Restricted Boltzmann machine based stock market trend prediction

Qiubin Liang · Wenge Rong · Jiayi Zhang · Zhang Xiong

32 citations0/0 files storedInternational Joint Conference on Neural Networks (IJCNN)

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Neural Networks and Applications

Abstract

For the past decades, stock prediction has been a popular topic in financial applications. Many approaches including machine learning based and statistical models have been employed to forecast price changes in stock market. Considering the power of Restricted Bolztmann Machine (RBM) for feature extraction, we propose to incorporate RBM and several classifiers to predict short-term stock market trend. In this paper, eleven technical indicators are firstly inferred by using trading data, e.g., close price, lowest price, open price and highest price. Afterwards, these technical indicators are conveyed to binary values by using a trend deterministic preparation layer. We apply a RBM to extract features from binary valued features from the last step. The experimental study demonstrates this model's effectiveness compared with several traditional methods.

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1970EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK*2003From Efficient Markets Theory to Behavioral Finance 2014Stock Price Prediction Using the ARIMA Model 2014Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques 2004A hybrid ARIMA and support vector machines model in stock price forecasting 2016Deep Learning for Finance: Deep Portfolios 1998Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks 2014A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data

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