Deep Learning for Stock Market Prediction

The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.

[1]  Saman Haratizadeh,et al.  CNNpred: CNN-based stock market prediction using a diverse set of variables , 2019, Expert Syst. Appl..

[2]  Salah Bouktif,et al.  Augmented Textual Features-Based Stock Market Prediction , 2020, IEEE Access.

[3]  Lu Zhang,et al.  An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms , 2020 .

[4]  Norman Matloff,et al.  Statistical Regression and Classification: From Linear Models to Machine Learning , 2017 .

[5]  Chulwoo Han,et al.  Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies , 2017, Expert Syst. Appl..

[6]  K. S. Adewole,et al.  Stock Trend Prediction Using Regression Analysis - A Data Mining Approach , 2011 .

[7]  Ömer Kaan Baykan,et al.  Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange , 2011, Expert Syst. Appl..

[8]  Jiangtao Ren,et al.  An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in Chinese stock exchange market , 2020, Appl. Soft Comput..

[9]  Amir Mosavi,et al.  Deep Learning for Stock Market Prediction , 2020, Entropy.

[10]  David Hsieh.  Chaos and Nonlinear Dynamics: Application to Financial Markets , 1991 .

[11]  Jae Joon Ahn,et al.  Is Deep Learning for Image Recognition Applicable to Stock Market Prediction? , 2019, Complex..

[12]  Lee-Ing Tong,et al.  Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming , 2011, Knowl. Based Syst..

[13]  Xiao Zhong,et al.  Predicting the daily return direction of the stock market using hybrid machine learning algorithms , 2019, Financial Innovation.

[14]  Katarzyna Poczeta,et al.  Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece , 2019, Algorithms.

[15]  Peter Tiño,et al.  Learning long-term dependencies in NARX recurrent neural networks , 1996, IEEE Trans. Neural Networks.

[16]  Krishna Kumar,et al.  Blended computation of machine learning with the recurrent neural network for intra-day stock market movement prediction using a multi-level classifier , 2019, International Journal of Computers and Applications.

[17]  Chao Wu,et al.  Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm , 2011, Knowl. Based Syst..

[18]  Li-Chiu Chang,et al.  Reinforced recurrent neural networks for multi-step-ahead flood forecasts , 2013 .

[19]  Impact of the stock market capitalization and the banking spread in growth and development in Latin American: A panel data estimation with System GMM , 2017 .

[20]  David C. Yen,et al.  Predicting stock returns by classifier ensembles , 2011, Appl. Soft Comput..

[21]  ChongEunsuk,et al.  Deep learning networks for stock market analysis and prediction , 2017 .

[22]  Ha Young Kim,et al.  ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module , 2018, Expert Syst. Appl..

[23]  K. P. Soman,et al.  Stock price prediction using LSTM, RNN and CNN-sliding window model , 2017, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[24]  Mohammad Hossein Fazel Zarandi,et al.  A hybrid fuzzy intelligent agent‐based system for stock price prediction , 2012, Int. J. Intell. Syst..

[25]  Svetlana Borovkova,et al.  An Ensemble of LSTM Neural Networks for High-Frequency Stock Market Classification , 2018, Journal of Forecasting.

[26]  Arash Ghanbari,et al.  Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting , 2010, Knowl. Based Syst..

[27]  Kyung-shik Shin,et al.  Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction , 2019, Neural Computing and Applications.

[28]  Jinho Lee,et al.  Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network , 2019, IEEE Access.

[29]  Mahdi Pakdaman Naeini,et al.  Stock market value prediction using neural networks , 2010, 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM).

[30]  M. Balamurugan,et al.  An Effective Time Series Analysis for Equity Market Prediction Using Deep Learning Model , 2019, 2019 International Conference on Data Science and Communication (IconDSC).

[31]  Francisco Guijarro,et al.  Forecasting stock market trend: a comparison of machine learning algorithms , 2020 .

[32]  L I OliveiraAdriano,et al.  Computational Intelligence and Financial Markets , 2016 .

[33]  A. Ijspeert.  The Handbook of Brain Theory and Neural Networks , 2015 .

[34]  Sahil Shah,et al.  Predicting stock market index using fusion of machine learning techniques , 2015, Expert Syst. Appl..

[35]  Sahil Shah,et al.  Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques , 2015, Expert Syst. Appl..

[36]  Amir Mosavi,et al.  Data Science in Economics , 2020, ArXiv.

[37]  Debahuti Mishra,et al.  Stock market prediction using Firefly algorithm with evolutionary framework optimized feature reduction for OSELM method , 2019, Expert Syst. Appl. X.

[38]  Kyung-shik Shin,et al.  Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction , 2018, Sustainability.

[39]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[40]  Wen Long,et al.  Deep learning-based feature engineering for stock price movement prediction , 2019, Knowl. Based Syst..

[41]  Michel Ballings,et al.  Evaluating multiple classifiers for stock price direction prediction , 2015, Expert Syst. Appl..

[42]  Javier Oliver,et al.  HYBRID FUZZY NEURAL NETWORK TO PREDICT PRICE DIRECTION IN THE GERMAN DAX-30 INDEX , 2018, Technological and Economic Development of Economy.

[43]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[44]  Prateek Pandey,et al.  Stock Market Prediction Using Optimized Deep-ConvLSTM Model , 2020, Big Data.

[45]  Snehanshu Saha,et al.  Predicting the direction of stock market prices using tree-based classifiers , 2019, The North American Journal of Economics and Finance.

[46]  Haruna Isah,et al.  Stock Market Analysis: A Review and Taxonomy of Prediction Techniques , 2019, International Journal of Financial Studies.

[47]  T. Rao,et al.  An Introduction to Bispectral Analysis and Bilinear Time Series Models , 1984 .

[48]  Zhisong Pan,et al.  Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets , 2018 .

[49]  Khaled Rasheed,et al.  Stock market prediction with multiple classifiers , 2007, Applied Intelligence.

[50]  Adriano Lorena Inácio de Oliveira,et al.  Expert Systems With Applications , 2022 .

[51]  Harry Eugene Stanley,et al.  Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market? , 2018, IEEE Access.

[52]  Victor Chang,et al.  An innovative neural network approach for stock market prediction , 2018, The Journal of Supercomputing.

[53]  Yudong Zhang,et al.  Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network , 2009, Expert Syst. Appl..

[54]  J. Michael Herrmann,et al.  Lagged correlation-based deep learning for directional trend change prediction in financial time series , 2018, Expert Syst. Appl..

[55]  Yan Chen,et al.  Stock Market Trend Prediction Using High-Order Information of Time Series , 2019, IEEE Access.

[56]  Guang Liu,et al.  A Numerical-Based Attention Method for Stock Market Prediction With Dual Information , 2019, IEEE Access.

[57]  Zhao Zhigang.  Stock Price Forecast Based on Bacterial Colony RBF Neural Network , 2007 .

[58]  Hasan Dehghan Dehnavi,et al.  Evaluating the Employment of Technical Indicators in Predicting Stock Price Index Variations Using Artificial Neural Networks (Case Study: Tehran Stock Exchange) , 2012 .

[59]  Karl Aberer,et al.  Robust Online Time Series Prediction with Recurrent Neural Networks , 2016, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[60]  Emilio Soria Olivas,et al.  Handbook of Research on Machine Learning Applications and Trends : Algorithms , Methods , and Techniques , 2009 .

[61]  Esmaeil Hadavandi,et al.  Hybridization of evolutionary Levenberg-Marquardt neural networks and data pre-processing for stock market prediction , 2012, Knowl. Based Syst..