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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.”
Q: What is the history of the fund?

A: Quantitative Investment Strategies has been part of Goldman’s Asset Management business since 1989, and I joined the team in 2005. The team started to invest using quantitative techniques that were being pioneered in academic institutions around the world. Behavioral economists and financial researchers were identifying explicit characteristics of stocks (called factors) that were useful in predicting future stock returns.

Examples of those characteristics were factors such as ‘value’: value stocks tend to outperform growth stocks. Our investment business was focused on creating portfolios that invested based on these types of factors.

We started managing large-cap equity strategies in the late 1980s, before getting into managing small-cap strategies using similar data-driven models in the late 1990s (we launched our first U.S. small cap mutual fund in 1997). The Goldman Sachs Small Cap Growth Insights Fund was launched in 2007, driven by client need to have a specific small-cap growth option and a specific way of investing in the small-cap growth space using more quantitative models.

In terms of the broader set of assets, the Quantitative Investment Strategies group manages approximately $100 billion in assets. Of that about $20 billion is managed by the team that I am a part of, our quantitative equity team. We manage about $1 billion in U.S. small-cap strategies and approx. $2.5 BB in ex-US (international) small-cap strategies. The Small Cap Growth Fund has over $300 million in assets.

Q: How do you define your investment philosophy?

A: The core beliefs that guide our investment thinking, have not changed over 30 years. We are data-driven. We use a very clear, transparent set of criteria to identify stocks and then evaluate whether they are attractive or not.

Until about 10 years ago, quantitative analysis utilized mostly financial-statement-based metrics, such as calculating the price to book ratio of companies, the return on equities, and perhaps the stock price movements. However, in the past 5 to 10 years we have been able to analyze much larger data sets on companies. This allows us to better understand what’s going on inside companies (their operations, intra-quarter sales, etc.) but what is also going on in markets that these companies are in (like themes/trends in the broader market). Our computers can process unstructured or text-based data sets, volumes of information, to identify which companies look attractive. But our philosophy of investing has not changed; it has always been very data-driven.

Conceptually, we identify companies that are cheap, growing and high-quality; as well as ones that are benefiting from positive themes and trends in the market; and that have the right amount of public sentiment around them. But we believe the kinds of data that are available and the way we can use that data objectively has really given us a competitive advantage.

Our goal is to identify attractive stocks that differ from what the rest of the market thinks is obvious and attractive, because we use unique alternative data and a more quantitative approach.

Q: What is unique about investing in small-cap companies?

A: The number-one factor that makes investing in small-cap companies interesting is that this asset class is clearly a little less efficient than, for example, the U.S. large-cap segment. There’s less information available on small-cap companies, fewer analysts covering them, fewer newsletters being written about them.

But the fact that the small-cap space has a little less traditional research, that it is a little less efficient, offers us greater opportunities to drive alpha from an active management standpoint. It actually works to our advantage; it gives us a bit of an informational edge.

Another important aspect of the small-cap space is the breadth of the universe: there are a lot of small-cap opportunities. This breadth allows us to diversify our portfolios across lots of companies that we find attractive. The broad size of the small-cap universe is often considered a challenge by many traditional investors but for data-driven quantitative investors it is actually an attractive quality.

A third aspect that defines the small-cap segment is that it is a slightly less liquid segment of the market. That matters to us most in deciding upon and quantifying the true net-of-transaction cost benefit of buying or selling each company.

Q: What is your investable market capitalization range?

A: This is a small-cap growth fund, so the benchmark to beat is the Russell 2000 Growth Index, but we allow ourselves to buy any stock in the Russell 2000 Index. If we think there is an opportunity there, for us it is a good way to drive outperformance. Having said that, we still ensure that our portfolios have characteristics and attributes that are very similar to the Russell 2000 Growth index even though we may invest in some stocks that are outside of the index.

At the high end some companies are almost in the $5 billion range, but the vast majority of the stocks in our investment universe are in the range of $500 million to $4 billion or $5 billion.

Q: Would you describe the pillars of your investment strategy?

A: We analyze all stocks in the Russell 2000 Index. So, there are about 2,000 stocks in our investment universe, and every single day we re-estimate and re-calculate what we think their expected return is going to be over the next year relative to the market. To do that estimation of expected return we look at a large set of data on those companies, generating an explicit view on every company in the universe. We want to have a very robust and complete set of data on all the companies in order to forecast their returns.

The metrics we look for fall into four broad categories. First, we want to identify whether or not a stock is trading at a discount to its industry peers. So we do a bit of valuation analysis, like using traditional financial statement data to build bottom-up value models, or looking at some different unique alternative data sources that we think are useful in better forecasting company valuation. The end goal here is to identify if the stock is trading at a discount.

The second pillar is an assessment of a company’s quality, its profitability, and its fundamental strength. The goal here is to identify high-quality companies that are stable and well-capitalized but also that are gaining market share, are profitable, and will offer a surprise on the upside when they announce their earnings. Here, too, we use a whole host of traditional financial statement data to run some of these calculations, but we also use other data sources to enhance our return predictions. For example, over the past two years we have been using web traffic data, looking at how many people are on the company website and how much time they are spending there to help us quantify a good or growing business that is going to surprise on the upside from an earnings standpoint.

The third pillar is sentiment, what people think about a company. We do that analysis with proprietary tools and technologies we have built, where our computers read news articles and earnings call transcripts to see what a company’s management is saying about itself, or analyst research reports to see what they are saying about a company. A whole host of machines are reading unstructured data, coupled with analyses of market variables like credit default swap spreads, options data, short interest data, to see whether people are shorting the stock or not. And all that helps us form an opinion on what the sentiment is about the company overall.

And finally, the fourth pillar is an analysis of themes and trends to which a stock is exposed—looking at a company’s own stock price movements and those of its customers, its suppliers and competitors and partners, and companies engaging in similar types of businesses around the world. There is a whole family of metrics that tries to capture whether a company is benefiting from positive themes and trends in the stock market.

Those last two pillars—sentiment, and themes and trends—are more technical in nature, while the first two—valuation, and assessment of a company’s profitability and quality—are more fundamental in nature. Overall we use a very large set of characteristics of companies in those four categories to rank and evaluate companies, and then to forecast their expected returns, every single day.

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Sources: Data collected by 123jump.com and Ticker.com from company press releases, filings and corporate websites. Market data: BATS Exchange. Inc