Prediction of stock return by LSTM neural network

Qiao, Risheng; Chen, Weike; Qiao, Yu

doi:10.1080/08839514.2022.2151159

Cited by 8 publications

(7 citation statements)

References 29 publications

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“…In recent years, numerous scholars have concentrated on the evaluation of prediction models. Regarding LSTM, Sairam and K [5] and Qiao et al [6] both found that this model exhibits superior accuracy. LSTM neural networks can extract information from extensive original data without relying on prior knowledge of predictors [6].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Regarding LSTM, Sairam and K [5] and Qiao et al [6] both found that this model exhibits superior accuracy. LSTM neural networks can extract information from extensive original data without relying on prior knowledge of predictors [6]. With respect to ARIMA, Goyal and Raj [7] demonstrated that this model can be applied to both univariate and multivariate time series data, yielding satisfactory results in terms of prediction accuracy and error rates.…”

Section: Literature Reviewmentioning

confidence: 99%

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The stock price of BYD prediction using LSTM and ARIMA

2023

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The increasing attention towards low-carbon initiatives has led to a surge in interest for products and services that contribute to a sustainable future. As a result, cap-andtrade policies, green bonds, and low-carbon stocks have emerged as significant areas of investigation. This study aims to explore the predictability of low-carbon stock prices, using BYD (Build Your Dream), a prominent new energy vehicle brand, as a case study. To effectively analyze and forecast BYD stock closing prices, we have evaluated various models and determined that Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models exhibit superior performance in comparison to other alternatives. Employing a range of evaluation metrics, such as Standard Deviation (STD), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), we demonstrate that the selected models exhibit a satisfactory level of fit. In future research endeavors, we aspire to expand the scope of our investigation to encompass additional facets of low-carbon stocks and their potential impact on the broader financial landscape.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

The stock price of BYD prediction using LSTM and ARIMA

2023

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“…Such a technique fits well for rolling windows as the windows can operate on a weekly basis. Once the model predicts, the window is shifted ahead, by the number of periods equal to test set to continue training [ 58 ]. The one-week-based multi-horizon prediction has also been found [ 59 ] to be the most pragmatic, considering that real-world applications mainly require forecasts up to one week.…”

Section: Stock Market Forecastingmentioning

confidence: 99%

Causality-driven multivariate stock movement forecasting

Díaz Berenguer,

Da,

Bossa

et al. 2024

PLoS ONE

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Our study aims to investigate the interdependence between international stock markets and sentiments from financial news in stock forecasting. We adopt the Temporal Fusion Transformers (TFT) to incorporate intra and inter-market correlations and the interaction between the information flow, i.e. causality, of financial news sentiment and the dynamics of the stock market. The current study distinguishes itself from existing research by adopting Dynamic Transfer Entropy (DTE) to establish an accurate information flow propagation between stock and sentiments. DTE has the advantage of providing time series that mine information flow propagation paths between certain parts of the time series, highlighting marginal events such as spikes or sudden jumps, which are crucial in financial time series. The proposed methodological approach involves the following elements: a FinBERT-based textual analysis of financial news articles to extract sentiment time series, the use of the Transfer Entropy and corresponding heat maps to analyze the net information flows, the calculation of the DTE time series, which are considered as co-occurring covariates of stock Price, and TFT-based stock forecasting. The Dow Jones Industrial Average index of 13 countries, along with daily financial news data obtained through the New York Times API, are used to demonstrate the validity and superiority of the proposed DTE-based causality method along with TFT for accurate stock Price and Return forecasting compared to state-of-the-art time series forecasting methods.

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“…By considering the Shanghai and Shenzhen stock markets from 2019 to 2021 as the contextual backdrop, the authors employ the LSTM neural network to extract features from the original data. Subsequently, the rolling window method is employed to predict future stock market behavior and conduct analysis [10].…”

Section: Specific Applicationmentioning

confidence: 99%

Studying Stock Prediction in the Context of Machine Learning Exemplified by Analyzing SVM and LSTM Models

Zheng

2024

HSET

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The impact of stocks on economic development is significant, with most companies employing quantitative finance methods to analyze and trade stocks. In the present day, there is a growing plethora of stock prediction methods, requiring individuals to carefully select the appropriate means of prediction. This study aims to present the highly regarded machine learning algorithms of recent years. Machine learning enables the extraction of trading strategies by learning patterns from historical stock data. Deep learning, a subset of machine learning, eliminates the need for researchers to manually extract features, instead utilizing neural network models that have proven effective for stock analysis. This paper also provides an extensive analysis of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), elucidating their algorithmic principles and assessing their strengths and weaknesses based on the findings of other researchers. It is observed that SVM is effective for predicting small quantities, but lacks suitability for non-stationary data prediction due to the non-informative nature of financial time series data, which significantly diminishes the accuracy of SVM predictions. In contrast, LSTM emerges as the most comprehensive single algorithm, exhibiting high accuracy and efficiency across all aspects. The paper also explores advanced hybrid models, which demonstrate that combining the advantages of multiple models can lead to higher accuracy and efficiency. The future of stock prediction algorithms holds promise for development, with the potential to construct an intelligent system that automatically selects data for analysis.

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Prediction of stock return by LSTM neural network

Cited by 8 publications

References 29 publications

The stock price of BYD prediction using LSTM and ARIMA

The stock price of BYD prediction using LSTM and ARIMA

Causality-driven multivariate stock movement forecasting

Studying Stock Prediction in the Context of Machine Learning Exemplified by Analyzing SVM and LSTM Models

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