Equities hot hand – do winners keep winning?

The Casual Link to Opportunity

Equities hot hand – do winners keep winning?

9 January 2020 Uncategorized 0

EFN427 – Behavioural finance group assignment
Hot Hand: Is Performance Predictable?

short line

Submission date: 22 September, 2019

Group 10: XX XX, XX XX, XX XX and Adam Atkins


Introduction 

Background

In their seminal 1974 paper, Tversy and Kahneman discussed the phenomena of heuristics — or mental shortcuts — that are used to make decisions under uncertainty. They explained that many of our decisions are based on beliefs concerning the likelihood of uncertain events and heuristics help to reduce the complex task of assessing probabilities and predicting outcomes to simpler judgmental operations. While heuristics help to navigate this uncertainty, they sometimes lead to severe and systematic errors of judgement, for example thinking that an event is more or less likely to happen than it actually is based on a proper understanding of the situation (Tversky & Kahneman, 1974; Ackert & Deaves, 2010).

The “hot hand” fallacy was first observed by Gilovich, Vallone, and Tversky (1985), which the paper defined as the claim that players are more likely to make a successful shot if their previous shot was successful. It stems from the representativeness heuristic, a shortcut people use to judge the probability of an event by how well it represents certain salient features of the population from which it was drawn (Tversky & Kahneman, 1974). In the case of basketball, it can lead to faulty decision making by players overestimating the likelihood that they or another player will have a successful shot following a winning streak. Gilvovich et al concluded that the sense of being “hot” does not predict hits or misses.

The hot hand fallacy has also been studied in the context of financial decision making, where it has been shown that investors and financial advisors are influenced by the past performance of funds when making decisions, despite evidence to the contrary (Rabin & Vayanos, 2010). However, some studies that have found limited findings of persistence in performance of funds, where this has been observed over a short period following a streak (Hendriks et al, 1993).

This paper will use a case study to assess if there is any evidence of persistence in the performance of equity funds by analysing the annual returns data for Australian share market and that of different equity funds over a ten year period. It will explore if there is any truth to the hot hand or if it is indeed a fallacy.

CanSuper investment

CanSuper is a medium-sized superannuation fund with a membership of 45,690 and investment assets of nearly $3.2 billion.

A high proportion of CanStar’s assets are invested in the Australian equity (share) market and the investment philosophy of trustees is to manage risk through diversification. Therefore, diversified equity funds are of particular interest to CanSuper’s committee.

It has been proposed by CanSuper committee members that indicators of past performance (i.e. returns) be used as a filter in selecting the future pool of funds to invest. This is broadly based on the assumption that a hot hand effect does in fact exist and that persistent performance in previous years will influence performance in future years.

This paper will make specific recommendations to the CanSuper committee based on findings from an analysis of the annual returns data for Australian share market and that of different equity funds over a ten year period. 

Analysis and discussion

For the purposes of our analysis, we have used two different measures. We looked at the number of consecutive periods of successful performance and also looked at average returns across the total period. Both measures have been used in prior studies to document the “hot hand” effect, though it appears that the consecutive periods measure is the widely accepted measure (Bocskocsky et al, 2014; Grose & Kargidis, 2012).

Firstly, we found evidence that some funds fairly consistently outperform the ASX200. We looked at outperformance both in terms of the total number of years over 2004-13 that the funds outperformed as well as the highest number of consecutive years (i.e. a streak) of outperformance. Table 1 presents our findings. For example, twenty-nine funds outperformed the ASX200 in seven or more years over 2004-13.  Further, the longest ‘streak’ was achieved by one fund for seven consecutive years, with another nine funds outperforming for six consecutive years.

Table 1: Number of years funds outperformed the ASX200 over 2004-2013

In general, funds experiencing a ‘hot hand’ or outperforming over a number of years, deliver higher average annual returns than other funds and the benchmark. Chart 1 shows the relationship between average annual returns and the number of years of outperformance.

