| Research Brief: Advantages of a Quantitative Approach|
Over the last three decades the exponential growth in computing power, the development of sophisticated analytical tools, and significant improvements in the accuracy and size of research databases have led to tremendous advances in the fields of finance, econometrics, and statistics. As a result, quantitative portfolio management strategies have become increasingly more powerful and effective for all asset classes.
As quantitative strategies can provide meaningful return, risk, and cost advantages over traditional subjective strategies when properly designed and implemented, they have steadily gained in popularity in recent years. This trend is expected to continue as investors more fully recognize the disciplined manner in which quantitative strategies can increase the probability of long-term success.
- Quantitative strategies can be time-tested to determine the significance of their edge:
The primary objective of active portfolio management is to generate alpha (excess risk-adjusted returns) over what is available at negligible cost with a passive alternative. Given that the capital markets are fairly efficient and that the additional costs1 associated with active management can be high, generating alpha in a consistent manner is not an easy task. It is therefore critical that managers have a well-defined, statistically significant, and sustainable edge if they are expected to outperform.
Unfortunately, due to the reliance of traditional strategies on subjective forecasts and qualitative judgments that are difficult to quantify, few managers can provide the data necessary to determine with a high degree of confidence the source or significance of their edge. Without this information, it is difficult to ascertain whether their investment performance is the result of skill or simply luck.
Quantitative strategies have an advantage in this regard as, due to their mechanical nature, they can be empirically tested over long periods of time and through several types of market environments to better understand their behavior. Accordingly, any edge can be statistically evaluated as to its significance, and allocation decisions can be made with greater confidence. In addition to Midwest’s internal research (1952 to 20052), there are several independent long-term studies (1937 to 19623; 1968 to 19904; 1951 to 20035) that clearly demonstrate the meaningful edge that can be achieved with a properly designed and implemented quantitative strategy.
- Quantitative strategies can eliminate behavioral biases from the investment process:
Due to their subjective nature, many traditional strategies suffer from various behavioral and emotional biases6 which can hinder consistently superior results. Greed and fear are powerful forces, and research in behavioral finance provides compelling evidence that investors often act irrationally, and repeatedly make sub-optimal decisions when confronted with uncertainty.7
By eliminating behavioral biases from the investment process, a sound quantitative strategy enables investors to make decisions that are emotionally difficult, but have the highest probability of generating superior results over the long-term. Accordingly, temporary price fluctuations and random market "noise" do not cause sub-optimal short-term deviations from a viable long-term plan. This discipline allows quantitative strategies to exploit behavioral inefficiencies, rather than contribute to them.
- Quantitative strategies can evaluate a large opportunity set:
Because investment decisions are driven by objective computer models, a vast number of securities can be monitored and evaluated on a real-time basis to efficiently identify and capitalize on the best reward/risk opportunities. This is in stark contrast to traditional strategies which are significantly constrained by the tremendous amount of time and resources required to subjectively evaluate each security on a case-by-case basis. The number of securities that can be effectively monitored in this manner is limited, and consequently many attractive opportunities are missed with regularity. This opportunity loss often outweighs the benefits of additional security-specific insights generated through such a case-by-case process.
- Quantitative strategies can identify virtually all sources of risk in a portfolio:
Effective risk management is crucial to long-term investment success. Not only is it important to understand the sources of risk in a portfolio, but also the manner in which they are compensated. Whereas investors should expect to be rewarded for exposure to systematic market, sector, and style risks, security-specific and other non-systematic risks often go uncompensated since they can be eliminated through diversification.
Comprehensive empirical research into the nature of these risks is the foundation of sound quantitative strategies. Statistical analysis can be used to separate risks that are well-rewarded from those that are not, and exposure can be managed accordingly.8 With traditional strategies the underlying risks are often unclear, and portfolios can be inadvertently exposed to many which are either unnecessary or lack adequate compensation.
- Quantitative strategies can reduce operational risks:
Investors are also subject to manager-specific operational risks that can lead to sub-par performance. These risks are notably less severe with quantitative strategies:
- Style drift: Because quantitative strategies are well-defined, consistent, and disciplined, they should not be subject to style drift.
- Management turnover: The departure of key managers at quantitative firms has little impact as the investment strategies are mechanical and not dependent on the subjective abilities of any single individual.
- Ethical and legal risk: A quantitative portfolio can be constructed to address most investor concerns (social, environmental, etc.). Once established only minor adjustments should be necessary and legal issues (insider trading, front-running, etc.) should be virtually non-existent.
