The Effect of Social Media on Market Liquidity

Hu, Tianyou; Tripathi, Arvind

doi:10.2139/ssrn.2661099

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…use NB and compute bullishness using two methods Hu and Tripathi (2015). use NB and Support Vector Machine then compute bullishness and agreement indexes.…”

mentioning

confidence: 99%

The Effect of Social and News Media Sentiments on Financial Markets

2016

SSRN Journal

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“…use NB and compute bullishness using two methods Hu and Tripathi (2015). use NB and Support Vector Machine then compute bullishness and agreement indexes.…”

mentioning

confidence: 99%

The Effect of Social and News Media Sentiments on Financial Markets

2016

SSRN Journal

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“…The assessment and measurement of investor sentiment thus have become an important research topic to evaluate its effects on the financial market (Baker and Wurgler, 2007 ). As such, researchers increasingly leverage a variety of financial data sources and employ computational approaches to extract investor sentiment from the unstructured textual information, such as financial news media (Tetlock et al, 2008 ; Boudoukh et al, 2013 ; Heston and Sinha, 2017 ), Internet stock message board (Tumarkin and Whitelaw, 2001 ; Hou and Tripathi, 2015 ), and social media (Xu and Zhang, 2013 ; Wang et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

“…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 ).…”

Section: Introductionmentioning

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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A Comparison of Commercial Sentiment Analysis Services

et al. 2023

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Empirical insights into promising commercial sentiment analysis solutions that go beyond the claims of their vendors are rare. Moreover, due to the constant evolution in the field, previous studies are far from reflecting the current situation. The goal of this article is to evaluate and compare current solutions using two experimental studies. In the first part of the study, based on tweets about airline service quality, we test the solutions of six vendors with different market power, such as Amazon, Google, IBM, Microsoft, Lexalytics, and MeaningCloud, and report their measures of accuracy, precision, recall, (macro)F1, time performance, and service level agreements (SLA). Furthermore, we compare two of the services in depth with multiple data sets and over time. The services tested here are Google Cloud Natural Language API and MeaningCloud Sentiment Analysis API. For evaluating the results over time, we use the same data set as in November 2020. In addition, further topic-specific and general Twitter data sets are used. The experiments show that the IBM Watson NLU and Google Cloud Natural Language API solutions may be preferred when negative text detection is the primary concern. When tested in July 2022, the Google Cloud Natural Language API was still the clear winner compared to the MeaningCloud Sentiment Analysis API, but only on the airline service quality data set; on the other data sets, both services provided specific benefits and drawbacks. Furthermore, we detected changes in the sentiment classification over time with both services. Our results motivate that an independent, critical, and longitudinal experimental analysis of sentiment analysis services can provide interesting insights into their overall reliability and particular classification accuracy beyond marketing claims to critically compare solutions based on real data and analyze potential weaknesses and margins of error before making an investment.

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The Effect of Social Media on Market Liquidity

Cited by 7 publications

References 17 publications

The Effect of Social and News Media Sentiments on Financial Markets

The Effect of Social and News Media Sentiments on Financial Markets

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

A Comparison of Commercial Sentiment Analysis Services

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