That said, Chart 1 also shows that a number of funds that consistently outperformed the ASX200, but did so only modestly. For example, of the twenty-nine funds that outperformed in at least seven out of ten years, only seventeen funds achieved an average annual return above 2%. This is important as the Australian Investors’ Association reports that management fees on funds are generally between 0.7 and 2% (Australian Investors’ Association, 2012). Hence, these firms may not be generating outperformance once fees are taken into account and a low cost passive fund or ETF could be better value.

Chart 1: Average annual relative return vs number of years of outperformance of the ASX200

Broadly, we found that the funds that achieved higher average returns, also took more risk. Chart 2 presents our findings with risk measured by the standard deviation of returns. Similarly, Chart 3 shows that funds that achieved a consistent number of years of outperformance relative to the ASX200 generally also pursued riskier strategies (represented by a standard deviation of returns above that of the ASX200).  

In part, this reflects the nature of the funds that consistently outperformed. The top twenty performing funds are dominated by small-to-medium cap funds and geared funds, which are inherently more risky. Further analysis here looking at the outperformance of solely large cap, diversified strategies would be useful for CanSuper.

Chart 2: Standard deviation of returns vs average outperformance of the ASX200

Chart 3: Standard deviation of returns vs number of years of outperformance of the ASX200

We also thought it would be interesting to analyse the top-five and bottom-five dollar value funds for a scenario in which an investor deposited AUD$1,000 at year zero and withdrew at the end of the period at year ten. These findings are represented in Table 2 and Table 3.

First consider the top-five dollar value funds (Table 2). The top-performers had 7-15% higher standard deviation, outperformed the ASX200 by 6-10% and stayed in the top 10% of funds.

Now consider the bottom-five dollar value funds (Table 3). The bottom 10% of performers generally stayed in the bottom 10%, and there were two big falls from top 10% to bottom-five.

The bottom-performing funds all had standard deviations near double the market, and while they had average returns not too far off the ASX200, they performed much worse than the ASX200 in bad years and did not perform as well as the ASX200 in good years — so, in each case their dollar returns were significantly below the ASX200 benchmark.

Some of the bottom-five dollar value funds had an average return that exceeded the market and yet are still in the bottom-five in absolute dollar value.

This finding is of significant value as it demonstrates that average returns above market should not necessarily be taken at face-value and require closer and more detailed examination — if an investor took only the face-value returns figure and reinvested in the fund, they would have been worse off over the duration of the ten-year period than if they had selected a simple low-cost ETF that emulated the ASX200 (even though average returns were apparently higher).

Looking back at the top-performing funds we observe that three of these funds were in the top ten in the first year, and that two of these funds were in the top third (at rankings twenty-eight and twenty-nine) before climbing up to be in the top five.

From this we can observe that there is some tendency for “winners” to keep winning, and that there is some tendency for those that become “winners” to continue to win. More broadly, the top 10% in year one stayed in the top 30% by year ten, except for the worst two performers, Maple-Brown Abbott Aus Geared Eq and Maple-Brown Abbott Aus Geared Eq W, which were together from top twenty down to bottom five. These outliers could be explained by their poor use of leverage as these were both geared funds.

Investors may be interested in the annual percentage returns and regard these as a fair predictor of future performance, however what really matters is dollar returns — a high average return does not equate to a high dollar return if the occasional bad years are devastating.

Table 2: Top five dollar value fund returns for if an investor deposited AUD$1,000 at year zero and withdrew at the end of the period in year ten

Top 5 Dollar value funds Ranking at year 0 and finish Average annual returns Compare to ASX200 avg returns Average Standard deviation Dollar value at end of period
ASX200 benchmark n/a 12.11% n/a 22.26% $2,089.26
Hyperion Small Growth Companies 9 to 1 21.52   +9.41% 31.73%   $3,739.21  
NovaPort Premier Smaller Companies 29 to 2 19.69 +7.58%   32.27% $3,135.79
Smallco Investment 4 to 3 22.27   +10.15%   36.94%   $3,121.92  
NovaPort WS Smaller Companies 28 to 4 19.16   +7.05%   32.09%   $3,058.30  
BT PPSI-BT WS Smaller Companies 11 to 5 18.18% +6.07% 29.75% $3,034.21

