Asset and portfolio management

Quantitative investing: Focusing on the return-to-volatility ratio

It is of paramount importance that an investor considers returns in risk-adjusted terms rather than in isolation, as the return on any investment must be considered relative to the amount of risk taken. In the world of quantitative asset management, one of the main measures of risk is volatility. Consequently, quants seek to achieve the highest return-to-volatility ratio in order that investors are appropriately compensated for the amount of volatility that their portfolio is expected to experience.

Case study: The long-short strategy

In creating its long-short strategy, the Quantitative Asset Management team at Commerzbank (QAM) uses techniques that seek to reduce volatility. 

First, the team adopts a careful screening process to reduce the initial universe of over half a million equities down to a select universe containing only those European stocks that satisfy the minimum standards for quality and liquidity. From this universe of approximately half a thousand stocks, the team then screens thousands of different data items to identify clusters of stocks with similar characteristics called factors. The team aims to keep a diversified portfolio of stocks, yet it also has clear limits to restrict the number of stocks in the portfolio to ensure that exposure to specific factors is not diluted.

Second, at a portfolio level, four factor strategies – carry, liquidity, momentum and value – are combined using the concept of risk budgeting to form the long component of the strategy. The risk-budgeting approach reduces exposure to the more volatile factor strategies, while increasing exposure to the more stable factor strategies. By inversely weighting factors based on their volatility, a riskbudgeting approach creates a truly diversified portfolio whereby each factor has an equal volatility contribution to the portfolio.

Third, market exposure is hedged by taking short positions in relevant index futures. Short positions in index futures are based on the market beta of the portfolio, as well as a mechanism that monitors market direction and reacts accordingly. More specifically, this reactionary mechanism ensures that as the underlying market begins to underperform, a period usually characterised by high volatility, the strategy can increase the size  of its short positions in the relevant index futures, and thereby decreases its net long exposure to the market. Conversely, as the market outperforms, a period usually characterised by relatively low volatility, the strategy decreases the size of its short positions and thereby increases its net long exposure to the market. This mechanism shifts the strategies beta exposure to European equity markets in a range of roughly 0.1 to 0.8.

Thus, our conservative approach ensures that our long-short strategy seeks to increase performance without paying a high price in terms of volatility.

Performance

The below figures (Chart 1) highlight the benefits of using our low volatility long-short strategy over a pure buy-and-hold strategy for European equity markets. The most notable benefits are the return-to-volatility ratio, the maximum drawdown, and the time to recovery.

Chart 1: European equities: The benefits of a long-short versus a buy-and-hold strategy
Chart 1: European equities: The benefits of a long-short versus a buy-and-hold strategy
Source: Bloomberg, Commerzbank Corporate Clients. Data from September to end of January 2018. Data before January 2018 result from backtesting. All data result from simulation.
Daily price data have been used.

Commerzbank
long-short strategy

European
equity markets

Annualised return

12.0%

7.6%

Annualised volatility

6.1%

18.4%

Annualised return/annualised volatility

1.95

0.41

Maximum drawdown

–10.2%

–58.7%

Maximum time to recovery (days)

576

2,369

Correlation

23.0%

Source: Bloomberg, Commerzbank Corporate Clients. Data from September to end of January 2018. Data before January 2018 result from backtesting. All data result from simulation.
Daily price data have been used.

The improved performance of the long-short strategy is the result of three drivers of return: equity market exposure (beta), a reactionary mechanism that adjusts according to volatility (tactical beta), and careful stock selection (alpha).

Delivery of the strategy

The strategy can be delivered in a variety of wrappers, including managed accounts. Investors have full transparency and a clear view of the strategy holdings over time.

Our philosophy

In Commerzbank’s Quantitative Asset Management team, our philosophy is to generate the most attractive risk-adjusted returns for investors. We pride ourselves on our portfolio construction process, which successfully merges active and systematic portfolio management in one by deploying scientifically-driven models that are carefully adapted to changing markets.

For more information regarding liquid alternatives or Quantitative Asset Management at Commerzbank AG, please contact QAM@commerzbank.com.

Disclaimer: The results do not represent those of actual trading since the strategy depicted did not exist. The results represent back-tested simulated performance by means of a retroactive application of the strategy. There are frequently sharp differences between simulated performance results and the actual results subsequently achieved by a particular strategy.
Past performance is not a reliable indicator of future results. Commerzbank long-short strategy is simulated performance while the equity markets’ line is past performance.