Ensemble Application of Transfer Learning and Sample Weighting for Stock Market Prediction

Merello, Simone; Ratto, Andrea Picasso; Oneto, Luca; Wang, Zhaoxia

doi:10.1109/ijcnn.2019.8851938

Cited by 14 publications

(3 citation statements)

References 25 publications

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“…Their findings suggest that the construction of a semantic vine improves on the arbitrary vine-growing method in the context of robust correlation estimation and multi-period asset allocation. Merello and colleagues [43] showed that the return predictions generated by a regression approach are more meaningful concerning the "buy" or "sell" signals provided by classification approaches during trading. They found that the application of transfer learning and sample weighting over different market fluctuations has been instrumental in enhancing performance.…”

Section: Sentic Computing and Financementioning

confidence: 99%

“…Dridi, Atzeni and Recupero [42] proposed a supervised method and found that semantic features and semantic frames [34] Quantification of Investor Emotion in Financial News by Analyzing the Stock Price Reaction Atzeni, Dridi and Recupero (2018) [36] Using Frame-Based Resources for Sentiment Analysis within the Financial Domain Xing, Cambria and Welsch (2018) [37] Intelligent Asset Allocation via Market Sentiment Views Picasso et al (2018) [38] Technical Analysis and Sentiment Embeddings for Market Trend Prediction Xing, Cambria and Welsch (2018) [39] Natural Language Based Financial Forecasting: a Survey Malandri et al (2018) [40] Public Mood-Driven Asset Allocation: the Importance of Financial Sentiment in Portfolio Management Upreti et al (2019) [41] Knowledge-Driven Approaches for Financial News Analytics Dridi, Atzeni and Recupero (2019) [42] FineNews: Fine-Grained Semantic Sentiment Analysis on Financial Microblogs and News Merello et al (2019) [43] Ensemble Application of Transfer Learning and Sample Weighting for Stock Market Prediction Xing, Cambria and Welsch (2019) [44] Growing Semantic Vines for Robust Asset Allocation Xing, Cambria and Zhang (2019) [45] Sentiment-Aware Volatility Forecasting Akhtar, Ekbal and Cambria (2020) [46] How Intense Are You? Predicting Intensities of Emotions and Sentiments Using Stacked Ensemble can be applied successfully to sentiment analysis within the financial domain, thus leading to better results.…”

Section: Sentic Computing and Financementioning

confidence: 99%

See 1 more Smart Citation

Does Twitter Affect Stock Market Decisions? Financial Sentiment Analysis During Pandemics: A Comparative Study of the H1N1 and the COVID-19 Periods

et al. 2021

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Investors are constantly aware of the behaviour of stock markets. This affects their emotions and motivates them to buy or sell shares. Financial sentiment analysis allows us to understand the effect of social media reactions and emotions on the stock market and vice versa. In this research, we analyse Twitter data and important worldwide financial indices to answer the following question: How does the polarity generated by Twitter posts influence the behaviour of financial indices during pandemics? This study is based on the financial sentiment analysis of influential Twitter accounts and its relationship with the behaviour of important financial indices. To carry out this analysis, we used fundamental and technical financial analysis combined with a lexicon-based approach on financial Twitter accounts. We calculated the correlations between the polarities of financial market indicators and posts on Twitter by applying a date shift on tweets. In addition, correlations were identified days before and after the existing posts on financial Twitter accounts. Our findings show that the markets reacted 0 to 10 days after the information was shared and disseminated on Twitter during the COVID-19 pandemic and 0 to 15 days after the information was shared and disseminated on Twitter during the H1N1 pandemic. We identified an inverse relationship: Twitter accounts presented reactions to financial market behaviour within a period of 0 to 11 days during the H1N1 pandemic and 0 to 6 days during the COVID-19 pandemic. We also found that our method is better at detecting highly shifted correlations by using SenticNet compared with other lexicons. With SenticNet, it is possible to detect correlations even on the same day as the Twitter posts. The most influential Twitter accounts during the period of the pandemic were The New York Times, Bloomberg, CNN News and Investing.com, presenting a very high correlation between sentiments on Twitter and stock market behaviour. The combination of a lexicon-based approach is enhanced by a shifted correlation analysis, as latent or hidden correlations can be found in data.

show abstract

Section: Sentic Computing and Financementioning

confidence: 99%

Section: Sentic Computing and Financementioning

confidence: 99%

Does Twitter Affect Stock Market Decisions? Financial Sentiment Analysis During Pandemics: A Comparative Study of the H1N1 and the COVID-19 Periods

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Most of the existing studies focus on only one stock market, in the sense that stock markets differ from each other because of the trading rules, while different markets may share some common phenomenon that can be leveraged for prediction by approaches such as transfer learning. There are already a few studies showing positive results for cross-market analysis Lee et al, 2019;Merello et al, 2019;Nguyen & Yoon, 2019;, it is worth exploring in the following studies. In Lee et al (2019), the model is trained only on US stock market data and tested on the stock market data of 31 different countries over 12 years.…”

Section: Cross-market Analysismentioning

confidence: 99%

Applications of deep learning in stock market prediction: recent progress

Jiang

2020

Preprint

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Stock market prediction has been a classical yet challenging problem, with the attention from both economists and computer scientists. With the purpose of building an effective prediction model, both linear and machine learning tools have been explored for the past couple of decades. Lately, deep learning models have been introduced as new frontiers for this topic and the rapid development is too fast to catch up. Hence, our motivation for this survey is to give a latest review of recent works on deep learning models for stock market prediction. We not only category the different data sources, various neural network structures, and common used evaluation metrics, but also the implementation and reproducibility. Our goal is to help the interested researchers to synchronize with the latest progress and also help them to easily reproduce the previous studies as baselines. Base on the summary, we also highlight some future research directions in this topic.

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Modeling Tweet Dependencies with Graph Convolutional Networks for Sentiment Analysis

2022

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Ensemble Application of Transfer Learning and Sample Weighting for Stock Market Prediction

Cited by 14 publications

References 25 publications

Does Twitter Affect Stock Market Decisions? Financial Sentiment Analysis During Pandemics: A Comparative Study of the H1N1 and the COVID-19 Periods

Does Twitter Affect Stock Market Decisions? Financial Sentiment Analysis During Pandemics: A Comparative Study of the H1N1 and the COVID-19 Periods

Applications of deep learning in stock market prediction: recent progress

Modeling Tweet Dependencies with Graph Convolutional Networks for Sentiment Analysis

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