Welcome to the fourth issue of the third volume of The Journal of Investment Strategies. In this issue you will find four papers that cover a diverse set of topics: behavioral finance, portfolio optimization, systematic momentum strategies and credit trading.
In the first paper of the issue, "Quantifying irrational sentiment", Todd Feldman introduces us to an area of behavioral finance research related to irrational sentiment detection: this represents a first for our journal. What I find most interesting here is that Feldman apparently succeeds in constructing meaningful and intuitive indexes for rational and irrational content in aggregate investor behavior, using the same widely available sets of data that most of us have been using for years. By looking at the statistical patterns that are peculiar to the loss-aversion and recency biases, he is able to construct rational and irrational sentiment indexes for the broad US market. He then argues that by looking at the gap between the two indexes one can detect periods in which behavior is particularly irrational (either irrationally fearful or irrationally exuberant). It is interesting that these indexes are apparently better at catching episodes of irrational fear, but it is also remarkable that they catch the irrational optimism that was present just before the major equity market crashes of 1987 and 2008. I like the fact that this paper demonstrates a very pragmatic and useful avenue for bringing insights from behavioral finance into quantitative finance, and perhaps even into the world of systematic strategy construction. I find this a promising area of research and hope that we will have more papers like Feldman's in our journal in the future.
In the issue's second paper, "The stochastic-volatility, jump-diffusion optimal portfolio problem with jumps in returns and volatility", Floyd B. Hanson provides a rigorous treatment of the optimal portfolio problem in continuous time with significant generalizations allowing for jumps in returns and volatility. Continuing the line of work that started with Merton in the log-normal jump-diffusion setting, and that many researchers have both corrected and broadened for various relaxed assumptions, Hanson gives a quick summary of the literature on this well-studied topic and proceeds to show where new results can be obtained: in particular, when allowing jumps in volatility (volatility jumps are, as we know from empirical research, a very prominent feature of markets). The paper proceeds to derive the optimal allocation and consumption solutions under the constant relative risk aversion utility function, which allows the author to treat special cases including power- and log-dependence of the utility function (the latter corresponds to the Kelly criterion). The paper also offers specific computation methods for numerically solving the optimality equations with various constraints.
The third paper in this issue, "Momentum strategies with the L1 filter" by Tung-Lam Dao, offers another view on systematic trend-following strategies, a topic that has already received well-deserved attention from our journal in the past. The paper considers, in particular, a specific method of trend detection and strategy construction, called L1 filtering, that allows detection of not only the long-term trends but also short term deviations from them. The remarkable aspect of this paper compared with many others in the area is that both the mathematical methodology of trend detection and the computational techniques used to obtain numerical results are very rigorous and give researchers a solid foundation for deriving a robust strategy. Of course, rigor of a purely mathematical nature is not enough to ensure that historical backtests of systematic strategies will withstand future market challenges, but the absence of mathematical rigor is usually enough to ensure the opposite. As in any such model, there remain a number of calibration parameters, and their choice affects the model performance. One would therefore hope that the selection of these parameters can be done from first principles, rather than purely by optimization, in order to avoid possible overfitting that can occur in such complex and data-sensitive procedures.
The issue's fourth paper, "Hedging iTraxx credit default swap index trading on an intraday basis: an empirical study" by Cheng-Ran Du and Tim Brunne, gives a rare glimpse into the world of credit derivatives trading from a systematic perspective. Moreover, the authors are interested in the intraday trading risks of the iTraxx CDS index, which is itself a topic that not many people have addressed in the literature. The authors'approach is very pragmatic: they try to identify a set of liquid exchange-traded derivatives contracts (futures) that could potentially offer a risk-reduction potential to the iTraxx position. Considering DAX and EuroStoxx futures, as well as Bund futures, the authors find that the DAX based hedging is most efficient for iTraxx, but that the efficiency of the hedge remains fairly low. I think that these findings are both intuitive and (probably) robust, even if practical use of such hedging is, at best, limited. Still, in certain situations when an over-the counter trader is stuck with a large risk position and finds himself in a turbulent market, even a modest reduction in risk can be very welcome.
As we draw the third volume of The Journal of Investment Strategies to a close, on behalf of the editorial board I would like to thank our readers and contributing authors for their continued support and interest.We are confident that future volumes will provide you with even more compelling reasons to keep reading and writing.
Arthur M. Berd
General Quantitative LLC
We discuss various implementations of L1 filtering in order to detect some properties of noisy signals.
In this paper we examine the effectiveness of intraday hedging models for credit default swap index trading by means of more liquidly traded exchange-based futures contracts.
The author uses behavioral finance theory to create a measure that detects when stock markets become irrational.
The stochastic-volatility, jump-diffusion optimal portfolio problem with jumps in returns and volatility
The risk-averse optimal portfolio problem is treated with consumption in continuous time for a stochastic jump-volatility-jump-diffusion (SJVJD) model for both the risky asset and the volatility.