Playing by the rules – the power of quantitative management
Quantitative equity fund management is underlain by a structured and disciplined process based on predefined rules. Hence, irrationality can be not only avoided; the resulting market inefficiency can be exploited for systematic outperformance. To achieve this, quantitative fund managers basically use the same factors as traditional managers. They also call on fundamental and technical indicators, such as dividend yield, earnings growth and revisions, price momentum and volatility. It is therefore a fallacy to affirm that quantitative fund management is not fundamental fund management. It must also stand out from the many, purely passive approaches. Indeed, in quantitative approaches, the computers’ only role is to calculate and evaluate large amounts of data, whereas setting the parameters for data analysis and calculations as the interpretation of results is the sole responsibility of quantitative portfolio managers. Their intervention is key to the process. To achieve active performance they therefore need not only mathematical knowledge and a feel for numbers, but also an understanding of economics and familiarity with the equity markets.
At a glance – Properties of quantitative fund management
- The investment process is rigorous, clearly structured and transparent
- Quantitative screening can be used to select attractive shares from a broad investment universe
- Systematic assessment guarantees high objectivity and discipline in implementation
- All markets are covered with consistent quality
- The model’s predictive capacity may vary depending on the market environment
The main difference between quantitative portfolio management and conventional approaches is far more in its capacity to assess a large set of data on a large number of markets, its rigorous application and the systematic evaluation of available indicators. This makes it possible to identify and select “hidden champions” from the crowd that might go completely unnoticed in a traditional approach based on fundamental analysis given its too narrow focus on a limited investment universe. A home bias or big-cap bias, i.e., the systematic overweighting of domestic or large companies merely because one knows them better, can’t happen in a quantitative model, as the stocks are selected consistently on the basis of model indicators and factors. The company’s “story” and one’s personal opinion on the company are deliberately erased in order to eliminate intuition and irrationality. The investment decision is thus consistent, understandable and repeatable in each individual case.
At the risk of oversimplifying, non-quantitative fund managers go into greater depth as they research precisely the companies in which they invest. Quantitative fund managers go into greater breadth by
using computing power to tap into a far wider investment universe in their search for attractive companies.
Success factors for achieving outperformance
A systematic stock-picking model must first identify factors representing investment styles whose systematic analysis helps generate a positive active performance vs. the market. Here are some examples:
Value: Fundamentally attractively priced shares
Momentum: Shares with solid medium-term performance track-records
Revision: Shares with positive trends in analyst forecasts
Growth: Shares with good fundamental growth indicators
Risk: Shares will low risk indicators, such as volatility and beta
Value, revision and growth are fundamental factors based on indicators such as corporate earnings or analyst forecasts. Momentum and risk are technical and are based on the share price history, while the value factor takes into account multiples such as the price/earnings ratio and dividend yield.
Each factor is underlain by several indicators that represent this investment style. For example, value uses indicators such as price/earnings ratio, dividend yield etc.. We have found that the higher the number of indicators analysed, the more stable the factor’s performance.
Reducing portfolio risk through a multi-factor approach
Many investors tend to focus on certain factors. For example, value approaches have long been among the favourite investment approaches. But this requires acceptance of phases of steep underperformance. Pure style portfolios such as so-called “smart-beta” products deliver added value only to very long-term-oriented investors, who are willing to stick out long periods of weakness.
However, many investors are not willing to do so. They sell off or sell down their exposure after a phase of weakness, thus missing out on the subsequent rally. This is a good argument for multi-factor approaches, which combine several factors. The “value” factor invests in undervalued stocks and, hence, also in “turnaround stories” that have suffered steep losses. Conversely, the trend-following, “momentum” factor invests in stocks that have an especially strong performance track-record. Accordingly, the “value” and “momentum” factors select more volatile shares, and the “risk” factor deliberately chooses stocks that are less volatile.
The variety of factors manifests itself mathematically in the low or even negative correlation of the relative returns of the factor portfolios. There are always market phases in which a certain investment style does not work. Moreover, a single factor portfolio can focus very closely on certain sectors by choosing an overwhelming proportion of shares from the same sector, thus foregoing optimal diversification.
However, combining various factors can reduce portfolio risk through diversification. When one factor is not working, other factors can pick up the slack. In particular, a combination of fundamental factors (value, revision and growth) and technical ones (momentum and risk) has proven to be advantageous. Here again the rigour of the quantitative process is on display, in that it is possible to stick with investments in individual factors and investment styles during phases of poor performance, in order to take part in the subsequent rally.
Multi-factor strategies – a turnkey solution
From a diversification point of view, the question arises as to how best to determine factor weightings. It is useful to do this in two steps. First, a strategic weighting is established via Markowitz optimisation (maximising the information ratio) based on long-term historical risk-reward indicators. However, it is apparent that optimisation alone based on long-term risk indicators does not guarantee optimum diversification. The theoretical basic assumption of portfolio optimisation that the risk properties of factor portfolios, particularly their correlation with one another, is constant over time, has been shown in practice to be unrealistic. During tense market phases in particular, long-term lightly correlated investment styles can move in a single direction. In this case, active risk cannot be reduced through diversification between factors.
This is why in a second step and each time the portfolio is turned over, dynamic factor weightings are determined on the basis of short-term historical risk parameters. Factors that are closely correlated with one another or that are highly volatile are thereby underweighted. This reduces active risk even further.
As a result, factor portfolios are combined that are slightly, or even negatively, correlated. This reduces the active risk of the entire portfolio through diversification, but it also allows to collect the returns that the individual factor portfolios generate. From five portfolios with very high tracking errors, a comprehensive portfolio is formed with moderate tracking error. But this is not a quasi-index or passive portfolio, as seen in a high active share (deviation from the benchmark). This is a typical indicator of a quantitative equity fund: on the one hand highly active positioning with – when examined on an isolated basis – a highly risky (hence with high potential returns) positioning in the individual stocks and investment styles but, on the whole, a diversified portfolio with aggregate risk under control.
Despite having a long experience in markets and techniques, no professional from the financial industry, not even a quant, has yet found the philosopher’s stone or the secret formula for investing in the right shares so as to outperform the market. Even so, quantitative stock-selection models make it possible, though their systematic approach, to identify promising investment opportunities, to actively position oneself, and to make investment decisions in the future as well, based on the exact same rules. This gives investors the opportunity, at negligible risk, to outperform the market routinely in the medium- to long-term, i.e., to beat the indices and ETFs. A stock-picking system, when used consistently, also makes it possible to achieve greater reliability and consistency in future investment outcomes.
Dr. Carsten Große-Knetter
Global Head of Quantitative Equity, ODDO BHF Asset Management
Portfolio Manager, ODDO BHF Asset Management