PARTNER CONTENT
By Melissa Brown, CFA
Head of Investment Decision Research, SimCorp
Quantitative tools for hedge funds, such as risk and return models and optimizers, have been around for decades, but many portfolio managers and traders who take a fundamental approach at best, remain skeptical of their value. Some may even refuse to use the tools.
In this article, we set out to convince the skeptics that these quant tools can be useful, even without full-scale adoption. Here are five advantages that non-quants should know about quant tools:
1. Quantitative tools provide a “second opinion”
Quant tools enforce discipline. Risk models signal that it is time to rebalance to stay within a required risk budget. Return models may signal that an asset has become overvalued and it is time to trim or sell the position. They can help managers separate the company from its stock and avoid the problem of “falling in love” with a name. I read a recent paper by William Bernstein published in Advisor Perspectives. In it, he relates a famous quote from Charles Ellis: “one wins on Wall Street by being smarter, harder working, or more disciplined than competitors.” The first two propositions, he points out, are nearly impossible; there’s always someone smarter and harder working than you. That leaves us with discipline, as suggested by rigorous models, as an approach managers can count on to help stand out from the crowd and win against competition.
2. Quantitative tools help in understanding portfolio risk
Quant tools can help you understand the risks you are taking, ones that go beyond knowing the markets, themes or individual securities you invest in really well. Using risk models can be especially important for hedge funds. A true hedge relies on understanding how the long and short sides of a portfolio are correlated, and properly designed covariance matrices from risk models can provide much better information on whether one bet truly hedges another. It is often the mismatch in hedges that can cause problems. For a simplified example, assume a fund is long some technology stocks and short others, and appears to be sector neutral. However, the longs could all be larger names, creating a size bias. Or, they could all be semiconductor companies. If the shorts are all in software companies the hedge is also less than perfect. The manager may be quite happy with that risk but should know it is there.
3. Quantitative tools align with clients’ analyses and mandates
Using ex-ante risk analysis and ex-post factor performance attribution can help fundamental managers speak the same language as intermediaries such as investment consultants, fund-of-fund managers and direct clients. They also help asset owners compare performance of managers according to the same criteria and show that the manager is doing what they are expected to do. A hedge fund wants to show, and clients want to see, that the fund manager is taking risk on the bets she expects to pay off, avoiding other risks, and importantly, is appropriately hedged.
4. Quantitative tools can help build efficient portfolios
An optimizer can examine hundreds or thousands of combinations of assets to tell us which one gives the best return for the risk we are willing to take, or which one yields the lowest risk for a given return target. The optimizer will ensure that the expected risk contribution of each asset is commensurate with its expected return, and that a bet on one factor adequately hedges a bet on another. Optimizers are capable of incorporating different asset classes as well. Portfolios built using heuristics are much more likely to be inefficient. Optimization may not be suitable for every type of hedge fund strategy, but it can benefit many types such as equity long-short, relative value, and even global macro funds.
5. Finally, quantitative tools can enhance performance
Risk models and performance attribution also highlight all the bets a manager is taking, some of which may be unintended (seen through risk analysis) and have a negative payoff (highlighted in return attribution). Identifying these potential drags on performance can help a manager avoid them, while at the same time maintaining their conviction on individual names. See our case study on a how quantitative tools can improve a portfolio’s returns for more detail, but the bottom line is that by acknowledging and avoiding unintended exposures managers can substantially improve their performance without diluting their convictions.
Learn more about the Axioma offering for hedge funds.
Melissa Brown, CFA, Head of Investment Decision Research, SimCorp – Before joining Axioma (now part of SimCorp) in 2011, Melissa was an MD and head of the institutional business at Wintrust Capital Management. Before that, she spent 10 years at Goldman Sachs Asset Management, ultimately serving as a Partner and co-head of Client Portfolio Management in the QIS Group. Before joining GSAM she was Director of Quantitative Research at Prudential Securities, where she popularized the idea of the “cockroach theory” of earnings surprise and appeared on Institutional Investor’s “All-Star” list for 10 straight years.
