Journal articles on the topic 'Prices – Statistical methods'
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Ellingerová, Helena, Zora Petráková, and Ingrida Skalíková. "Statistical Methods in Building Industry to Determine Prices Indices." Tehnički glasnik 14, no. 4 (December 9, 2020): 458–65. http://dx.doi.org/10.31803/tg-20200604105846.
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Tender price is often affected by the location of the construction, which is usually determined by the investor, and it has an impact on the traffic in the particular location. Individual time of supply and the method of realization play an important role as well. They both are determined by the investor along with the designer of the particular construction. Contractors often complain about the lack of time needed for the preparation of their tender prices. Therefore, it is necessary to look for the possibilities how to reliably speed up this process at the same time taking into account all of the specific features of a structure. This article deals with the application of two statistical methods. The Pareto analysis, which can be used during the design of the tender price, and the extrapolation method, which can be used for the estimation of the price development, based on the regression analysis of the time series. The results of the article particularly serve to contractors in the building industry to better prepare their price offers in tenders. The findings of this document may also be applicable in other countries which have a similar economic profile as Slovakia.
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Rudko, G. I., M. M. Kurylo, V. V. Bala, and Yu S. Makovskyi. "METHODS FOR PRICE DETERMINATION (JUSTIFICATION) AT ECONOMIC-GEOLOGICAL EVALUATION OF COAL DEPOSITS." Мінеральні ресурси України, no. 4 (December 28, 2018): 45–48. http://dx.doi.org/10.31996/mru.2018.4.45-48.
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The purpose of research is systematization and analysis of methods of price determining for geological and economic assessment of coal deposits in domestic and international practice. Price indicators and income from sale of coal affect significantly reserves value, profitability of their development, and determine industrial significance of reserves. In domestic practice commodity exchanges, contractual, regulated, world and transfer prices are used. In international practice coal prices are formed at the result of futures, spot or stock exchange contracts. Now international coal trade realizes in the framework of futures contracts and spot transactions. In recent years, short-term contracts prevail, rarely it’s used medium-term contracts. A sequence of coal pricing for geological and economic assessment has been determined, which is the following: classification of coal by grades and classes in accordance with current standards; statistical analysis of prices by grades and classes, coal enrichment products; determination of a system of discounts/surcharges to the price of each class depending on coal quality; correction of actual producer prices for assessment reserves. The values of surcharges or discounts for individual indicators of coal quality are determined. The sensitivity analysis of reserves value and profitability from changes in selling coal prices has been carried out. The determination of the coal price or enrichment products requires a detailed justification depending on the stage of geological and feasibility study of reserves. For detailed assessment of explored or exploited deposits it is reasonable to use actual prices of coal sales for the previous period and contract prices for future periods in the presence of medium and long-term contracts. For preliminary geological and economic assessment, it is possible to use the price of the analogue deposit, which is developed, or wholesale coal prices with correction by quality.
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Riansut, Warangkhana. "Forecasting of Wollongong Prices via the Use of Statistical Methods." Journal of Applied Science 20, no. 2 (September 6, 2021): 65–79. http://dx.doi.org/10.14416/j.appsci.2021.02.007.
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Lin, Lisha, Yaqiong Li, Rui Gao, and Jianhong Wu. "The numerical simulation of Quanto option prices using Bayesian statistical methods." Physica A: Statistical Mechanics and its Applications 567 (April 2021): 125629. http://dx.doi.org/10.1016/j.physa.2020.125629.
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Gaca, Radosław. "Parametric and Non-Parametric Statistical Methods in the Assessment of the Effect of Property Attributes on Prices." Real Estate Management and Valuation 26, no. 2 (June 1, 2018): 83–91. http://dx.doi.org/10.2478/remav-2018-0018.
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Abstract One of the basic problems in the comparison-based property valuation process is to determine the influence of property attributes on their price differential. Due to the qualitative character of the majority of property attributes as well as to the distributions of both prices and attributes, their effect on the price differential is increasingly often assessed by means of non-parametric statistical methods. As a tool for determining the effect of attributes on prices, many authors propose parametric methods, in particular multiple regression models. The study presents a comparison of the results of property market attribute weight estimation obtained by means of the Spearman rank correlation coefficient with the ceteris paribus adjustment and the multiple regression model based on a set of transactions with built-up land property. In both of the analyzed methods, qualitative variables were modeled with the use of the Osgood semantic differential scale. The results of the analysis show the equivalence of the applied methods. Property attribute weights calculated using the method based on the rank correlation coefficient with the ceteris paribus adjustment and the multiple regression model, both with the same level of relevance, showed almost identical values. This indicates that both parametric and non-parametric methods can be used to estimate weights.
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Webster, Michael, and Rory C. Tarnow-Mordi. "Decomposing Multilateral Price Indexes into the Contributions of Individual Commodities." Journal of Official Statistics 35, no. 2 (June 1, 2019): 461–86. http://dx.doi.org/10.2478/jos-2019-0020.
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Abstract This article describes methods for decomposing price indexes into contributions from individual commodities, to help understand the influence of each commodity on aggregate price index movements. Previous authors have addressed the decomposition of bilateral price indexes, which aggregate changes in commodity prices from one time period to another. Our focus is the decomposition of multilateral price indexes, which aggregate commodity prices across more than two time periods or countries at once. Multilateral indexes have historically been used for spatial comparisons, and have recently received attention from statistical agencies looking to produce temporal price indexes from large and high frequency price data sets, such as scanner data. Methods for decomposing these indexes are of practical relevance. We present decompositions of three multilateral price indexes. We also review methods proposed by other researchers for extending multilateral indexes without revising previously published index levels, and show how to decompose the extended indexes they produce. Finally, we use a data set of seasonal prices and quantities to illustrate how these decomposition methods can be used to understand the influence of individual commodities on multilateral price index movements, and to shed light on the relationships between various multilateral and extension methods.
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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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Afanasyev, V. N. "Statistical Methods in the Study of Changes in the Structure and Elements of the Cost of Electricity Generation." Vestnik NSUEM, no. 4 (December 29, 2019): 286–303. http://dx.doi.org/10.34020/2073-6495-2019-4-286-303.
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The growth of tariffs and prices in the Russian Federation is largely determined by the growth of electricity prices. Need to know why electricity is becoming more expensive. The article presents the analysis of the system of statistical methods used in the study of changes in the structure and elements of the cost of electricity production. Statistical tools are being discussed to identify and measure the factors behind the rise in electricity prices, and to conduct a detailed causal analysis. Special emphasis is placed on statistical technologies used in the study of changes in individual elements and the cost structure as a whole. Special emphasis is placed on statistical technologies used in predicting changes in individual elements and the cost structure as a whole. The main goal of such a forecast is to develop a strategy for the behavior of the economic entity and formulate of its activity plan.
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Chuluunsaikhan, Tserenpurev, Ga-Ae Ryu, Kwan-Hee Yoo, HyungChul Rah, and Aziz Nasridinov. "Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea." Agriculture 10, no. 11 (October 30, 2020): 513. http://dx.doi.org/10.3390/agriculture10110513.
