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Article
Publication date: 17 May 2019

Divya Aggarwal

The purpose of this paper is to review and discuss the literature focusing on defining and measuring sentiments so as to understand their role in stock market behavior.

Abstract

Purpose

The purpose of this paper is to review and discuss the literature focusing on defining and measuring sentiments so as to understand their role in stock market behavior.

Design/methodology/approach

Critical review of the literature by analyzing myriad scholarly articles. The study is based on an analysis of 81 scholarly articles to critically analyze the approach toward defining and measuring market sentiments. The articles have been examined to identify and critique different classification of sentiment measures. A discussion is built to scrutinize the sentiment measures under the purview of theoretical underpinnings of the investor sentiment theory as well.

Findings

With more than five decades of research, the sentiment construct in finance literature is still ill-defined. Myriad empirical proxies of sentiment measures have led to conflicting results. The sentiment construct defined in financial theories needs to be revisited from the lens of sentiments defined in psychology.

Research limitations/implications

The study is limited to analyzing the role of individual and institutional sentiments in equity markets. There is a need to explore sentiments with respect to different investment styles and strategies along with the type of investors.

Practical implications

Developing a suitable sentiment proxy can result in devising profitable trading strategies for investors. Understanding factors driving investor sentiments will help regulators to become more proactive and frame better policies.

Originality/value

This paper has leveraged psychology literature to highlight the limitations in development of sentiment construct in finance literature. By identifying stylized facts from reviewing the empirical literature, it highlights areas for future research.

Details

Qualitative Research in Financial Markets, vol. 14 no. 2
Type: Research Article
ISSN: 1755-4179

Keywords

Article
Publication date: 14 September 2020

Qiang Bu and Jeffrey Forrest

The purpose of this study is to investigate whether the direct and indirect sentiment measures are similar in explaining mutual fund performance.

Abstract

Purpose

The purpose of this study is to investigate whether the direct and indirect sentiment measures are similar in explaining mutual fund performance.

Design/methodology/approach

The authors examine the role of direct and indirect sentiment measures on fund performance in two scenarios. One is when a sentiment measure is added to market models, and the other is when it used independently. Also, the authors propose a system science theory to explain the findings.

Findings

The authors find that both direct and indirect sentiment measures are integral to the benchmark models to explain fund performance. However, while the explanatory power of the direct sentiment index is robust when used independently or collectively, the indirect sentiment measures can explain fund performance only when used along with other market factors.

Originality/value

Given the number of sentiment measures, it is critical to determine whether these measures contain the same information of sentiment. This paper represents the first study on this topic.

Details

International Journal of Managerial Finance, vol. 17 no. 3
Type: Research Article
ISSN: 1743-9132

Keywords

Book part
Publication date: 1 January 2014

James A. Kitts

The research community currently employs four very different versions of the social network concept: A social network is seen as a set of socially constructed role

Abstract

Purpose

The research community currently employs four very different versions of the social network concept: A social network is seen as a set of socially constructed role relations (e.g., friends, business partners), a set of interpersonal sentiments (e.g., liking, trust), a pattern of behavioral social interaction (e.g., conversations, citations), or an opportunity structure for exchange. Researchers conventionally assume these conceptualizations are interchangeable as social ties, and some employ composite measures that aim to capture more than one dimension. Even so, important discrepancies often appear for non-ties (as dyads where a specific role relation or sentiment is not reported, a specific form of interaction is not observed, or exchange is not possible).

Methodology/Approach

Investigating the interplay across the four definitions is a step toward developing scope conditions for generalization and application of theory across these domains.

Research Implications

This step is timely because emerging tools of computational social science – wearable sensors, logs of telecommunication, online exchange, or other interaction – now allow us to observe the fine-grained dynamics of interaction over time. Combined with cutting-edge methods for analysis, these lenses allow us to move beyond reified notions of social ties (and non-ties) and instead directly observe and analyze the dynamic and structural interdependencies of social interaction behavior.

Originality/Value of the Paper

This unprecedented opportunity invites us to refashion dynamic structural theories of exchange that advance “beyond networks” to unify previously disjoint research streams on relationships, interaction, and opportunity structures.

Article
Publication date: 2 August 2021

Lee M. Dunham and John Garcia

This study examines the effect of firm-level investor sentiment on a firm's level of financial distress.

Abstract

Purpose

This study examines the effect of firm-level investor sentiment on a firm's level of financial distress.

Design/methodology/approach

The authors use Bloomberg's firm-level, daily investor sentiment scores derived from firm-level news and Twitter content in a beta-regression model to explain the variability in a firm's financial distress.

