The Dangerous Education Gap

A large body of research on the behavior of individual investors has demonstrated that low levels of financial knowledge, in addition to biases in the selection and processing of information, drive suboptimal financial choices.

Among the findings from the literature are:

  • Men tend to be more financially literate than women, independent of country of residence, marital status, educational level, age, income and their possible role as decision-makers.
  • Women tend to be less confident than men, though they are more aware of their own limits. This is a positive finding, because overconfidence, which may lead to excessive risk taking, excessive trading and lack of diversification, is negatively related to performance. The result is that women outperform men. Note that it has been found that higher levels of education may lead to higher levels of overconfidence. (Any financial advisor who has worked with doctors would confirm this!)
  • Degree of financial knowledge tends to be positively correlated with education and wealth.
  • Individuals with a high degree of financial literacy tend to exhibit a higher risk tolerance and a higher degree of patience, as well as greater willingness to spend time acquiring financial knowledge.
  • Financial experience, as measured through the ownership of financial instruments and the holding period length of risky assets, is positively associated with financial knowledge.
  • Regret aversion (coupled with low literacy) may deter the demand for professional help if individuals anticipate the possibility that advisors will highlight mistakes in their previous decisions.

Monica Gentile, Nadia Linciano and Paola Soccorso contribute to the literature on investor education and behavior with their March 2016 working paper, “Financial Advice Seeking, Financial Knowledge and Overconfidence: Evidence from the Italian Market.”

The authors write: “The effectiveness of both investor education and financial advice may be challenged by individuals’ behaviours and reactions. Unbiased financial advice can substitute for financial competence only if unsophisticated investors seek the support of professional advisors. Furthermore, advice may not reach overconfident investors deciding on their own on the basis of self-assessed rather than actual capability. Conventional financial education initiatives may exacerbate overconfidence and/or other biases distorting further investors’ decision-making process.”

Drawing on data from more than 1,000 Italian households, the authors analyzed the relationship between investors’ propensity to seek professional investment advice, financial knowledge and self-confidence, as well as the determinants of financial knowledge and self-confidence.

Following is a summary of their findings:

  • The majority of individuals exhibited a very low degree of financial literacy. For example, almost half of respondents weren’t able to describe inflation, 55% incorrectly defined risk diversification, 57% did not correctly define the risk/return relationship, 72% were not able to compare investment options across expected returns and 67% showed insufficient understanding of simple interest rates.
  • Nearly 45% of investors preferred informal advice (that is, consulting relatives, friends and colleagues) to professional advice.
  • The authors, in general, found a lower level of financial knowledge among women, resulting in a gender gap.
  • Loss aversion was quite widespread. Overall, 55% of respondents weren’t willing to take financial risk, implying a chance of loss and 17% would disinvest after even a very little loss. However, financial knowledge is positively related to risk aversion. The more financial knowledge someone has, the less risk averse that investor tends to be.
  • Financial literacy positively affected financial advice seeking. The higher the level of financial literacy, the more likely it is that professional advice will be sought.
  • Financial knowledge was negatively related to high levels of investor self-confidence. Less knowledgeable investors were more confident of their skill (skill they do not, in fact, have). Overconfidence, in turn, discourages demand for advice (by the very people who need it the most).
  • Overconfidence was prevalent. For example, among individuals reporting an understanding of basic financial products equal or higher than the average person, 30% weren’t able to correctly define inflation and 44% couldn’t solve a simple-interest problem.
  • Financial advice acts as a complement rather than as a substitute of financial capabilities.

Gentile, Linciano and Soccorso concluded that their results confirm “concerns about regulation of financial advice being not enough to protect investors who need it most.”

They continue: “Additionally, our findings suggest that investor education programmes may be beneficial not only directly, i.e. by raising financial capabilities, but also indirectly, i.e. by enhancing people’s awareness of their financial capability and by hindering overconfident behaviours and behavioural biases. This latter outcome mitigates the worries about financial education fueling confidence without improving competence, thus leading to worse decisions.”

Summary

One of the great tragedies is that most Americans, having taken a biology course in high school, know more about amoebas than they do about investing. Despite its obvious importance to every individual, our education system almost totally ignores the field of finance and investments. This remains true unless you attend an undergraduate business school or pursue an MBA in finance. Without a basic understanding of finance and markets, there’s simply no way for investors to make prudent decisions.

Making matters worse is that far too many investors think they know how markets work, when the reality is quite different. As humorist Josh Billings noted: “It ain’t what a man don’t know as makes him a fool, but what he does know as ain’t so.”

