We use expectations of the short rate inferred from the term structure of interest rates to test several well-known models of behavioral biases and information frictions. We classify signals about future short rates by their cost of acquisition and find evidence of overreaction to high-cost signals and underreaction to low-cost signals, providing support for the overconfidence bias. We show that our results are unlikely to be driven by time-varying risk premia. The biases are so large that the market's forecast errors are larger at all horizons than for forecasts obtained by assuming that the short rate follows a random walk.

We investigate the decision-making behaviour of fund managers within the framework of Cumulative Prospect Theory (CPT). Employing monthly data from nearly 200,000 funds across all asset classes, investment styles and geographical regions between 1990 and 2022, we estimate the parameters of the CPT value and probability-weighting functions using hierarchical Bayesian estimation. Our findings reveal that fund managers consistently exhibit many of the behavioural traits that have been widely documented in the experimental psychology literature, although often with parameter values that are significantly different those that have been reported in these studies and which have been subsequently used in empirical research. We show that there are statistically significant differences in fund managers' behavioural characteristics between different asset classes, and between different fund categories within each asset class. We also show that fund size plays an important role in determining fund managers' behavioural traits, while manager tenure has only limited impact.

Cryptocurrencies are characterized by high volatility and low correlations with traditional asset classes, and present an intriguing investment opportunity. However, their inherent risks and regulatory uncertainties make direct investment challenging for many investors. This paper addresses this challenge by proposing a replication framework that employs machine learning to create synthetic portfolios that replicate the risk-adjusted return profile and diversification benefits of Bitcoin, by far the largest cryptocurrency by market share. We show that the synthetic portfolios offer a compelling alternative to direct investment in Bitcoin, delivering superior risk-adjusted returns net of trading costs while mitigating the risks that are associated with holding Bitcoin directly. Furthermore, the synthetic portfolios provide better diversification benefits and lower tail risk.

In this paper, we investigate the performance of behavioural portfolio strategies. We incorporate the short-term and long-term memory of the investor, thus recasting the behavioural portfolio choice process in a dynamic setting. We evaluate the out-of-sample performance of a behavioural investor in relation to both a naive investor who invests in an equally weighted portfolio and a rational investor, who maximises expected mean-variance utility. We report a number of findings. First, from an expected utility perspective, neither the rational investor nor the CPT investor achieves a risk-adjusted return or certainty equivalent return that significantly outperforms that of the naive investor. Second, from a CPT utility perspective, the behavioural investor outperforms both the rational and naive investors. Third, the CPT investor typically displays highly concentrated, lottery-like asset allocations, low turnover and highly stable portfolio allocations. Fourth, the addition of the investor's memory into the portfolio choice process increases both diversification and turnover, leading to improved investment performance. Finally, by allocating more weight to positively skewed assets and increasing portfolio concentration, the probability weighting function has more impact than the utility function on the behavioural investor's performance. Our results are robust to the choice of reference return, estimation sample size, probability estimates, the probability weighting function and portfolio weight constraints. (c) 2021 Elsevier B.V. All rights reserved.

We investigate the role of the average risk across stocks in predicting subsequent market returns using measures of risk that capture the higher moments of the return distribution including variance, skewness and kurtosis, as well as measures of tail risk that combine these. We find that average tail risk has statistically and economically significant predictive ability for market returns, even after controlling for market tail risk, suggesting that average idiosyncratic tail risk contains information about future returns. Average tail risk dominates other measures of average risk that have been documented in the literature, such as variance and skewness. Our results are robust to the inclusion of control variables that capture business cycle effects, and to the use of different measures of tail risk.

We investigate the predictability of the G10 currencies with respect to lagged currency returns from the perspective of a U.S. investor, using the maximally predictable portfolio (MPP) approach of Lo and MacKinlay (1997). Out-of-sample, the MPP yields a higher Sharpe ratio, higher cumulative return and lower maximum drawdown than both an equal-weighted portfolio of the currencies and an equal-weighted portfolio of momentum trading strategies. The MPP has performed particularly well since the 2008 financial crisis, in contrast with the momentum portfolio, the value of which declined significantly over this period. Our results are robust to the estimation window length, the type and level of portfolio weight constraints, and transaction costs.

In this article, we develop one- and two-component Markov regime-switching conditional volatility models based on the intraday range and evaluate their performance in forecasting the daily volatility of the S&P 500 Index. We compare the performance of the models with that of several well-established return- and range-based volatility models, namely EWMA, GARCH, and FIGARCH models, the Markov regime-switching GARCH model, the hybrid EWMA model, and the CARR model. We evaluate the in-sample goodness of fit and out-of-sample forecast performance of the models using a comprehensive set of statistical and economic loss functions. To assess the statistical performance of the models, we use mean error metrics, directional predictive ability tests, forecast evaluation regressions, and pairwise and joint tests; and to appraise the economic performance of the models, we use value at risk coverage tests and risk management loss functions. We show that the proposed range-based Markov switching conditional volatility models produce more accurate out-of-sample forecasts, contain more information about true volatility, and exhibit similar or better performance when used for the estimation of value at risk. Our results are robust to the choice of volatility proxy, estimation sample size, out-of-sample evaluation period, and alternative error distributions.

We investigate the impact that the publication of the Bank of England’s Financial Stability Report (FSR) has on the stock returns and credit default swap spreads of UK financial institutions. Examining a sample of 73 UK-listed banks and other financial institutions, we find that publication of the FSR is, on average, associated with no abnormal returns. We extend our analysis to examine the extent to which policies and the sentiment in the FSR are predictable, which would explain the observed lack of abnormal returns. We find that both sentiment and announced policies are predictable. We also examine the extent to which the release of the FSR reduces information asymmetry in financial markets, but do not find strong evidence.

