Academic literature on the topic 'Profit Australia Forecasting'
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Journal articles on the topic "Profit Australia Forecasting"
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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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Hammer, GL, DP Holzworth, and R. Stone. "The value of skill in seasonal climate forecasting to wheat crop management in a region with high climatic variability." Australian Journal of Agricultural Research 47, no. 5 (1996): 717. http://dx.doi.org/10.1071/ar9960717.
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In Australia, and particularly in the northern part of the grain belt, wheat is grown in an extremely variable climate. The wheat crop manager in this region is faced with complex decisions on choice of planting time, varietal development pattern, and fertiliser strategy. A skilful seasonal forecast would provide an opportunity for the manager to tailor crop management decisions more appropriately to the season. Recent developments in climate research have led to the development of a number of seasonal climate forecasting systems. The objectives of this study were to determine the value of the capability in seasonal forecasting to wheat crop management, to compare the value of the existing forecast methodologies, and to consider the potential value of improved forecast quality. We examined decisions on nitrogen (N) fertiliser and cultivar maturity using simulation analyses of specific production scenarios at a representative location (Goondiwindi) using long-term daily weather data (1894-1989). The average profit and risk of making a loss were calculated for the possible range of fixed (i.e. the same every year) and tactical (i.e. varying depending on seasonal forecast) strategies. Significant increase in profit (up to 20%) and/or reduction in risk (up to 35%) were associated with tactical adjustment of crop management of N fertiliser or cultivar maturity. The forecasting system giving greatest value was the Southern Oscillation Index (SOI) phase system of Stone and Auliciems (1992), which classifies seasons into 5 phases depending on the value and rate of change in the SOI. The significant skill in this system for forecasting both seasonal rainfall and frost timing generated the value found in tactical management of N fertiliser and cultivar maturity. Possible impediments to adoption of tactical management, associated with uncertainties in forecasting individual years, are discussed. The scope for improving forecast quality and the means to achieve it are considered by comparing the value of tactical management based on SO1 phases with the outcome given perfect prior knowledge of the season. While the analyses presented considered only one decision at a time, used specific scenarios, and made a number of simplifying assumptions, they have demonstrated that the current skill in seasonal forecasting is sufficient to justify use in tactical management of crops. More comprehensive studies to examine sensitivities to location, antecedent conditions, and price structure, and to assumptions made in this analysis, are now warranted. We have examined decisions related only to management of wheat. It would be appropriate to pursue similar analyses in relation to management decisions for other crops, cropping sequences, and the whole farm enterprise mix.
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Oliver, Y. M., and M. J. Robertson. "Quantifying the benefits of accounting for yield potential in spatially and seasonally responsive nutrient management in a Mediterranean climate." Soil Research 47, no. 1 (2009): 114. http://dx.doi.org/10.1071/sr08099.
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Crop yield potential is a chief determinant of nutrient requirements, but there is little objective information available on the gains in profitability that can be made by accounting for the influences of soil type and season on yield potential when making fertiliser decisions. We conducted such an assessment using crop growth simulation coupled to nutrient response curves for wheat-growing at 4 locations in the low-medium rainfall zone of Western Australia. At each location, the yield potential was simulated on 10 soil types with plant-available water capacity (PAWC) ranging from 34 to 134 mm, which represent the major soils types in Western Australia. Soil survey maps were available to quantify soil type variability and the historical climate record (1974–2005) for seasonal variability. The benefits possible for fertiliser (NPK) management that takes account of variation in crop yield potential due to season and soil type by having ‘perfect knowledge’ ranged from $2 to 40/ha. Seasonal variation was more important than soil type for the better soils (high PAWC), providing two-thirds of the benefit of perfect knowledge. On low PAWC soils, knowledge of soils and seasonal influences on yield potential were similar contributors to profit gains. An assessment of one yield forecasting system showed that about 50% of the maximum gains could be captured if seasons could be categorised as below, at, or above average at the time the fertiliser decision is made. In each catchment, 30–40% of fields showed scope for benefits in accounting for within-field variation in soil type due to large variation in PAWC, and therefore yield. Maximum profit gains and reductions in nutrient excess were greater in the low rainfall locations and also on the low PAWC soil types.
