Recurrent neural network and a hybrid model for prediction of stock returns

Rather, Akhter Mohiuddin; Agarwal, Arun; Sastry, V. N.

doi:10.1016/j.eswa.2014.12.003

Cited by 408 publications

(143 citation statements)

References 34 publications

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“…Other more elaborate nonlinear modeling approaches have also been used. Patel et al (2015), Patel et al (2014) and Rather et al (2014) , for instance, employed multiple or hybrid models involving SVR (Artificial Neural Regression), random forest and GA (Genetic Algorithms) to forecast stock market indicators.…”

Section: Dynamical Bayesian Factor Graphmentioning

confidence: 99%

Stock market trend prediction using dynamical Bayesian factor graph

Wang

Zhao

et al. 2015

Expert Systems with Applications

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Section: Dynamical Bayesian Factor Graphmentioning

confidence: 99%

Stock market trend prediction using dynamical Bayesian factor graph

Wang

Zhao

et al. 2015

Expert Systems with Applications

View full text Add to dashboard Cite

“…The study conducted sentiment analysis using Twitter data to verify a learning model and to investigate the relationships with various other stochastic events. Rather, Agarwal, and Sastry () developed a hybrid model composed of a recurrent neutral network and a multiple linear regression and attempted to overcome the limitations associated with each. According to the researchers, these three methods are useful for the prediction of cryptocurrency prices, particularly bitcoin.…”

Section: Previous Related Workmentioning

confidence: 99%

Advanced social media sentiment analysis for short‐term cryptocurrency price prediction

Wołk

2019

Expert Systems

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In recent years, the scrutiny of bitcoin and other cryptocurrencies as legal and regulated components of financial systems has been increasing. Bitcoin is currently one of the largest cryptocurrencies in terms of capital market share. Therefore, this study proposes that sentiment analysis can be used as a computational tool to predict the prices of bitcoin and other cryptocurrencies for different time intervals. A key characteristic of the cryptocurrency market is that the fluctuation of currency prices depends on people's perceptions and opinions, not institutional money regulation. Therefore, analysing the relationship between social media and web search is crucial for cryptocurrency price prediction. This study uses Twitter and Google Trends to forecast the short‐term prices of the primary cryptocurrencies, as these social media platforms are used to influence purchasing decisions. The study adopts and interpolates a unique multimodel approach to analyse the impact of social media on cryptocurrency prices. Our results prove that people's psychological and behavioural attitudes have a significant impact on the highly speculative cryptocurrency prices.

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“…Recently there has been a resurgence of interest in deep learning, whose basic structure is best described as a multilayer neural network [31]. Some literatures have established various models based on deep neural networks to improve the prediction ability of high-frequency financial time series [32,33]. The ability of deep neural networks to extract abstract features from data is also attractive, Chong et al [12] applied a deep feature learning-based stock market prediction model, which extract information from the stock return time series without relying on prior knowledge of the predictors and tested it on high-frequency data from the Korean stock market.…”

Section: Stock Market Prediction Methodsmentioning

confidence: 99%

Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets

Zhou

Pan

et al. 2018

Mathematical Problems in Engineering

163

110

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Stock price prediction is an important issue in the financial world, as it contributes to the development of effective strategies for stock exchange transactions. In this paper, we propose a generic framework employing Long Short-Term Memory (LSTM) and convolutional neural network (CNN) for adversarial training to forecast high-frequency stock market. This model takes the publicly available index provided by trading software as input to avoid complex financial theory research and difficult technical analysis, which provides the convenience for the ordinary trader of nonfinancial specialty. Our study simulates the trading mode of the actual trader and uses the method of rolling partition training set and testing set to analyze the effect of the model update cycle on the prediction performance. Extensive experiments show that our proposed approach can effectively improve stock price direction prediction accuracy and reduce forecast error.

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Recurrent neural network and a hybrid model for prediction of stock returns

Cited by 408 publications

References 34 publications

Stock market trend prediction using dynamical Bayesian factor graph

Stock market trend prediction using dynamical Bayesian factor graph

Advanced social media sentiment analysis for short‐term cryptocurrency price prediction

Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets

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