Search results

This is a literature survey paper. The purpose of this paper is to focus on the latest developments in textual analysis on China’s financial markets, highlighting its differences from existing works in the US markets.

The authors review the literature and carry out an experiment of sentiment analysis based on a small sample of Chinese news articles.

Based on the experiment of sentiment analysis, there is limited evidence on the association between sentiment and other contemporaneous or future returns.

The supply of financial textual information has grown exponentially in the past decades. Technological advancements in recent years make the programming-based analysis an effective tool to digest such information. The authors highlight the use of credible textual information and discuss directions of research in this important field.

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

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.

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.

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.

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

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.

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.

This paper aims to examine if Malaysian public listed companies have expressed any specific sentiment(s) when publishing their financial performance during the COVID-19 pandemic.

The disclosed sentiments contained in the management discussion and analysis section of the companies’ annual reports were extracted by means of computer-automated textual analysis through the linguistic inquiry and word counts and the Loughran–McDonald Financial Sentiment Dictionary. Next, a correlation analysis was conducted. Finally, a qualitative content analysis (QCA) was conducted to confirm these sentiments.

The analysis shows that companies adopted various tones of sentiments when communicating with their stakeholders. Most companies used negative sentiments to voice their concerns about how the COVID-19 pandemic has impacted upon their business operations. Only a few companies reflected positive sentiments, whilst those that experienced operating losses also expressed uncertainty.

This study may assist either the regulators or accounting bodies to introduce a reporting framework that public companies can adopt during natural hazards. It also provides useful insights to (potential) investors to enable them to better understand the business landscape. For future research, the same study could be conducted on more countries so that their experiences can be used to better understand the business phenomenon from a global perspective.

This study is one of few studies to adopt automated textual analysis and QCA to examine the exhibited sentiments when public companies reported their financial performance during the COVID-19 pandemic.

The purpose of this paper is to investigate whether the Islamic religious occasions can, through their impact on investor sentiment, affect returns in six Arab financial markets.

In this paper, the authors test the effect of the occasions of Hajj pilgrimage, Ramadan, Eid-al-Fitr, Mawlid and Ashura during the period of 2001-2016 on Saudi Arabia, Dubai, Kuwait, Egypt, Qatar and Bahrain financial markets. Three measures of investor sentiment are used: trading volume, high minus low and psychological line index.

Higher effect of investor sentiment on returns is detected after Hajj pilgrimage than that before Hajj pilgrimage in all studied financial markets. The positive emotions during Ramadan contribute significantly to the increase in returns in Arab financial markets. Results indicate that most of studied financial markets exhibit a significant effect of investor sentiment on returns during the first 10 days and the second 10 days of Ramadan. Empirical results indicate that Eid-al-Fitr affects the relation between investor sentiment and returns in Saudi Arabia, Kuwait, Qatar and Dubai financial markets. Relationship between investor sentiment and returns is not is not significantly affected by the Mawlid occasion, except in the Dubai and Kuwait financial markets.

The Islamic occasions of the Hajj pilgrimage, Ramadan and Eid-al-Fitr affect significantly the relation between investor sentiment and returns.

The purpose of this paper is to examine the impact of corporate governance, investor sentiment and financial liberalization on downside systematic risk and the interplay of socio-political turbulence on this relationship through static and dynamic panel estimation models.

The evidence is based on a sample of 230 publicly listed non-financial firms from Pakistan Stock Exchange (PSX) over the period 2008–2018. Furthermore, this study analyzes the data through Blundell and Bond (1998) technique in the full sample as well sub-samples (big and small firms).

The authors document that corporate governance mechanism reduces the downside risk, whereas investor sentiment and financial liberalization increase the investors’ exposure toward downside risk. Particularly, the results provide some new insights that the socio-political turbulence as a moderator weakens the impact of corporate governance and strengthens the effect of investor sentiment and financial liberalization on downside risk. Consistent with prior studies, the analysis of sub-samples reveals some statistical variations in large and small-size sampled firms. Theoretically, the findings mainly support agency theory, noise trader theory and the Keynesians hypothesis.

Stock market volatility has become a prime area of concern for investors, policymakers and regulators in emerging economies. Primarily, the existence of market volatility is attributed to weak governance, irrational behavior of market participants, the liberation of financial policies and sociopolitical turbulence. Therefore, the present study provides simultaneous empirical evidence to determine whether corporate governance, investor sentiment and financial liberalization hinder or spur downside risk in an emerging economy. Furthermore, the work relates to a small number of studies that examine the role of socio-political turbulence as a moderator on the relationship of corporate governance, investor sentiment and financial liberalization with downside systematic risk.

This study aims to predict the financial performance of Islamic banks with sentiments of management from the textual information in annual reports.