Findings

The results indicate that improvements (deterioration) in investor sentiment derived from both news articles and Twitter content lead to a decrease (increase) in the average firm's financial distress level. We also find that the effect of sentiment derived from Twitter on a firm's financial distress is significantly stronger than the sentiment derived from news articles.

Research limitations/implications

Our proxy for financial distress is Bloomberg's financial distress measures, which may be an imperfect measure of financial distress. Our results have important implications for market participants in assessing the determinants of financial distress.

Practical implications

Our sample period covers four years (2015–2019), which is determined by Bloomberg sentiment data availability.

Social implications

Market participants are increasingly using social media to express views on firms and seek information that might be used to determine a firm's level of financial distress. Our study links investor sentiment derived from social media (Twitter) and traditional news articles to financial distress.

Originality/value

By examining the relationship between a firm's sentiment and its financial distress, this paper advances our understanding of the factors that drive a firm's financial distress. To our knowledge, this is the first study to link US firms' investor sentiment derived from firm-level news and Twitter content to a firm's financial distress.

Details

Managerial Finance, vol. 47 no. 12
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 14 April 2022

Dave Berger

This study creates a measure of investor sentiment directly from retail trader activity to identify misvaluation and to examine the link between sentiment and subsequent returns.

Abstract

Purpose

This study creates a measure of investor sentiment directly from retail trader activity to identify misvaluation and to examine the link between sentiment and subsequent returns.

Design/methodology/approach

Using investor reports from a large discount brokerage that include measures of activity such as net buying, net new accounts and net new assets, this study creates a measure of retail trader sentiment using principal components. This study examines the relation between sentiment and returns through conditional mean and regression analyses.

Findings

Retail sentiment activity coincides with aggregate Google Trends search data and firms with the greatest sensitivity to retail sentiment tend to be small, young and volatile. Periods of high retail sentiment precede poor subsequent market returns. Cross-sectional results detail the strongest impact on subsequent returns within difficult to value or difficult to arbitrage firms.

Originality/value

This study links a rich measure of retail trader activity to subsequent market and cross-sectional returns. These results deepen our understanding of noise trader risk and aggregate investor sentiment.

Details

Review of Accounting and Finance, vol. 21 no. 2
Type: Research Article
ISSN: 1475-7702

Keywords

Article
Publication date: 18 May 2021

Prajwal Eachempati and Praveen Ranjan Srivastava

A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the…

Abstract

Purpose

A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment.

Design/methodology/approach

Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results.

Findings

Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6.

Originality/value

The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.

Article
Publication date: 24 April 2020

Lee M. Dunham and John Garcia

The purpose of this paper is to examine the effect of firm-level investor sentiment on a firm's share liquidity.

Abstract

Purpose

The purpose of this paper is to examine the effect of firm-level investor sentiment on a firm's share liquidity.

Design/methodology/approach

The authors use Bloomberg's firm-level, daily investor sentiment scores derived from firm-level news and Twitter content in a regression model to explain the variability in a firm's share liquidity.

Findings

The results indicate that improvements (deterioration) in investor sentiment derived solely from Twitter content lead to a decrease (increase) in the average firm's share liquidity. Results, although not as strong, are opposite for investor sentiment derived solely from news articles: improvements (deterioration) in news sentiment leads to an increase (decrease) in the average firm's share liquidity.

Research limitations/implications

The proxy for share liquidity is the bid-ask spread, which may be an imperfect measure of liquidity. The Amihud illiquidity measure was used as an alternative proxy and yield similar results. The results have important implications for investors in assessing the determinants of share liquidity.

Practical implications

The sample period covers four years (2015–2018), which is determined by the availability of the Bloomberg sentiment data.

Social implications

Investors increasing use of social media to express views on particular stocks and seek information that might be used in the investment decision-making process. The study links investor sentiment derived from social media (Twitter) to share liquidity.

Originality/value

By examining the relationship between a firm's sentiment and the firm's share liquidity, this paper advances the authors' understanding of the factors that drive a firm's share liquidity. To the authors' knowledge, this is the first study to link investor sentiment derived from firm-level news and Twitter content to a firm's share liquidity.

Details

Managerial Finance, vol. 47 no. 1
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 9 July 2018

Jessica Roxanne Ruscheinsky, Marcel Lang and Wolfgang Schäfers

The purpose of this paper is to determine systematically the broader relationship between news media sentiment, extracted through textual analysis of articles published by…

Abstract

Purpose

The purpose of this paper is to determine systematically the broader relationship between news media sentiment, extracted through textual analysis of articles published by leading US newspapers, and the securitized real estate market.

