Abstract The extent to which financial analysts provide “herd” rather than “bold” (or anti-herd) earnings forecasts has important implications for market efficiency. Identifying any contributing factor(s) for financial analyst herding behavior can lead to policies to help reduce such harmful conduct. Our 2 proxies for intangible asset intensity are found to have a differential impact on analyst herding

Abstract The authors investigate the empirical relation among investor sentiment, valuation uncertainty, and announcements of changes in analyst recommendation decisions among U.S. firms. Recent behavioral finance evidence shows market sentiment to have predictive content that affects the classical relationship between analyst recommendations and stock return dynamics. Contrary to this evidence, the

Abstract The author examines whether the direct and indirect sentiment measures are distinct from each other. The author finds that the 2 types of sentiment measures have a relatively low correlation between them. The direct sentiment measures have significant explanatory power on contemporaneous stock returns, whereas the indirect sentiment measures have a lagging effect in such explanatory power

Abstract Motivated by the mixed findings in prior archival studies, this study conducts three experiments to examine the relationships among analysts’ intrinsic valuation estimates (V), stock recommendations (REC) and stock returns. Experiment 1, built on implications from prospect theory, provides direct observations on analysts’ asymmetric use of the valuation to price (V/P) ratio in making their

Abstract Recent SEC guidelines enabled many Fortune 500 companies to actively adopt social media, such as Twitter, to disseminate information. In this paper, we analyze the relationship between tweets by corporations and stock returns. Our study used over 1.2 million corporate tweets made by thirty companies in the Dow Jones Industrial Average between April 2013 and July 2020. The shocks from the frequency

Abstract This study investigates the relationship between founder-led firms and non-securities (operational) litigation risk. We postulate lower operational litigation risk for founder-led firms than for nonfounder-led firms based on founder-CEOs’ limited agency conflicts and stronger emotional attachment to the firms they establish. Our empirical results suggest that having a founder as CEO mitigates

Abstract Using a machine-learning technique known as random forests, we analyze the role of investor confidence in forecasting monthly aggregate realized stock-market volatility of the United States (US), over and above a wide-array of macroeconomic and financial variables. We estimate random forests on data for a period from 2001 to 2020, and study horizons up to one year by computing forecasts for

Abstract In this paper we apply text mining methodologies on a set of 10,000 Central Bank speeches to construct a financial dictionary, based on which we use Google Trends indices to measure people’s interest in financial news. Particularly, we investigate the relationship between these indices and financial market turbulence leveraging on Deep Learning techniques, which are benchmarked against a variety

Abstract This paper investigates how past stock returns affect investors’ lottery demand. We show that the maximum daily return (MAX) effect, first documented by Bali et al. (Maxing Out: Stocks as Lotteries and the Cross-Section of Expected Returns) is predominantly concentrated among stocks that have worst returns in the recent past. In contrast, for stocks that have recorded best past returns, the

Abstract This paper establishes a direct link between (anti) herding behavior in currency markets and investor sentiment, proxied by a social media based investor happiness index built on Twitter feed data. Our analysis of daily data for nine developed market currencies suggests that the foreign exchange market is generally characterized by strong anti-herding behavior. Utilizing the quantile-on-quantile

Abstract The Sapir-Whorf hypothesis theorizes that the structure of a language may affect the way that its speakers think. We investigate if there is a significant difference in dividend policy of firms headquartered in a country whose primary language uses a strong future-time reference (FTR) compared with a country that uses a weak FTR language. We posit that use of future tense to describe future

Abstract Financial information from firms often contains biased information. In this study, we posit and experimentally test the idea that investors will have difficulty in unraveling known biases in management’s earnings forecasts but will be most likely to fully adjust when the information about bias is in quantitative, EPS form and the investor’s judgment is compatible with that bias information

Abstract We operationalize Stein’s (2009) overcrowding concept in the context of investor response to daily stock return shocks and examine its underlying behavioral motivation. We define chasing at the overshooting point as evidence of overcrowding. Using trading data by investor type from Korea, we find that while institutional investors display informed trading, they fail to recognize the predictable

Abstract In this paper, we use a Quantile Structural Vector Autoregressive (QSVAR) model, estimated over the quarterly period of 1975:Q3 to 2017:Q3, to analyze whether the impact of monetary policy shocks on growth rate of real house price in the United States is contingent on the initial state of housing market sentiment. We find that contractionary monetary policy reduces growth rate of real house

Abstract A company’s operational uncertainty resulting from economic activity could be measured by its contingent liabilities. When the contingent liabilities are forecasted by the analysts, the dispersed analysts’ forecasts might constitute another source of uncertainty. In this study, the Uncertainty (contingent liabilities) of Uncertainty (dispersed analysts’ forecasts) is measured by the UOU indicator

