Building a Better Mousetrap: Enhanced Dollar Cost Averaging
Lee M. Dunham (Creighton University) and Geoffrey Friesen (University of Nebraska) | Dec 2011
This paper presents a simple, intuitive investment strategy that improves upon the popular dollar-cost-averaging (DCA) approach. The investment strategy, which we call enhanced dollar-cost-averaging (EDCA), is a simple, rule-based strategy that retains most of the attributes of traditional DCA that are appealing to most investors but yet adjusts to new information, which traditional DCA does not. Simulation results show that the EDCA strategy reliably outperforms the DCA strategy in terms of higher dollar-weighted returns about 90% of the time and nearly always delivers greater terminal wealth for reasonable values of the risk premium. EDCA is most effective when applied to high volatility assets, when cash flows are highly sensitive to past returns, and during secular bear markets. Historical back-testing on equity indexes and mutual funds indicates that investor dollar-weighted returns can be enhanced by between 30 and 70 basis points per year simply by switching from DCA to EDCA.

Momentum and Reversal: Does What Goes Up Always Come Down?
Jennifer Conrad (University of North Carolina) (UNC) and M. Deniz Yavuz (Purdue) | Feb 2012
We examine whether risk characteristics of stocks interact with return continuation and reversals. We find that a momentum portfolio whose winner stocks are chosen from high expected return securities, and whose loser stocks are chosen from low expected return securities, has significant momentum profits, but shows no evidence of subsequent reversals. In contrast, a momentum portfolio that buys low expected return winners and sells high expected return losers has no significant momentum but strong reversals. Overall, we find evidence that intermediate-horizon momentum and longer-horizon reversal patterns may not be linked. Our results have implications for several explanations of momentum profits.
Diversifying Diversification Strategies: Model Averaging in Portfolio Optimization
Felix Miebs (European Business School) | Feb 2012
The literature on portfolio optimization in the presence of parameter uncertainty has suggested several approaches to mitigate the impact of estimation error on portfolio performance. However, empirical evidence finds no single approach that can achieve a consistently higher risk-adjusted performance than 1/N. In this paper, I propose three averaging rules that synthesize the established approaches in order to mitigate the impact of estimation error on portfolio performance. The evaluation of the proposed averaging rules on empirical and simulated datasets shows that each rule achieves a consistently higher risk-adjusted performance than 1/N, while all individual portfolio strategies considered in the averaging exercise do not. I find that the observed performance gains are economically and statistically significant. The performance gains are attributable to persistent diversification effects between the portfolio strategies under consideration, as well as to empirical characteristics in portfolio returns that are exploited by one of the averaging rules.
Managing Risk Exposures Using the Risk Budgeting Approach
Benjamin Bruder and Thierry Roncalli (Lyxor Asset Management) | Jan 2012
The ongoing economic crisis has profoundly changed the industry of the asset management, by putting risk management at the heart of most investment processes. This new risk-based investment style does not rely on returns forecasts and is therefore assumed to be more robust. In 2011, it has particularly encountered a great success with the achievement of minimum variance, ERC and risk parity strategies in portfolios of several large institutional investors. These portfolio constructions are special cases of a more general class of allocation models, known as the risk budgeting approach. In a risk budgeting portfolio, the risk contribution from each component is equal to the budget of risk defined by the portfolio manager. Unfortunately, even if risk budgeting techniques are widely used by market practitioners, they are few results about the behavior of such portfolios in the academic literature. In this paper, we derive the theoretical properties of the risk budgeting portfolio and show that its volatility is located between those of minimum variance and weight budgeting portfolios. We also discuss the existence, uniqueness and optimality of such a portfolio. In a second part of the paper, we propose several applications of risk budgeting techniques for risk-based allocation, like risk parity funds and strategic asset allocation, and equity and bond
alternative indexations.
The Sharpe Ratio Indifference Curve
David Bailey (Lawrence Berkeley National Laboratory) and Marcos Lopez de Prado (Tudor Investment Corp) | Feb 2012
The problem of capital allocation to a set of strategies could be partially avoided or at least greatly simplified with an appropriate procedure for strategy approvals. This paper proposes such procedure. We begin by splitting the capital allocation problem into two tasks: Strategy approval and portfolio optimization. Then we argue that the goal of the second task is to beat a naïve benchmark, and the goal of the first task is to identify which strategies improve the performance of such naïve benchmark. This is a very appealing result, because it doesn’t leave all the work to the optimizer, which should add robustness to the final outcome.
We introduce the concept of Sharpe ratio Indifference Curve, which represents the space of pairs (candidate strategy’s Sharpe ratio, candidate’s correlation to the approved set) for which the Sharpe ratio of the expanded approved set is remains constant. This proves that selecting strategies (or portfolio managers) solely based on past Sharpe ratio will lead to suboptimal results, particularly when we ignore the impact that these decisions will have on the average correlation of the portfolio.