Fellow, Director of Studies and
College Teaching Officer (Assistant Professor) in Economics
Downing College, University of Cambridge
I am currently a Fellow of Downing College in the University of Cambridge, where I direct studies in Economics. I also give supervisions (small group classes) for the College in Quantitative Methods (mathematics and statistics), Econometrics, Macroeconomics and Finance. In addition, I am currently working with the Centre for Healthcare Leadership and Enterprise at the Judge Business School on a project to produce forecasts of coronavirus incidence for local public health authorities. I will be lecturing the Further Econometrics (Time Series) MFin course at the Judge Business School in 2021. I have previously lectured on Financial Programming in R, which is part of the University of Cambridge MPhil in Economics and Finance Applied Asset Management module.
Work in progress
Is Regulatory Short Sale Data a Profitable Predictor of UK Stock Returns?
Paper on SSRN at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3682230.
Regulator-required public disclosures of net short positions do not provide a profitable investment signal for UK stocks. While long-short (zero initial outlay) portfolios based on this signal usually make a profit on average, it is rarely statistically significant in either gross or risk-adjusted terms. The issue is that the short sides of the portfolios make substantial losses. This is true even when using information in the trend in disclosures to form portfolios, rather than using the most recent disclosures, which is a more standard procedure. Unit initial outlay portfolios based on the disclosures that are allowed to take short positions do not reliably significantly outperform the market. Certain long-only unit initial outlay portfolios based on the disclosures do reliably significantly outperform the market. However, this out-performance is economically modest: about 1 percentage point a year in gross and risk-adjusted terms.
JEL Classification: G11, G14
The Value of Using Predictive Information Optimally
Paper on SSRN at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3682225.
For mean-variance investors, using predictive information unconditionally optimally produces better portfolios than using predictive information conditionally optimally. The latter is more usually done in practice. Empirically, the unconditionally optimal portfolios have higher Sharpe ratios and certainty equivalents than the conditionally optimal portfolios. They also have lower turnover, leverage, losses and draw-downs. Moreover, measures of the whole distribution tend to prefer the unconditionally optimal portfolios, especially once transaction costs are accounted for. With transaction costs, the unconditionally optimal portfolios often second-order stochastically dominate the conditionally optimal portfolios. The unconditionally optimal portfolios are also preferred in terms of Sharpe ratio, certainty equivalent, costs, losses, draw-downs and stochastic dominance to mean-variance optimal portfolios that do not use predictive information. However, whether unconditionally optimal portfolios are preferred to minimum variance or 1/N portfolios depends on the asset universe.
JEL Classification: G11, G14, G17
Do Consumption-Based Asset Pricing Models Explain Own-History Predictability in Stock Market Returns? (With Oliver Linton)
Paper on SSRN at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3173586
We show that three prominent consumption-based asset pricing models - the Bansal-Yaron, Campbell-Cochrane and Cecchetti-Lam-Mark models - cannot explain the own-history predictability properties of stock market returns. We show this by estimating these models with GMM, deriving ex-ante expected returns from them and then testing whether the difference between realised and expected returns is a martingale difference sequence, which it is not. Furthermore, a semi-parametric test suggests that lagged returns have too much predictive power over current returns to be consistent with the state variables which explain market returns being the same as the state variables which explain market returns in any of the three models.
JEL Classification: C52, C58, G12
Lectures/classes I currently or have previously given at the University of Cambridge for graduate students and supervisions* for undergraduates
*Supervisions are small-group classes
FINANCIAL PROGRAMMING IN R - MPhil Economics & Finance
Course for MPhil in Economics and Finance Students taking the "Applied Asset Pricing" module which covers financial applications in R. These include plots (ggplot2 and highcharter) and calculations (in xts and tidyverse/tidyquant) and implementation of various portfolio construction (e.g. paramteric portfolio policies) and analysis (e.g. statistical factor modelling) techniques, as well an introduction to machine learning (gradient boosting and random forests) and spanning tests.
FURTHER ECONOMETRICS (TIME SERIES) - MFin
Course for MFin students at the University of Cambridge Judge Business School, covering structural time series models, the Kalman filter, VARs/VECMs and other advanced time series techniques.
PART IIA ECONOMETRICS
Second-year course covering cross-sectional OLS in detail, multiple equation models, IV/2SLS, panel data methods (fixed effects and first differencing), probit/logit, elements of forecasting in time series analysis.
PART IIB BANKING AND FINANCE
Third-year course covering the role of the financial sector in developing countries, introductory-intermediate asset pricing, the micro-foundations of banking, the macroeconomic significance of banks and introductory corporate finance.
PART I QUANTITATIVE METHODS IN ECONOMICS
First-year course in maths and statistics for economists, covering calculus, linear algebra, difference and differential equations, probability theory and an introduction to econometrics.
PART IIA MACROECONOMICS
Second-year course covering intertemporal macroeconomics, unemployment & labour markets, monetary economics and international economics.