Modified Neural Network Algorithms for Predicting Trading Signals of Stock Market Indices

Tilakaratne, C. D.; Mammadov, Musa; Morris, Sidney A.

doi:10.1155/2009/125308

Cited by 15 publications

(9 citation statements)

References 21 publications

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“…Identifying potential inputs or feature selection is an important task when neural networks are used for classification. Literature provides evidence for intermarket influences on stock markets [1][2][3][4]. Parametric, non-parametric and graphical techniques were used to identify potential intermarket influences as well as the lag relationships of the interested market itself.…”

Section: Potential Inputs Identificationmentioning

confidence: 99%

“…The stock market is one of the leading financial markets in this regard due to the importance and interest of many stakeholders. The profitability of investing in financial markets is directly proportional to its predictability [1][2][3][4]. Efficient market hypothesis indicates that market prices fully reflect all available information, and "beating the market" is very difficult.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Probabilistic Neural Network Model for Directional Prediction of a Stock Market Index

2019

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Financial market prediction attracts immense interest among researchers nowadays due to rapid increase in the investments of financial markets in the last few decades. The stock market is one of the leading financial markets due to importance and interest of many stakeholders. With the development of machine learning techniques, the financial industry thrived with the enhancement of the forecasting ability. Probabilistic neural network (PNN) is a promising machine learning technique which can be used to forecast financial markets with a higher accuracy. A major limitation of PNN is the assumption of Gaussian distribution as the distribution of input variables which is violated with respect to financial data. The main objective of this study is to improve the standard PNN by incorporating a proper multivariate distribution as the joint distribution of input variables and addressing the multi-class imbalanced problem persisting in the directional prediction of the stock market. This model building process is illustrated and tested with daily close prices of three stock market indices: AORD, GSPC and ASPI and related financial market indices. Results proved that scaled t distribution with location, scale and shape parameters can be used as more suitable distribution for financial return series. Global optimization methods are more appropriate to estimate better parameters of multivariate distributions. The global optimization technique used in this study is capable of estimating parameters with considerably high dimensional multivariate distributions. The proposed PNN model, which considers multivariate scaled t distribution as the joint distribution of input variables, exhibits better performance than the standard PNN model. The ensemble technique: multi-class undersampling based bagging (MCUB) was introduced to handle class imbalanced problem in PNNs is capable enough to resolve multi-class imbalanced problem persisting in both standard and proposed PNNs. Final model proposed in the study with proposed PNN and proposed MCUB technique is competent in forecasting the direction of a given stock market index with higher accuracy, which helps stakeholders of stock markets make accurate decisions.

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Section: Potential Inputs Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Probabilistic Neural Network Model for Directional Prediction of a Stock Market Index

2019

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“…C. D. Tilakaratne (2009) inferred that the modified neural network algorithms predicted the trading signals better than the standard feed forward Neural Network algorithm. Such predictions enabled the users to make financial decisions, for example, whether it is wise to hold, sell or buy company"s shares.…”

Section: Restaurant Business P a G E | 104mentioning

confidence: 99%

Predicting Stock Price Using Artificial Neural Networks

Nigama¹,

Alamelu²,

Selvabaskar³

et al. 2019

Res. Bus.

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Stock market facilitates the economic activities that contribute to a nation’s growth and prosperity. This is viewed as one of the lucrative avenues for financial investment. Although the stock market is a thrilling and potential opportunity to grow one’s money, it brings along with it certain challenges, because, there is no universal rule that suggests profitable investments. Investors, corporate and advisors employ several techniques like fundamental and technical analysis, trend analysis and other analysis to suggest stocks that will give best yields but such tools are neither consistent nor foolproof in the prediction of stock prices. But human exertions to convert the tacit knowledge into explicit knowledge has never found any alternate. More, the uncertainties, more the efforts to know them with certainty. Digital economy with its advanced technological tools aids the pursuit of not only understanding uncertainties but also predicting the future with maximum precision. The most prominent techniques in the technological realm includes the usage of artificial neural networks (ANNs) and Genetic Algorithms. This paper discusses the stock prices forecasting ability of Bombay stock exchange trend using genetically evolved neural networks, the input being the closing price of the previous five years and output being the price for the next day. Risk (Standard deviation), Average Return, variance and Market price are chosen as indicators of the performance. The objective of this study is to give an overview of the application of artificial neural network in predicting stock market.

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“…This global optimization algorithm is designed for solving continuous optimization problems with defined box constraints. The efficiency of the algorithm has been demonstrated in solving many difficult practical problems (see [13] and references therein).…”

Section: Steady-state Vibrationsmentioning

confidence: 99%

Optimization of improved suspension system with inerter device of the quarter-car model in vibration analysis

et al. 2010

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In this paper, we analyze an improved suspension system with the incorporated inerter device of the quarter-car model to obtain optimal design parameters for maximum comfort level for a driver and passengers. That is achieved by finding the analytical solution for the system of ordinary differential equations, which enables us to generate an optimization problem whose objective function is based on the international standards of admissible acceleration levels (ISO 2631-1, Mechanical Vibration and Shock-Evaluation of Human Exposure to Whole-Body Vibration-Part 1, 1997). The considered approach ensures the highest level of comfort for the driver and passengers due to a favorable reduction in body vibrations. Numerical examples, based on actually measured road profiles, are presented at the end of the paper to prove the validity of the proposed approach and its suitability for the problem at hand.

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Modified Neural Network Algorithms for Predicting Trading Signals of Stock Market Indices

Cited by 15 publications

References 21 publications

An Improved Probabilistic Neural Network Model for Directional Prediction of a Stock Market Index

An Improved Probabilistic Neural Network Model for Directional Prediction of a Stock Market Index

Predicting Stock Price Using Artificial Neural Networks

Optimization of improved suspension system with inerter device of the quarter-car model in vibration analysis

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