Deep learning in the stock market—a systematic survey of practice, backtesting, and applications

Olorunnimbe, Kenniy; Viktor, Herna L.

doi:10.1007/s10462-022-10226-0

Cited by 55 publications

(16 citation statements)

References 73 publications

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“… Lastly, the use of deep learning methods in stock price prediction comes with the risk of overfitting, especially when the model becomes too complex [87]. Future research could look into the application of regularization techniques or ensemble methods to mitigate this risk.…”

Section: Resultsmentioning

confidence: 99%

Integrating Social Media Data and Historical Stock Prices for Predictive Analysis: A Reinforcement Learning Approach

Li,

Zhang

2023

IJACSA

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The reliance on data collection for assessing individual behavior and actions has intensified, particularly with the proliferation of digital platforms. People often use the Internet to express their opinions and experiences about various products and services on social media and personal websites. Concurrently, the stock market, a key driver of commercial and industrial growth, has seen a surge in research focused on predicting market trends. The vast array of information on social media regarding public sentiment towards current events, coupled with the known impact of financial news on stock prices, has led to the application of data mining techniques for understanding market volatility. This research proposes a novel method that integrates social media data, encompassing public sentiment, news, and historical stock prices, to predict future stock trends. The approach involves two primary phases. The first phase develops a sentiment analysis (SA) model using three dilated convolution layers for feature extraction and classification. Addressing the challenge of unbalanced classification, a reinforcement learning (RL)-based strategy is employed, wherein an agent receives varied rewards for accurate classification, with a bias towards the minority class. Additionally, a unique clustering-based mutation operator within a differential equation (DE) framework is introduced to initiate the backpropagation (BP) process. The second phase incorporates an attention-based long short-term memory (LSTM) model, merging historical stock prices with sentiment data. An experimental analysis of the study dataset is conducted to determine optimal values for significant parameters, including the reward function.

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Section: Resultsmentioning

confidence: 99%

Integrating Social Media Data and Historical Stock Prices for Predictive Analysis: A Reinforcement Learning Approach

Li,

Zhang

2023

IJACSA

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“…MAE is a measure of how far the predicted value is from the actual value by calculating the absolute value of the difference between the two and then taking the average [29]. To calculate MAE, we can use (8) [30].…”

Section: Performance Evaluationmentioning

confidence: 99%

Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

Priyatno,

Ramadhan Sudirman,

Musridho

2024

IJECE

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Stock price prediction using machine learning is a rapidly growing area of research. However, the large number of features that can be used can complicate the learning process. The feature selection method that can be used to overcome this problem is recursive feature elimination. Standard recursive feature elimination carries the risk of producing inaccurate algorithms because the top-ranked features are not necessarily the most important features. This research proposes a feature selection method that combines important features and nonparametric correlation in recursive feature elimination for stock price prediction. The data features used are technical indicators and stock price history. The recursive feature elimination method is modified with important features and nonparametric correlation features. The strategy for combining important features and non-parametric features is average weight, 25:75% weight, 75:25% weight, maximum weight, and minimum weight. The performance evaluation results show that the proposed feature selection method succeeded in obtaining small error values. The proposed method for predicting PT Bank Rakyat Indonesia Tbk (BBRI) stock prices obtains mean squared error, root mean square error, mean absolute error, and mean absolute percentage error evaluation values of 0.0000336, 0.00577, 0.00459, and 1.78%, respectively. This shows that recursive feature elimination with feature selection that combines important features and non-parametric correlation works better than the original recursive feature elimination at predicting stock prices.

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“…6: Formulate forget gate and input gate as given in equations ( 6) and (7) 7: Update old memory cell and produce output gate as given in equations ( 8), ( 9), (10)…”

Section: Multivariate Box Jenkins Long Short Term Memory Forecasting ...mentioning

confidence: 99%

“…Yet another method for bankruptcy prediction employing Extreme Gradient Boosting for learning an ensemble of decision trees for economic prediction was presented in [9]. An organized survey on deep learning in stock market was investigated in [10].…”

Section: Introductionmentioning

confidence: 99%

Box Jenkins Neural Network Keynecian Reinforcement Learning Based Financial Big Data Analysis for Optimal Prediction

Zhang

2022

Preprint

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In the digital era, Economic and Finance information is huge and attainable. The financial industry is a key to stimulating the evolution of the national economy, and with the immense availability of huge data makes the process of financial data analysis both a time consuming and tedious decision making in the financial market. By analyzing this Economic and Finance information significantly and easily, valuable perceptions can be provided. For rational economic growth, information pertaining to economic affairs and likelihoods is critical to decision-makers like governmental bodies, financial institutions and banks. However, precise predictions have been demand owing to the intricacy and unpredictability of financial and economic systems in the midst of persistent changes in economic environments. This work provides to approaches for better economic prediction and decision-making using a novel method called, Multivariate Box Jenkins Neural Network-based Keynecian Reinforcement Learning (MBJNN-KRL). In this work, Financial Data Analysis Deep Economic Prediction model that contains a Multivariate Box Jenkins Long Short Term Memory Forecasting model, for better economic prediction and convergence speed even in case of large data is first designed. The deep reinforcement learning technique is then adapted to retrain neural networks and rebalance the aggregate expenditure periodically. We combined the multivariate Multivariate Box Jenkins Long Short Term Memory Forecasting and the Keynecian Reinforcement Learning models into a single integrated reinforcement learning model named Multivariate Box Jenkins Neural Network-based Keynecian Reinforcement Learning (MBJNN-KRL). Finally, distinct sets of experiments are carried out on real large-scale data set, and the results entirely prove the efficiency and robustness of the proposed method in arbitrary economic prediction based on financial data. Our empirical data revealed that the MBJNN-KRL method could attain combative financial performance compared to traditional methods in terms of predictive accuracy, convergence speed and prediction error.

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Deep learning in the stock market—a systematic survey of practice, backtesting, and applications

Cited by 55 publications

References 73 publications

Integrating Social Media Data and Historical Stock Prices for Predictive Analysis: A Reinforcement Learning Approach

Integrating Social Media Data and Historical Stock Prices for Predictive Analysis: A Reinforcement Learning Approach

Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

Box Jenkins Neural Network Keynecian Reinforcement Learning Based Financial Big Data Analysis for Optimal Prediction

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