Stock market return predictability: Google pessimistic sentiments versus fear gauge

Habibah, Ume; Rajput, Suresh Kumar Oad; Sadhwani, Ranjeeta

doi:10.1080/23322039.2017.1390897

Cited by 13 publications

(15 citation statements)

References 22 publications

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“…In recent years, a number of studies have explored information retrieval and knowledge discovery from texts using data mining and natural language processing from social networks; they have provided the possibility for us to measure public psychological pressure from social networks [28]- [30]. However, because the related assessment indexes are missing, the efficiency of information analysis and processing for public opinion analysis is severely limited.…”

Section: Public Sentiment Analysis In Social Network 1) the Related Research Of Public Sentiment Analysismentioning

confidence: 99%

A Public Psychological Pressure Index for Social Networks

Zhang¹,

Jin²,

Zhang³

et al. 2020

IEEE Access

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With the worldwide proliferation of social networks, public opinion analysis of data generated by social networks has become an important field of research. Social networks have become a major platform for public opinion formation and diffusion, and analyzing public opinion through social network data plays an important role across numerous fields, including political science, economics, commerce, finance, international trade, public policy implementation and so on. Nevertheless, the corresponding quantitative indexes of public opinion analysis have not yet been developed, and the theoretical foundation underpinning such indexes has yet to be established. How to measure public opinion through social network data is a significant problem in need of the development of a series of quantitative assessment indices and social computing methods that can be used to solve this problem. This paper proposes both the concept of a public psychological pressure index and its calculation method, making it a fundamental work in the field of public opinion analysis. The maximum entropy principle is introduced to the social computing domain in this paper and positions it as the theoretical foundation underpinning such indexes.INDEX TERMS Information entropy, intelligence and security informatics, public information security, public opinion analysis, public psychological pressure index, social computing.

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Section: Public Sentiment Analysis In Social Network 1) the Related Research Of Public Sentiment Analysismentioning

confidence: 99%

A Public Psychological Pressure Index for Social Networks

Zhang¹,

Jin²,

Zhang³

et al. 2020

IEEE Access

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“…This additional utilization of estimation results is rarely found in existing research. Other related research, again, is mostly focused on developed countries such as Habibah et al (2017) on the American market, Kim et al (2019) on the Norwegian, Bank et al (2011) on German, Dzielinski (2012) also selected developed markets (American, Australian, Canadian, UK, German and Japanese), Zhang Wei et al (2013) chose Shanghai, Takeda and Wakao (2014) the Japanese market, Yang et al (2019) and Zhang et al (2018) the Chinese one, etc.…”

Section: Related Researchmentioning

confidence: 99%

Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets

Škrinjarić¹

2019

IJFS

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This research observes a time varying relationship between stock returns, volatilities and the online search volume in regard to selected CESEE (Central, Eastern and South-Eastern European) stock markets. The main hypothesis of the research assumes that a feedback relationship exists between stock returns, volatilities and the investor’s attention variable (captured by the online search volume). Moreover, the relationship is assumed to be time varying due to changing market conditions. Previous research does not deal with the time-varying multi-directional relationship. Thus, the contribution to existing research consists of estimating the aforementioned relationship between return, volatility and the search volume series for selected CESEE countries by using a novel approach of spillover indices within the VAR (Vector AutoRegression) model framework. The results indicate that the Google search volume affects the risk series more than the return series on the selected markets.

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“…They find that these measures of market attention are potential gauges for investor sentiment. Habibah, Rajput, and Sadhwani (2017) used the pessimistic investor sentiments (market crash and bear market) to predict the stock returns. VIX is also used as a measure of sentiment because it has been considered as a leading indicator of investor sentiment and realized market volatility in the literature.…”

Section: Public Interest Statementmentioning

confidence: 99%

“…Klemola et al (2016) have used Google Search Volume Index for Google search terms "market crash, bear market, bull market, and market rally" to predict near-term future stock returns. Habibah et al (2017) have used the pessimistic GSVIs and VIX to predict the stock returns. To forecast patterns of market activity, pessimistic media content variables are used by Tetlock (2007), whereas Preis, Moat, and Stanley (2013) have used Google search terms to predict movements in the equity market.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They find that increases in volatility cause decrease in return on S&P500 index. There is a significant empirical evidence that VIX is used as a good predictor for future market volatility (Whaley, 2000;Blair, Poon, & Taylor, 2001;Engle & Gallo, 2006;Dennis & Strickland, 2002;Giot & Laurent, 2007;Habibah et al, 2017). Sarwar (2012) examines the relationship between VIX and Stantdard and Poor's (S&P) 100, 500 and 600 indices returns and finds the negative relationship between VIX and return on indices.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Do sentiment indices impact the premium of prominent pricing factors?

Sadhwani

Rajput

Habibah

2018

Cogent Economics & Finance

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This study investigates whether Google Search Volume Indices (GSVIs) bring shifts in the expected return of prominent pricing factors in comparison to the Volatility Index (VIX). The results show that compared to VIX, GSVIs bring less significant changes in expected premium on Fama-French's five-factors and q-factors. Pessimistic sentiment indices (Market Crash and Bear Market) predict more significant variation in the premium of prominent pricing factors than optimistic sentiment indices (Market Rally and Bull Market), and also have a significant correlation with VIX representing downside risk. Furthermore, the sentiment indices are better in predicting premium on five-factors than q-factors.

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Stock market return predictability: Google pessimistic sentiments versus fear gauge

Cited by 13 publications

References 22 publications

A Public Psychological Pressure Index for Social Networks

A Public Psychological Pressure Index for Social Networks

Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets

Do sentiment indices impact the premium of prominent pricing factors?

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