The result is that individuals make investments without the basic knowledge required to understand the implications of their decisions. It’s as if they took a trip to a place they have never been with neither a road map nor directions. Lacking formal education in finance, most investors make decisions based on accepted conventional wisdom—ideas that have become so ingrained that few individuals question them.

And some spend far more time watching reality TV shows than they do investing in their own financial literacy. Given the important role that financial literacy plays in achieving financial goals, this is dangerous behavior.

 

 

This commentary originally appeared September 26 on ETF.com

By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party Web sites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them.

The opinions expressed by featured authors are their own and may not accurately reflect those of the BAM ALLIANCE. This article is for general information only and is not intended to serve as specific financial, accounting or tax advice.

© 2016, The BAM ALLIANCE

Research on Luck versus Skill in Mutual Fund Performance Highlights Active Management’s Shortcomings

In many walks of life, trying to discern the lucky from the skilled can be a difficult task. For example, it seems like every time a professional sports draft occurs, debate again flares up over whether the evaluation of college (or even high school) athletes is an exercise in skill or in luck.

Were the Portland Trail Blazers unskilled in the 1984 NBA draft when they selected Sam Bowie with the second overall pick, just ahead of Michael Jordan? Or were they just unlucky? (Bowie went on to an uneventful and injury-plagued career, while Jordan led the Chicago Bulls to six championships and is considered by many to be the greatest basketball player ever.)

The point is that sometimes it’s not easy to distinguish luck from skill. This difficulty extends to the evaluation of active mutual fund managers. With that in mind, we’ll review the academic literature on the subject of luck versus skill in mutual fund performance. We will examine the results of five studies. The first is by Bradford Cornell.

Skill Vs. Serendipity

Cornell, who contributed to the literature with his study “Luck, Skill, and Investment Performance,” which was published in the Winter 2009 issue of The Journal of Portfolio Management, noted: “Successful investing, like most activities in life, is based on a combination of skill and serendipity. Distinguishing between the two is critical for forward-looking decision-making because skill is relatively permanent while serendipity, or luck, by definition is not. An investment manager who is skillful this year presumably will be skillful next year. An investment manager who was lucky this year is no more likely to be lucky next year than any other manager.” The problem is that skill and luck are not independently observable.

Because this is the case, we are left with observing performance. However, we can apply standard statistical analysis to help differentiate the two, which is precisely what Cornell did. He used Morningstar’s 2004 database of mutual fund performance to analyze a homogenous sample of 1,034 funds that invest in large-cap value stocks.

Cornell’s findings are consistent with the previous research. The great majority (about 92%) of the cross-sectional variation in fund performance is due to random noise. This result demonstrates that “most of the annual variation in performance is due to luck, not skill.” Cornell concluded: “The analysis also provides further support for the view that annual rankings of fund performance provide almost no information regarding management skill.”

French & Fama Weigh In

Our second study is by Eugene Fama and Kenneth French, who contributed to the literature with their study, “Luck versus Skill in the Cross-Section of Mutual Fund Returns,” which was published in the October 2010 issue of The Journal of Finance. They found fewer active managers (about 2%) were able to outperform their three-factor (beta, size and value)-model benchmark than would be expected by chance.

Stated differently, the very-best-performing traditional active managers have delivered returns in excess of the Fama-French three-factor model. However, their returns have not been high enough to conclude they have enough skill to cover their costs, or that their good returns were due to skill rather than luck.

Fama and French concluded: “For (active) fund investors the simulation results are disheartening.” They did concede their results appear better when looking at gross returns (the returns without the expense ratio included). But gross returns are irrelevant to investors unless they can find an active manager willing to work for free.

Increasing Difficulty

Our third study is “Conviction in Equity Investing” by Mike Sebastian and Sudhakar Attaluri, which appears in the Summer 2014 issue of The Journal of Portfolio Management. Their study is of interest because it showed a declining ability to generate alpha. The authors found:

  • Since 1989, the percentage of managers who evidenced enough skill to basically match their costs (showed no net alpha) has ranged from about 70% to as high as about 90%, and by 2011, was at about 82%.
  • The percentage of unskilled managers has ranged from about 10% to about 20%, and by 2011, was at about 16%.
  • The percentage of skilled managers, those showing net alphas (demonstrating enough skill to more than cover their costs), began the period at about 10%, rose to as high as about 20% in 1993, and by 2011 had fallen to just 1.6%, virtually matching the results of the aforementioned paper by Fama and French.

