Optimal Characteristic Portfolios (with Jose Olmo)
Characteristic-sorted portfolios are the workhorses of modern empirical finance, deployed widely to evaluate anomalies and construct asset pricing models. We propose a new method for their estimation that is simple to compute; makes no ex-ante assumption on the nature of the relationship between the characteristic and returns; and does not require ad hoc selections of percentile breakpoints or portfolio weighting schemes. Characteristic portfolio weights are implied directly from data, through maximizing the expected value of a mean-variance utility function estimated non-parametrically over the cross section of the assets. To illustrate the method we use it to evaluate the size, value and momentum anomalies.
Option-implied Physical Probabilities (with Thierry Post & Valerio Poti)
Results of Empirical Likelihood Ratio tests support the joint specification of the density estimates and the pricing kernel for stock index options by Constantinides, G. M., J. C. Jackwerth, and S. Perrakis, 2009, Mispricing of S&P 500 Index Options, Review of Financial Studies 22, 12471277. The test results include implied probabilities which are shown to be superior density forecasts for stock index returns compared with the original density estimator and the estimated risk-neutral density, using both statistical and economic goodness criteria. Further improvements of predictive ability are obtained by refining the initial density estimates and the pricing kernel system.
Know when to hold’em: Profitability from adapting technical trading rules (with Bartosz Gebka, Robert Hudson & Andrew Urquhart)
Under the Adaptive Market Hypothesis, temporary inefficiencies arise in markets until these are learned by utility optimizing agents. The activities of these agents tend to make the market efficient, but only to the learned inefficiency, with new inefficiencies continuously arising until they in turn are learned by agents. Under this market ecology an adaptive trading strategy may perform better than a static one, potentially even better than the best static rule in hindsight. We test for this and compare adaptive rule performance, against both the buy \& hold portfolio and a more rigorous benchmark of the best static trading rule selected over the whole sample ex-post.
Correcting Professional Forecaster Uncertainty Measures for Herding Bias (with Tapas Mishra & Simon Wolfe)
We use leading measures of economic policy uncertainty, macroeconomic uncertainty and financial uncertainty, to test whether professional forecasters herd under uncertainty. Corrections using a herding model lead to more accurate forecasts of GDP across all categories of professional forecasters evaluated.
Can Volatility-Based Investment Regimes be Market-Timed?
Recent research findings suggest long-term investment benefits through scaling returns by recent realized volatility. In this paper we apply a new methodology to define market regimes based on investor utility and investigate the informational content of a number of realized and implied volatility measures to forecast these regimes.
Photo taken at Alcázar, Seville, Spain.