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Fama and French win “Best Quant Paper 2018”

Savvy Investor curates the best pensions and investment white papers from around the world. Having uploaded more than 25,000 papers since launch, they have a unique platform from which to host these Awards. The Savvy Investor Awards are judged on the basis of the quality and readability of the paper and its appeal to their institutional investor audience.

Call us sentimental, but we’re delighted to be awarding the Savvy Investor trophy for the best quant paper of 2018 to Eugene Fama and Kenneth French. Unlike some earlier papers authored by this duo, the winning paper is in no way ground-breaking. However, it reminds us all of the nature of equity market volatility, and the implications for long-term investment returns. As the name suggests, it is a “volatility lesson” for professional investors, coming from two of the most respected names in the business.

See the winning papers below, or visit Savvy Investor for the full list of winners and short-listed papers across all 15 categories. 

Best Quant Paper 2018

Volatility Lessons (Financial Analysts Journal – CFA Institute)
In this paper, Fama and French examine the return distribution of equities versus cash over a variety of time periods, and show that the probability of negative equity returns over three and five-year periods is substantial. Interestingly, for longer-term horizons (say 10 or 20 years) there is a marked increase in right skewness and kurtosis. In other words, compared to a normal distribution of returns, the left tail almost disappears and the likelihood of negative equity returns versus cash diminishes substantially. Another key conclusion from the data relates to drawing inferences about future risk premia from observed returns over 3-, 5- or 10-year periods. The duo argue that, due to the high volatility, the evidence from such a “short” time period will be too “noisy” to be reliable.

Robust Asset Allocation for Robo-Advisors (Amundi Asset Management)
Quant researchers from Amundi Asset Management examine the challenges faced by robo-advisors attempting to automate the portfolio allocation and rebalancing process. This is a detailed, complex and formula-rich paper which will appeal primarily to quant managers and analysts involved in portfolio optimization, specifically using a mean-variance approach.

The Correlation See-Saw (Axioma)
The correlation of returns between different asset classes is critical to overall portfolio risk. However, these relationships are not necessarily stable over time. Axioma analyzes the way that shifts in cross-asset correlations impact overall portfolio risk, examining a case study of the first five months of 2018 when there was an unusual pattern of correlation reversals. How should this impact an investor’s approach to risk analytics?

Combining Investment Signals in Long/Short Strategies (Goldman Sachs Asset Management)
This paper examines the best way to combine quantitative investment signals in the context of managing a long-short portfolio. Is it better to create one combined signal, or is it preferable to consider the portfolio exposures indicated by each signal and combine the different exposures? The authors carry out their own empirical study and compare the results with other academic evidence.

If We Don’t Believe Markets are “Efficient”, What Do We Believe? (Winton)
Despite the well-known faults that are inherent in the efficient market hypothesis, it still underpins several prominent investment strategies. The authors of this paper examine an ecological theory that could be more applicable to financial markets.

The Current State of Quantitative Equity Investing (CFA Institute Research Foundation)
In this 74-page paper, CFA Institute Research Foundation reviews the concepts of risk and return, anomalies and the onset of factor investing, as well as the influence of big data on the quantitative equity field.

Pulling the Goalie: Hockey and Investment Implications (Cliff Asness/Aaron Brown)
Harkening back to the 1980 ‘Miracle on Ice,’ the authors build a model to determine the precise time that a hockey coach should choose to pull the goalie when behind. They then apply these lessons to a portfolio management environment.

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