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Knowing the prices of agricultural commodities in advance can provide governments, farmers, and consumers with various advantages, including a clearer understanding of the market, planning business strategies, and adjusting personal finances. Thus, there have been many efforts to predict the future prices of agricultural commodities in the past. For example, researchers have attempted to predict prices by extracting price quotes, using sentiment analysis algorithms, through statistical information from news stories, and by other means. In this paper, we propose a methodology that predicts the daily retail price of pork in the South Korean domestic market based on news articles by incorporating deep learning and topic modeling techniques. To do this, we utilized news articles and retail price data from 2010 to 2019. We initially applied a topic modeling technique to obtain relevant keywords that can express price fluctuations. Based on these keywords, we constructed prediction models using statistical, machine learning, and deep learning methods. The experimental results show that there is a strong relationship between the meaning of news articles and the price of pork.
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Marushkevych, Dmytro, and Yevheniia Munchak. "Estimation of Parameters and Verification of Statistical Hypotheses for Gaussian Models of Stock Price." Lietuvos statistikos darbai 55, no. 1 (December 20, 2016): 91–101. http://dx.doi.org/10.15388/ljs.2016.13871.
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We construct models of asset prices on the Ukrainian stock market and analyse their applicability by checkingappropriate statistical hypotheses using actual observed data. We also analyse the presence of jumps in the dynamics ofdifferent assets and estimate the Hurst coefficient for the logarithm of the price of the asset by two different methods.
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Grigoreva, D. R., G. A. Gareeva, and A. Yu Ishimova. "COMPUTER TECHNOLOGIES IN STATISTICAL METHODS ON THE EXAMPLE OF POLYMER’S PRICES ANALYSIS." Scientific and Technical Volga region Bulletin 7, no. 1 (February 2017): 77–79. http://dx.doi.org/10.24153/2079-5920-2017-7-1-77-79.
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Kopytets, Nataliia. "Analysis of the price situation in the cattle meat market." Ekonomika APK 313, no. 11 (November 27, 2020): 52–59. http://dx.doi.org/10.32317/2221-1055.202011052.
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The purpose of the article is to investigate the current price situation in the cattle meat market, taking into account the peculiarities of the beef price chain “production – processing –trade – consumer”. Research methods. The following methods have been used in the research process: abstract and logical, system analysis – for generalize theoretical positions, formulating conclusions; comparative analysis – for compare indicators and identify trends in their change over time; statistical – for assessing the cattle meat market; tabular – for visual representation of the research results; monographic – for detailing the price situation in the beef market; graphic – for identify and illustrate the trends of the research economic phenomena. Research results. An analysis of the price situation in the cattle meat market with details of individual species priced in dif-ferent areas of the country. Trends and regularities of dynamics of prices for cattle and products of processing in wholesale and retail trade are estimated. There is a clear tendency of annual increase in prices for cattle meat market in Ukraine during 2017-2020. It was found that the increase in purchase prices for young cattle causes an increase in wholesale and retail prices for various types of meat. It is justified that the price is important to all cattle meat market participants. The level of prices affects the efficiency of both individual producers and the development of the economy of any country. Prices clearly reflect the processes of production, exchange, distribution and consumption. Scientific novelty. It is specified that under the conditions of low purchasing power of most of the population of Ukraine, the actual retail prices for beef and veal within the trade network are quite high and do not contribute to the growth of demand for this type of meat. Practical significance. The research results can be useful for all participants in the food chain “production – processing – distribution – consumption” of the cattle market. Tabl.: 1. Figs.: 1. Refs.: 23.
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C. Larson, Alexander, Rita L. Reicher, and David William Johnsen. "Threshold effects in pricing of high-involvement services." Journal of Product & Brand Management 23, no. 2 (April 14, 2014): 121–30. http://dx.doi.org/10.1108/jpbm-04-2013-0278.
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Purpose – The purpose of this research is to test for price threshold effects in the demand for high-involvement services for small businesses. Design/methodology/approach – The authors use a stated preference choice-based conjoint study of small business telecommunications demand. Using survey data, individual-level parameter estimates for a demand model are achieved via the Hierarchical Bayes method of estimation. Findings – For demand for small business telecommunications services, the authors find very strong positive impacts of nine-ending and zero-ending prices on the demand for a common bundle of telecommunications services (wired telephone service, broadband internet, and cellular telephone service), even at prices so high a shift in the left-most digit does not occur. Practical implications – The advertising, brand, or product manager or statistician who assumes threshold effects are not extant in high-involvement service demand may find conventional demand estimation methods lead to erroneous conclusions and less effective pricing strategies. Originality/value – In the statistical literature on price-ending effects on product demand, most products for which demand is modelled are low-involvement consumer products priced at less than ten monetary units per unit of product. There is a lacuna in this price-ending effects literature regarding small businesses and high-involvement services offered at three-digit prices via monthly subscription. This research indicates that testing for threshold effects should be de rigeur in the methodology of demand estimation for telecommunications or other high-involvement services.
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Kokot, Sebastian, and Marcin Bas. "Evaluation of the Applicability of Statistical Methods in Studies on Price Dynamics on the Real Estate Market." Real Estate Management and Valuation 21, no. 1 (May 1, 2013): 49–58. http://dx.doi.org/10.2478/remav-2013-0007.
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Abstract The specific character of the real estate market is the reason why observations of transaction prices seen as statistical variables are taken in a non-standard way. In the traditional approach each time period or specific moments of time are attributed with one observation of a studied variable per one object. In the case of the real estate market, this is not possible since transactions relate to different objects, i.e., properties, and occur at irregular, or even random, moments. This is why traditional methods used to examine the dynamics of economic phenomena must be adapted to specific conditions on the real estate market. Keeping that in mind, the aim of this paper is to adapt classical statistical examination methods of dynamics to specific conditions of the real estate market followed by the actual examination of the dynamics of real estate prices in three sub-segments of the housing market in Szczecin. On its basis, the authors evaluate various methods of examining real estate price dynamics in terms of their applicability in real estate appraisal procedures and, in a broader perspective, present characteristic phenomena that can be observed on the real estate market.
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Suradi, Jessica Prania, and Selly Eriska Marisa. "ANALISIS DAMPAK HARGA MINYAK MENTAH DUNIA, TINGKAT SUKU BUNGA DAN KURS VALUTA ASING TERHADAP INDEKS HARGA SAHAM PERTAMBANGAN PERIODE 2014 – 2016." Jurnal Bina Manajemen 8, no. 2 (March 31, 2020): 1–17. http://dx.doi.org/10.52859/jbm.v8i2.84.
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This study aims to look at the effect of world crude oil prices, interest rates, and foreign exchange rates on the mining sector stock price index for the 2014-2016 period. The research method used is descriptive statistical methods with quantitative research types. This study also uses analytical methods such as multiple regression analysis through t test and F test. Based on the F test (simultaneous) shows that world oil prices, interest rates, and foreign exchange rates affect simultaneously on the mining sector stock price index for the period 2014-2016 , while the t test (partial) shows that world crude oil prices a positive but not significant effect on the mining stock price index for the period 2014-2016, the interest rate has a negative effect and significant to the mining sector stock price index for the period 2014-2016, and the foreign exchange rate has a negative and significant effect on the price index mining sector shares in the 2014-2016 period.
