Journal ArticleDOI
Surveying stock market forecasting techniques - Part II: Soft computing methods
TLDR
This paper surveys more than 100 related published articles that focus on neural and neuro-fuzzy techniques derived and applied to forecast stock markets to show that soft computing techniques are widely accepted to studying and evaluating stock market behavior.Abstract:
The key to successful stock market forecasting is achieving best results with minimum required input data. Given stock market model uncertainty, soft computing techniques are viable candidates to capture stock market nonlinear relations returning significant forecasting results with not necessarily prior knowledge of input data statistical distributions. This paper surveys more than 100 related published articles that focus on neural and neuro-fuzzy techniques derived and applied to forecast stock markets. Classifications are made in terms of input data, forecasting methodology, performance evaluation and performance measures used. Through the surveyed papers, it is shown that soft computing techniques are widely accepted to studying and evaluating stock market behavior.read more
Citations
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Journal ArticleDOI
Deep learning with long short-term memory networks for financial market predictions
TL;DR: This work deploys LSTM networks for predicting out-of-sample directional movements for the constituent stocks of the S&P 500 from 1992 until 2015 and finds one common pattern among the stocks selected for trading – they exhibit high volatility and a short-term reversal return profile.
Journal ArticleDOI
A review of unsupervised feature learning and deep learning for time-series modeling ☆
TL;DR: This paper overviews the particular challenges present in time-series data and provides a review of the works that have either applied time- series data to unsupervised feature learning algorithms or alternatively have contributed to modifications of featurelearning algorithms to take into account the challenges present.
Journal ArticleDOI
Predicting direction of stock price index movement using artificial neural networks and support vector machines
TL;DR: This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index, finding that average performance of ANN model was found significantly better than that of SVM model.
Journal ArticleDOI
Using artificial neural network models in stock market index prediction
TL;DR: The effectiveness of neural network models which are known to be dynamic and effective in stock-market predictions are evaluated, including multi-layer perceptron (MLP), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) to extract new input variables.
Journal ArticleDOI
Deep learning networks for stock market analysis and prediction
TL;DR: A systematic analysis of the use of deep learning networks for stock market analysis and prediction using five-minute intraday data from the Korean KOSPI stock market as input data to examine the effects of three unsupervised feature extraction methods.
References
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Journal ArticleDOI
Forecasting stock market movement direction with support vector machine
TL;DR: This paper investigates the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index and proposes a combining model by integrating SVM with the other classification methods.
Journal ArticleDOI
A hybrid ARIMA and support vector machines model in stock price forecasting
Ping-Feng Pai,Chih-Sheng Lin +1 more
TL;DR: This investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems and results of computational tests are very promising.
Proceedings ArticleDOI
Stock market prediction system with modular neural networks
TL;DR: The authors developed a number of learning algorithms and prediction methods for the TOPIX (Tokyo Stock Exchange Prices Indexes) prediction system, which achieved accurate predictions, and the simulation on stocks trading showed an excellent profit.
Journal ArticleDOI
Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index
TL;DR: Empirical results show that the PNN-based investment strategies obtain higher returns than other investment strategies examined in this study.
Journal ArticleDOI
Application of neural networks to an emerging financial market: forecasting and trading the Taiwan stock index
TL;DR: In this paper, a probabilistic neural network (PNN) is used to forecast the direction of index return after it is trained by historical data, and the results show that the PNN-based investment strategies obtain higher returns than other investment strategies examined in this study.