOC), and resistant organic carbon (ROC) (primarily char) are often used to represent the active, slow, and inert carbon pools used in soil carbon models. However, these fractions are difficult to measure directly, and mid infrared (MIR) spectroscopic techniques are increasingly being investigated to quantify these fractions and total organic carbon (TOC). This study examined the change in MIR-predicted pools of TOC, POC, HOC, and ROC in response to different crop management between two time periods (1981 and 2008) in a long-term wheat cropping trial in Queensland, Australia. The aims were (i) to assess the ability of MIR to detect changes in carbon stocks compared with direct measurements of TOC (LECO-TOC); and (ii) to assess how well the behaviour of POC, HOC, and ROC corresponded with the active, slow, and inert conceptual carbon pools. Significant declines in carbon stocks were observed over time using both LECO-TOC and MIR-predicted stocks of TOC, POC, HOC, and ROC, although MIR-TOC under-estimated loss by 27–30% compared with LECO-TOC. The decline in MIR-POC and MIR-HOC was consistent with the expected behaviour of the active and slow conceptual pools; however, the decline in ROC was not consistent with that of the inert pool. In addition, MIR measurements did not accurately detect differences in the rate of carbon loss under different crop management practices.
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Ji, Xuan, Jiachen Wang, and Zhijun Yan. "A stock price prediction method based on deep learning technology." International Journal of Crowd Science 5, no. 1 (March 5, 2021): 55–72. http://dx.doi.org/10.1108/ijcs-05-2020-0012.
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Purpose Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data. Design/methodology/approach This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price. Findings The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price. Originality/value In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.
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Degiannakis, Stavros, and Apostolos Kiohos. "Multivariate modelling of 10-day-ahead VaR and dynamic correlation for worldwide real estate and stock indices." Journal of Economic Studies 41, no. 2 (March 4, 2014): 216–32. http://dx.doi.org/10.1108/jes-06-2012-0082.
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Purpose – The Basel Committee regulations require the estimation of value-at-risk (VaR) at 99 percent confidence level for a ten-trading-day-ahead forecasting horizon. The paper provides a multivariate modelling framework for multi-period VaR estimates for leptokurtic and asymmetrically distributed real estate portfolio returns. The purpose of the paper is to estimate accurate ten-day-ahead 99%VaR forecasts for real estate markets along with stock markets for seven countries across the world (the USA, the UK, Germany, Japan, Australia, Hong Kong and Singapore) following the Basel Committee requirements for financial regulation. Design/methodology/approach – A 14-dimensional multivariate Diag-VECH model for seven equity indices and their relative real estate indices is estimated. The authors evaluate the VaR forecasts over a period of two weeks in calendar time, or ten-trading-days, and at 99 percent confidence level based on the Basle Committee on Banking Supervision requirements. Findings – The Basel regulations require ten-day-ahead 99%VaR forecasts. This is the first study that provides successful evidence for ten-day-ahead 99%VaR estimations for real estate markets. Additionally, the authors provide evidence that there is a statistically significant relationship between the magnitude of the ten-day-ahead 99%VaR and the level of dynamic correlation for real estate and stock market indices; a valuable recommendation for risk managers who forecast risk across markets. Practical implications – Risk managers, investors and financial institutions require dynamic multi-period VaR forecasts that will take into account properties of financial time series. Such accurate dynamic forecasts lead to successful decisions for controlling market risks. Originality/value – This paper is the first approach which models simultaneously the volatility and VaR estimates for real estate and stock markets from the USA, Europe and Asia-Pacific over a period of more than 20 years. Additionally, the local correlation between stock and real estate indices has statistically significant explanatory power in estimating the ten-day-ahead 99%VaR.
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Ma, Le, Chunlu Liu, and Anthony Mills. "Construction labor productivity convergence: a conditional frontier approach." Engineering, Construction and Architectural Management 23, no. 3 (May 16, 2016): 283–301. http://dx.doi.org/10.1108/ecam-03-2015-0040.
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Purpose – Understanding and simulating construction activities is a vital issue from a macro-perspective, since construction is an important contributor in economic development. Although the construction labor productivity frontier has attracted much research effort, the temporal and regional characteristics have not yet been explored. The purpose of this paper is to investigate the long-run equilibrium and dynamics within construction development under a conditional frontier context. Design/methodology/approach – Analogous to the simplified production function, this research adopts the conditional frontier theory to investigate the convergence of construction labor productivity across regions and over time. Error correction models are implemented to identify the long-run equilibrium and dynamics of construction labor productivity against three types of convergence hypotheses, while a panel regression method is used to capture the regional heterogeneity. The developed models are applied to investigate and simulate the construction labor productivity in the Australian states and territories. Findings – The results suggest that construction labor productivity in Australia should converge to stable frontiers in a long-run perspective. The dynamics of the productivity are mainly caused by the technology utilization efficiency levels of the local construction industry, while the influences of changes in technology level and capital depending appear limited. Five regional clusters of the Australian construction labor productivity are suggested by the simulation results, including New South Wales; Australian Capital Territory; Northern Territory, Queensland, and Western Australia; South Australia; and Tasmania and Victoria. Originality/value – Three types of frontier of construction labor productivity is proposed. An econometric approach is developed to identify the convergence frontier of construction labor productivity across regions over time. The specified model can provides accurate predictions of the construction labor productivity.
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Prevedouros, Panos D. "Origin-Specific Visitor Demand Forecasting at Honolulu International Airport." Transportation Research Record: Journal of the Transportation Research Board 1600, no. 1 (January 1997): 18–27. http://dx.doi.org/10.3141/1600-03.
