Efficient factor investing strategies - A ‘Sharper’ approach to harvesting factor premiums

Efficient factor investing strategies - A ‘Sharper’ approach to harvesting factor premiums

27-08-2014 | Research

There is a shift towards allocating to the factor premiums momentum, value and low volatility. However, since common factor indexes are a suboptimal way to harvest factor premiums, this paper shows the improved results of a more sophisticated approach. Factor strategies developed by Robeco lead to higher returns, while lowering the risks, resulting in higher Sharpe ratios*.

  • David Blitz
    Head of Quant Research
  • Joop  Huij
    Head of Factor Investing Equities and Factor Index Research

Speed read

  • Investors increasingly allocate strategically to factor premiums such as value, momentum, and low volatility in their asset allocation.
  • But common factor indexes are suboptimal since these indexes are typically exposed to unrewarded risks.
  • More efficient approaches to harvest the value, momentum, and low-volatility premiums yield Sharpe ratios that are two to three times larger than those obtained with common factor indexes.
  • We consider different factor-premium portfolios. The best portfolio approach depends on investor preferences.

Smart-beta approaches have pitfalls

Factor investing is an investment approach based on research that shows that factor premiums such as value, momentum and low volatility can outperform market-cap weighted benchmarks. While beta-factor or smart-beta approaches have proven that they can capture factor premiums, they can be exposed to a number of pitfalls like  taking uncompensated risks; high turnover leading to higher trading costs; and going against other factor premiums. The risk and returns of three factor indexes compared to the market (MSCI World) are shown in Table 1. 

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Table 1. Performance generic factor strategies
Source: Robeco, MSCI

The table above shows that the three factor indexes lead to higher Sharpe ratios (return/volatility ratios). However, a more sophisticated approach is needed to harvest factor premiums more efficiently.

A better approach leads to higher Sharpe ratios

More efficient strategies to harvest factor premiums lead to higher Sharpe ratios. Since 2005, the Robeco Quantitative Research team has concentrated on analyzing, evaluating and designing better factor strategies. These strategies are removing unrewarded risks. An example of an unrewarded risk we remove is distress risk: the risk of a company having difficulty paying off its financial obligations to its creditors. It is not necessary to take on distress risk to profit from the value premium.

In addition we focus on getting more exposure to factor premiums through a higher concentration and active share: the percentage of stock holdings that differs from the index. Better strategies also involve limiting unnecessary turnover to avoid costs. Last but not least, we avoid negative exposure to other factor premiums, since the positive effects of harvesting one factor premium could otherwise be canceled out by the negative effects of other premiums.
The results of this improved approach are shown in Table 2. 

Table 2. Performance Robeco factor strategies
Source: Robeco, MSCI

Across the board, the improvements in the Sharpe ratios come from both an increase in returns and a decrease in risks. 

Optimal factor mix depends on investor preferences

Finally, Robeco’s Quantitative Research team considered alternative approaches to constructing factor-premium portfolios: an equally weighted (1/N) portfolio, a maximum-return portfolio, a minimum-volatility and a risk-weighted portfolio.

All of the portfolios show an improvement in the Sharpe ratio (return/volatility ratio) from 0.5 for the market portfolio to roughly 0.9 to 1.1 for the various factor-premium portfolios. But there are substantial differences in returns, volatilities and tracking errors. The optimal portfolio for investors depends on specific preferences such as coverage-ratio stability or maximizing expected return.

*Please note: this article was initially published on April 5th 2013. We have updated the article's figures. The current sample period is May 1988-December 2013.

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