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Bródka, Dawid, and Marcin Chciałowski. "PRICE VOLATILITY IN MACROECONOMIC STRUCTURE OF PRODUCTION IN POLAND." Acta Scientiarum Polonorum. Oeconomia 16, no. 3 (September 30, 2017): 5–14. http://dx.doi.org/10.22630/aspe.2017.16.3.28.
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In this article an empirical analysis of price volatility was conducted, on the basis of Polish macroeconomic data from 2010–2016 and theoretical framework proposed by Austrian School of Economics. The research was carried out using a number of statistical methods used on price indices representing different stages of production. The analysis allowed to establish conclusions about differing degree of price movements throughout Polish economy and its sectors, where prices of goods produced at the beginning of the structure were characterised by higher volatility than those produced in other branches. No statistically significant difference in price volatility was noted between consumer goods and intermediate goods stage.
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Öhman, Peter, and Darush Yazdanfar. "The nexus between stock market index and apartment and villa prices." International Journal of Housing Markets and Analysis 10, no. 3 (June 5, 2017): 450–67. http://dx.doi.org/10.1108/ijhma-09-2016-0069.
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Purpose The purpose of this study is to investigate the Granger causal link between the stock market index and housing prices in terms of apartment and villa prices. Design/methodology/approach Monthly data from September 2005 to October 2013 on apartment prices, villa prices, the stock market index, mortgage rates and the consumer price index were used. Statistical methods were applied to explore the long-run co-integration and Granger causal link between the stock market index and apartment and villa prices in Sweden. Findings The results indicate that the stock market index and housing prices are co-integrated and that a long-run equilibrium relationship exists between them. According to the Granger causality tests, bidirectional relationships exist between the stock market index and apartment and villa prices, respectively, supporting the wealth and credit-price effects. Moreover, variations in apartment and villa prices are primarily caused by endogenous shocks. Originality/value To the authors’ best knowledge, this study represents a first analysis of the causal nexus between the stock market and the housing market in terms of apartment and villa prices in the Swedish context using a vector error-correction model to analyze monthly data.
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Navickaitė, Roberta. "Application of Statistical Methods to the Assessment of Prices of Klaipeda City Apartments." Lietuvos statistikos darbai 53, no. 1 (December 20, 2014): 64–77. http://dx.doi.org/10.15388/ljs.2014.13896.
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The paper analyses the use of a nonlinear regression model, generalised linear model and generalised additive model(semi-parametric regression model) for creating real estate valuation models. These models are applied to data on transactions inKlaipeda city apartments. The aim is to create real estate valuation regression models applying various statistical methods and tocompare them with each other. The practical aspects of creating regression models are analysed and conclusions are presented in thepaper.
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Gupta, Shashi, Himanshu Choudhary, and D. R. Agarwal. "An Empirical Analysis of Market Efficiency and Price Discovery in Indian Commodity Market." Global Business Review 19, no. 3 (February 15, 2018): 771–89. http://dx.doi.org/10.1177/0972150917713882.
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The present article is an attempt to empirically investigate the long-term market efficiency and price discovery in Indian commodity futures market. The study has been conducted with eight commodities which include two agricultural commodities, two industrial commodities, two precious metal and two energy commodities. Sophisticated statistical methods like restricted cointegration and vector error correction model (VECM) are used to analyse the spot and futures prices time series. Restricted cointegration test shows that near-month futures prices for all the commodities are cointegrated with the spot prices but futures prices of all the commodities are inefficient to predict the future spot price. Indian commodity futures market evidenced as the thinly traded market (Kumar & Pandey, 2013, Journal of Indian Business Research, 5(2), 101–121) rejects the null hypothesis of efficiency and unbiasedness for all the eight commodities which reconfirms the result of Fortenbery and Zapata (1997, Journal of Futures Markets, 17(3), 279–301). The presence of short-term biases in the Indian futures market is evidenced in the results of VECM model which indicates the presence of informational efficiency. The statistically significant value of past prices of spot and futures confirm the short-term inefficiency and biasedness. The significant value of error correction term (ECT) of futures prices suggests that commodity futures are the most important indicator of commodity price movements. The important implication of the results is for market traders. They can use the futures prices to discover the new equilibrium and earn profits by transmitting it to the spot market. The better understanding of the interconnectedness of these market would be useful for policymakers who try to establish stability in the financial markets.
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MÜNNIX, MICHAEL C., RUDI SCHÄFER, and THOMAS GUHR. "STATISTICAL CAUSES FOR THE EPPS EFFECT IN MICROSTRUCTURE NOISE." International Journal of Theoretical and Applied Finance 14, no. 08 (December 2011): 1231–46. http://dx.doi.org/10.1142/s0219024911006838.
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We present two statistical causes for the distortion of correlations on high-frequency financial data. We demonstrate that the asynchrony of trades as well as the decimalization of stock prices has a large impact on the decline of the correlation coefficients towards smaller return intervals (Epps effect). These distortions depend on the properties of the time series and are of purely statistical origin. We are able to present parameter-free compensation methods, which we validate in a model setup. Furthermore, the compensation methods are applied to high-frequency empirical data from the NYSE's TAQ database. A major fraction of the Epps effect can be compensated. The contribution of the presented causes is particularly high for stocks that are traded at low prices.
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Mohapatra, Avilasa, Smruti Rekha Das, Kaberi Das, and Debahuti Mishra. "Applications of neural network based methods on stock market prediction: survey." International Journal of Engineering & Technology 7, no. 2.6 (March 11, 2018): 71. http://dx.doi.org/10.14419/ijet.v7i2.6.10070.
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Financial forecasting is one of the domineering fields of research, where investor’s money is at stake due to the rise or fall of the stock prices which unpredictable and fluctuating. Basically as the demand for stock markets has been rising at an unprecedented rate so its prediction becomes all the more exciting and challenging. Prediction of the forthcoming stock prices mostly Artificial Neural Network (ANN) based models are taken into account. The other models such as Bio-inspired Computing, Fuzzy network model etc., considering statistical measures, technical indicators and fundamental indicators are also explored by the researchers in the field of financial application. Ann’s development has led the investors for hoping the best prediction because networks included great capability of machine learning such as classification and prediction. Most optimization techniques are being used for training the weights of prediction models. Currently, various models of ANN-based stock price prediction have been presented and successfully being carried to many fields of Financial Engineering. This survey aims to study the mostly used ANN and related representations on Stock Market Prediction and make a proportional analysis between them.
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Barańska, Anna, and Beata Śpiewak. "The Influence of Chosen Statistical Methods of Detecting Outliers on Property Valuation Result." Real Estate Management and Valuation 29, no. 1 (March 1, 2021): 87–97. http://dx.doi.org/10.2478/remav-2021-0008.
