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This paper computes the pricing errors of S&P 500 index by employing the valuation model developed by Doran et al. (2009) and investigates its response to individual and institutional investor sentiments. This study contributes to the literature by looking at both rational and quasi-rational sentiments and how noise trading and investments based on fundamentals affect pricing errors.

This paper computes the pricing errors of S&P 500 index by employing the valuation model developed by Doran et al. (2009) and investigates its response to individual and institutional investor sentiments.

Results show that pricing errors are persistent and stock prices systematically deviate from their intrinsic values. The authors also find that both individuals and institutional investors form their expectations based on risk factors as well as noise; however, institutional investors seems to be more driven by rational factors. The findings also suggest that institutional investors have a significant power to cause pricing errors due to unpredictable changes in their sentiments while small investors lack such ability to move stock prices away from their intrinsic values. Additionally, this paper finds that quasi-rational (rational) investor sentiments have positive (negative) impact on pricing errors suggesting that trading based on noise is an important determinant of pricing errors while investors' expectations stemming from fundamentals play an important role in improving market efficiency.

The impact of rational outlook due to changes in fundamentals seems to be greater than that of noise on the pricing errors, consistent with both risk-based and behavioral models of the asset pricing literature.

Our study contributes to the existing literature in the following ways: first, the authors employ most recent data to compute mispricing for the market index and investigate if it is persistent and systematic. Second, the authors decompose sentiment variables into rational and quasi-rational components and trace their dynamics to better understand the role of risk factors and noise in the formation of sentiments. Third, the authors investigate the relative impact of individual and institutional investor sentiments on mispricing. Lastly, the authors examine the response of pricing errors to both rational and quasi-rational sentiments of individual and institutional investors.

The purpose of this paper is to examine the relative effects of rational and quasi-rational sentiments of individual and institutional investors on a set of smart beta fund returns. The magnitudes of the impacts of institutional investor sentiments are greater than those of individual investor sentiments. In addition, both rational and quasi-rational sentiments of individual and institutional investors have significant impacts on smart beta fund returns. The magnitudes of the impacts of quasi-rational sentiments are greater than those of the rational sentiments for both types of investors (quasi-rational sentiments of institutional investors have the maximum impact). These results are consistent with the arguments that professional investors consider the sentiments of individual investors as contrarian leading indicators which are mainly driven by noise while conform the sentiments of institutional investors which are driven by more rational factors. A majority of smart beta funds in the sample outperform the S&P500 returns in the short term but fail to consistently beat the market. The authors find evidence that smart beta funds with consistently high returns are relatively less (more) driven by individual (institutional) investor sentiments. Overall, the authors argue that smart beta funds appear to follow quasi-rational sentiments of both individual and institutional investors that are not rooted in economic fundamentals.

The results of the impulse functions generated from a multivariate model suggest that the smart beta fund returns are negatively (positively) impacted by individual (institutional) investor sentiments.

The magnitudes of the impacts of institutional investor sentiments are greater than those of individual investor sentiments. In addition, both rational and quasi-rational sentiments of individual and institutional investors have significant impacts on smart beta fund returns. The magnitudes of the impacts of quasi-rational sentiments are greater than those of the rational sentiments for both types of investors (quasi-rational sentiments of institutional investors have the maximum impact).

These results are consistent with the arguments that professional investors consider the sentiments of individual investors as contrarian leading indicators which are mainly driven by noise while conform the sentiments of institutional investors which are driven by more rational factors. A majority of smart beta funds in the sample outperform the S&P500 returns in the short term but fail to consistently beat the market. The authors find evidence that smart beta funds with consistently high returns are relatively less (more) driven by individual (institutional) investor sentiments. Overall, the authors argue that smart beta funds appear to follow quasi-rational sentiments of both individual and institutional investors that are not rooted in economic fundamentals.

Researchers have proposed characteristics‐based pricing models as an alternative to risk‐based pricing models. While supported empirically, these characteristic‐based models lack theoretical support. This paper seeks to reformulate an asset‐pricing model (RAPM) to demonstrate why firm characteristics help to explain stock returns.

The RAPM is grounded in an economic setting where two groups of agents hold different beliefs about firm fundamental values, and the more sophisticated group (rationals) adopts contrarian strategies against the naïve group (quasis). The model is derived in a static equilibrium within the consumption‐investment framework with heterogeneous agents.

The key theoretical result is a parsimonious equation of cross‐sectional expected returns that not only are specified by the traditional risk‐return relation, but also are determined by contrarian adjustments at both market‐wide and firm‐specific levels. When the model is taken to empirical specifications, it leads to consistent explanations for the behaviors of growth and value stocks, and for size and book‐to‐market effects.