Our fourth study is “Scale and Skill in Active Management,” which appeared in the April 2015 issue of the Journal of Financial Economics. The authors, Lubos Pastor, Robert Stambaugh and Lucian Taylor, provided further insight into why the hurdles to generating alpha have been growing. Their study covered the period 1979 to 2011 and more than 3,000 mutual funds. They concluded that fund managers have become more skillful over time.

They write: “We find that the average fund’s skill has increased substantially over time, from -5 basis points (bp) per month in 1979 to +13 bp per month in 2011.” However, they also found that the higher skill level has not been translated into better performance.

They reconcile the upward trend in skill with no trend in performance by noting: “Growing industry size makes it harder for fund managers to outperform despite their improving skill. The active management industry today is bigger and more competitive than it was 30 years ago, so it takes more skill just to keep up with the rest of the pack.”

Pastor, Stambaugh and Taylor came to another interesting conclusion: The rising skill level they observed was not due to increasing skill within firms. Instead, they found that “the new funds entering the industry are more skilled on average than the existing funds. Consistent with this interpretation, we find that younger funds outperform older funds in a typical month.” For example, the authors found that “funds aged up to three years outperform those aged more than 10 years by a statistically significant 0.9% per year.”

They hypothesized this is the result of newer funds having managers who are better educated or better acquainted with new technology, though they provide no evidence to support that thesis. They also found all fund performance deteriorates with age, as industry growth creates decreasing returns to scale, and newer, and more skilled, funds create more competition.

A Factor-Based Analysis

Our fifth and final study is “Mutual Fund Performance through a Five-Factor Lens,” an August 2016 research paper by Philipp Meyer-Brauns of Dimensional Fund Advisors. His sample contained 3,870 active funds over the 32-year period 1984 to 2015.

Benchmarking their returns against the newer Fama-French five-factor model (which adds profitability and investment to beta, size and value), he found an average negative monthly alpha of -0.06% (with a t-stat of 2.3). He also found that about 2.4% of the funds had alpha t-stats of 2 or greater, which is slightly fewer than what we would expect by chance (2.9%).

Meyer-Brauns also found that the distribution of actual alpha t-stats had shifted to the left of what would be expected from chance if all managers were able to produce excess returns over the five-factor model sufficient to cover their costs.

He concluded: “There is strong evidence that the vast majority of active managers are unable to produce excess returns that cover their costs.” He added that “funds do about as well as would be expected from extremely lucky funds in a zero-alpha world. This means that ex-ante, investors could not have expected any outperformance from these top performers.”

Summary

In 1998, at a time when about 20% of actively managed mutual funds were outperforming their risk-adjusted benchmarks, Charles Ellis called active management a loser’s game. What he meant was that, while it certainly was possible to win the game by selecting funds that would outperform, the odds of doing so were so poor that it wasn’t prudent to try.

Today the combination of academics having converted what once was alpha into beta (a common factor explaining returns)—thus eliminating potential sources of alpha—and that increasingly skilled competition has raised the hurdles, now only about 2% of actively managed mutual funds are generating statistically significant alpha. And that’s even before the impact of taxes on taxable investors.

The choice is yours. You could try to beat overwhelming odds and attempt to find one of the few active mutual funds that will deliver future alpha. Or you could accept market returns by investing passively in the factors to which you desire exposure. The academic research shows that investing in passively managed funds is playing the winner’s game.

This commentary originally appeared August 22 on ETF.com

By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party Web sites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them.

The opinions expressed by featured authors are their own and may not accurately reflect those of the BAM ALLIANCE. This article is for general information only and is not intended to serve as specific financial, accounting or tax advice.

© 2016, The BAM ALLIANCE

The Influence of Recent Market Returns on the Risk Tolerance of Individual Investors

 

The recency effect—that the most recent observations have the largest impact on an individual’s memory and, consequently, on perception—is a well-documented cognitive bias. This bias could impact investment behavior if individuals focus only on the most recent returns and project them into the future. Such behavior may lead investors to experience a reduction in their risk tolerance (which, in turn, can lead to selling) after a bear market, when valuations are lower and expected returns are higher. Conversely, recency may lead investors to experience an increase in their risk tolerance (which, in turn, can lead to buying) after a bull market, when valuations are now higher and expected returns are lower.

The Recency Effect

The recency effect nudges investor behavior in a direction contradictory to economic theory, which states that relative risk aversion is a function of increasing wealth (the marginal utility of wealth declines as wealth increases). Strong market returns would increase investor wealth, and thus we should see a reduction in investor risk tolerance.