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Birt, Jacqueline L., Kala Muthusamy, and Poonam Bir. "XBRL and the qualitative characteristics of useful financial information." Accounting Research Journal 30, no. 01 (May 2, 2017): 107–26. http://dx.doi.org/10.1108/arj-11-2014-0105.
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Purpose eXtensible Business Reporting Language (XBRL) is an internet-based interactive form of reporting language that is expected to enhance the usefulness of financial reporting (Yuan and Wang, 2009). In the UK and the USA, XBRL is mandatory, and in Australia, it is voluntarily adopted. It has been reported that in the not too distant future, XBRL will be the standard format for the preparation and exchange of business reports (Gettler, 2015). Using an experimental approach, this study assesses the usefulness of financial reports with XBRL tagged information compared to PDF format information for non-professional investors. The authors investigate participants’ perceptions of usefulness in relation to the qualitative characteristics of relevance, understandability and comparability. Design/methodology/approach This paper uses an experimental approach featuring a profit-forecasting task to determine if participants perceive XBRL-tagged information to be more useful compared to PDF-formatted information. Findings Results reveal that financial information presented with XBRL tagging is significantly more relevant, understandable and comparable to non-professional investors. Originality/value The authors address a gap in the literature by examining XBRL usefulness in Australia where XBRL adoption will be mandated within the not too distant future. Currently, the voluntary adoption of XBRL by preparers and users is low, possibly, because of a lack of awareness about XBRL and its potential benefits. This study yields significant implications for the accounting regulators in creating more awareness on the benefits of using XBRL and to create an impetus for XBRL adoption.
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Yang, Yi, Yao Dong, Yanhua Chen, and Caihong Li. "Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia." Abstract and Applied Analysis 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/504064.
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Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting methods have been proposed and researched in this domain, it is very difficult to forecast electricity price with only one traditional model for different behaviors of electricity price. In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO) based on the generalized autoregressive conditional heteroskedasticity (GARCH) model and support vector machine (SVM) to improve the forecasting accuracy. First, both GARCH model and SVM are developed to forecast short-term electricity price of New South Wales in Australia. Then, ACO algorithm is applied to determine the weight coefficients. Finally, the forecasting errors by three models are analyzed and compared. The experiment results demonstrate that the combined model makes accuracy higher than the single models.
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Liu, Jacie Jia. "A Study on Link Functions for Modelling and Forecasting Old-Age Survival Probabilities of Australia and New Zealand." Risks 9, no. 1 (January 2, 2021): 11. http://dx.doi.org/10.3390/risks9010011.
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Forecasting survival probabilities and life expectancies is an important exercise for actuaries, demographers, and social planners. In this paper, we examine extensively a number of link functions on survival probabilities and model the evolution of period survival curves of lives aged 60 over time for the elderly populations in Australasia. The link functions under examination include the newly proposed gevit and gevmin, which are compared against the traditional ones like probit, complementary log-log, and logit. We project the model parameters and so the survival probabilities into the future, from which life expectancies at old ages can be forecasted. We find that some of these models on survival probabilities, particularly those based on the new links, can provide superior fitting results and forecasting performances when compared to the more conventional approach of modelling mortality rates. Furthermore, we demonstrate how these survival probability models can be extended to incorporate extra explanatory variables such as macroeconomic factors in order to further improve the forecasting performance.
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Hoque, Ariful, Thi Ngoc Quynh Le, and Kamrul Hassan. "Does currency smirk predict foreign exchange return?" Investment Management and Financial Innovations 17, no. 3 (September 23, 2020): 219–30. http://dx.doi.org/10.21511/imfi.17(3).2020.17.