The RAPM is a one‐period model that assumes that “rationals” have perfect knowledge about “quasis” sentiment parameter and their relative market weights. In future research, it is planned to extend this static model to multiple periods to incorporate a learning process by which “rationals” learn these parameters over time.

The RAPM clearly identifies four criteria for implementing arbitrage opportunities in investments. These criteria formalize the common practices in the mutual/hedge fund industry.

The paper develops an original framework that formally supports the characteristics‐based models. It offers insights for researchers in behavioral finance and guidelines for investment practitioners.

Investors’ fear can be rational, emanating from the natural dynamics of economic fundamentals, or it can be quasi rational and not attributable to any known risk factors. Using VIX from Chicago Board Options Exchange as a proxy for investors’ fear, the purpose of this paper is to consider the following research questions: to what extent does noise play a role in the formation of investors’ fear? To what extent is the impact of fear on S&P 500 index returns driven by rational reactions to new information vs fear induced by noise in stock market returns? To what extent do S&P 500 index returns display asymmetric behavior in response to investor’s rational and quasi rational fear?

In a two-step process, the authors first decompose investors’ fear into its rational and irrational components by generating two additional variables representing fear induced by rational expectations and fear due to noise. The authors then estimate a three-vector autoregression (VAR) model to examine their relative impact on S&P 500 returns.

Impulse responses generated from a 13-variable VAR model show that investors’ fear is driven by risk factors to some extent, and this extent is well captured by the Fama and French three-factor and the Carhart four-factor models. Specifically, investors’ fear is negatively related to the market risk premium, negatively related to the premium between value and growth stocks, and positively related to momentum. The magnitude and duration of the impact of the market risk premium is almost twice that of the impact of the premium on value stocks and the momentum of investors’ fear. However, almost 90 percent of the movement in investors’ fear is not attributable to the 12 risk factors chosen in this study and thus may be largely irrational in nature. The impulse responses suggest that both rational and irrational fear have significant negative effects on market returns. Moreover, the effects are asymmetric on S&P 500 index returns wherein irrational upturns in fear have a greater impact than downturns. In addition, the component of investors’ fear driven by irrationality or noise has more than twice the impact on market returns in terms of magnitude and duration than the impact of the rational component of investors’ fear.

The results are consistent with the view that one of the most important drivers of stock market returns is irrational fear that is not rooted in economic fundamentals.

Based on the theoretical framework, this paper analyzes the sentiment-herding relationship in emerging stock markets (ESMs). First, it aims to examine the effect of investor sentiment on herding. Second, it seeks the direction of causality between sentiment and herding time series.

The present study applies the Exponential Generalized Auto_Regressive Conditional Heteroskedasticity (EGARCH) model to capture the volatility clustering of herding on the financial market and to investigate the role of the investor sentiment on herding behaviour. Then the vector autoregression (VAR) estimation uses the Granger causality test to determine the direction of causality between the investor sentiment and herding. This study uses a sample consisting of stocks listed on the Shanghai Composite index (SSE) (348 stocks), the Jakarta composite index (JKSE) (118 stocks), the Mexico IPC index (14 stocks), the Russian Trading System index (RTS) (12 stocks), the Warsaw stock exchange General index (WGI) (106 stocks) and the FTSE/JSE Africa all-share index (76 stocks). The sample includes 5,020 daily observations from February 1, 2002, to March 31, 2021.

The research findings show that the sentiment has a significant negative impact on the herding behaviour pointing out that the higher the investor sentiment, the lower the herding. However, the results of the present study indicate that a higher investor sentiment conducts a higher herding behaviour during market downturns. Then the outcomes suggest that during the crisis period, the direction is one-way, from the investor sentiment to the herding behaviour.

The findings may have implications for universal policies of financial regulators in EMs. We have found evidence that the Emerging investor sentiment contributes to the investor herding behaviour. Therefore, the irrational investor herding behaviour can increase the stock market volatility, and in extreme cases, it may lead to bubbles and crashes. Market regulators could implement mechanisms that can supervise the investor sentiment and predict the investor herding behaviour, so they make policies helping stabilise stock markets.

The originality of this paper lies in investigate the sentiment-herding relationship during the Surprime crisis and the Covid-19 epidemic in the EMs.

The purpose of this paper is to investigate a unique sample of lottery-like stocks and contextualize their short-run price behavior with respect to behavioral principles.

The authors conduct a short-run event-study of the abnormal returns for stock market investments in Australian small-cap oil and gas (O & G) explorers centered on the drilling commencement (spudding) of 157 wildcat oil or gas wells drilled between January 2000 and June 2010.

