Using the novel daily commodity-specific Thomson Reuters Market Psych sentiment data derived from news, social media, press releases, and regulatory filings, this study investigates the asymmetric impact of news and social media sentiment on the futures return connectedness of agricultural commodities. We construct time-varying connectedness measures for agricultural commodities futures returns at different quantiles and show how these spillover measures depend on news sentiment under extreme events. Our results show that the impact of news media sentiment on agricultural commodity connectedness depends on the quantiles (lower, median, upper) and the type of sentiment (traditional news or social media). In particular, we find that social media sentiment has a statistically significant impact on the magnitude of shocks each commodity transmits to others, at both the lower and upper quantiles, indicating that the media sentiment effect is more substantial during extreme market periods.

This study attempts to understand how to reduce the continued influence of misleading financial news and examine the effectiveness of corrective efforts such as ex-ante disclosure of compensation of a promoter/writer and the immediacy of ex-post correction. In the 2 (disclosure vs. no disclosure) × 2 (immediate correction vs. delayed correction) experiment, student participants received a (fake) news article including or excluding the author’s affiliation and compensation scheme and were then provided a correction invalidating its content. The correction was provided either immediately or with a delay. The results showed that although participants were susceptible to the influence of positive yet misleading news, providing warnings about potential economic conflicts of interest seemed to temper this enthusiasm among investors. Participants exhibited greater receptiveness to explicit corrections when the initial news article included a compensation disclosure, and when they were not immediately prompted to process the correction. Our findings imply that delaying corrections may offer distinct advantages, as it provides investors with the opportunity to assimilate the compensation disclosure information into their investment decisions over time. Additionally, our results indicate that a dual approach involving conflict of interest disclosure and subsequent correction can be an effective long-term strategy in mitigating investors’ vulnerability to misinformation.

We provide empirical evidence that risk-averse investors become anxious about investments in stocks whose realized losses reveal the downside of risk. Contrary to short-term reversal and in support of convex risk aversion, the latter stocks yield significantly lower returns in the subsequent period. Our findings are based on a novel measure of time-varying risk aversion, but can also be observed when using a well-established measure of risk aversion. Moreover, anxiety predicts cross-sectional returns in out-of-sample tests, suggesting that risk-averse investors’ preferences drive empirical risk premia.

This paper focuses on the relationship between financial analysts’ recommendations and press sentiment from the perspective of the attention-grabbing theory. Specifically, attention-grabbing should not be enough to explain the effect that media coverage has on investment decisions, since investors are wary of making a mistake and anticipate the regret of a future loss. Our case study pertains to a column reporting on secondhand information and analysts’ recommendations. Once the column did not report the analysts’ advice anymore, we hypothesized investors also assess the sentiment of the column to make sure they are not making a costly mistake. Event studies on abnormal returns and multivariate analyses show that for columns with explicit analysts’ recommendations the attention-grabbing mechanism directs buying decisions while has no influences on selling decision. In the absence of explicit recommendations, investors transform the columns’ content into implicit recommendations leading their buying decisions when the sentiment is highly positive.

The study investigates the impact of sector and market-wide illiquidity shocks on herding within US Real Estate Investment Trusts (REITs), on a sub-sector level, including health, hotel, mortgage, residential, retail and Warehouse REITs. Using daily data from January 2014 to February 2022, and consistent with noise trader risk theory, the research confirms the existence of herding behavior within US REITs on a sub-sector level, along with identifying that herding effects are intense on days with negative market returns as compared to days with positive market returns. Assessing the impact of investor sentiments via the VIX index, we find that herding behavior rises with a hike in investor uncertainty and fear. Motivated by the presence of a structural break within our data set corresponding to the Covid-19 outbreak, we use a Markov Switching approach and find significant evidence of sub-sector herding being more intense during the crash regime/covid-19 phase, relative to the expansionary phase. When assessing illiquidity, our results confirm that i) during the expansionary phase only expected illiquidity (market and sector-wide) enhances sub-sector herding within US REITs while ii) during the crash phase only unexpected illiquidity (market and sector-wide) enhances sub-sector herding within US REITs.