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The development of a PC-based and easy-to-use-and-update econometric model system for forecasting arrivals at the Honolulu International Airport is presented. A model system instead of a single model was designed so that differential growth rates from various origins as well as arrivals affected by curfews at the origin or the destination, or both, can be estimated. The airport system of the state facilitates the only mode of transportation into and out of Hawaii. Planning based on reliable demand forecasts is therefore essential. Separate models of arrivals from Australia and New Zealand, Canada, Germany, Korea, and the United Kingdom were specified and estimated using the Cochrane-Orcutt regression method. Several diagnostic tests were employed to arrive at the final models, as problems of correlation (over time) and collinearity (among variables) were present. Independent variables include the gross domestic product, population, monetary exchange rate, and unemployment rate of the origin countries. Historical values for the independent variables were taken from the publications of international organizations. Variables for wars that tend to affect flying security and natural disasters in Hawaii that affect the supply of tourist accommodations were included in the model specifications.
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Kokic, Philip, Rohan Nelson, Holger Meinke, Andries Potgieter, and John Carter. "From rainfall to farm incomes—transforming advice for Australian drought policy. I. Development and testing of a bioeconomic modelling system." Australian Journal of Agricultural Research 58, no. 10 (2007): 993. http://dx.doi.org/10.1071/ar06193.
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In this paper we report the development of a bioeconomic modelling system, AgFIRM, designed to help close a relevance gap between climate science and policy in Australia. We do this by making a simple econometric farm income model responsive to seasonal forecasts of crop and pasture growth for the coming season. The key quantitative innovation was the use of multiple and M-quantile regression to calibrate the farm income model, using simulated crop and pasture growth from 2 agroecological models. The results of model testing demonstrated a capability to reliably forecast the direction of movement in Australian farm incomes in July at the beginning of the financial year (July–June). The structure of the model, and the seasonal climate forecasting system used, meant that its predictive accuracy was greatest across Australia’s cropping regions. In a second paper, Nelson et al. (2007, this issue), we have demonstrated how the bioeconomic modelling system developed here could be used to enhance the value of climate science to Australian drought policy.
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Baldock, J. A., I. Wheeler, N. McKenzie, and A. McBrateny. "Soils and climate change: potential impacts on carbon stocks and greenhouse gas emissions, and future research for Australian agriculture." Crop and Pasture Science 63, no. 3 (2012): 269. http://dx.doi.org/10.1071/cp11170.
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Organic carbon and nitrogen found in soils are subject to a range of biological processes capable of generating or consuming greenhouse gases (CO2, N2O and CH4). In response to the strong impact that agricultural management can have on the amount of organic carbon and nitrogen stored in soil and their rates of biological cycling, soils have the potential to reduce or enhance concentrations of greenhouse gases in the atmosphere. Concern also exists over the potential positive feedback that a changing climate may have on rates of greenhouse gas emission from soil. Climate projections for most of the agricultural regions of Australia suggest a warmer and drier future with greater extremes relative to current climate. Since emissions of greenhouse gases from soil derive from biological processes that are sensitive to soil temperature and water content, climate change may impact significantly on future emissions. In this paper, the potential effects of climate change and options for adaptation and mitigations will be considered, followed by an assessment of future research requirements. The paper concludes by suggesting that the diversity of climate, soil types, and agricultural practices in place across Australia will make it difficult to define generic scenarios for greenhouse gas emissions. Development of a robust modelling capability will be required to construct regional and national emission assessments and to define the potential outcomes of on-farm management decisions and policy decisions. This model development will require comprehensive field datasets to calibrate the models and validate model outputs. Additionally, improved spatial layers of model input variables collected on a regular basis will be required to optimise accounting at regional to national scales.
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Ghosh, Papiya, and Brishti Guha. "THE STUDY OF RELATIONSHIP BETWEEN TOBIN’S Q AND US STOCK PERFORMANCE OF SELECTED FIRMS." International Journal of Advanced Economics 1, no. 2 (June 22, 2020): 85–94. http://dx.doi.org/10.51594/ijae.v1i2.56.
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The objective of study was to test the dynamic effects of changes in Tobin’Q on stock prices of selected 249 US public companies of different industry categories. Panel unit roots tests and cointegration tests are implemented. Next, DOLS and GMM models are estimated. Annual data for the 2004-2012 period are used for the above selected US companies. Panel unit root tests provide somewhat mixed evidence of non-stationarity of both variables. There is clear evidence of cointegration between the above variables. The negative coefficient of the error-correction term shows convergence toward long-run equilibrium, though at slow pace. The estimates also reveal shortrun net positive interactive feedback effects between the variables. Both DOLS and GMM estimates display similar picture of overvaluation of stocks in terms of upward movement in Tobin’s Q beyond 0-to-1 range. For most parts of the sample period, the US stock market was in declining mode due to heightening of economic uncertainties during the Great Recession and several years beyond. Tobin’s Q should be improved to boost stock prices. This is more of a long-run phenomenon. In the short run, both reinforce each other. The topic is unique and the existing literature on this topic is scant. Relatively new econometric techniques have been applied for estimation using panel data. The results are quite insightful, in our view.
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Ma, Le, Richard Reed, and Jian Liang. "Separating owner-occupier and investor demands for housing in the Australian states." Journal of Property Investment & Finance 37, no. 2 (March 4, 2019): 215–32. http://dx.doi.org/10.1108/jpif-07-2018-0045.
