Investigating Deep Stock Market Forecasting with Sentiment Analysis

Liapis, Charalampos M.; Karanikola, Aikaterini; Kotsiantis, Sotiris

doi:10.3390/e25020219

Cited by 13 publications

(12 citation statements)

References 64 publications

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“…Hence, the selected day-to-day sentiment and emotion scores extracted from related tweets are incorporated into the feature space and used in a multivariate setting to predict the closing prices of 15 stocks. The investigation builds on the results presented in [2] in the sense that the aforementioned work, which works within the same framework as the present one regarding base learners and data, enables us to reject a fairly large number of methods, keeping only those that exhibit good behavior. Thus, the experimental framework starts with five top-performing methods and includes the investigation of a number of possible weighted ensemble forecasting procedures.…”

Section: Introductionmentioning

confidence: 88%

Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting

Liapis,

Kotsiantis

2023

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The use of deep learning in conjunction with models that extract emotion-related information from texts to predict financial time series is based on the assumption that what is said about a stock is correlated with the way that stock fluctuates. Given the above, in this work, a multivariate forecasting methodology incorporating temporal convolutional networks in combination with a BERT-based multi-label emotion classification procedure and correlation feature selection is proposed. The results from an extensive set of experiments, which included predictions of three different time frames and various multivariate ensemble schemes that capture 28 different types of emotion-relative information, are presented. It is shown that the proposed methodology exhibits universal predominance regarding aggregate performance over six different metrics, outperforming all the compared schemes, including a multitude of individual and ensemble methods, both in terms of aggregate average scores and Friedman rankings. Moreover, the results strongly indicate that the use of emotion-related features has beneficial effects on the derived forecasts.

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Section: Introductionmentioning

confidence: 88%

Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting

Liapis,

Kotsiantis

2023

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“…Additionally, RMSLE penalizes underestimation more heavily than overestimation, making it a suitable choice for situations where underestimation is more costly than overestimation. (29) We used Root Mean Squared Logarithmic Error (RMSLE) and R-squared because they are advantageous for evaluating time series forecasting models in specific contexts. RMSLE is useful for its sensitivity to relative errors rather than absolute ones, making it ideal for data with a wide range of values.…”

Section: Rmslementioning

confidence: 99%

“…It's also scale-independent, allowing for comparisons across different datasets or scales. (29,30,31,32,33,34) These metrics provide benefits over traditional ones like MAE or RMSE by offering better interpretability, being more suited to certain types of data (like those with non-linear relationships or uneven variance) and offering robustness against specific types of errors. (35,36,37,38,39,40,41)…”

Section: Rmslementioning

confidence: 99%

Utilizing Machine Learning and Deep Learning for Predicting Crypto-currency Trends

El Youssefi,

Hessane,

Zeroual

et al. 2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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In the dynamic and often volatile world of the cryptocurrency market, accurately predicting future market movements is crucial for making informed trading decisions. While manual trading involves traders making subjective judgments based on market observations, the development of algorithmic trading systems, incorporating Machine Learning and Deep Learning, has introduced a more systematic approach to trading. These systems often employ technical analysis and machine learning techniques to analyze historical price data and generate trading signals. This study delves into a comparative analysis of two charting techniques, Heikin-Ashi and alternate candlestick patterns, in the context of forecasting single-step future price movements of cryptocurrency pairs. Utilizing a range of time windows (1 day, 12 hours, 8 hours, ..., 5 minutes) and various regression algorithms (Huber regressor, k-nearest neighbors regressor, Light Gradient Boosting Machine, linear regression, and random forest regressor), the study evaluates the effectiveness of each technique in forecasting future price movements. The primary outcomes of the research indicate that the application of ensemble learning methods to the alternate candlestick patterns consistently surpasses the performance of Heikin-Ashi candlesticks across all examined time windows. This suggests that alternate candlestick patterns provide more reliable information for predicting short-term price movements. Additionally, the study highlights the varying behavior of Heikin-Ashi candlesticks over different time windows

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“…Regarding the real-time BITCOIN price prediction, [57] leveraged RNNs equipped with LSTMs in conjunction with data extracted from Twitter and Reddit. [58] presented an extensive comparative study encompassing thirty contemporary Deep Learning models, with the aim of not only shedding light on model performance but also exploring a multitude of sentiment feature configurations. For a more comprehensive understanding of the application of modern Deep Learning methods in the FSA domain, additional information can be found in the following survey papers: [59][60][61].…”

Section: Literature Reviewmentioning

confidence: 99%

Financial sentiment analysis: Classic methods vs. deep learning models

Karanikola,

Davrazos,

Liapis

et al. 2023

IDT

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Sentiment Analysis, also known as Opinion Mining, gained prominence in the early 2000s alongside the emergence of internet forums, blogs, and social media platforms. Researchers and businesses recognized the imperative to automate the extraction of valuable insights from the vast pool of textual data generated online. Its utility in the business domain is undeniable, offering actionable insights into customer opinions and attitudes, empowering data-driven decisions that enhance products, services, and customer satisfaction. The expansion of Sentiment Analysis into the financial sector came as a direct consequence, prompting the adaptation of powerful Natural Language Processing models to these contexts. In this study, we rigorously test numerous classical Machine Learning classification algorithms and ensembles against five contemporary Deep Learning Pre-Trained models, like BERT, RoBERTa, and three variants of FinBERT. However, its aim extends beyond evaluating the performance of modern methods, especially those designed for financial tasks, to a comparison of them with classical ones. We also explore how different text representation and data augmentation techniques impact classification outcomes when classical methods are employed. The study yields a wealth of intriguing results, which are thoroughly discussed.

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Investigating Deep Stock Market Forecasting with Sentiment Analysis

Cited by 13 publications

References 64 publications

Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting

Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting

Utilizing Machine Learning and Deep Learning for Predicting Crypto-currency Trends

Financial sentiment analysis: Classic methods vs. deep learning models

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