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Abstract The subject of the thesis concerns the application of selected statistical methods searching for outliers in the process of determining the value of real estate, based on a functional model adjusted to market data. The collected research material consisted of data on land properties, which were the subject of transactions on local markets, for which there was no information regarding the specific conditions of concluding the sale agreement. After the initial selection of data regarding the purpose of the property in the local plan, the type of property rights being sold and the size of the shares sold - a functional model was adjusted to the obtained data, showing the relationship between the price being the dependent variable and the features of the property being the independent variables. Then, two statistical methods of searching for outliers which are significantly different in their algorithms, i.e. Cook’s distance and robust estimation method called Pope’s method, were applied to each model. The last stage was to determine the model values of selected properties and to compare the obtained results with the known transaction prices of the parcels being the subject of the valuation. The conducted research allowed for the verification of the influence of significantly different statistical methods searching for outliers on the property valuation result and its accuracy.
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Cellmer, Radosław. "The Possibilities and Limitations of Geostatistical Methods in Real Estate Market Analyses." Real Estate Management and Valuation 22, no. 3 (October 1, 2014): 54–62. http://dx.doi.org/10.2478/remav-2014-0027.
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Abstract In the traditional approach, geostatistical modeling involves analyses of the spatial structure of regionalized data, as well as estimations and simulations that rely on kriging methods. Geostatistical methods can complement traditional statistical models of property transaction prices, and when combined with those models, they offer a comprehensive tool for spatial analysis that is used in the process of developing land value maps. Transaction prices are characterized by mutual spatial correlations and can be considered as regionalized variables. They can also be regarded as random variables that have a local character and a specific probability distribution. This study explores the possibilities of applying geostatistical methods in spatial modeling of the prices of undeveloped land, as well as the limitations associated with those methods and the imperfect nature of the real estate market. The results are discussed based on examples, and they cover both the modeling process and the generated land value maps.
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Astuti, Astuti, Elly Susanti, and Hery Pandapotan Silitonga. "ANALISIS DAMPAK RASIO KEUANGAN PERUSAHAAN TERHADAP HARGA SAHAM PADA PERUSAHAAN YANG TERCATAT PADA JII." Jesya (Jurnal Ekonomi & Ekonomi Syariah) 3, no. 2 (May 31, 2020): 108–217. http://dx.doi.org/10.36778/jesya.v3i2.202.
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The research method uses qualitative data, secondary data sources, using documentation methods, classic data assumption test analysis techniques, coefficient of determination, hypothesis testing. The object of research in companies incorporated in the Jakarta Islamic Index in the period 2014 - 2018. The results of this study by F statistical tests show that all independent variables influence the dependent variable. In statistical test t current ratio has a negative and significant effect on stock prices. Size and Debt to Asset Ratio influence and are not significant on stock prices. Return on Assets has a positive and significant effect on stock prices.
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Gupta, Saloni. "Statistical Arbitrage: Profits through Pairs Trading." Journal of Business Management and Information Systems 2, no. 1 (June 30, 2015): 140–48. http://dx.doi.org/10.48001/jbmis.2015.0201013.
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Statistical arbitrage is a popular device among hedge fund managers and assets management professionals. It refers to simultaneous buying and selling two different capital assets to earn super-normal profit. By identifying persistent anomalies that violate the efficient market hypothesis, statistical methods can be used to create a trading strategy to generate profit with high probability. A pair trading is one such trading strategy which is based on statistical arbitrage process. Pairs trading can be simple in concept, but can be one of the most complex types of trading in practice. The starting point of this strategy is that stocks that have historically had the same trading patters will have so in future as well. If there is a deviation from the historical mean this creates a trading opportunity, which can be exploited. Gains are earned when the price relationship is resorted. The basic premise of this strategy is that stock prices follow a mean reverting process. The objective of this paper is to identify arbitrage opportunities and calculating profits earned through these opportunities by using statistical tools.
Many questions need to be answered before one can implement such strategy viz. which pair of stocks should be traded, how much do we buy/sell of each stock, how to catch the signal of an opportunity (i.e opening a position) and when to close the position so that profit could be earned. In this paper we have taken daily closing prices from 1/1/2010 to 1/1/2011 of thirty scrips of BSE-Sensex to form pairs. Pairs are formed on the basis of minimum distances between two stocks. We have decided not to invest anything. That is, purchase the same rupee amount of the long stock as we sell of the short stock so that strategy is self-financing. We open a position when the absolute value of the difference gets larger than two of its historical standardization. To unwind the position, we wait until the first time it crosses zero. To calculate the profit/loss of this strategy, we have used “R-Software”. It is observed that profit could be earned through pairs trading if it is applied without losing patience. By identifying persistent anomalies that violate the efficient market hypothesis, statistical methods can be used to create a trading strategy to generate profit with high probability.
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Viviani, Emma, Luca Di Persio, and Matthias Ehrhardt. "Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case." Energies 14, no. 2 (January 11, 2021): 364. http://dx.doi.org/10.3390/en14020364.
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In this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods for point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, and we then exploit Neural Networks approaches to derive a hybrid model for probabilistic type forecasting. We show that our solution reaches the highest standard both in terms of efficiency and precision by testing its output on German electricity prices data.
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Viviani, Emma, Luca Di Persio, and Matthias Ehrhardt. "Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case." Energies 14, no. 2 (January 11, 2021): 364. http://dx.doi.org/10.3390/en14020364.
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In this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods for point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, and we then exploit Neural Networks approaches to derive a hybrid model for probabilistic type forecasting. We show that our solution reaches the highest standard both in terms of efficiency and precision by testing its output on German electricity prices data.
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Jasińska, Elżbieta, and Edward Preweda. "Statistical Modelling of the Market Value of Dwellings, on the Example of the City of Kraków." Sustainability 13, no. 16 (August 20, 2021): 9339. http://dx.doi.org/10.3390/su13169339.
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The analysis of a city’s spatial development, in terms of a location that meets the needs of its inhabitants, requires many approaches. The preliminary assessment of the collected material showed that there was real estate in the database whose price did not have market characteristics. For the correct formulation of the valuation model, it is necessary to detect and eliminate or reduce the impact of these properties on the valuation results. In this study, multivariate analysis was used and three methods of detecting outliers were verified. The database of 8812 residential premises traded on the primary market in Kraków was analyzed. In order to detect outliers, the following indices were determined: projection matrix, Mahalanobis distances, standardized chi test and Cook distances. Critical values were calculated based on the formulas proposed in the publication. The probability level was P = 0.95. The article shows that the selected methods of eliminating outliers—the methods of standardized residuals and the Cook’s distance method give similar regression models. Further analysis (with the use of classification tree methods) made it possible to distinguish zones that are homogeneous in terms of price dispersion. In these zones, a set of features influencing real estate prices were determined.
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Ehrlich, Gabriel, John Haltiwanger, Ron Jarmin, David Johnson, and Matthew D. Shapiro. "Minding Your Ps and Qs: Going from Micro to Macro in Measuring Prices and Quantities." AEA Papers and Proceedings 109 (May 1, 2019): 438–43. http://dx.doi.org/10.1257/pandp.20191004.
