Do Behavioral funds perform better than the market?

4/22/2011 The University of Hong Kong

Introduction

Behavioral funds, general defined as funds that practice some form of behavioral finance in their

investment strategies. These funds are correctly identified as incorporating behavioral finance in

their strategies and that their objective is to earn abnormal returns.

Individual investors may commit fallacies in the market exchange due to certain behavioral

biases, which make them suffer from investment. Below are some listed ones.

1. Overconfidence. Overconfidence is one of the common fallacies that individual

commits during investment. Often overconfidence will lead to too frequent trading

2. Emotion and social forces. Emotion can affect investors’ attitudes towards gain

and losses, and their allocation in money

3. Home bias. Home bias is one of the main reasons why investors fail to diversify

their investment.

4. Regret aversion. People will suffer from regret of commission, which leads them

to miss opportunities.

5. Risk aversion. People often sell winners too soon

6. Anchoring and representativeness bias: Anchoring bias can help understand why

people can’t hold losers too long, instead of selling them in time. Representativeness shows

the bias when investors selecting their portfolios

These biases will lead to systematic miss-predictions of stocks and loss of opportunities, and also

can lead to excess optimism or pessimism, all of which will apply negative effects on investment.

Investors are slow to recognize the signs of improvement, so stock prices may not rise as fast as

their earnings. Also, affected by good or bad news of certain stocks, the investors also tend to

overreact to the previous or current behavior of stocks, rather than deeply thinking about the

future performance.

Taking behavioral finance into consideration, behavioral funds are trying to avoid such irrational

behavior when arrange the portfolio investments. So that major behavioral funds are doing

highly diversified investments, and distributing their money into different underlying in different

markets. Moreover, they’re trying to use these drawbacks of investors’ irrational investment

decision based on careful analysis Compared with traditional funds, behavioral funds pay special

attention to the trading history and volatility of a certain underlying in order to find out possible

attitudes that investors feel about it, and try to predict the expectation investors hold about it.

Whenever there is a mispricing of assets, they’re going to long them, and vice versa. For example,

on the strategy introduction of Undiscovered Managers Behavioral Value Fund of JP Morgan, it

says they’re looking for investors' behavioral biases that may cause the market to underreact to

new, positive information about a company and then focus on that kind of stock.

However, does this kind of new managing method of funds overperform its peers? It remains to

be discovered. In this project, the main idea is to compare behavioral funds’ performance with

normal index funds and matched funds. Thus, we are doing a small empirical study with the

sample of 11 different behavioral funds. Several indexes, such as Treynor Ratio and Sharpe

Ration are referred in order to do the comparison, calculated with original figures of return,

volume and other essential information.

Analysis and methodology

1. We are going to measure risk-adjustment return of these Behavioral Funds. In these

processes, there are 5 parts that need to be considered in order for the analysis to develop

fruitfully.

Sample of Behavioral Funds: We emphasize the fact that our objective is not to identify all the

funds actually incorporating some form of behavioral finance in their investing strategies. Instead,

our objective is to analyze the funds most visibly associated with behavioral finance. Before we

concern ourselves with the entire population of behavioral funds, which is difficult to identify,

the group that is most observably connected to behavioral finance must first demonstrate that

its association with behavioral finance has generated benefits for its investors. We have

identified a group of mutual funds that claim to base their investment strategies on principles of

behavioral finance in order to capitalize on market inefficiencies and earn above average returns.

Similar to Reinhart and Brennan, we categorize a mutual fund as “behavioral” if we are able

to find a written statement in which the fund or its manager explicitly admits to utilizing

“behavioral finance” to make investment decisions. In a few cases, the funds actually have

“behavioral” as a part of their fund names. In most cases we were able to find direct quotes from

the fund managers or fund families themselves. In the remaining instances we found articles

about the fund managers linking their investment strategies to behavioral finance.

Actually，part from Morningstar (named behavioral funds), part from some researches, 11

in total with data available.

Total Assets(m) expense ratio 3-Y Annual

JIISX 13 1% 1.65%

JPGSX 673.8 1% 3.99%

JPIAX 1600 1% 1.74%

JPIVX 683 0.86% 1.31%

KDHAX 1800 1.16% -4.23%

KDSAX 2900 1.24% 7.12%

LMVTX 3700 1.76% -4.78%

LSVEX 2000 0.63% 0.50%

UBRLX 74.9 1.30% 6.97%

UBVLX 39.7 1.39% 13.07%

WOOPX 459.2 0.99% 5.49%

Time Range: 3 years, from 2008-3 to 2011-3

Reason: the most recent data; 3-year is a mid-term period, which is more reliable than

short-term performance while in long-term, it’s difficult to identify whether these funds use

consistent behavioral finance strategy; this period perfectly include two trends: a downward

trend from 2008-3 to 2009-2 and an upward trend from 2009-3 to 2011-3, in order to explore the

behavioral funds’ performance in different economic situation. (Two sides are almost in the

same level while downward period has a larger volatility)

Methods: 5 measurements, which will be explained in detail later. They are all risk adjusted

measurement.