Design/methodology/approach

The methodology is divided into two stages. First, roughly 125,000 US newspaper article headlines from Bloomberg, The Financial Times, Forbes and The Wall Street Journal are investigated with a dictionary-based approach, and different measures of sentiment are created. Second, a vector autoregressive framework is used to analyse the relationship between media-expressed sentiment and REIT market movements over the period 2005–2015.

Findings

The empirical results provide significant evidence for a leading relationship between media sentiment and future REIT market movements. Furthermore, applying the dictionary-based approach for textual analysis, the results exhibit that a domain-specific dictionary is superior to a general dictionary. In addition, better results are achieved by a sentiment measure incorporating both positive and negative sentiment, rather than just one polarity.

Practical implications

In connection with fundamentals of the REIT market, these findings can be utilised to further improve the understanding of securitized real estate market movements and investment decisions. Furthermore, this paper highlights the importance of paying attention to new media and digitalization. The results are robust for different REIT sectors and when conventional control variables are considered.

Originality/value

This paper demonstrates for the first time, that textual analysis is able to capture media sentiment from news relevant to the US securitized real estate market. Furthermore, the broad collection of newspaper articles from four different sources is unique.

Details

Journal of Property Investment & Finance, vol. 36 no. 5
Type: Research Article
ISSN: 1463-578X

Keywords

Article
Publication date: 15 January 2021

Ka Shing Cheung and Joshua Lee

Real estate is an asset that is traded in highly segmented, illiquid and informationally inefficient local markets. A short sale in real estate is almost infeasible and…

Abstract

Purpose

Real estate is an asset that is traded in highly segmented, illiquid and informationally inefficient local markets. A short sale in real estate is almost infeasible and therefore impedes informed rational arbitrageurs to trade against mispricing. Thus, real estate returns are prone to sentiment-driven behaviours. Will the impacts on asset returns be identical for different types of sentiment?

Design/methodology/approach

This study argues that not all sentiment effects are created equal. Using the bounds test of the autoregressive distributed lag (ARDL) models, this paper examines how occupier sentiment versus investor sentiment contributes to the short-run and long-run dynamics of commercial real estate returns in Australia.

Findings

The empirical evidence suggests that investor sentiment and occupier sentiment influence return asymmetrically after macroeconomic conditions are controlled for.

Practical implications

The sectoral analysis further reveals that sector-specific sentiment plays a significant role in explaining commercial real estate returns. Furthermore, notable improvement is found in producing more accurate prediction in returns, given that measures of occupier and investor sentiment are appropriately specified in the forecast.

Originality/value

This study is novel in the sense that it acknowledges the impacts of occupiers' and investors' sentiment may be fundamentally different. The unique innovation and contribution of this study to behavioural finance literature are based on a new dataset from the Royal Institute of Chartered Surveyors which includes a survey-based measure of investor sentiment and occupier sentiment.

Article
Publication date: 5 June 2017

Boonlert Jitmaneeroj

A large number of empirical studies investigate the determinants of price-earnings (P/E) ratio by focusing on fundamental factors. However, there has been an increasing…

1716

Abstract

Purpose

A large number of empirical studies investigate the determinants of price-earnings (P/E) ratio by focusing on fundamental factors. However, there has been an increasing concern that stock valuation is also driven by investor sentiment. This paper aims to extend the existing literature by exploring whether investor sentiment impacts the P/E ratio.

Design/methodology/approach

The paper examines the determinants of P/E ratio by applying latent variable models with investor sentiment as a latent variable and several fundamental factors as control variables. Investor sentiment is proxied by trading volume, advance-decline ratio and price volatility.

Findings

Using annual data of the US industries over the period of 1998-2014, the current paper produces new empirical evidence that investor sentiment significantly affects the P/E ratio. This result is robust to the inclusion of several control variables that have been documented to explain the P/E ratio.

Practical implications

The findings have important implications for investors, as downplaying sentiment can lead to significant errors in making equity investment choices based on the P/E ratio.

Originality/value

The analytical framework of the current paper is differentiated from the conventional analysis in which the P/E ratio is regressed against control variables and proxies for sentiment, thus falling into the trap of implicitly presupposing that proxies are perfect measures of investor sentiment. As all proxies may have measurement errors to the true but unobservable investor sentiment, the current paper uses latent variable models to shed new light on the influence of investor sentiment on the P/E ratio.

Details

Studies in Economics and Finance, vol. 34 no. 2
Type: Research Article
ISSN: 1086-7376

Keywords

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