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PurposeThere has been declining home ownership and increased acceptance of long-term renting in many western countries including Australia; this has created a problem when examining housing markets as there are dual demand and include both owner-occupiers and investors. The purpose of this paper is to examine the long-run relationship between house prices, housing supply and demand, and to estimate the effects of the two types of demand (i.e. owner-occupier and investor) on house prices.Design/methodology/approachThe econometric techniques for cointegration with vector error correction models are used to specify the proposed models, where the housing markets in the Australian states and territories illustrate the models.FindingsThe results highlight the regional long-run equilibrium and associated patterns in house prices, the level of new housing supply, owner-occupier demand for housing and investor demand for housing. Different types of markets were identified.Practical implicationsThe findings suggest that policies that depress the investment demand can effectively prevent the housing bubble from further building up in the Australian states. The empirical findings shed light in the strategy of maintaining levels of housing affordability in regions where owner-occupiers have been priced out of the housing market.Originality/valueThere has been declining home ownership and increased acceptance of long-term renting in many western countries including Australia; this has created a problem when examining housing markets as there are dual demand and include both owner-occupiers and investors. This research has given to the relationship between supply and dual demand, which includes owner-occupation and investment, for housing and the influence on house prices.
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Yong, Jaime, and Anh Khoi Pham. "The long-term linkages between direct and indirect property in Australia." Journal of Property Investment & Finance 33, no. 4 (July 6, 2015): 374–92. http://dx.doi.org/10.1108/jpif-01-2015-0005.
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Purpose– Investment in Australia’s property market, whether directly or indirectly through Australian real estate investment trusts (A-REITs), grew remarkably since the 1990s. The degree of segregation between the property market and other financial assets, such as shares and bonds, can influence the diversification benefits within multi-asset portfolios. This raises the question of whether direct and indirect property investments are substitutable. Establishing how information transmits between asset classes and impacts the predictability of returns is of interest to investors. The paper aims to discuss these issues.Design/methodology/approach– The authors study the linkages between direct and indirect Australian property sectors from 1985 to 2013, with shares and bonds. This paper employs an Autoregressive Fractionally Integrated Moving Average (ARFIMA) process to de-smooth a valuation-based direct property index. The authors establish directional lead-lag relationships between markets using bi-variate Granger causality tests. Johansen cointegration tests are carried out to examine how direct and indirect property markets adjust to an equilibrium long-term relationship and short-term deviations from such a relationship with other asset classes.Findings– The authors find the use of appraisal-based property data creates a smoothing bias which masks the extent of how information is transmitted between the indirect property sector, stock and bond markets, and influences returns. The authors demonstrate that an ARFIMA process accounting for a smoothing bias up to lags of four quarters can overcome the overstatement of the smoothing bias from traditional AR models, after individually appraised constituent properties are aggregated into an overall index. The results show that direct property adjusts to information transmitted from market-traded A-REITs and stocks.Practical implications– The study shows direct property investments and A-REITs are substitutible in a multi-asset portfolio in the long and short term.Originality/value– The authors apply an ARFIMA(p,d,q) model to de-smooth Australian property returns, as proposed by Bond and Hwang (2007). The authors expect the findings will contribute to the discussion on whether direct property and REITs are substitutes in a multi-asset portfolio.
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Gaughan, D. J., R. W. Mitchell, and S. J. Blight. "Impact of mortality, possibly due to herpesvirus, on pilchard Sardinops sagax stocks along the south coast of Western Australia in 1998-99." Marine and Freshwater Research 51, no. 6 (2000): 601. http://dx.doi.org/10.1071/mf99176.
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During progression of a mass mortality of Australian pilchards in late 1998 and early 1999, quantities of dead pilchards on the sea-surface, sea-floor and along beaches were estimated in three regions along southern Western Australia (WA) by use of transects. Total mortality was estimated at 17 590, 11193 and 144.4 t for Esperance, Bremer Bay and Albany respectively. Mortality rates at Esperance and Bremer Bay were similar at 74.5% and 64.7% respectively, with a mean of 69.6%. In contrast, estimated mortality at Albany was only 2.4%. Although the difference in total mortality between regions is probably related to differences in stock size, as determined by simulation models, the much lower estimate for Albany is probably an artefact of an over-estimated pilchard biomass and not due to large differences in actual mortality rates. Variability in estimates of both pilchard biomass and quantities killed resulted in a wide range of estimated mortality rates, with lower estimates for Esperance and Bremer Bay of 28.0% and 22.9% respectively. This represents a significant decline in the breeding stock of WA pilchards. If the impact was closer to the mean (69.6%), then pilchard stocks in WA are severely depressed.
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Ovenden, Jennifer R., Bree J. Tillett, Michael Macbeth, Damien Broderick, Fiona Filardo, Raewyn Street, Sean R. Tracey, and Jayson Semmens. "Stirred but not shaken: population and recruitment genetics of the scallop (Pecten fumatus) in Bass Strait, Australia." ICES Journal of Marine Science 73, no. 9 (May 8, 2016): 2333–41. http://dx.doi.org/10.1093/icesjms/fsw068.
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Abstract We report population genetic structure and fine-scale recruitment processes for the scallop beds (Pecten fumatus) in Bass Strait and the eastern coastline of Tasmania in southern Australia. Conventional population pairwise FST analyses are compared with novel discriminant analysis of principal components (DAPC) to assess population genetic structure using allelic variation in 11 microsatellite loci. Fine-scale population connectivity was compared with oceanic features of the sampled area. Disjunct scallop beds were genetically distinct, but there was little population genetic structure between beds connected by tides and oceanic currents. To identify recruitment patterns among and within beds, pedigree analyses determined the distribution of parent–offspring and sibling relationships in the sampled populations. Beds in northeastern Bass Strait were genetically distinct to adjacent beds (FST 0.003–0.005) and may not contribute to wider recruitment based on biophysical models of larval movement. Unfortunately, pedigree analyses lacked power to further dissect fine-scale recruitment processes including self-recruitment. Our results support the management of disjunct populations as separate stocks and the protection of source populations among open water beds. The application of DAPC and parentage analyses in the current study provided valuable insight into their potential power to determine population connectivity in marine species with larval dispersal.
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Lalwani, Vaibhav, and Madhumita Chakraborty. "Multi-factor asset pricing models in emerging and developed markets." Managerial Finance 46, no. 3 (December 2, 2019): 360–80. http://dx.doi.org/10.1108/mf-12-2018-0607.