The study uses data from 33 Islamic banks in six Islamic countries from 2006 to 2020. The authors estimate the model using the system GMM because it helps dealing with endogeneity problem, which are inherent in panel data.

The findings of the study reveal that there is a strong relationship between the sentiment expressed by management in annual reports and the current (future) financial performance of Islamic banks. The higher the positive sentiments of management, the better financial performance. In addition, the study also suggests that negative sentiments using term frequency-inverse document frequency is linked to a decrease in banks’ financial performance.

The study does not present the Islamic view on sentiment analysis in the context of Islamic scriptures due to the unavailability of a relevant dictionary.

The findings of the study suggest that developing accurate models with the help of textual information for performance prediction of Islamic banks help shareholders, regulators and policymakers avoid devastating events. Using textual information may also help reduce the information asymmetry between the management and shareholders, which may lead to more efficient bank supervision. The study can also help investors evaluate their prospective investments in the Islamic bank.

To the best of the authors’ knowledge, this study is the first of its kind that uses management sentiments for performance prediction of the Islamic banking sector. It may add a valuable contribution to the existing literature.

The main purpose of this study is to investigate the effect of investor sentiment on accounting conservatism in listed companies in the Tehran Stock Exchange (TSE).

In this paper, two models of Ball and Shivakumar (2006) and Basu (1997) have been used for measuring conditional conservatism in accounting. To measure investor sentiment, the author uses the Baker and Wurgler (2006, 2007) index. The research sample consists of 1,820 observations and 182 firms listed on TSE over a ten-year period between 2011 and 2020. This study uses panel data and multivariate regression analysis to test it hypotheses.

Consistent with this hypothesis that accounting conservatism will increase with investor sentiment, the results showed that Iranian firms recognize economic losses and bad news in a more timely manner during high sentiment periods than during low sentiment periods. This implies that Iranian managers recognize economic losses and bad news in earnings in a more timely manner during periods of high investor sentiment.

This finding provides significant evidence for investors and financial reporting standard-setters in Iran because by removing accounting conservatism from the conceptual framework, managers are not able to present conservative financial reports, and this can intensify the negative impact of investors sentiment in the Iranian capital market. Managers of Iranian companies can reduce information asymmetry and increase capital market efficiency by accelerating the disclosure of bad news. Thus, managers can strategically recognize losses and prevent investors from making emotional decisions that reduce their wealth.

To the best of the authors’ knowledge, this is the first study to empirically examine the impact of investor sentiment on accounting conservatism in a developing market called Iran. This study contributes to the corporate disclosure literature. Also, the result of this study contributes to standard-setters of accounting standards to improve the mandatory disclosure literature on more conservative accounting earnings.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

The purpose of this study is to determine which quantitative metrics are most representative of investor sentiment in the US equity markets. Sentiment is the aggregation of consumers', investors', and producers' thoughts and opinions about the future of the financial markets. By analyzing the change in popular economic indicators, financial market statistics, and sentiment reports, we can gain information on investor reactions. Furthermore, we will use machine learning techniques to develop predictive models that will attempt to forecast whether the stock market will go up or down based on the percent change in these indicators.

Behavioral finance and market microstructure studies suggest that the investor sentiment and liquidity are related. This paper aims to examine the aggregate sentiment–liquidity relationship in emerging markets (EMs) for both the sample period and crisis period. Then, it verifies this relationship, using the asymmetric sentiment.

This study uses a sample consisting of stocks listed on the SSE Shanghai composite index (348 stocks), the JKSE (118 stocks), the IPC (14 stocks), the RTS (12 stocks), the WSE (106 stocks) and FTSE/JSE Africa (76 stocks). This is for the period ranging from February, 2002 until March, 2021 (230 monthly observations). We use the panel data and apply generalized method-of-moments (GMM) of dynamic panel estimators.

The empirical analysis shows the following results: first, it demonstrates a significant relationship between the aggregate investor sentiment and the stock market liquidity for the sample period and crisis one. Second, referring to the asymmetric sentiment, we have empirically given proof that the market is significantly more liquid in times of the optimistic sentiment than it is in times of the pessimistic sentiment. Third, using panel causality tests, we document a unidirectional causality between the investor sentiment and liquidity in a direct manner through the noise traders and the irrational market makers and also a bidirectional causality in an indirect channel.

The results reported in this paper have implications for regulators and investors in EMs. Firstly, the study informs the regulators that the increases and decreases in the stock market liquidity are related to the investor sentiment, not financial shocks. We empirically evince that the traded value is higher in the crisis. Secondly, we inform insider traders and rational market makers that the persistence of increases in the trading activity in both quiet and turbulent times is associated with investor participants such as noise traders and irrational market makers.

The originality of this work lies in employing the asymmetric sentiment (optimistic/pessimistic) in order to denote the sentiment–liquidity relationship in EMs for the sample period and the 2007–2008 subprime crisis.