A Deep Learning Framework for Stock Prediction Using LSTM

In order to test the predictive power of the deep learning model, several machine learning methods were introduced for comparison. Empirical case results for the period of 2000 to 2017 show the forecasting power of deep learning technology. With a series of linear regression indicator measurement, we find LSTM networks outperform traditional machine learning methods, i.e., Linear Regression, Auto ARIMA, KNN.

[1]  Ben R. Marshall,et al.  Candlestick technical trading strategies : can they create value for investors? : a thesis presented in fulfilment of the requirements for the degree of Doctor of Philosophy in Finance at Massey University, Palmerston North, New Zealand , 2005 .

[2]  Yue Zhang,et al.  Deep Learning for Event-Driven Stock Prediction , 2015, IJCAI.

[3]  Shom Prasad Das,et al.  Support Vector Machines for Prediction of Futures Prices in Indian Stock Market , 2012 .

[4]  Rabab Kreidieh Ward,et al.  Distributed Compressive Sensing: A Deep Learning Approach , 2015, IEEE Transactions on Signal Processing.

[5]  Jianping Yin,et al.  A Stock Decision Support System Based on ELM , 2014 .

[6]  Chih-Chou Chiu,et al.  Financial time series forecasting using independent component analysis and support vector regression , 2009, Decis. Support Syst..

[7]  Yulei Rao,et al.  A deep learning framework for financial time series using stacked autoencoders and long-short term memory , 2017, PloS one.

[8]  Andrew W. Senior,et al.  Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition , 2014, ArXiv.

[9]  Justin A. Sirignano.  Deep learning for limit order books , 2016, Quantitative Finance.

[10]  Xiaolong Wang,et al.  A novel text mining approach to financial time series forecasting , 2012, Neurocomputing.

[11]  Huaiqing Wang,et al.  A Feature Fusion Based Forecasting Model for Financial Time Series , 2014, PloS one.

[12]  Bayu Distiawan Trisedya,et al.  Stock price prediction using linear regression based on sentiment analysis , 2015, 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS).

[13]  Thomas L. Burr.  Pattern Recognition and Machine Learning , 2008 .

[14]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[15]  Ping-Feng Pai,et al.  A hybrid ARIMA and support vector machines model in stock price forecasting , 2005 .

[16]  G. Caginalp,et al.  The Predictive Power of Price Patterns , 1998 .

[17]  Diego Klabjan,et al.  Implementing deep neural networks for financial market prediction on the Intel Xeon Phi , 2015, WHPCF@SC.

[18]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[19]  Ming Xiao,et al.  Dimethyl Sulfoxide Damages Mitochondrial Integrity and Membrane Potential in Cultured Astrocytes , 2014, PloS one.

[20]  E. Fama.  EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[21]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[22]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[23]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[24]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[25]  Chun-Xiao Nie,et al.  Analyzing the stock market based on the structure of kNN network , 2018, Chaos, Solitons & Fractals.

[26]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[27]  Jian Cao,et al.  Financial time series forecasting model based on CEEMDAN and LSTM , 2019, Physica A: Statistical Mechanics and its Applications.

[28]  Nicolas Huck,et al.  Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 , 2017, Eur. J. Oper. Res..

[29]  Achilleas Zapranis,et al.  Stock performance modeling using neural networks: A comparative study with regression models , 1994, Neural Networks.

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