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Purpose The purpose of this paper is to compare the performance of various multifactor asset pricing models across ten emerging and developed markets. Design/methodology/approach The general methodology to test asset pricing models involves regressing test asset returns (left-hand side assets) on pricing factors (right-hand side assets). Then the performance of different models is evaluated based on how well they price multiple test assets together. The parameters used to compare relative performance of different models are their pricing errors (GRS statistic and average absolute intercepts) and explained variation (average adjusted R2). Findings The Fama-French five-factor model improves the pricing performance for stocks in Australia, Canada, China and the USA. The pricing in these countries appears to be more integrated. However, the superior performance in these four countries is not consistent across a variety of test assets and the magnitude of reduction in pricing errors vis-à-vis three- or four-factor models is often economically insignificant. For other markets, the parsimonious three-factor model or its four-factor variants appear to be more suitable. Originality/value Unlike most asset pricing studies that use test assets based on variables that are already used to construct RHS factors, this study uses test assets that are generally different from RHS sorts. This makes the tests more robust and less biased to be in favour of any multifactor model. Also, most international studies of asset pricing tests use data for different markets and combine them into regions. This study provides the evidence from ten countries separately because prior research has shown that locally constructed factors are more suitable to explain asset prices. Further, this study also tests for the usefulness of adding a quality factor in the existing asset pricing models.
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FRAME, SAMUEL J., and CYRUS A. RAMEZANI. "BAYESIAN ESTIMATION OF ASYMMETRIC JUMP-DIFFUSION PROCESSES." Annals of Financial Economics 09, no. 03 (December 2014): 1450008. http://dx.doi.org/10.1142/s2010495214500080.
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The hypothesis that asset returns are normally distributed has been widely rejected. The literature has shown that empirical asset returns are highly skewed and leptokurtic. The affine jump-diffusion (AJD) model improves upon the normal specification by adding a jump component to the price process. Two important extensions proposed by Ramezani and Zeng (1998) and Kou (2002) further improve the AJD specification by having two jump components in the price process, resulting in the asymmetric affine jump-diffusion (AAJD) specification. The AAJD specification allows the probability distribution of the returns to be asymmetrical. That is, the tails of the distribution are allowed to have different shapes and densities. The empirical literature on the "leverage effect" shows that the impact of innovations in prices on volatility is asymmetric: declines in stock prices are accompanied by larger increases in volatility than the reverse. The asymmetry in AAJD specification indirectly accounts for the leverage effect and is therefore more consistent with the empirical distributions of asset returns. As a result, the AAJD specification has been widely adopted in the portfolio choice, option pricing, and other branches of the literature. However, because of their complexity, empirical estimation of the AAJD models has received little attention to date. The primary objective of this paper is to contribute to the econometric methods for estimating the parameters of the AAJD models. Specifically, we develop a Bayesian estimation technique. We provide a comparison of the estimated parameters under the Bayesian and maximum likelihood estimation (MLE) methodologies using the S&P 500, the NASDAQ, and selected individual stocks. Focusing on the most recent spectacular market bust (2007–2009) and boom (2009–2010) periods, we examine how the parameter estimates differ under distinctly different economic conditions.
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McAuley, Rory B., Colin A. Simpfendorfer, and Norm G. Hall. "A method for evaluating the impacts of fishing mortality and stochastic influences on the demography of two long-lived shark stocks." ICES Journal of Marine Science 64, no. 9 (September 20, 2007): 1710–22. http://dx.doi.org/10.1093/icesjms/fsm146.
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Abstract McAuley, R. B., Simpfendorfer, C. A., and Hall, N. G. 2007. A method for evaluating the impacts of fishing mortality and stochastic influences on the demography of two long-lived shark stocks. — ICES Journal of Marine Science, 64. Stochastic demographic models were developed for Carcharhinus obscurus and C. plumbeus populations off the west coast of Australia by resampling the input parameters for life tables from empirical biological data collected from commercial target fisheries and fishery-independent surveys. The models were used to examine the effects of multiple scenarios of age-specific survival, derived from the fishing mortality rates estimated from a tagging study on sharks and indirect estimates of natural mortality. In the absence of fishing, median estimates of the rates of intrinsic population increase (r) were 0.025 for both species. Inclusion of the age-specific fishing mortality rates estimated for C. obscurus recruits born in 1994 and 1995 resulted in the median estimates of r declining to 0.007 and 0.012, respectively, suggesting that recent harvest levels of mainly neonates by the target fishery were probably sustainable. However, the model also suggested that the population was more susceptible to exploitation of older sharks than was previously believed. The C. plumbeus model indicated that fishing mortality between 2001 and 2004 was probably unsustainable. The increasingly negative trend in median r estimates (from –0.032 to –0.049), and the population’s apparently limited capacity for density-dependent compensation through changes in fecundity, somatic growth and longevity, suggests that management intervention is necessary to prevent continued stock depletion.
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Adams, Carol A. "Conceptualising the contemporary corporate value creation process." Accounting, Auditing & Accountability Journal 30, no. 4 (May 15, 2017): 906–31. http://dx.doi.org/10.1108/aaaj-04-2016-2529.
