A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters
By Omer Berat Sezer, M. Ozbayoglu, Erdogan Dogdu. 2017
92 citations recorded by the source corpus.
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.
Subfields: Electrical and Electronic Engineering, Finance, Management Science and Operations Research, Economics & Operations Research, Evolutionary Computation
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Citations in the corpus
- Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “In some studies [188, 189, 193, 199, 219], innovative transformations of financial time”
- Deep Learning for Financial Applications : A Survey (2020, methodology): “[52] Stocks in Dow30 1997-2017 RSI DMLP with genetic algorithm Annualized return Spark MLlib, Java”
- Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks (2019, methodology): “Besides, Sezer et al. [26] used deep feedforward neural network with optimized technical analysis parameters that are selected by using evolutionary algorithms (genetic algorithm).”
- Large Language Models in Finance: A Survey (2023, methodology): “However, the emergence of deep neural networks introduced techniques like Recurrent Neural Networks (RNN), particularly Long Short-Term Memory (LSTM) networks [40], Convolutional Neural Networks (CNN), and transformers [51], which have proven effective in price forecasting.”