Malcolm P. Baker, Robert G. Kirby Professor of Business Administration, Harvard Business School, and director of research, Acadian Asset Management LLC, Boston, finds a puzzle in the lack of arbitrage between high- and low-risk stocks.
An “interesting empirical fact both in the U.S. and international (market) data is that low-risk stocks have tended to perform better than you would expect given their risk profile,” Mr. Baker said in an interview. “They really do reduce risk, but the average returns are equal to or higher than higher risk portfolios.”
Acadian is incorporating that low-risk anomaly as a managed volatility strategy in its portfolio management, Mr. Baker said.
In terms of measuring risk to arrive at the conclusion, Mr. Baker said, “There have been academic papers that have done it in various ways, either with standard deviation or with beta. They are reasonably correlated with each but they produce slightly different portfolios.”
“Risk and return aren't that related,” appearing as a more flat than inverse relationship, Mr. Baker said.
A paper that contributed to the application of the idea to investment management is “Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly,” in the January/February 2011 Financial Analysts Journal. It is co-authored by Mr. Baker and Brendan Bradley, Acadian director of managed volatility strategies, and Jeffrey Wurgler, the Nomura professor of finance, Stern School of Business, New York University; research associate, National Bureau of Economic Research; and Acadian senior consultant. Their article received a CFA Institute Graham and Dodd Scroll Award in 2011.
In their paper, the co-authors wrote, “Among the many candidates for the greatest anomaly in finance, a particularly compelling one is the long-term success of low-volatility and low-beta stock portfolios. Over 1968-2008, low-volatility and low-beta portfolios offered an enviable combination of high average returns and small drawdowns. This outcome runs counter to the fundamental principle that risk is compensated with higher expected return.”
Their study, as reported in their article, “applied principles of behavioral finance to shed light on the drivers of this anomalous performance and to assess the likelihood that it will persist.”
“(S)ome market participants are irrational” in some ways, and biases and overconfidence “lead to a demand for higher-volatility stocks that is not warranted by fundamentals,” pushing up price and driving down expected return, while the reverse occurs for lower-risk stocks, the co-authors wrote.
Their study examined why the “smart money” of sophisticated institutional investors “does not offset the price impact of any irrational demand” and overweight in low-volatility stocks. Market-capitalization-weighted benchmarks typically used by institutional investors “discourage investments in low-volatility stocks.”
In the interview, Mr. Baker said, “You have these institutional and behavioral effects combined with limits to arbitrage. That is a theoretical framework. That then gives you a template for thinking about how securities can be mispriced and what sorts of data might be useful in identifying patterns of stock returns.”
“And that's the big area of research on the alpha side in terms of predicting stock returns,” he said.
Among other investment techniques inspired by academic research, Mr. Baker said, “What's on the cutting edge ... (is) text analysis.”
Text analysis extracts information from text, whether it is in 10-K filings, newspapers or social media, Mr. Baker said. Using text analysis can contribute to predicting “short-term returns in ... individual securities or the market as a whole,” Mr. Baker said.
Acadian uses text as part of “multiple factors in explaining returns,” Mr. Baker said. “The goal is to convert the text into something that is a score, effectively” quantifying it.
It analyzes text for content that might contribute to informing investment decisions.
In terms of interpreting the content of a few articles, “you'd probably be able to do that better than a computer,” Mr. Baker said.
But Acadian's text analysis seeks to extract information from text on a large scale from thousands of articles every day, Mr. Baker said.
