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ANN Model to Predict Stock Prices at Stock Exchange Markets

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  • B. W. Wanjawa
  • L. Muchemi

Abstract

Stock exchanges are considered major players in financial sectors of many countries. Most Stockbrokers, who execute stock trade, use technical, fundamental or time series analysis in trying to predict stock prices, so as to advise clients. However, these strategies do not usually guarantee good returns because they guide on trends and not the most likely price. It is therefore necessary to explore improved methods of prediction. The research proposes the use of Artificial Neural Network that is feedforward multi-layer perceptron with error backpropagation and develops a model of configuration 5:21:21:1 with 80% training data in 130,000 cycles. The research develops a prototype and tests it on 2008-2012 data from stock markets e.g. Nairobi Securities Exchange and New York Stock Exchange, where prediction results show MAPE of between 0.71% and 2.77%. Validation done with Encog and Neuroph realized comparable results. The model is thus capable of prediction on typical stock markets.

Suggested Citation

  • B. W. Wanjawa & L. Muchemi, 2014. "ANN Model to Predict Stock Prices at Stock Exchange Markets," Papers 1502.06434, arXiv.org.
    • Handle: RePEc:arx:papers:1502.06434
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    Cited by:

    1. Ziyuan Xia & Jeffery Chen & Anchen Sun, 2021. "Mining the Relationship Between COVID-19 Sentiment and Market Performance," Papers 2101.02587, arXiv.org, revised Mar 2023.
    2. Barack Wamkaya Wanjawa, 2016. "Evaluating the Performance of ANN Prediction System at Shanghai Stock Market in the Period 21-Sep-2016 to 11-Oct-2016," Papers 1612.02666, arXiv.org.
    3. Jessie Sun, 2019. "A Stock Selection Method Based on Earning Yield Forecast Using Sequence Prediction Models," Papers 1905.04842, arXiv.org.
    4. Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
    5. Oluwaseun Oyebode & Desmond Eseoghene Ighravwe, 2019. "Urban Water Demand Forecasting: A Comparative Evaluation of Conventional and Soft Computing Techniques," Resources, MDPI, vol. 8(3), pages 1-18, September.
    6. Kerda Varaku, 2019. "Stock Price Forecasting and Hypothesis Testing Using Neural Networks," Papers 1908.11212, arXiv.org.
    7. Liu, Keyan & Zhou, Jianan & Dong, Dayong, 2021. "Improving stock price prediction using the long short-term memory model combined with online social networks," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    8. Rebecca Abraham & Mahmoud El Samad & Amer M. Bakhach & Hani El-Chaarani & Ahmad Sardouk & Sam El Nemar & Dalia Jaber, 2022. "Forecasting a Stock Trend Using Genetic Algorithm and Random Forest," JRFM, MDPI, vol. 15(5), pages 1-18, April.
    9. Barack Wamkaya Wanjawa, 2016. "Predicting Future Shanghai Stock Market Price using ANN in the Period 21-Sep-2016 to 11-Oct-2016," Papers 1609.05394, arXiv.org.

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