Comparison: S&P 500 Index is used as the benchmark of the market. We argue that a passive

investment strategy in US markets has largely become synonymous with investing in the S&P 500.

Therefore, our sample of index funds is comprised of the 3 largest S&P 500 index funds: VFINX,

PREIX, PEOPX. To construct a matched sample of actively managed funds, we match one actively

managed, non-behavioral fund to each of the 11 behavioral funds in our sample by matching on

the basis of total net assets and expense ratios in the month the behavioral funds enter our

sample. The first criterion is similarity in portfolio and non-behavioral funds. The second is

categorized in the similar size.

2. Data process:

Individual behavioral funds vs. Market based on Sharpe Ratio

Take JIISX for an example, we got most of our data from a combination of Yahoo Finance and

Bloomberg:

Date Adj Close

Price(monthly)

Monthly

Return S&P 500

Monthly

Return Differ

13-week T-bill(monthly

rate)

2011-4-1 20.64 0.00% 1328.17 0.18% -0.18% 0.04%

2011-3-1 20.64 1.52% 1325.83 -0.10% 1.63% 0.09%

2011-2-1 20.33 4.79% 1327.22 3.20% 1.60% 0.14%

2011-1-3 19.4 0.94% 1286.12 2.26% -1.33% 0.14%

2010-12-1 19.22 6.96% 1257.64 6.53% 0.43% 0.12%

…… …… …… …… …… …… ……

2008-7-1 18.98 -2.52% 1267.38 -0.99% -1.53% 1.63%

2008-6-2 19.47 -6.44% 1280 -8.60% 2.16% 1.71%

2008-5-1 20.81 4.47% 1400.38 1.07% 3.40% 1.85%

2008-4-1 19.92 4.24% 1385.59 4.75% -0.52% 1.34%

2008-3-3 19.11 #VALUE! 1322.7 #DIV/0! #VALUE! 1.27%

Sharpe Ratio=R−Rf

σ, which uses the portfolio’s total volatility. So this method can be used to

compare individual portfolio’s performance with the market and other kind of funds. A good

investment should show a positive Sharpe Ratio and the higher, the better.

JIISX S&P 500 Δ

Sharpe

Ratio

downward -1.548 -1.565 0.018

upward 1.874 1.759 0.115

total 0.095 -0.011 0.106

JIISX has outperformed the market consistently and did much impressively better in upward

trend. We use same methods to calculate other 10 Behavioral funds and get the following result:

win lose Cumulative

Probability

Statistical

Significance

Sharpe

Ratio

downward 8 3 0.113 ≈90%

upward 9 2 0.033 >95%

total 9 2 0.033 >95%

From this result, we can see the behavioral funds outperform the market with significant

consistency.

Aggregate Behavioral funds vs Market based on Treynor Ratio

Treynor Ratio=R−Rf

β, which only measures systematic risk. When many portfolios aggregated, the

nonsystematic could be eliminated. This method can be used to compare different aggregated

portfolios based on the same market. A good investment should show a positive Treynor Ratio

and the higher, the better.

With equal-weighted individual data, we then get aggregate data of Behavioral funds compared

with the market itself:

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

downward upward total

ΔSharpe Ratio

Aggregate

Behavioral

funds

S&P 500 Δ

Treynor

Ratio

downward -0.413 -0.405 -0.007

upward 0.335 0.276 0.059

total 0.030 -0.002 0.033

From this chart, we can see as a whole, Behavioral funds had almost the same performance with

the market in downward trend, only a little bit worse while impressively outperformed in upward

and total period.

Aggregate Behavioral funds vs Market based on Sortino Ratio:

Sortino Ratio=R−Rf

σd, which is mainly based on the Sharpe Ratio but different in measuring the

volatility. It only measures negative volatility. So this method can be used to reflect how the bad

volatility is compensated. Generally a portfolio with good volatility has a much higher Sortino

Ratio than its Sharpe Ratio.

