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Research Article Open access CC BY 3.0

Predicting Direction of Stock Prices Index Movement Using Artificial Neural Networks: The Case of Libyan Financial Market

Najeb Masoud

Journal of Economics, Management and Trade · pp. 597–619 · Published 9 Jan 2014

10.9734/BJEMT/2014/5519

Abstract

Aims: The aim of this paper is to present techniques indicators of artificial neural networks (ANNs) model using for predicting the exact movements of stock price in the daily Libyan Stock Market (LSM) index forecasting. Study Design: Research paper. Place and Duration of Study: Libyan stock market from January 2, 2007 to March 28, 2013.  Methodology: The data from an emerging market Libyan Stock Market are applied as a case study. Twelve technical indicators were selected as inputs of the proposed models. The forecasting ability of the ANN model is accessed using back-propagation neural network of errors such as MAE, RMSE, MAPE and R2. Two comprehensive parameter setting experiments for both the technical indicators and the levels of the index in the market were performed to improve their prediction performances. Results: The experimental statistical results show that the ANN model accurately predicted the direction of movement with the average prediction rate 91% of data analysis in its best case, which is a perfectly good outcome. The relationship strength between parameter combination and forecast accuracy measures such as MAE, MAPE, and RMSE is strong (R2≥0.99). The statistical and financial performance of this technique is evaluated and empirical results revealed that artificial neural networks can be used as a better alternative technique for forecasting the daily stock market prices. Conclusion: This study proved the significance of using twelve particular technical market indicators which gave also useful results in predicting the direction of stock price movement. To improve ANN model capabilities, a mixture of technical and fundamental factors as inputs over different time period were used to be an effective tool in forecasting the market level and direction.

Libyan stock market artificial neural networks prediction of stock price index technical indicators back-propagation errors

References (42)

  1. 1 Learning representations by back-propagating errors [DOI]
  2. 2 EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* [DOI]
  3. 3 Artificial neural networks: fundamentals, computing, design, and application [DOI]
  4. 4 Financial time series forecasting using support vector machines [DOI]
  5. 5 A hybrid ARIMA and support vector machines model in stock price forecasting [DOI]
  6. 6 Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange [DOI]
  7. 7 Neurocomputing: Foundations of Research
  8. 8 Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index [DOI]
  9. 9 Human Physiology: An Integrated Approach
  10. 10 Neural Networks for Chemists: An Introduction
  11. 11 Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index [DOI]
  12. 12 Neural Networks in Computer Intelligence
  13. 13 Artificial neural networks: an econometric perspective [DOI]
  14. 14 Neural networks an introduction [DOI]
  15. 15 Time series forecasting using neural networks vs. Box- Jenkins methodology [DOI]
  16. 16 Feedforward Neural Nets as Models for Time Series Forecasting [DOI]
  17. 17 Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support [DOI]
  18. 18 A Recursive Modelling Approach to Predicting UK Stock Returns [DOI]
  19. 19 Technical Analysis from A to Z
  20. 20 A Practical Guide to Forecasting Financial Market Volatility
  21. 21 The evaluation of extrapolative forecasting methods [DOI]
  22. 22 Pricing and hedging derivative securities with neural networks and a homogeneity hint [DOI]
  23. 23 A neural network approach to mutual fund net asset value forecasting [DOI]
  24. 24 Trading Equity Index Futures With a Neural Network [DOI]
  25. 25 An intelligent forecasting system of stock price using neural networks [DOI]
  26. 26 Forecasting futures trading volume using neural networks [DOI]
  27. 27 Graded forecasting using an array of bipolar predictions: application of probabilistic neural networks to a stock market index [DOI]
  28. 28 Estimating the cost of steel pipe bending, a comparison between neural networks and regression analysis [DOI]
  29. 29 BIOLOGICAL BRAIN‐INSPIRED GENETIC COMPLEMENTARY LEARNING FOR STOCK MARKET AND BANK FAILURE PREDICTION1 [DOI]
  30. 30 Artificial Neural Systems: A New Tool for Financial Decision-Making [DOI]
  31. 31 Networks and Chaos: Statistical and Probabilistic Aspects [DOI]
  32. 32 Forecasting Exchange Rates: a Comparative Analysis
  33. 33 The predictive ability and profitability of technical trading rules: does company size matter? [DOI]
  34. 34 Trading futures markets based on signals from a neural network [DOI]
  35. 35 Neural networks as a forecasting instrument for the polish stock exchange [DOI]
  36. 36 Multiple line-segments regression for stock prices and long-range forecasting system by neural network [DOI]
  37. 37 The combining prediction of the RMB exchange rate series based on diverse architectural artificial neural network ensemble methodology [DOI]
  38. 38 Artificial intelligence: a modern approach [DOI]
  39. 39 Neural networks for pattern recognition [DOI]
  40. 40 Introduction to the theory of neural computation [DOI]
  41. 41 Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest [DOI]
  42. 42 Support Vector Machines Approach to Predict the S&P CNX NIFTY Index Returns [DOI]

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