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Real estate investment trust (REIT) stocks are well known for limited management discretion in investment, financing, and payout policies, implying little information asymmetry between informed and uninformed investors. Besides, due to the renowned illiquidity and complexity of physical real estate markets, investors may be heterogeneously informed. The authors aim to investigate these arguments using REIT panel data from 1993 to 2010.

The authors simultaneously investigate the effects of heterogeneous information (PSOS) and information asymmetry (ADJPIN) on REIT excess returns by estimating panel data regressions controlling for both firm- and time-fixed effects.

The results confirm that heterogeneous information (PSOS) is significantly and positively associated with REIT excess returns while information asymmetry (ADJPIN) is insignificant when controlling for other variables well known for affecting REIT excess returns.

The effects of information asymmetry (ADJPIN) and heterogeneous information (PSOS) on REITs excess returns are rarely simultaneously discussed in the related literature, especially from the perspectives of limited managerial discretions, regulated dividend policy, and underlying asset liquidity (physical real estate markets). The results confirm the heterogeneous information arguments. Besides, the heterogeneous information (PSOS) effects become stronger when leverage and dividend yield are higher. Finally, the above effects of PSOS and ADJPIN on REIT excess returns are also robust during the real estate market growth period (2001-2008).

Product returns information gives firms an opportunity for continuous strategic adaptation by allowing them to understand the reasons for product returns, learning from them and improving their products and processes accordingly. By applying the Dynamic Capabilities (DCs) view in the context of closed-loop supply chains (CLSC), this study explores how firms can continuously learn from product returns information.

This study adopts a qualitative Delphi study-inspired approach. Experts from industry and academia are interviewed in two interview rounds. First round of interviews are based on extant research, while the second round allows the experts to elaborate and correct the results.

This study culminates into a conceptual model for incremental learning from product returns information. The results indicate incremental learning from product returns can potentially lead to a competitive advantage. Additionally, the authors identify the sources of information, capabilities along with their microfoundations and the manifestations of product return information. Three propositions are formulated embedding the findings in DC theory.

This study supports extant literature in confirming the value of product returns information and opens concrete avenues for research by providing several propositions.

This research elucidates the practices, processes and resources required for firms to utilize product returns information for continuous strategic adaptation. Practitioners can use these results while implementing continuous learning practices in their organizations.

This study presents the first systematic framework for incremental learning from product returns information. The authors apply the DC framework to a new functional domain, namely CLSC management and product returns management. Furthermore, the authors offer a concrete example of how organizational learning and DC intersect, thus advancing DC theoretical knowledge.

The purpose of this paper is to test whether financial analysts’ rationality in making stocks’ earnings forecasts is homogenous or not across different information regimes in stocks’ past returns.

By treating stocks’ past returns as the information variable in this study, the authors employ a threshold regression model to capture and test threshold effects of stocks’ past returns on financial analysts’ rationality in making earnings forecasts in different information regimes.

The results show that three significant structural breaks and four respective information regimes are identified in stocks’ past returns in the threshold regression model. Across the four different information regimes, financial analysts react to stocks’ past returns quite differently when making one-quarter ahead earnings forecasts. Furthermore, the authors find that financial analysts are only rational in a certain information regime of stocks’ past returns depending on a certain return-window such as one-quarter, two-quarter or four-quarter time period.

This study is different from those in the existing literature by arguing that there could exist heterogeneity in financial analysts’ rationality in making earnings forecasts when using stocks’ past returns information. The finding that financial analysts react to stocks’ past returns differently in the different information regimes of past returns adds value to the research on financial analysts’ rationality.

The purpose of this paper is to examine the reaction to earnings announcements in a small stock market.

The paper uses the traditional event study method to examine the information content of annual earnings announcements in the small Danish stock market from 1999‐2004.

The paper finds abnormal volatility in the days surrounding the announcements, indicating that they contain relevant information for the stock market. The abnormal volatility persists several days after the announcement, suggesting that the information environment of this small stock market works to decrease the speed of adjustment. In addition to this sign of inefficiency, the paper finds significant positive abnormal returns accompanying the announcements. These results are robust across various methodologies. Surprisingly, the paper finds a positive correlation between the information content and predisclosure information. This contradicts previous studies, and it is interpreted as evidence of a low level of pre‐announcement information. Confirming the results of similar studies, the paper finds that unexpected earnings are best proxied using a model based on consensus analyst forecasts.

This paper contributes to the existing literature by analyzing the information content of earnings announcements in a small stock market with accounting standards that are congruent with the International Accounting Standards.

Prior research has documented the role of information uncertainty in the cross-sectional variation in stock returns. Miller (1977) hypothesizes that if information uncertainty is caused by differences of opinion, prices will reflect only the positive beliefs due to short-sale constraints. These anomalous stock price behaviors may result from mispricing. In contrast, Merton (1974) asserts that default risk is a function of the uncertainty in the asset value process. Information uncertainty may be subsumed by credit or default risk. The paper aims to discuss these issues.

