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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.

ďThe output of our ranking model every day is a company-by-company expected return. Internally we call it the alpha of each company.Ē
Q: How does your investment process help in separating companies?

A: The output is a ranking for each one of the companies. In fact, it is more than a ranking, itís an expected return. The higher the company rank, the higher we think its expected return is going to be. So the output of our model every day is a company-by-company expected return. Internally we call it the alpha of each company.

Although it doesnít change very much day to day, we do see changes over time, especially when there is a lot of sentiment or news on a particular company, or new information that could change the expected return estimate of a company, or if a company announces earnings. But the real output of this investment process every single day is a company-by-company expected return. We purposely chose the kinds of data we use to predict expected returns over the next one year.

Part of our decision process is to look at the return we expect to get from a company and then decide whether the cost of getting into it and out of it justifies that expected return. Thatís a tradeoff that is much more important in less liquid segments of the market like small cap. We are cautious with getting in and out of securities, and need to have much more conviction in their potential returns when we do trade stocks.

Q: Can you give some examples to illustrate the process?

A: Airlines would be a good example. In the course of 2015, airlines saw a big move in oil prices and the sentiment around travel. Early in the year, a handful of airlines looked relatively attractive from a fundamental standpoint. Both the discounts relative to industry peers and their profitability along with the quality of their operations were attractive. Yet, on the sentiment side and the quantification of themes and trends there was not much that seemed compelling, and so we didnít really take any large positions.

Oil prices steadily decreased in mid-2015, and as a direct consequence we saw sentiment around these particular airlines change significantly. The news articles were a lot more bullish and the analysts were much more favorable. The marketís expectation of the segment and the themes and trends they were exposed to were much more bullish. So now coupled with decent fundamentals and the technicals of sentiment and themes and trends looking much more attractive, the expected returns started to look quite a bit higher.

A few more months later, however, Ebola became a large concern, and the net effect of Ebola and lower oil prices led to a more modest expectation of airlines going forward. Travel was going to come under pressure, as fewer and fewer people were going to feel comfortable traveling around the world. So we were a little more tempered in our positioning in airlines, because their expected returns were a little more modest. But the Ebola concern only lasted a few weeks and the lower oil prices were the dominant driver of sentiment, so we finished the year off pretty well, with our set of airlines being a reasonable contributor to the portfolioís expected return.

Thatís an example of how reevaluating our investment options almost daily, deciding whether they look attractive or not, aligning portfolios to what investors care about today, and making sure we are buying companies that are fundamentally attractive and that the investment public is excited about, plays in and helps us.

Q: How do you identify turning points?

A: One way is that for each data set we decide on the right horizon to look at in terms of aggregating what the data are saying. As a simple example, if a news article about a company today is negative, or is different from what it was yesterday, is that enough for us to act? Or do we want to see a more prolonged, consistent, meaningful change in sentiment around a company?

So each data set or signal we create is somewhat optimized to most effectively predict the one-year return. In some cases we look at very small and quick changes in information, and in some cases we look for much longer pervasive changes in what the data is telling us. On each and every data item, itís a matter of assessing whether a day-over-day or a month-over-month or a trend over three months is useful in predicting one-year return. Generally we find that on the sentiment side we need to look at a three-month change in a particular sentiment data source before it can be useful in predicting one-year return. That decision is made data source by data source.

At the heart of our portfolio construction process we use an optimizer to help us identify which stocksí expected returns have fallen enough that we can replace them with different stocks, generally in the same industries or in sectors whose expected returns are quite a bit higheróhigh enough to justify the cost of the trade. For instance, if we owned stock A and bought it when the expected return was 3% and now itís 1%, and there is stock B in the same industry and sector whose expected return is 5%, we would analyze if that is a good trade to make.

The decision process would mostly revolve around two questions. What is the cost of selling A and buying B, and what are the risks associated with owning B versus owning A? We generally make a set of tradeoffs in our portfolio construction process to rebalance the portfolio once a week. So while we reevaluate and recalculate expected returns of companies daily, itís not really until every week that we go in and reevaluate what we own and donít own, and use an optimizer to help us make these complex trade-offs while being as cost-effective and customized as possible.

In the small-cap space, where cost is such a paramount consideration, being cautious and only trading when there is a substantial improvement in expected return net of cost is part of how we structure our portfolio construction approach. Our proprietary expected return on the stock is driven by data that we think captures whether a company is high quality, cheap, has a positive sentiment, or benefits from themes and trends. But ultimately itís what we think is or isnít going to do well.

Q: What do you consider a good expected return?

A: Our expected return calculation estimates are relative to the market. We think itís sort of indifferent to our process whether the market is up 30% or 3%, or down 10%. Our expected return estimates tend to fall in the range of +/- 10% versus the market.

Q: What is your portfolio construction process?

A: In our portfolio construction we take modest active weights on industries and sectors; the majority of the return comes from selecting specific stocks, not sectors or industries. So we were not going to go all-in on airlines, but certainly airlines looked a little more attractive than others, and we took some small overweight positions in them in mid-2015.

We also moderate how much of a stock we buy relative to the Russell 2000 Growth Index. We do lots of analysis on lots of companies to make sure we end up with attractive companies in the portfolio. The key is to diversify the portfolio and limit single stock concentration.

The same goes for the sector / industry side. We donít want any one sector or industry to have a material overweight or underweight of more than 3% versus the benchmark, so we put guard rails on from the risk standpoint and make sure we target a beta of 1 relative to the Russell 2000 Growth Index.

We put these guard rails on risk, single stock concentration, sector or industry exposure, and beta. Then the optimizer also helps us from a risk and concentration standpoint in terms of names to sell or buy as a replacement, factoring in the risk and cost of buying and selling each stock. We also have daily estimates of the bid-ask spreads, commissions, taxes, fees and things like that.

Typically, we end up owning roughly 20% of the benchmark of the Russell 2000 Growth Index. Since the benchmark has around 1,200 stocks in it, about 20% of that would be 250 stocks. Right now we have closer to 300 stocks.

We monitor our portfolios daily and rebalance our small cap growth strategy approximately once a week. We assess if we should reduce or increase our position in stocks to improve the expected return of the portfolio subject to a set of risk considerations, like tracking error. Our strategy runs at a tracking error target of 3.5% relative to the Russell 2000 Growth Index, so we use risk models to quantify the tracking error of the portfolio and make sure itís within 3.5%. The turnover of the portfolio on an annualized basis ends up being about 125%.

Q: What is your sell discipline?

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