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Key macro indicators such as output, productivity, and inflation are based on a complex system across multiple statistical agencies using different samples and levels of aggregation. The Census Bureau collects nominal sales, the Bureau of Labor Statistics collects prices, and the Bureau of Economic Analysis constructs nominal and real GDP using these data and other sources. The price and quantity data are integrated at a high level of aggregation. This paper explores alternative methods for reengineering key national output and price indices using item-level data. Such reengineering offers the promise of greatly improved key economic indicators along many dimensions.
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Zawojska, Aldona, and Beata Horbowiec. "Ryzyko cenowe na rynku produktów rolno-żywnościowych: źródła, skutki i sposoby zarządzania." Zeszyty Naukowe SGGW - Ekonomika i Organizacja Gospodarki Żywnościowej, no. 115 (September 30, 2016): 37–57. http://dx.doi.org/10.22630/eiogz.2016.115.31.
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This paper aims to identify the causes and consequences of price risk in agri-food market as well as its mitigation methods. The research uses the scientific literature review approach and statistical analysis, applying the coefficients of variation of price indices for the global agricultural production and for the procurement of particular agricultural products in Poland over the period from 1995 to 2013. The price data is derived from the Central Statistical Office of Poland (GUS). Our study confirms the findings of other investigators that crop production is characterised by larger price fluctuations than animal production. An overview of empirical research shows that the volatility of agricultural input and output prices is transmitted along the food supply chain, thus exposing all its participants to price risk. In order to response to agricultural price uncertainty and volatility, instruments stabilizing agricultural markets as well as public and private risk management tools can be applied.
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Faliński, Przemysław. "INVESTMENT RISK MANAGEMENT BASED ON QUOTATIONS OF OIL COMPANIES, OIL AND DOLLAR." Zeszyty Naukowe Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach. Seria: Administracja i Zarządzanie, no. 53(126) (January 27, 2021): 37–45. http://dx.doi.org/10.34739/zn.2020.53.04.
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With the non-random movement of the prices of exchange trading objects in mind, by means of the methods and tools of chaos theory, it is possible to show that price changes are subject to the laws of deterministic chaos. This is a new look at this subject compared to the statistical methods that have been used for years, which in most cases assume that the distribution of the rate of returns of the examined series is normal. The aim of the study is to determine the nature of the changes in oil, dollar and Polish fuel prices: whether they are random or determined. In addition, the second aim is to investigate the cause and effect relationship between the price changes of the above-mentioned stocks. Tools such as rescaled range analysis, mean and variance stability analysis and technical analysis will be used. Conclusions resulting from the examination of the three indicated values should be interesting for capital market participants. The article ends with a short-term forecast for WIG-oil&gas.
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Zyga, Jacek. "Connection Between Similarity and Estimation Results of Property Values Obtained by Statistical Methods." Real Estate Management and Valuation 24, no. 3 (September 1, 2016): 5–15. http://dx.doi.org/10.1515/remav-2016-0017.
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Abstract The article discusses the topic of the application range of statistical methods in estimating property value on the grounds of a comparative approach. The analysis of application effects to estimate the unitary value of properties, respectively similar and dissimilar sets of market properties, by using the method of least squares and a linear price model. The prepared test set was developed from a priori assumed explanatory variable values as well as deterministically specified dependent variables (simulated prices) which were subjected to additional modification by a random factor. On the basis of the prepared set and series of accounting experiments, the estimation effects of any property out of a tested set were analyzed, understood as the determination of the value of the function of explanatory variables in the way of extrapolation or interpolation of values describing these variables. The experiments carried out show that the estimation of an explanatory variable for a random property out of a set of elements serving as the estimation base can be reliable only when it is related to the interpolation in the set of explanatory variables of this base. The application as an estimation base – a set in relation to which explanatory variables of the estimated property exceed the limits of corresponding variables, requires the completion of a basic set with records describing properties similar or close to the estimated property so that the values of explanatory variables for the estimated property are contained in the appropriate subsections of values of corresponding explanatory variables of the basic set. The paper refers to the issue of defining property market value indicating, by the prism of conducted experiments, that the estimation results obtained by means of statistical methods do not always meet the requirements of the statutory definition of market value, and hear rather in the direction of a result corresponding to the so-called “desk appraisal” result.
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Nataraj, S., C. Alvarez, L. Sada, A. A. Juan, J. Panadero, and C. Bayliss. "Applying Statistical Learning Methods for Forecasting Prices and Enhancing the Probability of Success in Logistics Tenders." Transportation Research Procedia 47 (2020): 529–36. http://dx.doi.org/10.1016/j.trpro.2020.03.128.
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Gani, Walid. "Can statistical methods help prove excessiveness of dominant firm's prices Evidence from the Tunisian Competition Council." International Journal of Economics and Business Research 20, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijebr.2020.10030419.
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Henri Drouhin, Pierre-Arnaud, and Arnaud Simon. "Are property derivatives a leading indicator of the real estate market?" Journal of European Real Estate Research 7, no. 2 (July 29, 2014): 158–80. http://dx.doi.org/10.1108/jerer-08-2013-0014.
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Purpose – This paper aims to analyze the statistical characteristics of changes in property forward prices. As highlighted in a survey conducted at the MIT Center for Real Estate in 2006, the relatively weak understanding in their prices is one of the most important barriers in their use. In this context, the analysis of the forward price term structure is essential. Do the short- and long-term forward prices behave similarly? Do property derivatives behave like other derivative assets or other related assets? This study also investigates the lead–lag relationship between spot and forward returns for different maturities. Design/methodology/approach – Using four years and nine months of data on the UK Investment Property Databank (IPD), all property total return swaps are examined. We strip the swaps into their forwards and study their statistical characteristics (the first four moments and their autocorrelation levels). The relationships among the forward contracts, the underlying asset (IPD index and IPD unsmoothed) and other assets (risk-free rate, listed real estate) are explored. Using the Yiu et al. (2005) methodology, the lead–lag relationship between the spot and the forwards is assessed. Findings – The index appears to be significantly less volatile and less efficient, in terms of correlation than its own derivative contracts. Moreover, changes in forward prices are leading indicators of the IPD index. Their risks tend to converge with the implied volatility of the REIT’s operating asset but without being affected by the general stock market risks. Regarding the forward price–discovery function, investors should collect information not only from the spot market but also, maybe primarily, from the derivative market. Originality/value – In this paper, we use a never-exploited database that is relative to the quotes of the UK IPD swaps. It is the first attempt to analyze the statistical characteristics of their changes. Our results show that these prices are clearly superior to the spot series, in terms of risks but without behaving affected by the tyranny of the past values. These findings may conduct to consider new methods to unsmooth current real estate indices. Characterized by a strong sensitivity to the changes in the information set, property derivative-based indicators should lead to increased efficiency in the spot market.
