Harnessing Behavioral Factors in the Investment Process: Behavioral Factors for Picking Equity Managers and Stocks
Behavioral finance is sweeping through the financial services industry. Financial advisors are the furthest along, introducing these concepts into their practices, including needs-based planning, outsourcing non-core activities such as investment management, and creating a reassuring behavioral experience for clients.
The advisors who have made this transition, according to a recent report by Cerulli Associates, grow faster and end up with wealthier, more loyal clients.1 Such a transition allows advisors to provide client value relative to their robo-advisor and index fund competition, as confirmed by Vanguard, Russell Investments, and others.2 Howard and Bunker (2018) provide a blueprint for transitioning to this type of advisory model.
Active equity investment managers face a challenge not unlike that faced by advisors, with large outflows over the past 10 years, roughly matched by low-cost index fund inflows. Active equity must deliver value relative to index offerings. I believe the best way to accomplish this is to harness behavioral factors in each aspect of the investment process, similar to how successful advisors have incorporated behavioral factors throughout their practices. Some of the material in this article is drawn from Howard and Voss (2019).
Behavioral Factors in Active Equity Selection and Evaluation
The goal of active equity management is to generate returns for investors that exceed the returns earned by simply and cheaply investing in an index fund. To many it seems logical to select funds that have achieved this goal in the past. Virtually everyone, professional or individual, relies heavily on a fund’s past performance when deciding upon the fund in which to invest.
The problem is that past performance is a noisy, often misleading, predictor of future performance. Every investment product is required to disclose exactly this: Past performance is not indicative of future performance. Reams of academic research confirm this to be the case.
But despite this overwhelming evidence, past performance is the primary criteria used for picking funds. This is an example of the representativeness bias, where decisions are made based on characteristics that have little or no predictive power but are emotionally appealing. And this bias is nowhere stronger than it is in investment markets.
The lack of predictive power is the consequence of two drivers, one an industry structure and the other the nature of investment returns themselves.
Closet Indexing Factory
Importance of Positive Return Skewness
Fund performance relative to its benchmark displays positive skewness as well. Kaplan and Kowara (2018) dramatically demonstrate this. Based on a worldwide sample of 5,500 active equity mutual funds, they find two-thirds outperform their benchmarks, gross of fees, over the 15-year period of 2003 through 2017. But the most surprising result was that the typical outperforming fund underperformed for a period of 9–12 years within this 15-year sample period. This means that the outperformance takes place in only three to six years out of a 15-year holding period.4
Kaplan and Kowara (2018) found mirror results for the typical fund that underperformed during this 15-year time period: it outperformed for a period of 9–12 years. They conclude that three-, five-, and 10-year performance is an unreliable predictor of a fund’s long-term performance and should not be used. The positive skewness that is so important for long-term fund performance is hidden for extended periods and the benefit of it can only be harvested with long holding periods.5
The evidence is clear: Past performance is an unreliable predictor of future performance. It turns out that fund manager behavior is predictive of future performance where past performance is not.
Manager Behavior and Strategy Consistency
An important fund manager behavior is the consistent pursuit of a narrowly defined strategy. The challenge is how to measure consistency. A common approach is to demand consistent returns over time. But I showed above that the best funds outperform at times and underperform at others. This is emotionally difficult for investors, but it is an unavoidable fact when investing in successful active equity funds because strategies don’t perform well in all kinds of markets.
Strategy consistency can be measured by focusing on the type of stocks in which a manager invests. For example, is a value fund invested in value stocks, or is it chasing an unrelated trend such as favoring growth stocks?
Using a top-down process, I like to evaluate the consistency of managers by comparing their holdings to other managers pursuing the same self-declared strategy. A pool of stocks most held by these strategy managers is created, referred to as one’s own-strategy stocks. For a manager following a valuation strategy, for example, the pool comprises stocks most held by other valuation funds.6
It makes intuitive sense to use a screen driven by those who are looking for similar stock characteristics. It is worth noting that strategy stock pools are in constant motion, because managers make buy and sell decisions based on ever-changing economic and market conditions. Unlike the fixed size or value boxes of the style grid, this produces a dynamic process in which stocks of most interest to the manager are changing constantly. That is, the best results are obtained when the investment team moves about the equity universe in pursuit of own-strategy stocks.
This is in stark contrast to an externally imposed style box that requires funds to limit their choices to a range of value or size in stocks.
Focusing on similar strategy stocks is logically appealing and it leads to better fund performance. Active equity funds holding the most similar strategy stocks (figure 1, quintile 5) outperform those holding the least similar strategy stocks by 212 basis points (bps). This confirms the advantage of focusing on stocks most held by others following the same strategy. The collective intelligence of active equity fund managers provides valuable information for identifying the most attractive pool of stocks upon which to focus.
