Feature selection and deep neural networks for stock price direction forecasting using technical analysis indicators

Peng, Yaohao; Albuquerque, Pedro Henrique Melo; Kimura, Herbert; Saavedra, Cayan Atreio Portela Bárcena

doi:10.1016/j.mlwa.2021.100060

Cited by 38 publications

(27 citation statements)

References 84 publications

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“…Peng et al [43] analyzed the factor zoo from a machine learning perspective, which has theoretical and empirical implications for fnance. Te authors discussed feature selection in the context of deep neural network models to predict the stock price direction.…”

Section: Literature Reviewmentioning

confidence: 99%

An Approach for a Multi-Period Portfolio Selection Problem by considering Transaction Costs and Prediction on the Stock Market

et al. 2023

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This paper addresses a method to solve a multi-period portfolio selection on the stock market. The portfolio problem seeks an investor to trade stocks with a finite budget and a given integer number of stocks to hold in a portfolio. The trade must be performed through a stockbroker that charges its respective transaction cost and has its minimum required trade amount. A mathematical model has been proposed to deal with the constrained problem. The objective function is to find the best risk-return rate; thus, Sharpe Ratio and Treynor Ratio are used as objective functions. The returns are the same for these ratios, but the risks are not Sharpe considering covariance and Treynor systematical risk. The returns are predicted using a Neural Net with Long-Short-Term Memory (LSTM). This neural net is compared with simple forecasting methods through Mean Absolute Percentage Error (MAPE). Computational experiments show the quality prediction performed by LSTM. The heteroskedastic risk is estimated by Generalized Autoregressive Conditional Heteroskedasticity (GARCH), adjusting the variance for every period; this risk measure is used in Sharpe Ratio. The experiment contemplates a weekly portfolio selection with 5 and 10 stocks in 122 weekly periods for each Chilean market ratio. The best portfolio is Sharpe Ratio with ten stocks, performing a 62.28% real return beating the market, represented by the Selective Stock Price Index (IPSA). Even the worst portfolio, Treynor Ratio, overcomes the IPSA cumulative yield with ten stocks.

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Section: Literature Reviewmentioning

confidence: 99%

An Approach for a Multi-Period Portfolio Selection Problem by considering Transaction Costs and Prediction on the Stock Market

et al. 2023

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“…Based on the past implicit state vector h t − 1 and the current input x t , it decides what information needs to be forgotten, what new information needs to be input, and what new memory information needs to be encoded to obtain h t as output. The LSTM layer at time t is calculated as shown in Equations ( 2)- (7).…”

Section: Detection Of News Text Sentimentsmentioning

confidence: 99%

“…Then, the neuron updates the state C t of the current time according to Equation (4). Finally, the output gate determines in what sense the new memory can be output based on C t , and the implicit state h t of the output is represented by Equation (7). In Equations ( 2)-( 7), W 0 and b 0 denote the weight matrix and bias vector, respectively.…”

Section: Detection Of News Text Sentimentsmentioning

confidence: 99%

“…The extant research on stock trend prediction has largely focused on the application of various econometric-based methods to predict stock trends based on structured and linear historical data, mainly using linear regression and parameter estimation techniques [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…CNN and BiLSTM algorithms are therefore able to learn relationships within time series without requiring large amounts of historical time series data. Similarly, BiLSTM and CNN have already been shown to provide highly accurate results for text mining tasks that require sequential modeling information [7]. Moreover, their implementation requires less time and effort [25].…”

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confidence: 99%

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A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Daradkeh

2022

Electronics

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Stock market analysis plays an indispensable role in gaining knowledge about the stock market, developing trading strategies, and determining the intrinsic value of stocks. Nevertheless, predicting stock trends remains extremely difficult due to a variety of influencing factors, volatile market news, and sentiments. In this study, we present a hybrid data analytics framework that integrates convolutional neural networks and bidirectional long short-term memory (CNN-BiLSTM) to evaluate the impact of convergence of news events and sentiment trends with quantitative financial data on predicting stock trends. We evaluated the proposed framework using two case studies from the real estate and communications sectors based on data collected from the Dubai Financial Market (DFM) between 1 January 2020 and 1 December 2021. The results show that combining news events and sentiment trends with quantitative financial data improves the accuracy of predicting stock trends. Compared to benchmarked machine learning models, CNN-BiLSTM offers an improvement of 11.6% in real estate and 25.6% in communications when news events and sentiment trends are combined. This study provides several theoretical and practical implications for further research on contextual factors that influence the prediction and analysis of stock trends.

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Stock Market Prediction Techniques Using Artificial Intelligence: A Systematic Review

Chaudhari¹,

Purswani²

2023

Lecture Notes in Networks and Systems

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Feature selection and deep neural networks for stock price direction forecasting using technical analysis indicators

Cited by 38 publications

References 84 publications

An Approach for a Multi-Period Portfolio Selection Problem by considering Transaction Costs and Prediction on the Stock Market

An Approach for a Multi-Period Portfolio Selection Problem by considering Transaction Costs and Prediction on the Stock Market

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Stock Market Prediction Techniques Using Artificial Intelligence: A Systematic Review

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