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Ranking Expected Returns in Small Caps
Goldman Sachs Small Cap Growth Insights Fund
Interview with: Osman Ali

Author: Ticker Magazine
Last Update: Apr 28, 9:45 AM ET
The ever-growing data processing power has enabled asset managers to harness the availability of a wide range of data, including financial and broader business-related metrics, as part of their quantitative models. Osman Ali, portfolio manager of the Goldman Sachs Small Cap Growth Insights Fund, explains how the management team relies on traditional and alternative data to invest in the highest ranked companies based on their expected return over the next one year.

“The output of our ranking model every day is a company-by-company expected return. Internally we call it the alpha of each company.”
A: It is the same discipline by which we buy stocks, because the expected return does not have to be above a particular threshold for us to like it or sell it. Every time we rebalance the portfolio we compare the expected return for a particular stock with another stock out there that is in the same sector, industry and country that has a better expected return, and similar risk properties and cost characteristics. If so, we would prefer to own at least some of that other stock because it’s got a higher expected return and perhaps sell out of a stock whose expected return has fallen.

Generally when a stock’s expected return starts to fall because the fundamentals or the sentiment and themes deteriorating, we start reducing our position proportionately and replacing it, generally with companies in the same industry or sector with higher expected returns. We make an explicit trade-off among return, risk and cost every time we rebalance the portfolio.

Q: How do you define and manage risk?

A: At the outset we think about operational risk and data risk. A lot of data comes into our investment process, and we have a whole host of integrity and data checks, with a team of people reviewing all the larger exceptions. It starts first and foremost with an operational and data integrity analysis that helps validate the inputs into our model, and ensures we are comfortable with them. We place a lot of effort on creating a framework for doing this.

At the portfolio level, risk has many different facets. We have our own risk models to measure volatility and tracking errors. We further ensure against adverse market events by putting portfolio constraints and guard rails on our investment process, limiting sector, industry or single-stock exposure, and beta. On top of an estimation of risk we have formal, weekly discussions on risks or events in the portfolio we may be missing or not capturing.

The senior portfolio managers meet with the Chief Investment Officers weekly to discuss portfolios and risks, to get a sense of what the other investors in the division are talking about from a risk standpoint.

During geopolitical events like the U.S. Presidential election and Brexit, we did lots of security analysis and other portfolio analyses. So risk for us involves a broad set of analyses; it’s part and parcel of making investment decisions and being cognizant of the unintended risks. We also have an independent risk management team outside of our portfolio management effort that does its own analysis on our portfolio—but for us it’s just making sure our decision process is as informed as possible.

There are always things that may not be fully captured, and we try to protect ourselves from that unknown.

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