FIGURE 1: U.S. ACTIVE EQUITY MUTUAL FUND AVERAGE ANNUAL ALPHA BY STRATEGY CONSISTENCY QUINTILE
Strategy consistency is measured as the percentage of own-strategy stocks held by the fund. Sample includes U.S. equity funds from 1997–2017, resulting in 288,000 fund-month observations. Sources: Morningstar and AthenaInvests.
The strategy-consistency results reported in figure 1 are in stark contrast to what has been uncovered regarding style-box consistency. Wermers (2012) finds that equity funds experiencing the largest style drift outperform those with the least style drift by about 300 bps. Asking fund managers to stay style-box consistent hurts performance because it forces them to invest in stocks outside their own strategy simply to track the style benchmark. Style-box consistency begets strategy inconsistency and, in turn, hurts performance.
High Conviction Stocks
Equally important is to allow managers to focus on their best idea stocks, also referred to as high-conviction stocks. One of the more interesting papers to demonstrate this is Cohen et al. (2010), who find, “… the U.S. stock market does not appear to be efficiently priced, since even the typical active mutual fund manager is able to identify stocks that outperform by economically and statistically large amounts.” These results, presented in figure 2, are based on the performance of the typical manager-ranked best ideas, as determined by relative portfolio weights. Figure 2 reveals that the best idea generates an average 10-percent annual alpha. Performance declines monotonically to around a 2.5-percent alpha for the 10th best idea stock. Thus, equity managers are superior stock pickers, can rank their best ideas, and generate large excess returns on their top stock picks.
FIGURE 2: STOCK SUBSEQUENT MONTHLY (BPS) ALPHA BY PORTFOLIO RANK
Based on Graph 3 in Cohen et al. (2010). Graph shows the average (blue line), and one standard deviation below (red line) over the subsequent quarter, six-factor risk-adjusted annual alpha for the most overweighted stock in a mutual fund portfolio, the next most overweighed, and so forth. Based on all active U.S. equity mutual funds from 1991 through 2005.
The relative weight methodology of Cohen et al. (2010) above can be used to rank the return potential of each stock held by an active equity mutual fund. The impact on a fund’s portfolio return of investing in highly ranked stocks (i.e., high-conviction stocks) is shown in figure 3. If a fund commits an additional 10 percent to its top 10 stocks, portfolio return improves by 61 bps. In turn, an additional 10 percent committed to the next 10 highest-ranked stocks bumps up portfolio return by 23 bps. However, if more is committed to stocks ranked lower than 20, fund returns decline, as indicated by the right-most bar in figure 3.
FIGURE 3: IMPACT OF FUND ALPHA OF INVESTING IN HIGH-CONVICTION BEST IDEA STOCKS
Based on single variable, subsequent gross fund alpha regressions estimated using a dataset of 44 million stock-month equity fund holdings over the period January 2001 to September 2014. Sources: Morningstar and AthenaInvest
As demonstrated above, there is a performance advantage if a fund invests exclusively in high-conviction stocks. However, the typical mutual fund holds 75 stocks (the median number of holdings) and is thus badly overdiversified, investing in three times more alpha-destroying stocks than alpha-building stocks. This provides further support for the argument that active equity funds should not grow too large (no larger than $1 billion in AUM) nor be asked to minimize tracking error, style drift, and volatility—all of which encourage investing in non-high-conviction stocks.
In practical terms, pick six to eight funds, each of them pursuing a different strategy, smaller (less than $1 billion in AUM), experiencing larger tracking error and style drift, and holding a smaller number of stocks (fewer than 30). As a secondary criterion, pick those funds with strong one-year returns. This is the only performance measure that has been shown to be predictive of future performance, no doubt due to the well-established existence of short-term momentum in equity markets.
Behavioral Factors in Stock Picking
Behavioral Price Distortions
The Arbitrage Challenge
Limits to Arbitrage
Hardnosed Buyer, Emotional Seller
Emotional selling decisions are a major problem for individual and professional investors alike. Essentia Analytics has studied manager sell decisions and finds they are affected by recent market movements, to the detriment of portfolio returns. They also find that there is a cycle in which a stock’s alpha reaches a peak and then declines well before the stock is sold. It is hard to usher a family member out of the portfolio (Woodcock et al. 2019).
Take the emotions out by developing an objective selling rule, preferably before the stock is even purchased. This reduces the many cognitive errors surrounding this decision and leads to improved fund performance. The goal is to become as hardnosed about selling as about buying.
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Behavioral Viewpoints features new topics each month which are intended to help advisors and investors gain a deeper understanding of how behavior shapes the investing landscape.
1. See “U.S. Advisor Edge: Customer Value (q3, 2019)," www.cerulli.com for more information.
2. See “Quantifying Your Value to Clients,” Vanguard (2016), https://advisors.vanguard.com/iwe/pdf/FaSqaaaB.pdf and “Five Key Ways Advisors Deliver Value in 2019,” Russell Investments (2019), https://russellinvestments.com/us/blog/five-key-ways-advisors-deliver-value-in-2019
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