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Purpose The purpose of this paper is to examine and explain the complex interrelationships which influence the ability of firms to create value for their providers of finance and other stakeholders (loosely referred to in practice as “integrated thinking”). In doing so it examines the interrelationships between: environmental, social and governance (ESG) risk; delivering on corporate strategy; non-financial corporate reporting; and, board oversight. Design/methodology/approach Interviews were conducted with board chairs and non-executive directors of large listed companies on the Johannesburg Stock Exchange (where Boards are required to have a social and ethics sub-committee and approve integrated reports which have been mandatory since 2010) and the Australian Stock Exchange (where Board directors’ liability legislation results in Boards being reluctant to adopt integrated reporting which is voluntary). Findings The research finds that contemporary reporting processes, and in particular those set out in the King III Code and the International Integrated Reporting Framework, influence cognitive frames enhancing board oversight and assisting organisations in managing complexity. This results in increased awareness of the impact of ESG issues together with a broader view of value creation despite investor disinterest. Research limitations/implications A number of avenues of research are suggested to further examine the interrelationships identified. Practical implications The research assists the development of practice and policy by articulating and enhancing the understanding of linkages, which loosely fall under the vague practitioner term “integrated thinking”. Social implications The conceptualisation can inform national and global discussions on the appropriateness of corporate reporting and governance models to achieve sustainable development and contribute to the Sustainable Development Goals. Originality/value The paper conceptualises emerging and complex interrelationships. The cross-country comparison allows an assessment of the extent to which different national social contexts with differing governance and reporting frameworks lead to different perspectives on, and approaches to, value creation.
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Ryan, Justin G., Christine T. Fyfe, and Clive A. McAlpine. "Biomass retention and carbon stocks in integrated vegetation bands: a case study of mixed-age brigalow-eucalypt woodland in southern Queensland, Australia." Rangeland Journal 37, no. 3 (2015): 261. http://dx.doi.org/10.1071/rj14023.
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Regrowth of native woody vegetation has the potential to provide an economically valuable source of carbon storage and other ecosystem services. There is a lack of readily applicable examples of how regrowth of forests and woodlands can be integrated with existing grazing production systems and provide soil-protection and water-retention benefits. A system of integrated vegetation bands (IVB) was applied to patchy regrowth of acacia and eucalypt vegetation in a grazed landscape of southern Queensland, Australia. Across a 39.8-ha catchment with 3–5% slope, regrowth of scattered native vegetation (18.4 ha) was surveyed and diameter at breast height and height for all woody plants were recorded. The IVB (6.3 ha) were then marked out as 25-m-wide bands set 100 m apart and offset at ~2–3% gradient to the contour line, retaining the densest/largest regrowth where possible. The data on diameter at breast height and height were analysed using allometric equations to compare aboveground biomass in the original regrowth condition (‘Original’) to that retained in the installed IVB (‘IVB-Riparian’). Estimates of aboveground biomass were calculated for the Original and IVB-Riparian and compared with three other potential regrowth-vegetation management ‘treatments’ in a desktop-modelling study. The models were designated as: (1) ‘Original’; (2) ‘Broad’ (broad-scale cleared with only a few large trees along a creek retained)’; (3) ‘Big Trees’ (only large trees >40 cm diameter at breast height retained); (4) ‘Riparian-IVB (bands of vegetation); and (5) ‘Riparian-IVB-Big Trees’ (large trees together with ‘IVB-Riparian’). In the non-forested area of the catchment, ‘Riparian-IVB-Big Trees’ (301 t), ‘Big Trees’ (249 t) and ‘Riparian-IVB’ (200 t) had the highest aboveground biomass retained, whereas ‘Broad’ resulted in the most pasture area (~33 ha) followed by ‘Riparian-IVB’ (~26 ha). The ‘Riparian-IVB’ treatment had the highest tree density within the vegetation bands and more than half (53%) of the original woody biomass in regrowth was retained on just under a quarter (23%) of the land area minimising the impact on the area of pasture/grazing land. This subsequently resulted in the ‘Riparian-IVB’ treatment having the highest carbon offset value (A$605 ha–1). The results demonstrate that the retention of native regrowth vegetation in either IVB or as large paddock trees can retain a large amount of aboveground biomass, with IVB having greater returns per hectare.
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Rogers, P. J., and T. M. Ward. "Application of a 'case-building approach' to investigate the age distributions and growth dynamics of Australian sardine (Sardinops sagax) off South Australia." Marine and Freshwater Research 58, no. 5 (2007): 461. http://dx.doi.org/10.1071/mf06181.
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Like many pelagic fish, sardine Sardinops sagax is difficult to age by counting structures in otoliths. Of 12968 whole otoliths analysed in the present study, <25% could be counted with accuracies of greater than ±1 zone. Estimates of average percentage error (APE) were high (11.37%). We used a ‘case building approach’ to corroborate a method for estimating age. Regressions of otolith weight–age from otoliths with high readabilities were used to estimate the age of fish with lower or unreadable otoliths. Growth rates determined from daily increments ranged from 0.36 to 0.75 mm day–1 for larvae and from 0.22 to 0.47 mm day–1 for juveniles. Estimates of von Bertalanffy growth coefficients were k = 0.32–1.07 year–1 and L∞ = 166.96–195.68 mm. Growth parameters estimated during the present study support previous assertions that levels of pelagic production in South Australian (SA) waters lie between those recorded in the predominantly oligotrophic waters off Western Australia (WA) and the productive waters off southern California and South Africa. Disparities in the age distributions of inshore and offshore samples suggest that age data from commercial catch samples may not be representative of the population. This finding has implications for the use of age-structured models to assess of stocks of S. sagax and other small pelagic fishes.
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Duppati, Geeta, and Mengying Zhu. "Oil prices changes and volatility in sector stock returns: Evidence from Australia, New Zealand, China, Germany and Norway." Corporate Ownership and Control 13, no. 2 (2016): 351–70. http://dx.doi.org/10.22495/cocv13i2clp4.
