Factors form the basis of performance. Just as an interviewer might consider “work experience” or “technical abilities” factors in a job interview, an investor might consider “value” and “momentum” as factors in a security or a portfolio.
Companies are assessed on how attractive they are based on one or more factors, and then ranked against other firms. Higher ranked companies may indicate a greater opportunity for alpha.1
Smart Beta2 strategies
Smart beta investing seeks to derive return from risk premia3 in the market; smart beta factors tend to be well known and easier to implement.4 For example, the “momentum” factor is well known and is based on the belief that stocks that have recently increased in price may continue to increase in price due to the bandwagon effect.
In comparison, equity alpha strategies typically seek to generate an informational advantage by utilizing various datasets to help identify securities that are priced too low or too high, and then buy or sell based on that information. For instance, quantitative investors seeking equity alpha could incorporate credit card data to potentially better predict sales growth before the market can price it in.
A thesis starts off with an idea, such as “Stocks that have recently increased in price will continue to increase in price due to the bandwagon effect.” Researchers will then aggregate relevant datasets—both traditional (e.g., stock prices, valuation metrics) and alternative (e.g., analyst reports, credit card data)—and analyze the raw data to form an investment view. The performance of the factor is evaluated for potential usefulness and significance.
It takes a team of investment professionals to devise theses, make decisions and implement factor investing strategies. From the advanced statistical analysis to the optimization of portfolios, to the understanding of clients’ needs, factor investing requires expertise. Explore Quantinomics™ to learn more about how GSAM uses factor investing to help pursue our clients’ investment objectives.
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