Understanding heuristics-based financial decision-making using behavioral portfolio strategies

PurposeThrough a portfolio choice model, the study empirically examines the influence of the heuristic simplification through peak-end rule (PER) and the associated neglect of the duration of the experience. The portfolio strategy adopted involves optimizing portfolios to capture the impact of heuristic-driven investors' experience of good and bad states. The study attempts to validate PER in an empirical context and is expected to generate trading rules, which would exploit pricing errors emerging out of the use of heuristics by investors.Design/methodology/approachThe empirical approach adopted in the study primarily examines returns to portfolios sorted according to various hedonic evaluation rules. Behavioral portfolios are constructed using hedonic experiences as conditioning variables.FindingsThe results imply that there is continued investor demand for such assets in the short run. An equal weight portfolio based on a three-month hedonic evaluation earns an average monthly return of 2.77% over the next 12 months.Originality/valueThe authors’ study may perhaps be the first attempt to use the peak-end heuristic in portfolio construction.

Implied Ambiguity: Mean-Variance Inefficiency and Pricing Errors

We investigate the optimal portfolio choice problem for an investor who has a utility function of the smooth ambiguity model. We identify necessary and sufficient conditions for a given portfolio to be optimal for such an investor. We define the implied ambiguity of a portfolio as the smallest ambiguity aversion coefficient with which the portfolio is optimal, and the measure of ambiguity perception as the part of the variability in asset returns that can be attributed to the ambiguity. We show that there are one-to-one relations between the implied ambiguity, the Sharpe ratio, and the pricing errors when the portfolio is taken as the pricing portfolio, and that the measure of ambiguity perception is determined by the Sharpe ratio and the alpha. Based on the U.S. stock market data, we assess how ambiguity averse the representative investor is and what types of stocks the investor perceives as having more ambiguous returns than others. This paper was accepted by Manel Baucells, behavioral economics and decision analysis.

Flexible Time-Varying Betas in a Novel Mixture Innovation Factor Model with Latent Threshold

This paper introduces a new methodology to estimate time-varying alphas and betas in conditional factor models, which allows substantial flexibility in a time-varying framework. To circumvent problems associated with the previous approaches, we introduce a Bayesian time-varying parameter model where innovations of the state equation have a spike-and-slab mixture distribution. The mixture distribution specifies two states with a specific probability. In the first state, the innovation variance is set close to zero with a certain probability and parameters stay relatively constant. In the second state, the innovation variance is large and the change in parameters is normally distributed with mean zero and a given variance. The latent state is specified with a threshold that governs the state change. We allow a separate threshold for each parameter; thus, the parameters may shift in an unsynchronized manner such that the model moves from one state to another when the change in the parameter exceeds the threshold and vice versa. This approach offers great flexibility and nests a plethora of other time-varying model specifications, allowing us to assess whether the betas of conditional factor models evolve gradually over time or display infrequent, but large, shifts. We apply the proposed methodology to industry portfolios within a five-factor model setting and show that the threshold Capital Asset Pricing Model (CAPM) provides robust beta estimates coupled with smaller pricing errors compared to the alternative approaches. The results have significant implications for the implementation of smart beta strategies that rely heavily on the accuracy and stability of factor betas and yields.

Ignoring Spatial and Spatiotemporal Dependence in the Disturbances Can Make Black Swans Appear Grey

Automated valuation models (AVMs) are widely used by financial institutions to estimate the property value for a residential mortgage. The distribution of pricing errors obtained from AVMs generally show fat tails (Pender 2016; Demiroglu and James Management Science, 64(4), 1747–1760 2018). The extreme events on the tails are usually known as “black swans” (Taleb 2010) in finance and their existence complicates financial risk management, assessment, and regulation. We show via theory, Monte Carlo experiments, and an empirical example that a direct relation exists between non-normality of the pricing errors and goodness-of-fit of the house pricing models. Specifically, we provide an empirical example using US housing prices where we demonstrate an almost perfect linear relation between the estimated degrees-of-freedom for a Student’s t distribution and the goodness-of-fit of sophisticated evaluation models with spatial and spatialtemporal dependence.

Dynamics of Equity Factor Returns and Asset Pricing

In empirical equity asset pricing, the stochastic discount factor (SDF) is implicitly modeled as a linear function of equity factors and is influenced by the empirical properties of the factor returns. We investigate the pricing error introduced by a misspecified SDF which ignores each of the following established empirical phenomena: autocorrelation, dynamics of covariances, dynamics of correlations, and heavy tails for the conditional factor return distribution. We consider near-linear SDFs and nonlinear specifications characterized by a high degree of risk aversion. We find that assuming constant covariances or constant correlations can significantly overprice certain equity portfolios at all risk-aversion levels and that ignoring fat tails can lead to large pricing errors for some derivative assets for highly nonlinear SDFs.

Inference for variance risk premium

PurposeWith the presence of pricing errors, the authors consider statistical inference on the variance risk premium (VRP) and the associated implied variance, constructed from the option prices and the historic returns.Design/methodology/approachThe authors propose a nonparametric kernel smoothing approach that removes the adverse effects of pricing errors and leads to consistent estimation for both the implied variance and the VRP. The asymptotic distributions of the proposed VRP estimator are developed under three asymptotic regimes regarding the relative sample sizes between the option data and historic return data.FindingsThis study reveals that existing methods for estimating the implied variance are adversely affected by pricing errors in the option prices, which causes the estimators for VRP statistically inconsistent. By analyzing the S&P 500 option and return data, it demonstrates that, compared with other implied variance and VRP estimators, the proposed implied variance and VRP estimators are more significant variables in explaining variations in the excess S&P 500 returns, and the proposed VRP estimates have the smallest out-of-sample forecasting root mean squared error.Research limitations/implicationsThis study contributes to the estimation of the implied variance and the VRP and helps in the predictions of future realized variance and equity premium.Originality/valueThis study is the first to propose consistent estimations for the implied variance and the VRP with the presence of option pricing errors.

Investor sentiments and pricing errors

PurposeThis 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.Design/methodology/approachThis 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.FindingsResults 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.Research limitations/implicationsThe 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.Originality/valueOur 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.