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The paper examines the exposure of sectoral stock returns to oil price changes in Australia, China, Germany, New Zealand and Norway over the period 2000-2015 using weekly data drawn from DataStream. The issue of volatility has important implications for the theory of finance and as is well-known accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management (e.g. in the calculation of hedge ratios and Value-at-Risk measures), and trading strategies (David and Ruiz, 2009). This study adopts GARCH and EGARCH to understand the relationship between the returns and volatility. The findings using GARCH (EGARCH) models suggests that in the case of Germany eight (nine) out of ten sectors returns can be explained by the volatility of past oil price in Germany, while in the case of Australia, six (seven) out of ten sector returns are sensitive to the oil price changes with the exception of Industrials, Consumer Goods, Health care and Utilities. While in China and New Zealand five sectors are found sensitive to oil price changes and three sectors in Norway, namely Oil & Gas, Consumer Services and Financials. Secondly, this paper also investigated the exposure of the stock returns to oil price changes using market index data as a proxy using GARCH or EGARCH model. The results indicated that the stock returns are sensitive to the oil price changes and have leverage effects for all the five countries. Further, the findings also suggests that sector with more constituents is likely to have leverage effects and vice versa. The results have implications to market participants to make informed decisions about a better portfolio diversification for minimizing risk and adding value to the stocks.
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Zhou, Shijie, David J. Vance, Catherine M. Dichmont, Charis Y. Burridge, and Peter J. Toscas. "Estimating prawn abundance and catchability from catch-effort data: comparison of fixed and random effects models using maximum likelihood and hierarchical Bayesian methods." Marine and Freshwater Research 59, no. 1 (2008): 1. http://dx.doi.org/10.1071/mf07090.
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Abundance and catchability are crucial quantities in fisheries management, yet they are very difficult to estimate, particularly for short-lived invertebrates. Using two distinct approaches – a standard non-hierarchical model (NH) and a hierarchical Bayesian model (HB) – abundance and catchability coefficients from a fishery depletion process for banana prawns (Penaeus merguiensis) in northern Australia were estimated. Non-hierarchical models treated each stock and year separately and individually, whereas the hierarchical models assumed some form of common underlying population from which the parameters for the individual cases generated by the combination of stock and year were drawn. Two HBs were considered. In HB1 it was assumed that annual abundance and catchability parameters came from separate populations, or distributions, for each stock. In HB2 it was assumed that these stock region distributions were not separate, but had their parameters drawn from a common distribution. Thus in HB2 all stocks shared information at the regional level. The results for both NH and HB methods were similar in most cases, indicating a fair degree of stability irrespective of the particular form of model chosen. However, the NH method suffered because the data were analysed in generally small sections and in many cases these sections were too small to allow precise estimation of both parameters and confidence intervals. The deviations of point estimates between the HB1, HB2 and NH models were more marked in catchability coefficient estimates than in abundance estimates, and large relative deviations typically occurred in stock regions and years with low fishing efforts, low catch or poor depletion trends over time. We conclude that the combined analysis using HB was superior because it could handle limited data, yielded credible interval estimates for all parameters and was computationally more efficient.
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Manickavasagam, Jeevananthan, and Visalakshmi S. "An investigational analysis on forecasting intraday values." Benchmarking: An International Journal 27, no. 2 (October 4, 2019): 592–605. http://dx.doi.org/10.1108/bij-11-2018-0361.
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Purpose The algorithmic trading has advanced exponentially and necessitates the evaluation of intraday stock market forecasting on the grounds that any stock market series are foreseen to follow the random walk hypothesis. The purpose of this paper is to forecast the intraday values of stock indices using data mining techniques and compare the techniques’ performance in different markets to accomplish the best results. Design/methodology/approach This study investigates the intraday values (every 60th-minute closing value) of four different markets (namely, UK, Australia, India and China) spanning from April 1, 2017 to March 31, 2018. The forecasting performance of multivariate adaptive regression spline (MARSplines), support vector regression (SVR), backpropagation neural network (BPNN) and autoregression (1) are compared using statistical measures. Robustness evaluation is done to check the performance of the models on the relative ratios of the data. Findings MARSplines produces better results than the compared models in forecasting every 60th minute of selected stocks and stock indices. Next to MARSplines, SVR outperforms neural network and autoregression (1) models. The MARSplines proved to be more robust than the other models. Practical implications Forecasting provides a substantial benchmark for companies, which entails long-run operations. Significant profit can be earned by successfully predicting the stock’s future price. The traders have to outperform the market using techniques. Policy makers need to estimate the future prices/trends in the stock market to identify the link between the financial instruments and monetary policy which gives higher insights about the mechanism of existing policy and to know the role of financial assets in many channels. Thus, this study expects that the proposed model can create significant profits for traders by more precisely forecasting the stock market. Originality/value This study contributes to the high-frequency forecasting literature using MARSplines, SVR and BPNN. Finding the most effective way of forecasting the stock market is imperative for traders and portfolio managers for investment decisions. This study reveals the changing levels of trends in investing and expectation of significant gains in a short time through intraday trading.
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Lee, Juhwan, Raphael A. Viscarra Rossel, Mingxi Zhang, Zhongkui Luo, and Ying-Ping Wang. "Assessing the response of soil carbon in Australia to changing inputs and climate using a consistent modelling framework." Biogeosciences 18, no. 18 (September 22, 2021): 5185–202. http://dx.doi.org/10.5194/bg-18-5185-2021.
