Buzzwords build momentum: Global financial Twitter sentiment and the aggregate stock market

Groß-Klußmann, Axel; König, Stephan; Ebner, Martin

doi:10.1016/j.eswa.2019.06.027

Cited by 55 publications

(21 citation statements)

References 58 publications

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“…where R pos i,k,t denotes the reading volume of the positive comment i of stock k on day t, R neg j,k,t denotes the reading volume of the negative comment j of stock k on day t. [30] assumes that investor sentiment in period t − 1 would affect the stock return in period t. In addition, Gross-Klussmann et al [52] and Xie and Wang [4] also propose similar assumption on daily frequency. So, according to these documents, we analyze the effect of investor sentiment at day t − 1 on the stock return, trading volume, and order imbalance of big trade at day t. e equations are as follows:…”

Section: Calculating Online Investormentioning

confidence: 99%

The Effect of Online Investor Sentiment on Stock Movements: An LSTM Approach

Wang

2020

Complexity

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With more and more investors exerting their voices through network forums or social media platforms, the relationships between online investor sentiment and stock movements have drawn more and more attention. In this paper, we crawl stock comments from China’s most popular online stock forum, East Money (www.eastmoney.com), and then develop a sentiment classifier using the LSTM method. Using the online investor sentiment of the stock forum, we explore the effect of online investor sentiment on the stock movements of CSI300. The results show that online investor sentiment has a significant positive impact on both stock return and trading volume and remains significant after controlling book-to-market ratio, BETA, and market value. Moreover, investor sentiment has a significant positive impact on order imbalance of big trade, which represents the main flow of money in the market. As a result, investor sentiment has a positive impact on the major fund flows in the market. In other words, an increase in investor sentiment can boost the major money flows in the market to some extent. From a practical point of view, investor sentiment can assist investors to make investment decisions and help the government to regulate the stock market.

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Section: Calculating Online Investormentioning

confidence: 99%

The Effect of Online Investor Sentiment on Stock Movements: An LSTM Approach

Wang

2020

Complexity

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“…Bollen et al (2011) conclude that the predictions of the stock indices can be refined through the study of information. Groß-Klußmann et al (2019) examine the relation between signals derived from the unstructured social media text data and financial market developments in a long-term time range and conclude that there is relationship between the social media text data and financial market performance. Gu & Kurov (2020) find that the information content of Twitter sentiment of an individual company can predict the future returns of the underlying stock.…”

Section: Literature Reviewmentioning

confidence: 99%

Financial Twitter Sentiment on Bitcoin Return and High-Frequency Volatility

Gao

Huang

Hua

2021

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This paper studies how sentiment affect Bitcoin pricing by examining, at an hourly frequency, the linkage between sentiment of finance-related Twitter messages and return as well as the volatility of Bitcoin as a financial asset. On the one hand, there was calculated the return from minute-level Bitcoin exchange quotes and use of both rolling variance and high-minus-low price to proxy for Bitcoin volatility per each trading hour. On the other hand, the mood signals from tweets were extracted based on a list of positive, negative, and uncertain words according to the Loughran-McDonald finance-specific dictionary. These signals were translated by categorizing each tweet into one of three sentiments, namely, bullish, bearish, and null. Then the total number of tweets were adopted in each category over one hour and their differences as potential Bitcoin price predictors. The empirical results indicate that after controlling a list of lagged returns and volatilities, stronger bullish sentiment significantly foreshadows higher Bitcoin return and volatility over the time range of 24 hours. While bearish and neutral financial Twitter sentiments have no such consistent performance, the difference between bullish and bearish ratings can improve prediction consistency. Overall, this research results add to the growing Bitcoin literature by demonstrating that the Bitcoin pricing mechanism can be partially revealed by the momentum on sentiment in social media networks, justifying a sentimental appetite for cryptocurrency investment.

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“…In particular, there has been a recent research trend of using social media sentiment analysis for financial decision support systems [3,4]. Regarding Twitter, the most common approach to retrieve texts is based on a keywords match by using the application programming interface (API).…”

Section: Introductionmentioning

confidence: 99%

“…Yet, this often results in misleading tweets. This problem was recently pointed out by [4], which detected a large amount of noisy tweets when using generic keywords for filtering stock index futures and thus needed to adopt a manually curated list of known financial experts to filter the data.…”

Section: Introductionmentioning

confidence: 99%

Twitter alloy steel disambiguation and user relevance via one-class and two-class news titles classifiers

Zola

Cortez

Brentari

2020

Neural Comput & Applic

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This paper addresses the nontrivial task of Twitter financial disambiguation (TFD), which is relevant to filter financial domain tweets (e.g., alloy steel or coffee prices) when no unique identifiers (e.g., cashtags) are adopted. To automate TFD, we propose a transfer learning approach that uses freely labeled news titles to train diverse one-class and two-class classification methods. These include different text handling transforms, adaptations of statistical measures and modern machine learning methods, including support vector machines (SVM), deep autoencoders and multilayer perceptrons. As a case study, we analyzed the domain of alloy steel prices, collecting a recent Twitter dataset. Overall, the best results were achieved by a two-class SVM fed with TFD statistical measures and topic model features, obtaining an 80% and 71% discrimination level when tested with 11,081 and 3,000 manually labeled tweets. The best one-class performance (78% and 69% for the same test tweets) was obtained by a term frequency-inverse document frequency classifier (TF-IDFC). These models were further used to generate a Financial User Relevance rank (FUR) score, aiming to filter relevant users. The SVM and TF-IDFC FUR models obtained a predictive user discrimination level of 80% and 75% when tested with a manually labeled test sample of 418 users. These results confirm the proposed joint TFD-FUR approach as a valuable tool for the selection of Twitter texts and users for financial social media analytics (e.g., sentiment analysis, detection of influential users).

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Buzzwords build momentum: Global financial Twitter sentiment and the aggregate stock market

Cited by 55 publications

References 58 publications

The Effect of Online Investor Sentiment on Stock Movements: An LSTM Approach

The Effect of Online Investor Sentiment on Stock Movements: An LSTM Approach

Financial Twitter Sentiment on Bitcoin Return and High-Frequency Volatility

Twitter alloy steel disambiguation and user relevance via one-class and two-class news titles classifiers

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