Crowds on Wall Street

Wang, Gang; Wang, Tianyi; Wang, Bolun; Sambasivan, Divya; Zhang, Zengbin; Zheng, Haitao; Zhao, Ben Y.

doi:10.1145/2675133.2675144

Cited by 42 publications

(3 citation statements)

References 36 publications

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“…Various techniques have been applied in machine learning approaches. For example, Wang et al [33] used the SVM, naive Bayes, and decision trees to classify StockTwits data into "bullish" and "bearish" and discovered that the SVM performed the best. Subsequently, models combined with lexicon-based techniques were established [34].…”

Section: Background and Related Studiesmentioning

confidence: 99%

Enhancing Financial Sentiment Analysis Ability of Language Model via Targeted Numerical Change-Related Masking

Jung,

Jang

2024

IEEE Access

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Sentiment analysis is a critical task that is highly beneficial to various financial tasks such as stock-price prediction, corporate credit rating, economic report analysis, and investment decision support.Researchers have used various methods to train pretraining language models (PLMs) for these tasks. Although most PLMs have achieved excellent performance, they can be further improved. In this study, we propose a new framework to strengthen numerical understanding, in particular for the FinBERT(Financial Bidirectional Encoder Representations from Transformers) model released in 2019, thus improving model performance in the task of sentiment analysis on financial news sentences. This method selects sentences containing numerical words from financial news articles, preferentially masks the words, and post-train the PLM. To evaluate the proposed methodology quantitatively, we apply the same post-training to different financial language models and compare the performance before and after the application using Financial Phrasebank, which is a representative benchmark dataset used in financial sentiment analysis. The experimental results show that the best performance is achieved when 50,000 sentences are used to post-train FinBERT, thus confirming the advantage of the proposed methodology for downstream tasks and highlighting the importance of using the correct amount of data. Additionally, we show that applying the proposed method to different language models improves the performance, particularly in low-resource environments with less training data. The findings of this study suggest that the PLM can improve aspects that it does not understand well, and that thd PLM performance can be improved by post-training it with task-and domain-appropriate datasets, in not only finance but also in other domains.

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Section: Background and Related Studiesmentioning

confidence: 99%

Enhancing Financial Sentiment Analysis Ability of Language Model via Targeted Numerical Change-Related Masking

Jung,

Jang

2024

IEEE Access

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“…In this research context, among the recent contributions in the literature, Li et al (2014) and index levels, model using sentiment analysis outperform the bag-of-words model in both validation and testing sets and that there is a minor difference between the models using the two different dictionaries. Wang et al (2015) work on sentiment analysis retrieved from SeekingAlpha articles and StockTwits messages, two social media platforms, and analyze their correlation with S&P 500 Index movements, finding the former has better explanatory power, even though sentimentbased investment strategies have generally poor performances. They use a dictionary-based approach for SeekingAlpha analysis, relying on LM, and a supervised ML approach for StockTwits, using labels assigned directly by users.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of public news sentiment on stock market index return and volatility

et al. 2023

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Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the implemented techniques and the type of source on which they are based. In this paper, we investigate the impact of the release of public financial news on the S&P 500. Using automatic labeling techniques based on either stock index returns or dictionaries, we apply a classification problem based on long short-term memory neural networks to extract alternative proxies of investor sentiment. Our findings provide evidence that there exists an impact of those sentiments in the market on a 20-minute time frame.We find that dictionary-based sentiment provides meaningful results with respect to those based on stock index returns, which partly fails in the mapping process between news and financial returns.

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“…Typically, investor sentiment on social media is extracted from user posts and aggregated at the asset level (Das and Chen, 2007), and it has been widely used to demonstrate its predictability of financial market performance, such as the trends of Dow Jones or S&P 500 Index (Bollen et al, 2011;Zheludev et al, 2014;Piñeiro-Chousa et al, 2016), stock price movement (Oh and Sheng, 2011;Zhang et al, 2012;Wang et al, 2015), abnormal returns (Ranco et al, 2015;Deng et al, 2018), earning surprises (Chen et al, 2014;Bartov et al, 2018), trading volume (Tan and Tas, 2021), and market volatility (Hou and Tripathi, 2015;Audrino et al, 2020). However, mixed evidence is also reported that investor sentiment does not have strong predictability of stock returns (Oliveira et al, 2013;Kim and Kim, 2014), or the magnitude of the effect is economically small (Nofer and Hinz, 2014).…”

mentioning

confidence: 99%

Understanding heterogeneity of investor sentiment on social media: A structural topic modeling approach

Han²

2022

Front. Artif. Intell.

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Investors nowadays post heterogeneous sentiments on social media about financial assets based on their trading preferences. However, existing works typically analyze the sentiment by its content only and do not account for investor profiles and trading preferences in different types of assets. This paper explicitly considers how investor sentiment about financial market events is shaped by the relative discussions of different types of investors. We leverage a large-scale financial social media dataset and employ a structural topic modeling approach to extract topical contents of investor sentiment across multiple finance-specific factors. The identified topics reveal important events related to the financial market and show strong heterogeneity in the social media content in terms of compositions of investor profiles, asset categories, and bullish/bearish sentiment. Results show that investors with different profiles and trading preferences tend to discuss financial markets with heterogeneous beliefs, leading to divergent opinions about those events regarding the topic prevalence and proportion. Moreover, our findings may shed light on the mechanism that underlies the efficient investor sentiment extraction and aggregation while considering the heterogeneity of investor sentiment across different dimensions.

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Crowds on Wall Street

Cited by 42 publications

References 36 publications

Enhancing Financial Sentiment Analysis Ability of Language Model via Targeted Numerical Change-Related Masking

Enhancing Financial Sentiment Analysis Ability of Language Model via Targeted Numerical Change-Related Masking

Impact of public news sentiment on stock market index return and volatility

Understanding heterogeneity of investor sentiment on social media: A structural topic modeling approach

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