We investigate the cross‐sectional relationship between stock returns and a number of measures of option‐implied beta. Using portfolio analysis, we show that the method proposed by Buss and Vilkov (2012, The Review of Financial Studies, 2525, 3113–3140) leads to a stronger relationship between implied beta and stock returns than other approaches. However, using the Fama and MacBeth (1973, Journal of Political Economy, 8181, 607–636) cross‐section regression methodology, we show that the relationship is not robust to the inclusion of other firm characteristics. We further show that a similar result holds for implied downside beta. We, therefore, conclude that there is no robust relation between option‐implied beta and returns.

We investigate the dynamics of the relationship between returns and extreme downside risk in different states of the market by combining the framework of Bali et al. [Is there an intertemporal relation between downside risk and expected returns? Journal of Financial and Quantitative Analysis, 2009, 44, 883–909] with a Markov switching mechanism. We show that the risk-return relationship identified by Bali et al. (2009) is highly significant in the low volatility state but disappears during periods of market turbulence. This is puzzling since it is during such periods that downside risk should be most prominent. We show that the absence of the risk-return relationship in the high volatility state is due to leverage and volatility feedback effects arising from increased persistence in volatility. To better filter out these effects, we propose a simple modification that yields a positive tail risk-return relationship in all states of market volatility.

We propose new systematic tail risk measures constructed using two different approaches. The first is a non-parametric measure that captures the tendency of a stock to crash at the same time as the market, while the second is based on the sensitivity of stock returns to innovations in market crash risk. Both tail risk measures are associated with a significantly positive risk premium after controlling for other measures of downside risk, including downside beta, coskewness and cokurtosis. Using the new measures, we examine the relevance for investors of the tail risk premium over different horizons.

In this paper, we investigate the dynamic relationship between financial market volatility, macroeconomic fundamentals and investor sentiment, employing a two-factor model to decompose volatility into a persistent long-run component and a transitory short-run component. Using a structural VAR model with Bayesian sign restrictions, we show that adverse shocks to aggregate demand and supply cause an increase in the persistent component of both stock and bond market volatility, and that adverse shocks to the persistent component of either stock or bond market volatility cause a deterioration in macroeconomic fundamentals. We find no evidence of a relationship between the transitory component of volatility and macroeconomic fundamentals. Instead, we find that the transitory component is more closely associated with changes in investor sentiment. Our results are robust to a wide range of alternative specifications.

Existing accounting-based forecasting models of earnings either do not fully consider information that is contained in stock prices or use an ad hoc specification that is not based on rigorous valuation theory. In this paper, we develop an earnings forecasting model built on the theoretical linkages between future earnings and stock prices as well as a number of accounting fundamental variables. We find that our model-based forecasts of earnings are in general less biased and more accurate than both existing model-based forecasts and analysts' consensus forecasts, at both shorter and longer horizons. We also show that the accuracy of both model-based forecasts and financial analysts' forecasts depend on firm-specific characteristics such as firm size and industry membership.

In this paper, we propose a gold price index that enables market participants to separate the change in the 'intrinsic' value of gold from changes in global exchange rates. The index is a geometrically weighted average of the price of gold denominated in different currencies, with weights that are proportional to the market power of each country in the global gold market. Market power is defined as the impact that a change in a country's exchange rate has on the price of gold expressed in other currencies. We use principal components analysis to reduce the set of global exchange rates to four currency 'blocs' representing the U.S. dollar, the euro, the commodity currencies and the Asian currencies, respectively. We estimate the weight of each currency bloc in the index in an error correction framework using a broad set of variables to control for the unobserved intrinsic value. We show that the resulting index is less volatile than the USD price of gold and, in contrast with the USD price of gold, has a strong negative relationship with global equities and a strong positive relationship with the VIX index, both of which underline the role of gold as a safe haven asset. (C) 2017 Elsevier Ltd. All rights reserved.

Standard models—based exclusively on macro-financial variables—have made little progress in explaining the behavior of exchange rates. In this paper, we introduce a neglected set of “soft power” factors capturing a country’s demographic, institutional, political and social underpinnings to uncover the “missing” determinants of exchange rate volatility over time and across countries. Based on a balanced panel dataset comprising 115 countries during the period 1996–2011, the empirical results are generally robust across different estimation methodologies and show a high degree of persistence in exchange rate volatility, especially in emerging market economies. After controlling for standard macroeconomic factors, we find that the “soft power” variables—such as an index of voice and accountability, life expectancy, educational attainment, the z-score of banks, and the share of agriculture relative to services—have a statistically significant influence on the level of exchange rate volatility across countries.

In this paper, we develop a long memory orthogonal factor (LMOF) multivariate volatility model for forecasting the covariance matrix of financial asset returns. We evaluate the LMOF model using the volatility timing framework of Fleming et al. [J. Finance, 2001, 56, 329–352] and compare its performance with that of both a static investment strategy based on the unconditional covariance matrix and a range of dynamic investment strategies based on existing short memory and long memory multivariate conditional volatility models. We show that investors should be willing to pay to switch from the static strategy to a dynamic volatility timing strategy and that, among the dynamic strategies, the LMOF model consistently produces forecasts of the covariance matrix that are economically more useful than those produced by the other multivariate conditional volatility models, both short memory and long memory. Moreover, we show that combining long memory volatility with the factor structure yields better results than employing either long memory volatility or the factor structure alone. The factor structure also significantly reduces transaction costs, thus increasing the feasibility of dynamic volatility timing strategies in practice. Our results are robust to estimation error in expected returns, the choice of risk aversion coefficient, the estimation window length and sub-period analysis.