Google scholar profile here.

**Publications: **

Momentum Has its Moments, with Pedro Santa-Clara. Journal of Financial Economics, volume 116, Issue 1, April 2015, Pages 111-120. Click here for a folder with the data and programs used to produce the tables in the paper.

**Working papers:**

**Abstract**: Portfolio optimization usually struggles in realistic out of sample contexts. I de-construct this stylized fact comparing historical estimates of the inputs of portfolio optimization with their subsequent out of sample counterparts. I confirm that historical estimates are often very imprecise guides of subsequent values but also find this lack of persistence varies significantly across inputs and sets of assets. Strikingly, the resulting estimation errors are not entirely random. They have predictable patterns and can be partially reduced using their own previous history. A plain Markowitz optimization using corrected inputs performs quite well, out of sample, namely outperforming the 1/N rule. Also the corrected covariance matrix captures the risk of optimal portfolios much better than the historical one.

**Conferences / seminars:** Catolica Business School (Lisbon), Deakin University, Schroder's official institutions seminar (Sydney), Portuguese Finance Network (2016), FMA Europe (2016), EFMA (2016), FMA Asia (2016).

**Abstract**:A direct measure of the cyclicality of momentum at a given point in time, its bottom-up beta with respect to the market, forecasts both the returns and the risk of the strategy. Challenging a potential risk-based explanation, a highly cyclical momentum portfolio forecasts both higher risk and lower returns for the strategy. The results show robustness out-of-sample (OOS) and controlling for other variables. One predictive regression of monthly momentum returns on its bottom-up beta produces an OOS R-square of 2.41%. This contrasts with the usual negative OOS R-squares of similar predictive regressions for the market excess return.

**Conferences / seminars: **University of Hull, Australasian Banking and Finance Conference (2016).** **

**Abstract**: We study the risk dynamics of the betting-against-beta anomaly. The strategy shows strong and predictable time variation in risk and no risk-return trade-off. A risk-managed strategy exploiting this achieves an annualized Sharpe ratio of 1.28 with a very high information ratio of 0.94 with respect to the original strategy. Similar strategies for the market, size, value, profitability, and investment factors achieve a much smaller information ratio of 0.15 on average. The large economic benefits of risk-scaling are similar to those of momentum and set these two anomalies apart from other equity factors. Decomposing risk into a market and a specific component we find the specific component drives our results.

**Conferences / Seminars**: **EFMA 2017 best paper award**, Spanish Finance Association (2017).

**Abstract**: We examine the risk-return trade-off among equity factors. We obtain a positive in-sample risk-return trade-off for the profitability (RMW) and investment (CMA) factors of Fama and French (2015, 2016), while for the market and momentum factors there is a negative relation. The out-of-sample forecasting power (of factor volatility for factor returns) is economically significant for both RMW and CMA: By constructing a trading strategy that relies on such predictability, we obtain annual Sharpe ratios above one and utility gains above 5% per year. We also find weak evidence that the factor variances are negatively correlated with the aggregate equity premium.

**Conferences / Seminars**: UNSW.

**Abstract**: Risk premiums for exposure to state variables that predict consumption growth are time-varying consistent with macro-finance theory. We first show that the relation between state variables, such as the term spread, and future consumption growth varies significantly over time. Consistent with an Intertemporal CAPM, we find that state variable risk premiums (in the cross-section of individual stocks) vary over time accordingly: Risk premiums increase by 5% (annualized) when a state variable predicts consumption growth strongly relative to its own history. We further show that this effect is magnified by time-variation in the variance of the state variables, which we argue to be driven by general macroeconomic uncertainty. Our conditional evidence contributes to recent literature that focuses on the unconditional pricing of state variable risk and finds mixed results.

**Conferences / Seminars**: UNSW.

**News:**

February 16, 2016 - My research on momentum featured in *BusinessThink*, UNSW's Business newsletter.