Unique and Alternative Data
Goldman Sachs International Equity Insights Fund
Author: Ticker Magazine
Last Update: Nov 13, 9:29 AM EST
|Investment managers are becoming increasingly capable of processing vast amounts of qualitative and quantitative data with advances in computing technology. Osman Ali, portfolio manager of the GS International Equity Insights Fund, benefits from the consistent input of a dedicated quantitative team in systematically analyzing constantly changing information in order to detect inflection points and make informed, forward-looking decisions.
“Our philosophy is to remove bias from the investment process by being extremely data driven and disciplined.”
By putting these four components together, the model identifies the companies most attractive to us: good businesses that are financially secure, that are growing but cheap, that have positive sentiment, and that are exposed to strong themes and trends in the market. There is no event-based data; rather, a set of metrics and characteristics updated every day helps us evaluate the prospects of each investment opportunity
Q: What is your research process?
Across all strategies, we look at over 13,000 companies around the world. Our investment universe for this fund includes the approximately 1,000 stocks in our benchmark, the MSCI EAFE Index.
Our research process begins by creating an expected return forecast for companies. It measures the expected returns relative to a stock’s industry peer group in a particular region – so, for example we’re trying to identify the most and least attractive companies within the European technology sector. This intra-industry, intra-region comparison means we’ll never face a scenario where the most attractive companies out of the 1,000 in the EAFE index are all Japanese utilities.
The next part of the process is based on our philosophy of building a diverse portfolio by investing across various countries and picking the right stocks in the right sectors. Because picking stocks is where the majority of our return comes from – and what the model was created to facilitate – we put guard rails on how much our weights can deviate from the benchmark with respect to sector, industry, and country.
Q: Would you describe your portfolio construction process?
On average, the fund has about 200 stocks, though that number sometimes rises during periods of higher market volatility or falls when volatility is lower.
To maximize excess return while keeping tracking error within our desired range, we use an optimizer to create ideal portfolio weights. Every three days, the optimizer measures each company’s return forecast, risk forecast, and cost forecast. It then decides which companies to overweight and underweight. Stocks that are especially attractive get a high active weight, but if they are risky or costly, that weight may be moderated.
We do have discretion in setting active deviations away from the benchmark, which may change from time to time vs. the benchmark weight. They tend to be in the range of plus or minus 2% – meaning stocks we love may be as high as 2% overweight, while unattractive ones might as much as 2% underweight. Most of our holdings fall in the middle of that range and are typically not near either limit.
Maximum sector and industry positions tend to be about plus or minus 4% the benchmark, but these are revisited from time to time. Country deviations might be larger than 4%, because we feel our country selection is a value-add. However, the portfolio remains one where the majority of active risk comes from our stock selection.
If a stock starts to get risky or its excess return forecast begins to fall, we might decide to replace it with another company in the same industry or sector – keeping the portfolio’s risk profile the same – but will only do so if the cost benefits of the trade also make sense.
Q: How do you define and control risk?
To beat the MSCI EAFE Index, we have to make measured bets against it every time we overweight or underweight a stock, sector, industry, or country – so for us, the most important risk consideration is tracking error, or active risk versus the benchmark. Ours is a modest 200 to 300 basis points on average. It’s such a crucial factor that we don’t think of the portfolio in terms of the number of stocks we own, but instead as the number of bets we’ve made versus the index (stocks that we don’t have a position in, but are benchmark constituents do also add to the portfolio’s tracking error).
We also have our own risk model that focuses on the active risk we take versus the active return we deliver. It’s paramount that we ensure that the ratio of excess return over active risk – or information ratio – is as high as possible. This means we’re getting the greatest amount of excess return while minimizing the excess risk needed to do so.
Q: What lessons did you learn from the financial crisis?
Although 2008 was a landmark moment, 2007 was a period of challenging performance for many quantitative investors. Back then, many of them were using the same types of models, data, and techniques to build portfolios. This worked fine until they all started selling at the same time, which resulted in liquidity-driven derisking and underperformance.
The most important lesson we learned from this is that active managers can best achieve an informational advantage by using unique and alternative data – which is why we’ve fixated so much on these types of data that go into our model.
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