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Abstract. Land use and management practices affect the response of soil organic carbon (C) to global change. Process-based models of soil C are useful tools to simulate C dynamics, but it is important to bridge any disconnect that exists between the data used to inform the models and the processes that they depict. To minimise that disconnect, we developed a consistent modelling framework that integrates new spatially explicit soil measurements and data with the Rothamsted carbon model (Roth C) and simulates the response of soil organic C to future climate change across Australia. We compiled publicly available continental-scale datasets and pre-processed, standardised and configured them to the required spatial and temporal resolutions. We then calibrated Roth C and ran simulations to estimate the baseline soil organic C stocks and composition in the 0–0.3 m layer at 4043 sites in cropping, modified grazing, native grazing and natural environments across Australia. We used data on the C fractions, the particulate, mineral-associated and resistant organic C (POC, MAOC and ROC, respectively) to represent the three main C pools in the Roth C model's structure. The model explained 97 %–98 % of the variation in measured total organic C in soils under cropping and grazing and 65 % in soils under natural environments. We optimised the model at each site and experimented with different amounts of C inputs to simulate the potential for C accumulation under constant climate in a 100-year simulation. With an annual increase of 1 Mg C ha−1 in C inputs, the model simulated a potential soil C increase of 13.58 (interquartile range 12.19–15.80), 14.21 (12.38–16.03) and 15.57 (12.07–17.82) Mg C ha−1 under cropping, modified grazing and native grazing and 3.52 (3.15–4.09) Mg C ha−1 under natural environments. With projected future changes in climate (+1.5, 2 and 5.0 ∘C) over 100 years, the simulations showed that soils under natural environments lost the most C, between 3.1 and 4.5 Mg C ha−1, while soils under native grazing lost the least, between 0.4 and 0.7 Mg C ha−1. Soil under cropping lost between 1 and 2.7 Mg C ha−1, while those under modified grazing showed a slight increase with temperature increases of 1.5 ∘C, but with further increases of 2 and 5 ∘C the median loss of TOC was 0.28 and 3.4 Mg C ha−1, respectively. For the different land uses, the changes in the C fractions varied with changes in climate. An empirical assessment of the controls on the C change showed that climate, pH, total N, the C : N ratio and cropping were the most important controls on POC change. Clay content and climate were dominant controls on MAOC change. Consistent and explicit soil organic C simulations improve confidence in the model's estimations, facilitating the development of sustainable soil management under global change.
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Feldpausch, T. R., J. Lloyd, S. L. Lewis, R. J. W. Brienen, M. Gloor, A. Monteagudo Mendoza, G. Lopez-Gonzalez, et al. "Tree height integrated into pantropical forest biomass estimates." Biogeosciences 9, no. 8 (August 27, 2012): 3381–403. http://dx.doi.org/10.5194/bg-9-3381-2012.
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Abstract. Aboveground tropical tree biomass and carbon storage estimates commonly ignore tree height (H). We estimate the effect of incorporating H on tropics-wide forest biomass estimates in 327 plots across four continents using 42 656 H and diameter measurements and harvested trees from 20 sites to answer the following questions: 1. What is the best H-model form and geographic unit to include in biomass models to minimise site-level uncertainty in estimates of destructive biomass? 2. To what extent does including H estimates derived in (1) reduce uncertainty in biomass estimates across all 327 plots? 3. What effect does accounting for H have on plot- and continental-scale forest biomass estimates? The mean relative error in biomass estimates of destructively harvested trees when including H (mean 0.06), was half that when excluding H (mean 0.13). Power- and Weibull-H models provided the greatest reduction in uncertainty, with regional Weibull-H models preferred because they reduce uncertainty in smaller-diameter classes (≤40 cm D) that store about one-third of biomass per hectare in most forests. Propagating the relationships from destructively harvested tree biomass to each of the 327 plots from across the tropics shows that including H reduces errors from 41.8 Mg ha−1 (range 6.6 to 112.4) to 8.0 Mg ha−1 (−2.5 to 23.0). For all plots, aboveground live biomass was −52.2 Mg ha−1 (−82.0 to −20.3 bootstrapped 95% CI), or 13%, lower when including H estimates, with the greatest relative reductions in estimated biomass in forests of the Brazilian Shield, east Africa, and Australia, and relatively little change in the Guiana Shield, central Africa and southeast Asia. Appreciably different stand structure was observed among regions across the tropical continents, with some storing significantly more biomass in small diameter stems, which affects selection of the best height models to reduce uncertainty and biomass reductions due to H. After accounting for variation in H, total biomass per hectare is greatest in Australia, the Guiana Shield, Asia, central and east Africa, and lowest in east-central Amazonia, W. Africa, W. Amazonia, and the Brazilian Shield (descending order). Thus, if tropical forests span 1668 million km2 and store 285 Pg C (estimate including H), then applying our regional relationships implies that carbon storage is overestimated by 35 Pg C (31–39 bootstrapped 95% CI) if H is ignored, assuming that the sampled plots are an unbiased statistical representation of all tropical forest in terms of biomass and height factors. Our results show that tree H is an important allometric factor that needs to be included in future forest biomass estimates to reduce error in estimates of tropical carbon stocks and emissions due to deforestation.
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Marriott, Ross J., Michael F. O'Neill, Stephen J. Newman, and Craig L. Skepper. "Abundance indices for long-lived tropical snappers: estimating standardized catch rates from spatially and temporally coarse logbook data." ICES Journal of Marine Science 71, no. 3 (October 8, 2013): 618–27. http://dx.doi.org/10.1093/icesjms/fst167.
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Abstract The Northern Demersal Scalefish Fishery has historically comprised a small fleet (≤10 vessels year−1) operating over a relatively large area off the northwest coast of Australia. This multispecies fishery primarily harvests two species of snapper: goldband snapper, Pristipomoides multidens and red emperor, Lutjanus sebae. A key input to age-structured assessments of these stocks has been the annual time-series of the catch rate. We used an approach that combined Generalized Linear Models, spatio-temporal imputation, and computer-intensive methods to standardize the fishery catch rates and report uncertainty in the indices. These analyses, which represent one of the first attempts to standardize fish trap catch rates, were also augmented to gain additional insights into the effects of targeting, historical effort creep, and spatio-temporal resolution of catch and effort data on trap fishery dynamics. Results from monthly reported catches (i.e. 1993 on) were compared with those reported daily from more recently (i.e. 2008 on) enhanced catch and effort logbooks. Model effects of catches of one species on the catch rates of another became more conspicuous when the daily data were analysed and produced estimates with greater precision. The rate of putative effort creep estimated for standardized catch rates was much lower than estimated for nominal catch rates. These results therefore demonstrate how important additional insights into fishery and fish population dynamics can be elucidated from such “pre-assessment” analyses.