- Transaction and other errors: A quantitative structure can reduce these events and, equally important, make them more transparent to fiduciaries.
- Quantitative strategies can reduce the risk of being fooled by randomness:
As previously mentioned, it is difficult for investors to differentiate skill from luck when evaluating active managers. Even a superior track record is insufficient evidence of investment ability, as research has shown that past performance is generally an unreliable indicator of future success.9 Therefore, additional information is necessary to make a reasonable determination. Because quantitative strategies can be tested over long-periods of time and risks can be thoroughly researched, investors have more data on which to base their decisions. This enables them to differentiate skill from luck with greater confidence, reduces their risk of being fooled by randomness,10 and helps to protect those with a fiduciary responsibility.
- Quantitative strategies can help investors to maintain confidence:
The increased informational content of quantitative strategies helps investors to maintain confidence during inevitable periods of underperformance. Without this confidence, investors often abandon a strategy due to increased uncertainty regarding the manager’s skill. Research indicates that this is not an optimal response, as the managers fired by institutional investors tend to subsequently outperform those that are hired.11 The discipline to maintain confidence and objectivity in the face of short-term underperformance is an important characteristic of successful long-term investors, and quantitative strategies enable that ability.
- Quantitative strategies can further diversify a multi-manager portfolio:
Because quantitative strategies identify opportunities in a unique manner, they are an excellent complement to traditional strategies already employed in a multi-manager portfolio. Their low correlation can help to decrease aggregate risk and improve return consistency.
- Quantitative strategies can be highly cost-effective:
Although often downplayed (especially during periods of superior performance), expenses have a dramatic impact on long-term portfolio growth when compounded over time. If excessive, they can quickly consume the excess returns generated by a skilled manager.
Because alpha is difficult to consistently generate in a fairly efficient market, any manager that can minimize expenses has a notable advantage. Moreover, research indicates that not only do low-cost managers outperform on average, but they do so by a greater amount than their cost savings.12
As quantitative strategies are driven by objective computer models, portfolios can be managed more efficiently and at lower cost than most traditional strategies. By eliminating the need for expensive and time-consuming qualitative research into each security in the portfolio, quantitative strategies can often save more in expenses than traditional strategies can generate through their deeper understanding of unique security-specific characteristics.
The capital markets are quite complex, and the irrationality of human nature makes it difficult for an individual to efficiently capture and objectively process all of the pertinent information. By reducing the investment equation to key time-tested variables and by eliminating subjectivity and emotion from the process, quantitative strategies can usually perform this function in a more optimal manner.
Although no single approach can guarantee future success, and quantitative strategies are not the sole solution to every investor’s needs, their notable advantages make them quite viable in today’s highly uncertain market environment. By helping investors more effectively manage their return, risk, and expenses, properly designed and implemented quantitative strategies can help shift the probability of long-term success more greatly in their favor.
"In the long-run, facts overpower emotions, but only if you let them."
- James P. O’Shaughnessy
1Portfolio management fees, transaction costs (commissions and markups/markdowns), market impact costs, etc.
2Please contact us for details.
3Francis S. Nicholson, "Price Ratios In Relation to Investment Results," Financial Analyst Journal 24 (Jan/Feb 1968):105-109.
4Josef Lakonishok, Andrei Shleifer, and Robert Vishny, "Contrarian Investment, Extrapolation, and Risk," Journal of Finance 49 (Dec 1994): 1541-1578.
5James P. O’Shaughnessy, What Works on Wall Street, 3rd ed. (New York, NY: McGraw-Hill, 2005).
6Amos Tversky, and Daniel Kahneman, "Judgment under Uncertainty: Heuristics and Biases," Science 185 (Sep 1974): 1124-1131.
7Meir Statman, "Behavioral Finance: Past Battles and Future Engagements," Association for Investment Management and Research (Nov/Dec 1989): 18-27.
8Louis Chan, Jason Karceski, and Josef Lakonishok, "The Risk and Return from Factors," Journal of Financial and Quantitative Analysis 33 (Jun 1988): 159-188.
9Mark M. Carhart, "On Persistence in Mutual Fund Performance," The Journal of Finance 52 (Mar 1977): 57-82.
10Nassim N. Taleb, Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets, 2nd ed. (New York: Texere, 2004).
11Amit Goyal, and Sunil Wahal, "The Selection and Termination of Investment Management Firms by Plan Sponsors," (AFA 2006 Boston Meetings Paper, May 2005).
12John C. Bogle, Common Sense on Mutual Funds, New ed. (New York: John Wiley and Sons, 2000), p. 67-81.
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