Humans have evolved to learn from our experiences. We are so good at this that we extrapolate recent experience to predict the future—unfortunately, often to our peril. Behavioral finance scholars refer to this phenomena as "recency bias"—our tendency to overweight recent events when making decisions about the future.
Harvard economist Andrei Shleifer has written extensively on this subject with what he calls the "extrapolation bias". In a paper he co-authored with Nicola Gennaioli and Yueran Ma titled, Expectations and Investment, Shleifer explains that investor expectations of future stock market returns are extrapolative: "They are high after a period of high market returns, and low after a period of low market returns." Shleifer went on to examine extrapolation bias in corporate decision making, using the DUKE CFO Magazine Business Outlook Survey. He found that, like investor expectations, CFO expectations for corporate earnings and spending decisions were also highly correlated with past stock market returns. In his book, Inefficient Markets, Shleifer summarizes thirty years of behavioral finance neatly stating, "People do not deviate from rationality randomly, but rather, most deviate in the same way...Investor sentiment reflects the common judgement errors made by a substantial number of investors, rather than uncorrelated random mistakes." That is to say, we extrapolate and herd.
In unrelated social science work, University of Pennsylvania professors, Phil Tetlock and Barbara Mellers, conducted a multi-year US Intelligence Advanced Research Project Activity (IARPA) funded study of decision making, aptly named The Good Judgement Project (GJP). From 2011 through 2014 the GJP team studied amateur and professional forecasters (US Central Intelligence Agency analysts) to learn what methods and traits separate poor forecasters from superforecasters. Researchers found that superforecasters were not more intelligent than their counterparts, but they did independently follow a basic set of rules that typically led to their outperformance. These rules included using a "base rate", or "reference class", when making a forecast. That is, before making a prediction, superforcasters study similar past situations and their outcomes to counter story-oriented thinking that may otherwise sway the herd. Further, Tetlock and Mellers also found that forecasts structurally improved the more participants were held accountable for their forecasts. They saw the biggest performance improvement when study participants were told that the GJP would keep track of their forecasts and be made visible to other participants. This gave participants more incentive to provide the most accurate forecasts.
Expectations vs. the reality of emerging markets
Inspired by the Shleifer paper, we sought to test the relevance of his analysis in our market. Using the well-known Institute for Supply Management's (ISM) Manufacturing Report on Business New Orders survey as a rough proxy for business expectations in the United States, we investigated how useful this broad survey of economic expectations has been to predict returns for emerging market debt (Exhibits 1 and 2). It turns out that the ISM is highly correlated with 12-month trailing returns and negatively, or weakly, correlated with 12-month future returns.2 After a period of high positive asset returns, survey participants forecast a continuation of good economic outcomes that should not be interpreted to equate to good investment outcomes. More than likely, following a period of high positive asset returns, current prices are well above fair value; making the expected future returns negative.
2We do not believe ISM survey respondents are overly influenced by emerging market debt returns, however we think the historically high correlation between US equity returns (a likely focus for US executives) and Emerging Market debt returns makes the logical link.
Exhibit 1: ISM vs. 12-month trailing spread returns
Source: Bloomberg, JPMorgan and Aegon AM US as of 12/18.
Exhibit 2: ISM vs. 12-month future spread returns
Source: Bloomberg, JPMorgan and Aegon AM US as of 12/17.
Our investment process
As humans, we recognize recency bias may influence our decision making. Therefore, we have honed our investment process with lessons from behavioral finance in mind. We conduct original research that often results in proprietary economic forecasts which we track and compare to the market. We also study similar past episodes of apparent market mis-valuation to establish what we think is a reasonable estimate of fair value given a similar set of circumstances.
At Aegon AM US, we subscribe to two core beliefs: Markets overreact and, consequently, asset prices deviate from fundamental value. Using the behavioral science language introduced above, we believe investors systematically extrapolate recent fundamentals and asset returns, potentially creating opportunities for value investors who can reasonably estimate a base rate before making investment decisions. We are value-oriented investors aware of the effects caused by certain behavioral traits many investors fall victim to and it is our goal to take advantage of these opportunities.
Gennaioli, Nicola, Ma, Yueran, and Schleifer, Andrei. "Expectations and Investment," NBER Macroeconomics Annual 2015, vol. 30. Eichenbaum and Parker, 2016.
Schleifer, Andrei. Inefficient Markets: an Introduction to Behavioral Finance. Oxford University Press, 2004.
Tetlock, P. E., & Mellers, B. A. "The Good Judgement Project: A large scale test of different methods of combining expert predictions." The University of Pennsylvania, 2012.
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