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Vochozka, Marek, Jakub Horak, and Tomas Krulicky. "Innovations in Management Forecast: Time Development of Stock Prices with Neural Networks." Marketing and Management of Innovations, no. 2 (2020): 324–39. http://dx.doi.org/10.21272/mmi.2020.2-24.
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Accurate prediction of stock market values is a challenging task for over decades. Prediction of stock prices is associated with numerous benefits including but not limited to helping investors make wise decisions to accumulate profits. The development of the share price is a dynamic and nonlinear process affected by several factors. What is interesting is the unpredictability of share prices due to the global financial crisis. However, classical methods are no longer sufficient for the application of share price development prediction.However, over-relying on prediction data can lead to losses in the case of software malfunction. This paper aims to innovate the prediction management when predicting the share price development over time by the use of neural networks. For the contribution, the data on the prices of CEZ, a.s. shares obtained from the Prague Stock Exchange database. The stock price data are available for the period 2012-2017. In the case of Statistica software, the multilayer perceptron networks (MLP) and the radial basis function networks (RBF) are generated. In the case of Matlab software, the Support Vector Regression (SVR) and the Back-Propagation Neural Network (BPNN) are generated. The networks with the best characteristics are retained and based on the statistical interpretation of the results, and all are applicable in practice. In all data sets, MLP networks show stable performance better than in the case of SVR and BPNN networks. As for the final assessment, the deviation of 2.26% occurs in the most significant differential of the maximal and the minimal prediction. It is not necessarily significant regarding the price of one stock. However, in the case of purchasing or selling a large number of stocks, the difference may seem significant. Therefore, in practice, the application of two networks is recommended: MLP 1-2-1 and MLP 1-5-1. The first network always represents a pessimistic, minimal prediction. The second one of the recommended networks is an optimistic, maximal prediction. The actual situation should correspond to the interval of the difference between the optimistic and pessimistic prediction. Keywords: Statistica software, Matlab software, stock price development, neural networks, prediction.
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Lutz, Jack, Theodore E. Howard, and Paul E. Sendak. "Stumpage Price Reporting in the Northern United States." Northern Journal of Applied Forestry 9, no. 2 (June 1, 1992): 69–73. http://dx.doi.org/10.1093/njaf/9.2.69.
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Abstract Data collection, processing, and dissemination methods of stumpage price reports in the northern United States vary considerably among the states due to differing objectives, markets, traditions, and budget constraints. Data are collected primarily from limited segments of the market with little quality control exerted by the compiling agencies. Prices are reported in terms of species, timber quality, and major product, and range from detailed lists to gross aggregates. Important areas for improving the price reports include more rapid dissemination, broader sampling of transactions, improved quality control and statistical analysis, and increased computerization. North. J. Appl. For. 9(2):69-73.
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Kobylińska, Katarzyna, and Radosław Cellmer. "The Use of Indicator Kriging for Analyzing Prices in the Real Estate Market." Real Estate Management and Valuation 24, no. 4 (December 1, 2016): 5–15. http://dx.doi.org/10.1515/remav-2016-0025.
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Abstract The patterns and relations between real estate prices and the factors which shape them can be presented, among others, in the form of traditional statistical models, as well as by means of geostatistical methods. In the case of research involving the diagnosis and prediction of transaction prices, the key role is played by the spatial aspect, hence the particular significance of geostatistical methods using spatial information. The main goal of the conducted research is to determine the probability of the occurrence of a price in a given location in space by means of geostatistical simulation - indicator kriging. Indicator kriging does not use the entirety of information included in a dataset, and can, therefore, be useful in situations when the assumptions involving the spatial stationarity of the examined phenomenon are not fulfilled by an entire dataset, but are fulfilled by a certain part of the set. The maps of the probability with which a regionalized variable (price) takes on particular values, limited by arbitrarily selected cutoff values, were prepared by means of indicator kriging. An alternative approach to the preparation of price probability maps is the determination of the spatial distribution of areas in which, with the assumed probability, the value of the price falls within the predetermined ranges. The paper presents both the essence as well as a theoretical description of the geostatistical simulation of a transaction on the real estate market, as well as the results of an experiment involving the transaction prices of real properties located in the north-western part of the city of Olsztyn. The result of the research is a set of virtual information about the places in which the transactions have occurred and about the prices of real estate, constituting a reflection of the market processes which may take place in the near future.
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LOFFREDO, MARIA I. "ON THE STATISTICAL PHYSICS CONTRIBUTION TO QUANTITATIVE FINANCE." International Journal of Modern Physics B 18, no. 04n05 (February 20, 2004): 705–13. http://dx.doi.org/10.1142/s021797920402432x.
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A short review is given of some research topics recently developed in the framework of quantitative finance and which can be referred to the effort of adapting methods and technologies of statistical physics to the analysis of economic systems. In particular we emphasize the role of a different, new perspective, in approaching financial problems, originated within the theory of complex systems and based on concepts like universality, scaling and correlation properties. Once applied to the time evolution of prices and volatility, this approach allows for the recognition of long-range and nonlinear effects in financial time series.
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Caporale, Guglielmo Maria, and Alex Plastun. "Daily abnormal price changes and trading strategies in the FOREX." Journal of Economic Studies 48, no. 1 (September 9, 2020): 211–22. http://dx.doi.org/10.1108/jes-11-2019-0503.
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PurposeThis paper explores abnormal price changes in the FOREX by using both daily and intraday data on the EURUSD, USDJPY, USDCAD, AUDUSD and EURJPY exchange rates over the period 01.01.2008–31.12.2018.Design/methodology/approachIt applies a dynamic trigger approach to detect abnormal price changes and then various statistical methods, including cumulative abnormal returns analysis, to test the following hypotheses: the intraday behaviour of hourly returns on overreaction days is different from that on normal days (H1), there are detectable patterns in intraday price dynamics on days with abnormal price changes (H2) and on the following days (H3).FindingsThe results suggest that there are statistically significant differences between intraday dynamics on days with abnormal price changes and normal days respectively; also, prices tend to change in the direction of the abnormal change during that day, but move in the opposite direction on the following day. Finally, there exist trading strategies that generate abnormal profits by exploiting the detected anomalies, which can be seen as evidence of market inefficiency.Originality/valueNew evidence on abnormal price changes and related trading strategies in the FOREX.
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Bradic-Martinovic, Aleksandra. "Stock market prediction using technical analysis." Ekonomski anali 51, no. 170 (2006): 125–46. http://dx.doi.org/10.2298/eka0670125b.
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Technical analysis (TA) is a form of analyzing market encompassing supply and demand of securities according to the study of their prices and trading volume. Using the appropriate methods, TA aims to identify price movements in the stock market, futures or currencies. In short, TA analysis is the process by which "future price movements are formulated according to the price history". TA originates from the work of Charles Dow and his conclusions about the global behavior of the market, as well as from Elliot Wave Theory. Dow did not regard its theory as a tool for stock market movement prediction, nor as a guide for investors, but as a kind of barometer of general market movements. The term TA methods encompasses all the methods used in tracking prices aiming to clearly predict future events. Many different methods, mainly statistical, are used in technical analysis, the most popular ones being: establishing and following trends using moving average, recognizing price momentum, calculating indicators and oscillators, as well as cycle analysis (structure indicators). It is also necessary to point out that TA is not a science in the true meaning of the term, and that methods it uses frequently deviate from the conventional manner of their use. The main advantage of these methods is their relative ease of use, aiming to give as clear picture as possible of price movements, while at the same time avoiding the use of complicated and complex mathematical methods. The reason for this is simple and is reflected in the dynamics of financial markets, where changes occur during short periods of time and where prompt decision-making is of vital importance.
