A deep learning method DCWR with HANet for stock market prediction using news articles

Albahli, Saleh; Awan, Awais; Nazir, Tahira; Irtaza, Aun; Alkhalifah, Ali; Albattah, Waleed

doi:10.1007/s40747-022-00658-0

Cited by 9 publications

(3 citation statements)

References 45 publications

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“…Both modeling structures emerged to be highly successful in the precise estimation of trends in highly volatile periods. There exists a sizeable literature wherein the sentiments of the external floating news and chaotic events have been leveraged to predict stock market trends [ 2 , 9 , 30 , 32 ].…”

Section: Cognate Literaturementioning

confidence: 99%

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies

Ghosh

Pamučar

2022

Complex Intell. Syst.

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Global financial stress is a critical variable that reflects the ongoing state of several key macroeconomic indicators and financial markets. Predictive analytics of financial stress, nevertheless, has seen very little focus in literature as of now. Futuristic movements of stress in markets can be anticipated if the same can be predicted with a satisfactory level of precision. The current research resorts to two granular hybrid predictive frameworks to discover the inherent pattern of financial stress across several critical variables and geography. The predictive structure utilizes the Ensemble Empirical Mode Decomposition (EEMD) for granular time series decomposition. The Long Short-Term Memory Network (LSTM) and Facebook’s Prophet algorithms are invoked on top of the decomposed components to scrupulously investigate the predictability of final stress variables regulated by the Office of Financial Research (OFR). A rigorous feature screening using the Boruta methodology has been utilized too. The findings of predictive exercises reveal that financial stress across assets and continents can be predicted accurately in short and long-run horizons even at the time of steep financial distress during the COVID-19 pandemic. The frameworks appear to be statistically significant at the expense of model interpretation. To resolve the issue, dedicated Explainable Artificial Intelligence (XAI) methods have been used to interpret the same. The immediate past information of financial stress indicators largely explains patterns in the long run, while short-run fluctuations can be tracked by closely monitoring several technical indicators.

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Section: Cognate Literaturementioning

confidence: 99%

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies

Ghosh

Pamučar

2022

Complex Intell. Syst.

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“…This change is caused by the influence of news quality and some other technical factors, and usually lasts from a few hours to a few days [9][10]. Since this is a short-term volatility, it is only relevant to some trailing and exiting stock investors [11][12]. Therefore, its analysis is not valued by long-term investors [13].…”

Section: Predictability Of the Securities Marketmentioning

confidence: 99%

Prediction Method and Optimization of Stock Trend Based on Neural Network

2021

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The stock market is characterized by the coexistence of high risks. In order to seek benefits and avoid risks, people do not hesitate to explore its laws and seek the best forecasting methods and methods. The purpose of this paper is to study the stock trend prediction method and optimization based on neural network. The predictability of the securities market is expounded. On the basis of the prediction model based on the historical stock transaction data, a stock prediction model integrating news and investor sentiment is constructed. The advantage of linear and complex time series forecasting problems, the stock training data is used as the input data of the improved CLSTM and BiLSTM deep neural network, and the stock trend forecasting model is trained. Continuously optimize and improve the model, select appropriate parameters to obtain a good stock trend prediction model. By continuously revising the LSTM level, the accuracy of the stock trend prediction model is significantly improved.

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“…With the rapid growth of stock trading data volume, traditional machine learning methods 2 of 19 began revealing shortcomings in modeling capability, failing to efficiently capture information within vast datasets. Deep-learning-based methods were then developed [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Ahmed et al [19] summarized the application of deep learning methods in stock price prediction.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach with Extensive Sentiment Analysis for Quantitative Investment

Li,

Hu,

Luo

2023

Electronics

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Recently, deep-learning-based quantitative investment is playing an increasingly important role in the field of finance. However, due to the complexity of the stock market, establishing effective quantitative investment methods is facing challenges from various aspects because of the complexity of the stock market. Existing research has inadequately utilized stock news information, overlooking significant details within news content. By constructing a deep hybrid model for comprehensive analysis of historical trading data and news information, complemented by momentum trading strategies, this paper introduces a novel quantitative investment approach. For the first time, we fully consider two dimensions of news, including headlines and contents, and further explore their combined impact on modeling stock price. Our approach initially employs fundamental analysis to screen valuable stocks. Subsequently, we built technical factors based on historical trading data. We then integrated news headlines and content summarized through language models to extract semantic information and representations. Lastly, we constructed a deep neural model to capture global features by combining technical factors with semantic representations, enabling stock prediction and trading decisions. Empirical results conducted on over 4000 stocks from the Chinese stock market demonstrated that incorporating news content enriched semantic information and enhanced objectivity in sentiment analysis. Our proposed method achieved an annualized return rate of 32.06% with a maximum drawdown rate of 5.14%. It significantly outperformed the CSI 300 index, indicating its applicability to guiding investors in making more effective investment strategies and realizing considerable returns.

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A deep learning method DCWR with HANet for stock market prediction using news articles

Cited by 9 publications

References 45 publications

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies

Prediction Method and Optimization of Stock Trend Based on Neural Network

A Deep Learning Approach with Extensive Sentiment Analysis for Quantitative Investment

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