Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators

Functional near-infrared spectroscopy (fNIRS) technology offers a promising avenue for assessing brain function across participant groups. Despite its numerous advantages, the fNIRS technique often faces challenges such as noise contamination and motion artifacts from data collection. Methods for improving fNIRS signal quality are urgently needed, especially with the development of wearable fNIRS equipment and corresponding applications in natural environments. To solve these issues, we propose a generative deep learning approach to recover damaged fNIRS signals from one or more measurement channels. The model could capture spatial and temporal variations in the time series of fNIRS data by integrating multiscale convolutional layers, gated recurrent units (GRUs), and linear regression analyses. Several extensive experiments were conducted on a dataset of healthy elderly individuals to assess the performance of the model. Collectively, the results demonstrate that the proposed model can accurately reconstruct damaged time series for individual channels while preserving intervariable relationships. Under two simulated scenarios of multichannel damage, the model maintains robust reconstruction accuracy and consistency in terms of functional connectivity. Our findings underscore the potential of generative deep learning techniques in reconstructing damaged fNIRS signals, offering a novel perspective for accurate data provision in clinical diagnosis and brain research.

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Decoding Bitcoin: leveraging macro- and micro-factors in time series analysis for price prediction

Jung,

Kim,

Lee

2024

PeerJ Computer Science

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Predicting Bitcoin prices is crucial because they reflect trends in the overall cryptocurrency market. Owing to the market’s short history and high price volatility, previous research has focused on the factors influencing Bitcoin price fluctuations. Although previous studies used sentiment analysis or diversified input features, this study’s novelty lies in its utilization of data classified into more than five major categories. Moreover, the use of data spanning more than 2,000 days adds novelty to this study. With this extensive dataset, the authors aimed to predict Bitcoin prices across various timeframes using time series analysis. The authors incorporated a broad spectrum of inputs, including technical indicators, sentiment analysis from social media, news sources, and Google Trends. In addition, this study integrated macroeconomic indicators, on-chain Bitcoin transaction details, and traditional financial asset data. The primary objective was to evaluate extensive machine learning and deep learning frameworks for time series prediction, determine optimal window sizes, and enhance Bitcoin price prediction accuracy by leveraging diverse input features. Consequently, employing the bidirectional long short-term memory (Bi-LSTM) yielded significant results even without excluding the COVID-19 outbreak as a black swan outlier. Specifically, using a window size of 3, Bi-LSTM achieved a root mean squared error of 0.01824, mean absolute error of 0.01213, mean absolute percentage error of 2.97%, and an R-squared value of 0.98791. Additionally, to ascertain the importance of input features, gradient importance was examined to identify which variables specifically influenced prediction results. Ablation test was also conducted to validate the effectiveness and validity of input features. The proposed methodology provides a varied examination of the factors influencing price formation, helping investors make informed decisions regarding Bitcoin-related investments, and enabling policymakers to legislate considering these factors.

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Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators

Cited by 3 publications

References 37 publications

Financial network communities and methodological insights: a case study for Borsa Istanbul Sustainability Index

Financial network communities and methodological insights: a case study for Borsa Istanbul Sustainability Index

Reconstructing damaged fNIRS signals with a generative deep learning model

Decoding Bitcoin: leveraging macro- and micro-factors in time series analysis for price prediction

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