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Bildirici, Melike, Nilgun Guler Bayazit, and Yasemen Ucan. "Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM." Energies 13, no. 11 (June 10, 2020): 2980. http://dx.doi.org/10.3390/en13112980.
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Under the influence of the COVID-19 pandemic and the concurrent oil conflict between Russia and Saudi Arabia, oil prices have exhibited unusual and sudden changes. For this reason, the volatilities of the West Texas Intermediate (WTI), Brent and Dubai crude daily oil price data between 29 May 2006 and 31 March 2020 are analysed. Firstly, the presence of chaotic and nonlinear behaviour in the oil prices during the pandemic and the concurrent conflict is investigated by using the Shanon Entropy and Lyapunov exponent tests. The tests show that the oil prices exhibit chaotic behavior. Additionally, the current paper proposes a new hybrid modelling technique derived from the LSTARGARCH (Logistic Smooth Transition Autoregressive Generalised Autoregressive Conditional Heteroskedasticity) model and LSTM (long-short term memory) method to analyse the volatility of oil prices. In the proposed LSTARGARCHLSTM method, GARCH modelling is applied to the crude oil prices in two regimes, where regime transitions are governed with an LSTAR-type smooth transition in both the conditional mean and the conditional variance. Separating the data into two regimes allows the efficient LSTM forecaster to adapt to and exploit the different statistical characteristics and ARCH and GARCH effects in each of the two regimes and yield better prediction performance over the case of its application to all the data. A comparison of our proposed method with the GARCH and LSTARGARCH methods for crude oil price data reveals that our proposed method achieves improved forecasting performance over the others in terms of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) in the face of the chaotic structure of oil prices.
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Annisa, Mutiara Lusiana, and Rizki Fitri Amalia. "PENGARUH STRUKTUR MODAL DAN PROFITABILITAS TERHADAP HARGA SAHAM (Studi Kasus Pada Perusahaan Asuransi yang Terdaftar Di Bursa Efek Indonesia Periode 2015 sampai dengan 2017)." BALANCE Jurnal Akuntansi dan Bisnis 3, no. 2 (November 1, 2018): 308. http://dx.doi.org/10.32502/jab.v3i2.1252.
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This research purpose to know and explaining the effect of capital structure and profitability stock prices case of studies at the insurance companies that listed on the Indonesia Stock Exchange from period time of 2015 untul 2017. The type of research used is explanatory causality research with quantitative approach. Based on sampling technique “purposive sampling” researcher used 11 companies that meets the criteria. The method of analysis used in this research is statistical descriptive analysis multiple linear analysis. The result of the methods show that the capital structure and profitability have a significant effect on the structure of the stock price. The results of research for capital structure has an insignificant the stock price and profitability has a significant the stock price.
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Parengkuan, Frangky Christoffel. "Analisis Sentimen Perubahan Harga Emas Dunia, Nilai Tukar Rupiah dan Indeks Harga Saham Gabungan terhadap Keputusan Membeli Produk Reksadana Saham." Jurnal Ilmiah Magister Managemen 4, no. 2 (December 1, 2018): 1–17. http://dx.doi.org/10.34010/jimm.v4i2.3768.
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The objective of this study are (1) to describe the exchange of world gold price, USD/IDR exchage rate, the movement in Indonesian Stocks Exchange Indexs (IHSG) and investor’s sentiment to buy Equity Fund (2) to verify the corelation against the world gold price, USD/IDR exchange rate and movement of Indonesian Stocks Exchange Indexs (3) to calculate how they will make effect to decision of buying Equity Fund. The Equity Funds come from all funds that sold by Bank Danamon Region 8 Jawa Barat with minimum existing for more than 10 years performance. Analytical tools that used in this study is path analysis with two statistical methods. The result showed that even in LISREL and SPSS the correlation between world gold prices and USD/IDR Exchange Rate is too low, but correlation between world gold prices and IHSG is strong enough. The strongest coorelation is on USD/IDR exchange rate and IHSG. When it is tested in path analysis, the result shows that all independent variabel have significant effect to investor sentiment of buying Equity Fund.
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Ani, Dorothy Patience, Emmanuel Adah Onoja, and Isaac Terna Humbe. "Partial Fuel Subsidy Removal in Nigeria." International Journal of Social Ecology and Sustainable Development 12, no. 1 (January 2021): 98–114. http://dx.doi.org/10.4018/ijsesd.2021010108.
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The ripple effects of the petrol crisis on the Nigerian economy is multi-faceted: price distortions, volatilities, dutch-disease, corruption, and inefficiencies. This study assessed the effects of partial fuel subsidy removal on agricultural sector and Nigerian economy. The study made use of secondary data obtained from Central Bank of Nigeria Statistical Bulletins, Petroleum Product Price Regulatory Agency (PPPRA), National Bureau of Statistics, Benue State Agricultural and Rural Development Authority (BNARDA), and FAO. Johansen co-integration model and t-test were the analytical tools used. After appropriate robustness checks and ensuring data stationarity, the study found that partial fuel subsidy removal had significant positive influence on the country's GDP, significantly reduced inflation rate, and also reduced life expectancy of Nigerians. Specifically, a percentage increase in petrol price significantly increases GDP by 9.8%; a percentage increase in petrol price increases the prices of rice and maize by 0.75% and 1.50% respectively. The study concludes that increased petrol price had positive effects on GDP and adverse effects on the prices of crop produce. Government should diversify and develop other economies and provide adequate infrastuctural facilities to cushion the effects of subsidy removal. Organic and low-input methods of farming should be adopted to reduce the need for fuel inputs to the food system at all levels.
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Žmuk, Berislav. "Capabilities of Statistical Residual-Based Control Charts in Short- and Long-Term Stock Trading." Naše gospodarstvo/Our economy 62, no. 1 (March 1, 2016): 12–26. http://dx.doi.org/10.1515/ngoe-2016-0002.
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Abstract The aim of this paper is to introduce and develop additional statistical tools to support the decision-making process in stock trading. The prices of CROBEX10 index stocks on the Zagreb Stock Exchange were used in the paper. The conducted trading simulations, based on the residual-based control charts, led to an investor’s profit in 67.92% cases. In the short run, the residual-based cumulative sum (CUSUM) control chart led to the highest portfolio profits. In the long run, when average stock prices were used and 2-sigma control limits set, the residual-based exponential weighted moving average control chart had the highest portfolio profit. In all other cases in the long run, the CUSUM control chart appeared to be the best choice. The acknowledgment that the SPC methods can be successfully used in stock trading will, hopefully, increase their use in this field.