Abstract A company’s operational uncertainty resulting from economic activity could be measured by its contingent liabilities. When the contingent liabilities are forecasted by the analysts, the dispersed analysts’ forecasts might constitute another source of uncertainty. In this study, the Uncertainty (contingent liabilities) of Uncertainty (dispersed analysts’ forecasts) is measured by the UOU indicator

Abstract The authors examine whether acquiring companies strategically time their quarterly earnings and merger and acquisition announcements. Acquiring companies have the ability and incentive to alter the sequence of these 2 types of announcements to optimize the market’s perception of the firm. The authors show that acquiring companies are more likely to announce earnings before mergers and acquisitions

Abstract We analyze the exchange rate forecasting performance under the assumption of selective attention. Although currency markets react to a variety of different information, we hypothesize that market participants process only a limited amount of information. Our analysis includes more than 100,000 news articles relevant to the six most-traded foreign exchange currency pairs for the period of 1979–2016

Abstract In contrast to the conventional view that analysts forecast optimistically, we provide evidence of the Negativity Bias. Analysts show negative forecast bias associated with their relative local income growth, whether the growth is positive or negative. The bias is stronger for negative growth than for positive growth. The negative bias also directly affects the bias of the next analysts of

Abstract Researchers have documented that individuals have a strong penchant for round numbers in a variety of settings, such as trading in financial markets. Beginning as early as the 1930s, empirical research has shown that security prices tend to cluster on round increments. This anomalous finding has persisted over time and in a wide range of different types of securities markets. We examine whether

Abstract This article examines whether incorporating investors’ uncertainty, as captured by the conditional volatility of sentiment, can help forecasting volatility of stock markets. In this regard, using the Markov-switching multifractal (MSM) model, we find that investors’ uncertainty can substantially increase the accuracy of the forecasts of stock market volatility according to the forecast encompassing

Abstract This paper uses the cross-sectional absolute deviation (CSAD) in static and time-varying versions to examine herding in the cryptocurrency market from April 2013 to November 2019. Results from the static model confirm the evidence of an anti-herding behavior over the considered period. However, the time-varying analysis suggests the presence of herding behavior around the end of 2013 and persists

Abstract This article uses public perceptions to forecast short-term fluctuations in asset prices. Based on four billion tweets scraped between 2009 and 2019, I perform textual analysis to construct daily sentiment indices. The sentiment indices allow us to forecast stock volatility jumps as well as expected jump levels. The implications of forecasting volatility jumps are substantive. First, volatility

Abstract This study introduces a comprehensive Google search volume based Bitcoin Sentiment Index (BSI) and investigates its symmetric and asymmetric association with Bitcoin's returns, volume, and volatility, and with United States Dollar (USD) exchange rates. Our results indicate a positive association of BSI with Bitcoin's returns and volume, but a negative relationship with its return volatility

Abstract Sentiment indicators have long been closely monitored by economic forecasters, notably to predict short-term moves in consumption and investment. Recently, housing sentiment indices have been developed to forecast housing market developments. Sentiment indices partly reflect economic determinants, but also more subjective factors, thereby adding information, particularly in periods of uncertainty

Abstract XBRL (eXtensible Business Reporting Language) has been touted as a new technology that may someday replace HTML (Hypertext Markup Language) as the standard method of statutory filings. This study offers some initial empirical evidence suggesting that retail investors do not use XBRL structured data as much as expected by the SEC (the U.S. Securities and Exchange Commission). The result shows

Abstract We elaborate on behavioral foundations for agent expectations in financial markets and propose a multicomponent model. Components in bounded rational processing and imitative processing that can underlie frequently cited “herding” in financial markets are proposed in the agent-based model. We define a transition function for the dominance of a component in the distance between market price

Abstract We develop a tractable model in which asset prices are driven by the epidemic spread of certain investment ideas. Once an idea “goes viral,” equilibrium prices exhibit a pattern of boom and bust. In turn, we identify a timeline of symptoms, which indicate whether a boom is in its early or later stages. Moreover, we find that prices start to decline while the number of infected agents, who

Abstract This paper reexamines the issue of behavioral heterogeneity in the stock market. In contrast to previously documented contemporaneous results, we test the issue by identifying and testing four new determinants of the proportion of fundamentalists and chartists in the stock market. Our empirical results are consistent with the following notions. First, the proportion of fundamentalists increases

Abstract This study tests the presence of herding behavior in commodity markets (energy, industrial metals, precious metals, grains food, and livestock) from a constant and time-varying perspective. Additionally, we investigate whether oil prices and major financial indicators drive herding in these markets. Using daily data over the period 2003–2017, the constant model based on the cross-sectional

Abstract Relying on over 300 million news sentiment scores from a global news source covering a battery of different news topics and their sentiment, we construct a novel measure called top news sentiment based on rolling correlations with equity returns. We find an effect of top news sentiment on stock returns with robust timeframes. These findings add to the sentiment literature with the first dynamically