PhD defence of Arco van Oord on Thursday 12 May 2016

About Arco van Oord

Arco van oord (1984) graduated in Quantitive Finance at the Erasmus University Rotterdam in 2006. In this year he also started as a Ph.D. student at the Erasmus Research Institute of Management on 'Active Portfolio Selection with Uncertainty in the Returns and Covariances'. Throughout the years the focus of his research shifted to the application of equity momentum. This has resulted in the publication of Chapter 3 in the Journal of Empirical Finance. Arco has presented his research at various international conferences. He has supervised several master students during their financial case studies and when writing their master thesis and has lectured on portfolio management in the master Quantitive Finance at the Erasmus University Rotterdam. In 2010 Arco joined the expert center on Risk and Asset Liability Management of De Nederlandsche Bank as a supervisor specialist on pension funds and insurance companies. In 2015 he moved to the Supervision Policy Department on Insurance Companies as a policy advisor. During his time at De Nederlandsche Bank Arco has also performed research on the interest risk management of pension funds as well as Dutch pension funds' investment costs.

Abstract of 'Essays on Momentum Strategies in Finance'

This thesis discusses several aspects and possible improvements of equity momentum strategies in finance. Equity momentum is the phenomenon that stocks have recently outperformed continue to outperform, while underperformers will continue to underperform. Equity strategies that exploit this phenomenon by buying the recent outperformers and short-selling the recent underperformers have prove to be profitable for investors. In this Nobel prize lecture in 2013 Eugene Fama referred to this performance of the momentum strategy as being the biggest challenge for the efficient market hypothesis.

Nevertheless, equity momentum is also known for its crash risk, wiping out years of average positive returns in just a few months, and the fact that its risk and returns vary over time. In this thesis different hedging strategies are applied to reduce momentum’s crash risk and time varying exposures without reducing its positive average returns. Furthermore, different recent improvements of momentum are combined in a mean-variance optimization set-up. Optimization also reduces momentum’s crash risk and its time varying exposures. Moreover it improves momentum’s Sharpe ratio for moderate transaction costs.

Finally, this thesis addresses momentum’s time varying risks and returns in a different way. A Bayesian latent factor model where the number of latent factors is allowed to vary over time is derived. Using the predictive likelihood approach this model is then applied to a residual industry momentum strategy. In turbulent times, like the crisis that started in 2008, the Bayesian latent factor model performs well in terms of risk and return characteristics.