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A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters

Ömer Berat Sezer · Ahmet Murat Özbayoğlu · Erdoğan Doğdu

118 citations0/0 files storedProcedia Computer Science

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Financial Analysis and Corporate Governance

Abstract

In this study, we propose a stock trading system based on optimized technical analysis parameters for creating buy-sell points using genetic algorithms. The model is developed utilizing Apache Spark big data platform. The optimized parameters are then passed to a deep MLP neural network for buy-sell-hold predictions. Dow 30 stocks are chosen for model validation. Each Dow stock is trained separately using daily close prices between 1996-2016 and tested between 2007-2016. The results indicate that optimizing the technical indicator parameters not only enhances the stock trading performance but also provides a model that might be used as an alternative to Buy and Hold and other standard technical analysis models.

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References

2008Surveying stock market forecasting techniques – Part II: Soft computing methods 2011Using artificial neural network models in stock market index prediction 2002Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index 2011Forecasting stock indices with back propagation neural network 2007A Hybrid Neurogenetic Approach for Stock Forecasting 2009Forecasting model of global stock index by stochastic time effective neural network 2014A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter

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