This study attempts to jointly identify the effects of anchoring and mean reversion on asset prices. In particular, we define “anchor reversion” as the tendency for stock prices to revert back toward an anchor, for example, the 52-week high. Our results show that the further a stock moves away from its 52-week high, the more likely it is to revert back toward that 52-week high in the following month. Conversely, if a stock moves toward its 52-week high in a given month, it is less likely to experience a large movement toward that 52-week high in the following month. Portfolios constructed according to a long-short anchor reversion strategy result in future returns that range from 1.40% to 1.62% per month. These results are robust to controls for various risk factors as well as several cross-sectional stock characteristics, such as the monthly reversal phenomena.

Based on the linkage between the carbon asset and its related assets, an European Union investor sentiment (EUIS) index reflecting investor behavior in the EU carbon market is constructed using the principal component analysis (PCA) method, and the dynamic correlation relationship between the EUIS index and the EU allowance (EUA) futures prices is analyzed. Furthermore, the European economic policy uncertainty (EEPU) index is introduced and the GARCH-MIDAS model is applied to discuss the impact of macroeconomic uncertainty on the EUIS index. The empirical results indicate that the EUIS index constructed from indicators of the carbon asset and its related assets can reflect the sentiments of carbon market investors. There is a dynamic correlation between the EUIS index and EUA futures, with reverse correlation dominating. The significant fluctuations in their correlation are related to the occurrence of major events. The volatility of the EUIS index is negatively affected by realized volatility and positively affected by EEPU. The model confidence set (MCS) test shows that the prediction accuracy of the GARCH-MIDAS model considering low-frequency macroeconomic variables is better than that of GARCH models.

We study how the degree of personal responsibility in prior investment decisions affects the likelihood of changing an investment after experiencing a gain or loss. To this end we conduct a lab-in-the-field experiment with professional participants from the finance and controlling department of a large infrastructure company. Consistent with our hypothesis and prior findings from student samples we observe that lower personal responsibility in the decision is associated with a higher likelihood of changing the investment project after a loss. However, this effect disappears with age, which we interpret as experience in the professional career.

This paper is an evaluation of long-term cumulative abnormal returns (CAR's) based on Twitter broadcasts by highly influential market agents. We look at the information content of Elon Musk, CEO of SpaceX, Tesla and Twitter Inc. and the former US President Donald Trump. The principal objectives of this research are twofold: 1.) To assess whether markets are semi-strong form efficient and consequentially whether or not returns can be derived from strategies based on such sporadic tweet releases (abstracting from ‘news’). For this purpose, event studies are conducted on multiple companies which were targeted by Musk and Trump tweets. A control group of all Dow Jones companies with earnings releases on Twitter is utilized. We find there appears to be a “pre-post-Twitter-drift” when the release is by exceedingly influential market personalities. The cumulative abnormal returns remain significant over long durations. This indicates that markets are not entirely semi-strong form efficient regarding social media releases and that trading on such tweets may be profitable (even after factoring in varying market phases). 2.) The paper introduces a new theme: long term CAR's of market information events. The paper also notes whether the tweet was during regular market hours or after-market hours.

This study considers two causes—inexperience and overreaction to price movements—of a well-established phenomenon on the stock market called “excessive trading”: large trading volumes leading to decreased net portfolio returns via increased transaction costs. Creating a stylized hold or trade-scenario in a computer laboratory experiment, I confirm both inexperience and overreaction as robust drivers of trading activity. The 2 × 2 experimental design featuring overlapping treatments enables me to disentangle the two direct effects from their interaction. The results show no signs of dependencies between the two channels, suggesting that psychological distortions are immutable personal characteristics that cannot be unlearned.