An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

Sharaf, M.M.; Hemdan, Ezz El‐Din; El‐Sayed, Ayman; El‐Bahnasawy, Nirmeen A.

doi:10.1007/s11042-022-14216-w

Cited by 22 publications

(3 citation statements)

References 22 publications

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“…The use of machine learning, deep learning, and reinforcement learning models for future stock price prediction has been the most popular approach of late [11][12][13][14][15][16][17][18][19][20][21][22][23]. Hybrid models are also proposed that utilize the algorithms and architectures of machine learning and deep learning and exploit the sentiments in the textual sources on the social web [24][25][26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

Portfolio Optimization: A Comparative Study

Sen,

Dasgupta

2024

Artificial Intelligence

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Portfolio optimization has been an area that has attracted considerable attention from the financial research community. Designing a profitable portfolio is a challenging task involving precise forecasting of future stock returns and risks. This chapter presents a comparative study of three portfolio design approaches, the mean-variance portfolio (MVP), hierarchical risk parity (HRP)-based portfolio, and autoencoder-based portfolio. These three approaches to portfolio design are applied to the historical prices of stocks chosen from ten thematic sectors listed on the National Stock Exchange (NSE) of India. The portfolios are designed using the stock price data from January 1, 2018, to December 31, 2021, and their performances are tested on the out-of-sample data from January 1, 2022, to December 31, 2022. Extensive results are analyzed on the performance of the portfolios. It is observed that the performance of the MVP portfolio is the best on the out-of-sample data for the risk-adjusted returns. However, the autoencoder portfolios outperformed their counterparts on annual returns.

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Section: Related Workmentioning

confidence: 99%

Portfolio Optimization: A Comparative Study

Sen,

Dasgupta

2024

Artificial Intelligence

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“…Results indicated that combining sentimental information from both sources improved model accuracy. In [16] Sharaf, Hemdan, El-Sayed and El-Bhnasawy presented a survey of sentiment analysis and stock analysis, followed by the introduction of an efficient proposed system that combines sentiment analysis of social news and historical data analysis. The proposed system faced limitations during the Covid-19 period.…”

Section: Related Workmentioning

confidence: 99%

Global Stock Price Forecasting during a Period of Market Stress using LightGBM

Guennioui,

Chiadmi,

Amghar

2024

IJCDS

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Accurately predicting future stock prices is crucial for investors, particularly during market stress, enabling informed decisions to mitigate losses and reduce financial exposure. While machine learning techniques have shown promise in this field, most studies have focused on local models. Global forecasting models, which are trained on a variety of time series, have demonstrated encouraging outcomes in outperforming local models. This article presents a global forecasting approach utilizing Light Gradient-Boosting Machine (LightGBM) to predict stock prices in the Moroccan market during highly volatile period. The study includes a sector-wise analysis, with the most volatile sector being evaluated, and correlated stocks from other sectors were considered to enhance the data. Our findings highlight the effectiveness of utilizing a global forecasting approach with an optimized LightGBM model for Moroccan stock price prediction, outperforming models such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). Additionally, incorporating data from multiple stocks improves the accuracy of stock price prediction.

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“…In (Sharaf et al 2022), news headlines pertaining to TSLA, AMZ, and GOOG stock were considered to obtain good-quality data to reduce spam tweets through feature selection methods. Sentiment analysis was also used for polarity detection and historical data mining, for which DL algorithms were used.…”

Section: Comprehensive Summary Of Theoretical Basismentioning

confidence: 99%

Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications

Sonkavde,

Dharrao,

Bongale

et al. 2023

IJFS

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The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models.

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An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

Cited by 22 publications

References 22 publications

Portfolio Optimization: A Comparative Study

Portfolio Optimization: A Comparative Study

Global Stock Price Forecasting during a Period of Market Stress using LightGBM

Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications

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