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This study examines the predictive power of implied volatility smirk to forecast foreign exchange (FX) return. The volatility smirk contains critical information, especially when the market experiences negative news. The Australian dollar, Canadian dollar, Swiss franc, Euro, and British pound options traded in the opening, midday and closing periods of the trading day are selected to estimate the currency smirk. Research results reveal that the currency smirk outperforms in forecasting FX returns. In addition, the steeper slope in the middle of the trading day suggests that the predictive power of currency smirk in the midday period is higher compared to the opening and closing periods. However, currency smirks’ predictability lasts for a short period, as the FX market is highly adept at incorporating the vital information embedded in the currency smirk. These findings imply that the currency smirk is distinctive for forecasting very short-term FX fluctuations, and the day- or overnight FX traders can use its uniqueness to profit from quick price swings in the 24-hour global FX market.
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Wu, Kehe, Yanyu Chai, Xiaoliang Zhang, and Xun Zhao. "Research on Power Price Forecasting Based on PSO-XGBoost." Electronics 11, no. 22 (November 16, 2022): 3763. http://dx.doi.org/10.3390/electronics11223763.
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With the reform of the power system, the prediction of power market pricing has become one of the key problems that needs to be solved in time. Power price prediction plays an important role in maximizing the profits of the participants in the power market and making full use of power energy. In order to improve the prediction accuracy of the power price, this paper proposes a power price prediction method based on PSO optimization of the XGBoost model, which optimizes eight main parameters of the XGBoost model through particle swarm optimization to improve the prediction accuracy of the XGBoost model. Using the electricity price data of Australia from January to December 2019, the proposed model is compared with the XGBoost model. The experimental results show that PSO can effectively improve the performance of the model. In addition, the prediction results of PSO-XGBoost are compared with those of SVM, LSTM, ARIMA, RW and XGBoost, and the average relative error and root mean square error of different power price prediction models are calculated. The experimental results show that the prediction accuracy of the PSO-XGBoost model is higher and more in line with the actual trend of power price change.
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Siu, Yam Wing. "Volatility Forecast by Volatility Index and Its Use as a Risk Management Tool Under a Value-at-Risk Approach." Review of Pacific Basin Financial Markets and Policies 21, no. 02 (May 27, 2018): 1850010. http://dx.doi.org/10.1142/s0219091518500108.
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This paper examines the predicting power of the volatility indexes of VIX and VHSI on the future volatilities (or called realized volatility, [Formula: see text] of their respective underlying indexes of S&P500 Index, SPX and Hang Seng Index, HSI. It is found that volatilities indexes of VIX and VHSI, on average, are numerically greater than the realized volatilities of SPX and HSI, respectively. Further analysis indicates that realized volatility, if used for pricing options, would, on some occasions, result in greatest losses of 2.21% and 1.91% of the spot price of SPX and HSI, respectively while the greatest profits are 2.56% and 2.93% of the spot price of SPX and HSI, respectively, making it not an ideal benchmark for validating volatility forecasting techniques in relation to option pricing. Hence, a new benchmark (fair volatility, [Formula: see text] that considers the premium of option and the cost of dynamic hedging the position is proposed accordingly. It reveals that, on average, options priced by volatility indexes contain a risk premium demanded by the option sellers. However, the options could, on some occasions, result in greatest losses of 4.85% and 3.60% of the spot price of SPX and HSI, respectively while the greatest profits are 4.60% and 5.49% of the spot price of SPX and HSI, respectively. Nevertheless, it can still be a valuable tool for risk management. [Formula: see text]-values of various significance levels for value-at-risk and conditional value-at-value have been statistically determined for US, Hong Kong, Australia, India, Japan and Korea markets.
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Benaouda, Djamel, and Fionn Murtagh. "Neuro-Wavelet Approach to Time-Series Signals Prediction: An Example of Electricity Load and Pool-Price Data." International Journal of Emerging Electric Power Systems 8, no. 2 (February 9, 2007). http://dx.doi.org/10.2202/1553-779x.1404.