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Volontyr, L., and L. Mykhalchyshyna. "Organizational and economic mechanism of grain sales: information component." Scientific Messenger of LNU of Veterinary Medicine and Biotechnologies 21, no. 92 (May 11, 2019): 81–89. http://dx.doi.org/10.32718/nvlvet-e9213.
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A significant part of the output of the agro-industrial complex of Ukraine is exported. Therefore, it is desirable to determine the optimal volume of products to be implemented each month. Prices for grain are formed depending on demand and supply, costs for production and sale, market fees, etc. The analysis of the price situation on the Ukrainian cities shows a large variation. The average price of 1 kg of grain crops does not give a full opportunity to characterize the price situation of the Ukrainian grain market. There is seasonal price cyclicality: their growth with the decrease of stocks and the reduction after harvesting, when mass sales of grain are carried out by producers who are not able to store the grown crops, and consumers make grain crops. In the article the solution of the economic-mathematical model of optimization of the calendar plan for the sale of agricultural products is developed and found. The model is considered from the standpoint of deterministic product prices and under the probabilistic nature of future market prices. The system of restrictions consists of two constraints: to determine the optimal size of grain crop harvesting of each type and the capacity of the warehouse. If future market prices are considered not deterministic, then the commodity producer always has the risk of receiving in the future revenue from the sale of products smaller than expected. A risk-averse person will be guided by two criteria when deciding to: maximize the expected total net income and minimize the dispersion of total net income. In this case, the model will be two-criterial and nonlinear. The method of supporting the process of determining the predominance of multi-criteria optimization is that the owner first of all has received information about the limits of the variation of the expected total net income and the standard deviation of income on the set of effective options for the calendar plan. The peculiarities of the individual attitude to risk are calculated by drawing information on the permissible levels of the indicated criterion. Further among all effective variants of the calendar plan of realization is calculated precisely the one that best reflects the individual predominance of the owner of the product. The following information is needed to construct a numerical model for grain sales: sales prices and the cost of storing 1 ton of grain crops to a certain month. The predicted values are based on a simple linear econometric model based on statistical sampling. The reliability of the econometric model is determined by the determination coefficient or on the basis of Fisher's F-criterion according to the theory of statistical hypotheses. Econometric models have weak extropolitic properties, so the forecast can be formed only short-term. The solution of the model showed: all kinds of grain crops, except for barley, are economically unprofitable to be implemented in such months as January, May, June, July and August. Wheat grades 3 and 6, corn is also unprofitable to be sold in September. Unlike other crops, barley is beneficial throughout the year. In February, the maximum sales of wheat is 2, 3 and 6 classes, in March the maximum sale of barley, and the minimum is in May. Maize has the maximum sales in May, and the minimum in September. The minimum sale of wheat depends on its class – September, April and December respectively 2, 3 and 6 classes. With such incomplete loading of warehouses, the profit from storage of grain crops will be 743 thousand. UAH. Thus, PJSC “Gnivan Grain Reciprocal Enterprise” is more likely to load its warehouses to improve its financial position. One of the ways of solving the problem of seasonal grain sales is to create a network of modern certified grain elevators, taking into account the logistically rational location, which will allow to keep enough grain in addition and of the proper quality. This will allow an increase in the efficiency of grain producers through the sale of grain at favorable market conditions in a wider range of time. Independent operators should also be encouraged to ensure that the quality of the grain is objectively measured. At present, the analysis of the work of the grain storage system shows that the high cost of services of active elevators is also a problem.
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Hill, Michael J., Stephen H. Roxburgh, John O. Carter, and Gregory M. McKeon. "Vegetation state change and consequent carbon dynamics in savanna woodlands of Australia in response to grazing, drought and fire: a scenario approach using 113 years of synthetic annual fire and grassland growth." Australian Journal of Botany 53, no. 7 (2005): 715. http://dx.doi.org/10.1071/bt04106.
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A spatially explicit state and transition model for assessing the interactive effects of grazing, fire and climate on carbon dynamics in Australian savannas is described. The model runs on a yearly time step. It is based on a sequential treatment of events within each year, involving, in order, growth of biomass, consumption of biomass by livestock and burning of the remaining fuel (growth minus consumption). The major drivers are 113 years of annual rainfall data, annual modelled rangeland growth, synthetic fire incidence and timing data, and data describing stocking rates in dry sheep equivalents. Baseline carbon stocks are derived from pre-settlement estimates from the VAST steady-state carbon model. State and transition models for nine vegetation zones define vegetation condition and consequent carbon stock. Change is mediated by key indices of driver variables such as grazing, growth and fire, a series of parameters and default thresholds, and a set of rules. The model is run for three 113-year spin-up cycles to establish a set of initial condition grids that represent a plausible synthetic current state. Sensitivity analyses on selected parameters and index thresholds showed that fire and growth thresholds were most important for woodland zones, and utilisation rates and degradation and recovery periods were most important for grassland zones. The model was used to examine a series of scenarios involving changes to grazing pressure and suppression of fire for different climate sequences. Changes to a few parameters enabled simulation of low and high levels of encroachment of woody weeds, and hence carbon accumulation, in the Astrebla (Mitchell) grasslands; and effective capture of the climate-induced variation in grassland condition, and hence potential for soil carbon loss, within Heteropogon spp.-dominated grasslands in northern Queensland. The scenario results suggest that this simple state and transition model with an annual time step provides a potentially useful and flexible scenario model framework for exploration of vegetation and consequent carbon dynamics of the Australian tropical savanna region.