Time series momentum strategies are about as old and ubiquitous as the CTA community that uses them, yet there has been surprisingly little recent academic work done in this space. Notable exceptions to this are two papers produced in 2012 and 2013 by Dr. Nick Baltas and Prof. Robert Kosowski of Imperial College London. Automated Trader talks to the authors about their work and some of the conclusions, which run counter to established thinking.

Automated Trader: What are the origins of your work in the field of time series momentum?

Nick Baltas: It has its origins in work I was doing a couple of years ago when I was preparing for my PhD and Robert was my supervisor. At the time, there was not a vast amount of material on momentum strategies in published academic literature. We felt that there was potential for making the strategy more robust as regards both volatility estimation and signal generation.

Automated Trader: What started as one paper now appears to be two. Why?

Robert Kosowski: In March 2012 we decided to split the original paper into two parts partly for reasons of space, but more importantly because we felt that there were further issues to be examined that would naturally cover distinct features of time-series momentum strategies. The paper ‘Momentum Strategies in Futures Markets and Trend-Following Funds’ examines matters such as the prevalence of time-series momentum strategies among CTAs by using benchmark strategies that have high explanatory power in the time-series of CTA index returns. It also considers (and rejects) the hypothesis that high flows of capital into CTA strategies cause capacity constraints.

The second paper, ‘Improving Time-Series Momentum Strategies: The Role of Trading Signals and Volatility Estimators’ is primarily focused on the mechanics of the time-series momentum strategy. In particular, it considers how two specific changes might enhance the overall profitability of a momentum strategy. One change was that the introduction of a more efficient volatility estimator could reduce unnecessary portfolio rebalancing and thereby also reduce transaction costs. The paper also examines how the quality of the momentum trading signal affects performance and posits an alternative model that in addition to long/short signals incorporates a stay flat/exit signal.

We feel that both papers are of interest to practitioners (including fund of funds) and academics.

Automated Trader: What was the motivation for investigating capacity constraints in the first paper.

CTA performance since 2009 has generally been weak, with various conventional CTA indices (e.g. BarclayHedge, Newedge indices) reporting negative yearly returns for 2009, 2011 and 2012. Yet over the same period flows into CTAs have been positive. That would superficially suggest that there are capacity constraints in the industry. We therefore decided to test this hypothesis, and in order to do so we needed to find some strategies that had explanatory power for CTAs that could be used as a proxy for their activity.

Automated Trader: Surely determining that those strategies should be momentum-based was a given, in view of their well-documented proliferation among CTAs?

Nick Baltas: That may be a widespread assumption, but it is interesting that previous studies which have asserted this have not actually provided any empirical evidence in support. We decided to remedy this by building a time series momentum strategy that we applied across monthly, weekly and daily time frames (which we termed Futures-based Trend-following Benchmarks or FTBs 1 ) and regressing this against a CTA index.

Dr. Nick Baltas

Automated Trader: What was your methodology for constructing the FTBs?

Robert Kosowski: We first tested for and found evidence of return predictability in the lagged returns across monthly, weekly and daily frequencies of our dataset. We then created a series of overlapping portfolios that were rebalanced at the end of each day/week/month, and tested for momentum profitability. The results showed that the momentum strategy generated an economically significant alpha and return at all three frequencies and were in the majority of cases significant at the 1% level. Several combinations of lookback and holding periods exhibited Sharpe Ratios greater than 1.25.