Optimal Quantitative Risk Factors (with Jose Olmo)
Asset pricing factors are widely constructed using characteristic-sorted portfolios of stocks. Both the number of quantile portfolios selected in this process and the portfolio weightings within these are agnostic of the true relationship between the candidate attribute and stock returns. In this paper we propose a non-parametric approach to constructing factor mimicking portfolios that is flexible in capturing any relationship between attribute and returns. All stocks are included in the factor mimicking portfolio with optimal weightings informed by the observed historical relationship between the candidate attribute and expected stock returns across the full cross section of the ranking attribute. We evaluate the new factors empirically for the size, value and momentum stock characteristics.
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.
Safe Haven or Risky Hazard? Bitcoin during the Covid-19 Bear Market (with Tom Conlon)
The Covid-19 bear market presents the first acute market losses since active trading of Bitcoin began. This market downturn provides a timely test of the frequently expounded safe haven properties of Bitcoin. In this paper, we show that Bitcoin does not act as a safe haven, instead decreasing in price in lockstep with the S&P 500 as the crisis develops. When held alongside the S&P 500, even a small allocation to Bitcoin substantially increases portfolio downside risk. Our empirical findings cast doubt on the ability of Bitcoin to provide shelter from turbulence in traditional markets.
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.
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.
Image: Alcázar, Seville, Spain.