Procedia Computer Science · 2017 · 112 citations · 11 references
ComputingComputer ScienceFinanceFinancial EconomicsMachine LearningQuantitative FinanceDeep LearningFinancial Time Series AnalysisNeural NetworksOptimization For Machine LearningStock MarketsStock ModelingBusiness Market ForecastingDeep Learning-based ForecastingFinancial Time Series ForecastingMachine Learning-based Price ForecastingMarket Forecasting SystemsNeural ForecastingNeural Time Series ForecastingPredictive Price Modeling
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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Forecasting stock indices with back propagation neural network
Jianzhou Wang, Jujie Wang, Zhe George Zhang et al. · Expert Systems with Applications · 2011 · 484 citations · Full text