Table 3: Bottom five dollar value fund returns for if an investor deposited AUD$1,000 at year zero and withdrew at the end of the period in year ten

Bottom 5 Dollar value funds Ranking at year 0 and finish Average annual returns Compare to ASX200 avg returns Average Standard deviation Dollar value at end of period  
ASX200 benchmark n/a 12.11% n/a 22.26% $2,089.26  
Maple-Brown Abbott Aus Geared Eq 14 to 250 10.93% -1.18%   38.42%   $882.45  
EQT Small Companies 244 to 249 11.16 -0.95% 46.43%   $1,238.18  
EQT SGH Wholesale Small Companies 239 to 248 12.46 0.35% 48.27%   $1,332.30  
Maple-Brown Abbott Aus Geared Eq W 6 to 247 14.88 2.76%   38.97%   $1,367.48  
PM Capital Australian Opportunities 238 to 246 9.81 -2.30% 27.53%   $1,406.26  

Lastly It is important to note some limitations on this analysis, and address the issue of capital flows.

Successful funds will attract more capital. If more capital flow in on a good year, or following a good year, then the following year, if good, will be even better since the fund has more capital to invest. In other words, due to the effects of capital flow, a hot hand may appear to get “hotter” in subsequent years.

Conversely, if a fund has a bad year, and investors withdraw, then there is less funds to rebound with in a subsequent good year, so losses compound and are not recovered. In this way, a cold hand may appear to get “colder” in subsequent years.

Summary of findings and recommendations

  • There exists some evidence to suggest that there is a tendency for “winners” to keep winning and for “losers” to keep losing.
  • In general, funds experiencing a ‘hot hand’ or outperforming over a number of years, deliver higher average annual returns than other funds and the ASX200 benchmark. However, most do so only modestly.
  • The higher performance of ‘hot’ funds may not be enough to warrant the subsequent higher fees associated with consistently high-performing funds.
  • Funds that achieved higher average returns also, generally, pursued riskier strategies. In part, this reflects the nature of the funds that consistently outperformed — mostly small-to-medium cap funds and geared funds, which are inherently more risky.
  • Interestingly, average returns above the market should not necessarily be taken at face-value as a predictor of future performance, as some funds had an average percentage return that exceeded that of the market, yet underperformed in terms of total dollar value (based on a scenario whereby AUD$1,000 was deposited at year zero and withdrawn at the end of the ten-year period). This is because a high average return does not equate to a high dollar return if the occasional bad years are devastating.
  • Successful funds attract more capital, and unsuccessful funds attract less capital, which affects the total capital they have to invest the following year. This may compound effects so that hot hands appear to get “hotter” and cold hands appear to get “colder” over subsequent years.

References

Ackert, L. F., & Deaves, R. (2010). Behavioral Finance: Psychology. Decision-Making, and Markets, 97-99.

Australian Investors’ Association. (2012). Managed funds. Retrieved from: https://www.investors.asn.au/education/other-investments/managed-funds/.

Ayton, P., & Fischer, I. (2004). The hot hand fallacy and the gambler’s fallacy: Two faces of subjective randomness?. Memory & cognition, 32(8), 1369-1378.

Bocskocsky, A., Ezekowitz, J., & Stein, C. (2014, March). The hot hand: A new approach to an old ‘fallacy’. In 8th Annual MIT Sloan Sports Analytics Conference (pp. 1-10).

Gilovich, T., Vallone, R., & Tversky, A. (1985). The hot hand in basketball: On the misperception of random sequences. Cognitive psychology, 17(3), 295-314.

Grose, C., & Kargidis, T. (2012). Persistence In Performance For Mutual Funds In Periods Of Crisis. Scientific Bulletin, 85.

Hendricks, D., Patel, J., & Zeckhauser, R. (1993). Hot hands in mutual funds: Short‐run persistence of relative performance, 1974–1988. The Journal of finance, 48(1), 93-130.

Rabin, M., & Vayanos, D. (2010). The gambler’s and hot-hand fallacies: Theory and applications. The Review of Economic Studies, 77(2), 730-778.

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. science, 185(4157), 1124-1131.

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