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Palát, M., Š. Dvořáková, and N. Kupková. " Consumption of beef in the Czech Republic." Agricultural Economics (Zemědělská ekonomika) 58, No. 7 (July 23, 2012): 308–14. http://dx.doi.org/10.17221/72/2011-agricecon.
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The paper is aimed at the demand for beef. Its objective is to evaluate the development of beef consumption in the market of the Czech Republic, and particularly to identify the factors affecting the level of demand for beef. It refers to the analysis of the development of beef consumption in the Czech Republic depending on its price and costs of the selected kinds of its near substitutes, when their relationships are evaluated through their relationships are evaluated methods of regression and correlation analysis. The paper proves statistically the existence of relations among these crucial factors determining the demand. There are, of course, other factors affecting the position and tendency of a demand curve. It refers, for example, to various tastes, customs, traditions, the degree of urbanization, the possible health benefits or risks, legislation or the expected decline or increase of prices of the particular kinds of meat. All factors mentioned above cannot be, however, included into the analysis because their values are not available and many of them cannot be even quantified. Results of the statistical analysis prove the fundamental role of final consumers in forming the demand for beef, when they are above all affected by prices of beef and its substitutes.
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Mansfield, Sarah J. "Generic drug prices and policy in Australia: room for improvement? A comparative analysis with England." Australian Health Review 38, no. 1 (2014): 6. http://dx.doi.org/10.1071/ah12009.
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Objective To assess the degree to which reimbursement prices in Australia and England differ for a range of generic drugs, and to analyse the supply- and demand-side factors that may contribute to these differences. Methods Australian and English reimbursement prices were compared for a range of generic drugs using pricing information obtained from government websites. Next, a literature review was conducted to identify supply- and demand-side factors that could affect generic prices in Australia and England. Various search topics were identified addressing potential supply-side (e.g. market approval, intellectual property protection of patented drugs, generic pricing policy, market size, generic supply chain and discounting practices) and demand-side (consumers, prescribers and pharmacists) factors. Related terms were searched in academic databases, official government websites, national statistical databases and internet search engines. Results Analysis of drug reimbursement prices for 15 generic molecules (representing 45 different drug presentations) demonstrated that Australian prices were on average over 7-fold higher than in England. Significant supply-side differences included aspects of pricing policy, the relative size of the generics markets and the use of clawback policies. Major differences in demand-side policies related to generic prescribing, pharmacist substitution and consumer incentives. Conclusions Despite recent reforms, the Australian Government continues to pay higher prices than its English counterpart for many generic medications. The results suggest that particular policy areas may benefit from review in Australia, including the length of the price-setting process, the frequency of subsequent price adjustments, the extent of price competition between originators and generics, medical professionals’ knowledge about generic medicines and incentives for generic prescribing. What is known about the topic? Prices of generic drugs have been the subject of much scrutiny over recent years. From 2005 to 2010 the Australian Government responded to observations that Pharmaceutical Benefits Scheme prices for many generics were higher than in numerous comparable countries by instituting several reforms aimed at reducing the prices of generics. Despite this, several studies have demonstrated that prices for generic statins (one class of cholesterol-lowering drug) are higher in Australia compared with England and many other developed countries, and prices of numerous other generics remain higher than in the USA and New Zealand. Recently there has been increasing interest in why these differences exist. What does this paper add? By including a much larger range of commonly used and costly generic drugs, this paper builds significantly on the limited previous investigations of generic drug prices in Australia and England. Additionally, this is the first comprehensive investigation of multiple supply- and, in particular, demand-side factors that may explain any price differences between these countries. What are the implications for practitioners? Practitioners may contribute to the higher prices of generic medications in Australia compared with England through relatively low rates of generic prescribing. There are also significant implications for health policy makers, as this paper demonstrates that if Australia achieved the same prices as England for many generic drugs there could be substantial savings for the Pharmaceutical Benefits Scheme.
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Horváth, József, and Sándor Kovács. "The Examination of the Effects of Value Modifying Factors on Dairy Farms." Acta Agraria Debreceniensis, no. 24 (October 11, 2006): 36–40. http://dx.doi.org/10.34101/actaagrar/24/3222.
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We wish to present a method to quantify the value modifying effects when comparing animal farms. To achieve our objective, multi-variable statistical methods were needed. We used a principal component analysis to originate three separate principal components from nine variables that determine the value of farms. A cluster analysis was carried out in order to classify farms as poor, average and excellent. The question may arise as to which principal components and which variables determine this classification.After pointing out the significance of variables and principal components in determining the quality of farms, we analysed the relationships between principal components and market prices. Some farms did not show the expected results by the discriminant analysis, so we supposed that the third principal component plays a great role in calculating prices. To prove this supposition, we applied the logistic regression method. This method shows how great a role the principal components play in classifying farms on the basis of price categories.
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McCord, Michael James, John McCord, Peadar Thomas Davis, Martin Haran, and Paul Bidanset. "House price estimation using an eigenvector spatial filtering approach." International Journal of Housing Markets and Analysis 13, no. 5 (November 14, 2019): 845–67. http://dx.doi.org/10.1108/ijhma-09-2019-0097.
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Purpose Numerous geo-statistical methods have been developed to analyse the spatial dimension and composition of house prices. Despite these advances, spatial filtering remains an under-researched approach within house price studies. This paper aims to examine the spatial distribution of house prices using an eigenvector spatial filtering (ESF) procedure, to analyse the local variation and spatial heterogeneity. Design/methodology/approach Using 2,664 sale transactions over the one year period Q3 2017 to Q3 2018, an eigenvector spatial filtering approach is applied to evaluate spatial patterns within the Belfast housing market. This method consists of using geographical coordinates to specify eigenvectors across geographic distance to determine a set of spatial filters. These convey spatial structures representative of different spatial scales and units. The filters are incorporated as predictors into regression analyses to alleviate spatial autocorrelation. This approach is intuitive, given that detection of autocorrelation in specific filters and within the regression residuals can be markers for exclusion or inclusion criteria. Findings The findings show both robust and effective estimator consistency and limited spatial dependency – culminating in accurately specified hedonic pricing models. The findings show that the spatial component alone explains 14.6 per cent of the variation in property value, whereas 77.6 per cent of the variation could be attributed to an interaction between the structural characteristics and the local market geography expressed by the filters. This methodological step reduced short-scale spatial dependency and residual autocorrelation resulting in increased model stability and reduced misspecification error. Originality/value Eigenvector-based spatial filtering is a less known but suitable statistical protocol that can be used to analyse house price patterns taking into account spatial autocorrelation at varying (different) spatial scales. This approach arguably provides a more insightful analysis of house prices by removing spatial autocorrelation both objectively and subjectively to produce reliable, yet understandable, regression models, which do not suffer from traditional challenges of serial dependence or spatial mis-specification. This approach offers property researchers and policymakers an intuitive but comprehensible approach for producing accurate price estimation models, which can be readily interpreted.