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Accurate electricity load and pool-price forecasting can provide a set of vital predicted information that helps generation, transmission and retailer participating companies to bid strategically into a deregulated electricity market in order to maximize their profits and increase returns to their stakeholders. Although a number of forecasting methods have been proposed to solve the short-term and long-term electricity load forecast, pool-price forecasting is a relatively new research area. In this article, we propose an autoregressive approach, based on a wavelet multiscale decomposition, for the prediction of one-hour ahead load and pool price based respectively on historical electricity load, and pool-price data. This approach is based on a multiple resolution decomposition of the signal using the redundant Haar à trous wavelet transform whose advantage is taking into account the asymmetric nature of the time-varying data. There is an additional computational advantage in that there is no need to re-compute the wavelet transform (wavelet coefficients) of the full signal if the electricity and pool price data (time series) is regularly updated. We assess results produced by this multiscale autoregressive method, in both linear and nonlinear variants, with single resolution autoregressive, multilayer perceptron, Elman recurrent neural network and the general regression neural network models. The input data consists of historical load and pool price data, which is collected over a period of 3 years (1999-2001), used for training, and 1 year (2002) used for testing. Experimental results are based on the New South Wales (Australia) electricity load and pool price data that is provided by the National Electricity Market Management Company.
Book chapters on the topic "Profit Australia Forecasting"
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Yao, Jing Tao, and Chew Lim Tan. "Neural Networks for Technical Forecasting of Foreign Exchange Rates." In Neural Networks in Business, 189–204. IGI Global, 2002. http://dx.doi.org/10.4018/978-1-930708-31-0.ch012.
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This chapter describes the application of neural networks in foreign exchange rate forecasting between American dollar and five other major currencies: Japanese yen, Deutsch mark, British pound, Swiss franc and Australian dollar. Technical indicators and time series data are fed to neural networks to mine, or discover, the underlying “rules” of the movement in currency exchange rates. The results presented in this chapter show that without the use of extensive market data or knowledge, useful prediction can be made and significant paper profit can be achieved for out-of-sample data with simple technical indicators. The neural-network-based forecasting is also shown to compare favorably with the traditional statistical approach.
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Sakri, Sapiah, Jaizah Othman, and Noreha Halid. "Hybridisation of Feature Selection and Classification Techniques in Credit Risk Assessment Modelling." In Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques. IOS Press, 2020. http://dx.doi.org/10.3233/faia200581.
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In recent years, the use of artificial intelligence techniques to manage credit risk has represented an improvement over conventional methods. Furthermore, small improvements to credit scoring systems and default forecasting can support huge profits. Accordingly, banks and financial institutions have a high interest in any changes. The literature shows that the use of feature selection techniques can reduce the dimensionality problems in most credit risk datasets, and, thus, improve the performance of the credit risk model. Many other works also indicated that various classification approaches would also affect the performance of the credit risk assessment modelling. In this research, based on the new proposed framework, we investigated the effect of various filter-based feature selection techniques with various classification approaches, namely, single and ensemble classifiers, on three credit datasets (German, Australian, and Japanese credit risk datasets) with the aim of improving the performance of the credit risk model. All single and ensemble classifier-based models were evaluated using four of the most used performance metrics for assessing financial stress models. From the comparison analysis between, with, and without applying the feature selection and across the three credit datasets, the Random-Forest + Information-Gain model achieved a better trade-off in improving the model’s accuracy rate with the value of 96% for the Australian credit dataset. This model also obtained the lowest Type I error with the value of 4% for the German credit dataset, the lowest Type II error with the value of 2% for the German credit dataset and the highest value of G-mean of 95% for the Australian credit dataset. The results clearly indicate that the Random-Forest + Information-Gain model is an excellent predictor for the credit risk cases.
Conference papers on the topic "Profit Australia Forecasting"
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Rupasinghe, Mihiran, Malka N. Halgamuge, and Nguyen Tran Quoc Vinh. "Forecasting Trading-Time based Profit-Making Strategies in Forex Industry: Using Australian Forex Data." In 2019 11th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2019. http://dx.doi.org/10.1109/kse.2019.8919432.
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