Rui Yao and Angela Curl—authors of a 2011 study, Do Market Returns Influence Risk Tolerance? Evidence from Panel Data, which appeared in the Journal of Family and Economic Issues—hypothesized that the recency effect would dominate rational economic behavior. They posited that risk aversion is negatively related to recent market returns (or, in other words, that risk tolerance is positively related to recent market returns).

Their study used data from the 1992, 1998, 2000, 2002 and 2006 interview waves of the Health and Retirement Study (HRS), an ongoing biannual study conducted by the University of Michigan and funded through the National Institute of Aging. The target population for the HRS is noninstitutionalized men and women, born from 1931 to 1941, living in the contiguous United States. Based on responses to a set of income gamble questions, researchers assigned participants to a risk tolerance level for each wave: most risk tolerant, second-most risk tolerant, third-most risk tolerant and least risk tolerant. Stock market performance was measured as a continuous variable using the S&P 500 Index’s trailing 12-month returns prior to each interview. The following is a summary of the authors’ findings:

  • Consistent with the recency theory and their hypothesis, there was a significant positive linear relationship between S&P 500 returns and respondent risk tolerance.
  • Controlling for time and other independent variables, a one percentage point increase in market returns increased the probability of taking substantial or high risk by 1%. A one-standard-deviation increase in S&P 500 returns increased the likelihood of taking substantial or high risk by 15.7%.
  • When the stock market is falling, average monthly investor risk tolerance scores are strongly correlated with changes in the S&P 500. However, when stock prices start to rise, changes in average risk tolerance seem to be largely uncorrelated with the market.

Yao and Curl also found that:

  • Each additional year of age above the sample mean decreased the likelihood of taking some risks by 2%—consistent with theory and prior research showing that the likelihood of being in the high-risk or some-risk groups decreases as people age.
  • Higher educational attainment was consistently predictive of higher levels of risk tolerance.
  • Investors with greater financial assets reported lower levels of risk tolerance. This is consistent with the theory of declining marginal utility of wealth.

The key finding is in direct conflict with rational economic theory. When market return becomes negative, wealth decreases. Therefore, risk aversion should decrease (and risk tolerance should increase). But Yao and Curl’s analysis found that risk tolerance fluctuated positively with market returns. While the loss of money, combined with loss aversion, contributes to an increase in risk aversion during a bear market decline, gains during a bull market lead to the well-documented “house money” effect and a decrease in risk aversion.

The authors concluded that investors don’t behave according to rational economic model assumptions, and that “such changes in risk tolerance in response to market returns may be an indication that investors, and possibly their financial advisors, overestimate their ability to understand risk and assess individual risk tolerance.”

These findings suggest that individuals invest more after periods when market returns are high and withdraw partially or even completely from the market after periods when returns have been poor. Yao and Curl reached the conclusion that their findings support “the projection bias hypothesis and confirms the recency effect.” What’s more, their findings on investor behavior are consistent with those from the field of behavioral finance.

Behavioral Finance

For example, Richard Thaler and Eric Johnson, authors of the 1990 study Gambling with House Money and Trying to Break Even: The Effect of Prior Outcomes on Risky Choice, found that individuals experience less dissatisfaction from losses after a prior gain and greater dissatisfaction after a prior loss. Thus, risk aversion is time-varying and dependent on prior outcomes.

Yao and Curl’s findings are also consistent with those of Robin Greenwood and Andrei Shleifer, authors of a 2014 study, Expectations of Returns and Expected Returns. They were able to document a strong negative correlation between investor expectations of stock returns and recent returns for the S&P 500—investors change their expectations of the reward from taking risk based on recent changes in stock market returns.

The financial crisis of 2008 provided a good example of how recency impacts investor risk tolerance. During the crisis, individual investors pulled out hundreds of billions of dollars from the equity market. The result was that, by 2010, portfolio allocations to risky assets had declined to their lowest level for people under the age of 35 in the history of the Survey of Consumer Finances.

A more recent example can be found by examining the returns from emerging markets and investor flows. From September 2014 through September 2015, the MSCI Emerging Market Index lost more than 23%.Investment Company Institute data shows that beginning in July 2015, emerging-market funds experienced net withdrawals in every single month. For the period from July 2015 through January 2016, total net withdrawals exceeded $13 billion.

Next week, we’ll examine some additional support in the research for Yao and Curl’s findings, as well as explore the relationship between the recency effect and loss aversion and investor overconfidence.

 

This commentary originally appeared April 26 on MutualFunds.com

By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party Web sites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them.

The opinions expressed by featured authors are their own and may not accurately reflect those of the BAM ALLIANCE. This article is for general information only and is not intended to serve as specific financial, accounting or tax advice.

© 2016, The BAM ALLIANCE