The authors employ various sorting techniques and Fama-MacBeth Regressions to test the hypotheses.

The authors provide empirical evidence consistent with Merton’s (1974) default risk hypothesis and inconsistent with Miller’s (1977) mispricing hypothesis.

Risk aversion and not misplacing is the primary factor driving information-related anomalies in equities markets.

It would be quite difficult to find arbitrage opportunities in equities markets because there appears to be little, if any, mis-pricing due to information uncertainties.

This study provides important information about the primary underlying information-related source of certain empirical anomalies in the cross-section of stock returns.

This study endeavours to examine the relationship between information asymmetry and expected stock returns at the National Stock Exchange (NSE) of India, with a sample of NIFTY 500 stocks for a period ranging from 1st April 2000 to 31st March 2018, by employing three different proxies of information asymmetry: number of transactions, institutional ownership and idiosyncratic volatility.

The return differential amongst information-sorted decile portfolios has been assessed to understand the effect of information risk on stock returns by employing (1) traditional measures of performance evaluation like mean, Sharpe,  Treynor and information ratios, (2) regression models like the capital asset pricing (CAPM), Fama and French three-factor, Carhart's four-factor, information-augmented CAPM, information-augmented Fama and French three-factor and information-augmented Carhart's four-factor models and (3) an autoregressive distributed lag (ARDL) model.

The empirical evidence indicated that as information asymmetry associated with portfolio increases, returns also expand to recompense investors for bearing information risk validating the existence of a significant positive relationship between information asymmetry and expected stock returns at the NSE. Amongst the various asset pricing models employed in this study, the information-augmented Fama and French three-factor model turned out to be the best in explaining cross-sectional variations in portfolio returns.

Strong information premium was observed such that high information stocks outperformed low information stocks which have strong inference for investors and portfolio managers, who all continuously look out for investment strategies that can lend hand to beat the market.

Easley and O'Hara (2004) proposed that stocks with more information asymmetry have higher expected returns. Very few studies have examined this relationship between information risk and stock returns that too restricted to the US market only, with a few on other emerging markets. No work has been conducted on the concerned issue in the Indian context. Therefore, it seems to be the first study to explore the relationship between information asymmetry and expected stock returns in the Indian securities market.

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.

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.

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.

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.

We examine the informational roles of trades and time between trades in the domestic and overseas US Treasury markets. A vector autoregressive model is employed to assess the information content of trades and time duration between trades. We find significant impacts of trades and time duration between trades on price changes. Larger trade size induces greater price revision and return volatility, and higher trading intensity is associated with a greater price impact of trades, a faster price adjustment to new information and higher volatility. Higher informed trading and lower liquidity contribute to larger bid–ask spreads off the regular daytime trading period.

To describe the best practices for complying with the increasingly large body of information returns required by the Internal Revenue Service of participants in the investment management industry and the severe penalties that apply to noncompliant taxpayers.

This technical paper describes the explosive growth of information returns and protective return filings required of investment management industry participants, based upon the author’s advising tax return preparers and taxpayers charged with filing these forms.

Each tax return filing season has demonstrated the ever-increasing and enormous waste of effort and money but no relief is in sight. The expectation of relief from the tax authorities at any level or from Congress and other legislative bodies, is remote.

This paper provides timely guidance from a practitioner in the field of tax compliance including a summary of current forms to be reviewed by tax practitioners with investment management industry clients, either on the manager or the investor side.

This study uses the unique setting of unusual market activity (UMA) replies to examine the market reaction and the effects of disclosure and investor protection amid information uncertainty.

A total of 1527 hand-collected UMA replies from the interlinked stock exchanges of Indonesia, Malaysia, Thailand and Singapore for the period of 2015–2017 were analysed using event study and Heckman two-step methods with market and matched control firm benchmarks.

The overall results support the uncertain information hypothesis. The UMA replies with new information were also found to reduce information uncertainty, but not information asymmetry, and they are complementary to investor protection in enhancing abnormal returns. The overall finding suggests that the UMA public query system can be an effective market intervention mechanism in improving information certainty and efficiency.

This study provides insight on the effects of news replies and investor protection on abnormal returns, and support for the uncertain information hypothesis. The finding is useful to policymakers and stock exchanges as they seek to understand how to alleviate investors' anxiety and to create an informationally efficient market. Nevertheless, this study is limited by the extensiveness of the hand-collected UMA replies and also the potential issue of simultaneity-induced endogeneity.

This study uses UMA replies and cross-country data taking into account the effects of market surroundings such as information uncertainty and the level of investor protection on market reaction.