Small-cap stock market investments associated with newly spudded wildcat O & G wells are negative NPV gambles rather than fair (zero NPV) investments. Once a wildcat well is spudded, the 30-day expected abnormal return is 6-8 percent: wealth-maximizing stockholders are advised to sell upon news of spudding, but gamblers may wish to hold on for the chance of a 10.6 percent 30-day average abnormal return (if the well is not plugged and abandoned). In the lead-up to each gamble the authors observe a significant pre-spudding stock price run-up on average, perhaps indicative of positively affected investors aroused by an easily imagined successful wildcat gusher as per evidence on the influence of image and affect on investors’ decisions (MacGregor et al., 2000; Loewenstein et al., 2001; Rottenstreich and Hsee, 2001; Peterson, 2002).

The wildcat drilling events considered in this paper are lottery-like by nature, and spudding represents the distinct moment when the gamble is unambiguously on, following shortly on from which investors either strike it lucky or strike out. The specifically small-cap wildcatters are typically heavily vested in one well at a time, therefore the sample stocks are uniquely lottery like. This differs from other studies which infer the lottery-like nature of their sample stocks from characteristics such as price and idiosyncratic volatility.

Chicago economists have pursued and applied the logic of price theory in any direction and as far it will go. This is their hallmark and their genius. The adoption of any principle, however, implies the selection of a correlative opportunity cost, a rule as fundamental as any other in price theory. The opportunity cost consists, in part, of the alternative accounts of the operation of the economy and economic policy-making, and in part, the coherence, conditions, and limits of Chicago's doctrines of theory and policy.

Symbolic activity in managerial decision making may be a tool for manipulation or a means for inspiration and empowerment. In each case there are assumptions, values, goals, creation of meaning, and a purpose for symbolic activity that differ. Explores the symbolic dimensions of three perspectives on decision making; the political, foolishness and autocommunication. Suggests that the perspectives differ in the “rational” or “arational” use made of symbolic activity. The absence of unity of symbol and substance is seen to characterize the more manipulative uses of symbolic activity while a holistic integration of the two is consistent with a concern for the welfare and empowerment of others. The process of this integration is linked to the spiritual qualities personified in the symbol of the child. Discusses some specific ways in which organizations engage in symbolic activity which benefits both the organizations and the participants.

This paper participates in the debate on market efficiency and correct approach for asset pricing through a comprehensive review of literature in favor, as well as against the long held belief of market efficiency. The purpose of this paper is to understand emerging trends in behavioral finance and establish its future potential as a mainstream alternative theory of asset pricing.

The review and discussion of literature is mainly divided into three different sections that are –theories supporting efficient market hypothesis (EMH); studies providing evidences from the stock market on the failure of EMH and studies on behavioral finance, discussing separately investors’ behavioral biases keeping in mind their effect on stock prices; and providing empirical evidences on the effect of investor sentiment on stock prices.

The review of literature from both the point of views has helped in understanding the market efficiency issue and changing dynamics of asset pricing approach. This is achieved by highlighting the gaps in the concept of market efficiency and also suggesting how these gaps can be bridged with a superior approach such as behavioral finance. Through further discussion of emerging trends in behavioral finance, the paper also points out gaps and how these can be abridged, for behavioral finance to be accepted as a mainstream alternative approach to EMH.

This is an extensive and one of a kind study that discusses market efficiency through discussion of EMH and behavioral finance side by side. With the help of such a study, researchers can precisely understand the need and can focus on the future course of action to make behavioral finance a mainstream approach to asset pricing.

Recent empirical and theoretical developments related to the microprocesses of institutional logics have helped to cultivate a powerful theory of agency. We build on these developments to show how the institutional logics perspective can shed light on important questions related to frame construction and how institutions matter. In particular, we show how the emergence of an economic democracy frame in post-war Sweden generated different efforts to define that frame with concrete ideas and practices linked to different logics – socialism and neoliberalism. We show how socialists tried to define economic democracy as requiring a radical transformation in the nature of ownership and control embedded in the innovative financial practice of wage earner’s funds. In contradistinction, conservatives drew on neoliberal ideas and extant mutual fund practices to construct alternative meanings and practices related to economic democracy that enrolled citizens in Capitalism without challenging extant Capitalist ownership structures. While mutual funds and wage earner’s funds initially existed in a state of parabiosis – existing side by side without much interrelationship – struggles over the meaning of economic democracy led these practices to become competing solutions in a framing contest. Implications for the study of institutional logics, frames and the social organization of society are discussed.