The Market Portfolio is NOT Efficient: Evidences, Consequences and Easy to Avoid Errors
Pablo Fernandez (University of Navarra), et al.
March 16, 2016
The Market Portfolio is not an efficient portfolio. There are many evidences that tell us that: the equal weighted indexes have beaten their market-value weighted indexes for many years, many easy-to-build portfolios (some “smart-beta”, “multifactors”) have beaten market-value weighted indexes. We document evidences about seven Equal weighted indexes that have had higher returns than the corresponding market-value weighted index: S&P500, MSCI Emerging Markets, FTSE 100, MSCI World. MSCI, DAX 30 and IBEX 35. However, many finance and investment books still recommend to diversify in the same relative proportions as in a broad market index such as the Standard & Poor’s 500, many funds compare their performance with the return of market-value weighted indexes. Without homogeneous expectations, the market portfolio cannot be an efficient portfolio for all investors.

Stock Portfolio Design and Backtest Overfitting
David H. Bailey (University of California), et al.
February 26, 2016
We demonstrate a computer program that designs a portfolio consisting of common securities, such as the constituents of the S&P 500 index, that achieves any desired profile via in-sample backtest optimization. Unfortunately, the program also shows that these portfolios typically perform erratically on more recent, out-of-sample data, which is symptomatic of selection bias. One implication of these results is that so-called smart beta funds, which are designed in-sample to deliver a desirable performance pro file, are likely to disappoint out-of-sample.

Portfolio Management with Drawdown-Based Measures
Marat Molyboga (Efficient Capital Mgt) and Christophe L’Ahelec (OTPP)
February 8, 2016
This paper analyzes the portfolio management implications of using drawdown-based measures in allocation decisions. We introduce modified conditional expected drawdown (MCED), a new risk measure that is derived from portfolio drawdowns, or peak-to-trough losses, of demeaned constituents. We show that MCED exhibits the attractive properties of coherent risk measures that are present in conditional expected drawdown (CED) but lacking in the historical maximum drawdown (MDD) commonly used in the industry. This paper introduces a robust block bootstrap approach to calculating CED, MCED and marginal contributions from portfolio constituents. First, we show that MCED is less sensitive to sample error than CED and MDD. Second, we evaluate several drawdown-based minimum risk and equal-risk allocation approaches within the large scale simulation framework of Molyboga and L’Ahelec (2016) using a subset of hedge funds in the managed futures space that contains 613 live and 1,384 defunct funds over the 1993-2015 period. We find that the MCED-based equal-risk approach dominates the other drawdown-based techniques but does not consistently outperform the simple equal volatility-adjusted approach. This finding highlights the importance of carefully accounting for sample error, as reported in DeMiquel et al (2009), and cautions against over-relying on drawdown-based measures in portfolio management.

Sharpe Ratio: International Evidence
Javier Vidal-García (Harvard University)
April 2016
There is no overall consensus about which measure is the most suitable for evaluating portfolios’ performance. Despite being affected by some of the statistical characteristics of returns, Sharpe ratio is the most widely used measure for portfolio performance evaluation. Thus, the other measures such as Treynor ratio or Modigliani and Modigliani (M2) are considered as alternatives to the Sharpe ratio. Using daily and monthly returns, our study confirms that these measures provide comparable results across countries, even examining the relative performance of mutual funds to a benchmark using the M2 approach. We obtain the same performance ranking regardless of the measure employed.

Equity Premium Prediction: Are Economic and Technical Indicators Unstable?
Fabian Baetje (Leibniz Universität Hannover) and Lukas Menkhoff (DIW Berlin)
February 2016
We show that technical indicators deliver stable economic value in predicting the U.S. equity premium over the out-of-sample period from 1966 to 2014. Results tentatively improve over time and beat alternatives over a large continuum of sub-periods. By contrast, economic indicators work well only until the 1970s, but thereafter they lose predictive power, even when the last crisis is considered. Translating the predictive power of technical indicators into a standard investment strategy delivers an annualized average Sharpe ratio of 0.55 p.a. (after transaction costs) for investors who had entered the market at any point in time.

On Risk – Building a Definition
Filipe Lemos (Novo Banco)
March 2016
This article aims to build through the collection of inputs from prior research, regulatory input and practitioner’s experience, a comprehensive definition of risk. Risk is not measurable uncertainty nor volatility. Risk is a three part concept: (1) risk is the potential that events may have an unexpected and noteworthy impact on results, i.e. a consequence of exposure while of pursuing objectives in an uncertain environment; (2) risk is ontological uncertainty, the unknown unknown; and (3) risk is the perception of risk, since how risk is individually perceived and socially amplified influences the risk experience and its subsequent effects. So the challenge is to deal with risk understanding the impossibility of predicting the future. We should learn from the past but simultaneously accept that not all past lessons address every issue coming our way.