Aggregate

Behavioral

funds

S&P 500 Δ

Sortino

Ratio

downward -1.842 -2.025 0.183

upward 4.370 3.676 0.695

total 0.204 -0.016 0.220

Sharpe

Ratio

downward -1.406 -1.565 0.160

upward 1.921 1.759 0.162

total 0.128 -0.011 0.138

-0.010

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

downward upward total

ΔTreynor Ratio

From this chart, we can see all the “ΔSortino Ratio” is positive and higher than “ΔSharpe Ratio”,

which means a big proportion of Behavioral funds’ volatility is good that actually lower the risk.

Especially in upward trend, it outperforms the market a lot.

Aggregate Behavioral funds vs Market based on Alpha and Information Ratio:

Alpha= R − Rf − β ∗ (RM − Rf), which is used to measure the excessive return gained. If Alpha

is positive, the fund is managed better than the market performance.

Information Ratio=α

tracking error, which is to measure the cost of actively managed funds. Active

management raises cost, which needs to be compensated from excess return. Generally, 0.5 is

good, 0.75 is very good while 1 is exceptional.

Aggregate Behavioral

funds

Alpha

downward -0.22%

upward 6.27%

total 3.52%

Information

Ratio

downward -0.234

upward 1.064

total 0.461

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

downward upward total

Δsortino Ratio

Δsharp Ratio

From this chart, we can see that Behavioral funds usually have a positive alpha while almost 0 in

downward trend. However, in downward trend, when cost is taken into account, Behavioral funds

may have a disadvantage though it’s cost efficient in upward trend.

Summary: With 5 methods, Behavioral funds consistently beat the market in both upward and

total period; while only underperform the market with Information Ratio in downward period.

Aggregate Behavioral funds vs Index Funds based on Treynor, Sharpe, Sortino and Alpha

Aggregate

Behavioral

funds

Aggregate

Index Δ

Treynor

Ratio

downward -0.413 -0.426 0.014

upward 0.335 0.311 0.024

total 0.030 0.018 0.012

Sharpe

Ratio

downward -1.406 -1.509 0.103

upward 1.921 1.904 0.017

total 0.128 0.083 0.045

Sortino

Ratio

downward -1.842 -1.978 0.136

upward 4.370 4.088 0.283

total 0.204 0.126 0.077

Alpha

downward -0.22% -1.92% 1.70%

upward 6.27% 3.39% 2.88%

total 3.52% 2.02% 1.50%

-40.00%

-20.00%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

downward upward total

Alpha

Information Ratio

From this chart, we can see all of theΔ is positive, which means Behavioral funds outperform the

Index Funds consistently.

Individual Behavioral funds vs matched Non-B funds:

Our basic filter for finding a sample of matched actively managed, non-behavioral funds to

the 11 of our sample based upon 3 main criteria:

Must be within the 80 – 120% range of the NAV of the behavioral fund.

Must fall in the same Morningstar category as the behavioral fund.

Expense ratio is closest to the behavioral fund

And for the case we do not immediately find a match, we loosen the requirements, such as the

range of NAV growing to 70 – 130%. And in the extreme case of nothing found, we just exclude

that fund from the testing.

similariy in

portfolio result

LSVEX 74.90% outperform

SSLAX 94.00% outperform

UBRLX 52.00% outperform

UBVLX 90.60% underperform

KDSAX 85.50% outperform

JPGSX 81.10% almost same

JPIAX 83.90% almost same

WOOPX 89.00% almost same

LMVTX 72% underperform

JIISX 83.90% almost same

JPIVX 83.90% almost same

0.000

0.050

0.100

0.150

0.200

0.250

0.300

downward upward total

ΔTreynor

ΔSharp

ΔSortino

ΔAlpha

Total outperform underperform almost

same

11 4 2 5

From this table, we can not see a significant advantage of Behavioral funds, which performs

almost the same with other actively managed funds using other strategy.

Summary: Behavioral funds do better than Index funds while do not have an significant

advantage among actively managed funds.

Where is the BOOM?

Lately there has been a lot of publicity surrounding the terms behavioral economics, behavioral

finance, behavioral trading and behavioral funds. This can be proven by the increased amount of

capital inflow to identified behavioral funds, as discussed by Colbin Wright, Veneesha Boney,

Prithviraj Banerjee in their research paper Are the disciples profiting from the doctrine (2006)1.

But if behavioral strategies work, then why isn’t there a much larger and wider implementation

process going on? What is taking the rest of the investor ecosystem to notice all this publicity, or

for the lack of better word – hype?

We attribute this slow implementation to 2 major points, price and difficulty of widespread

implementation and a force coined by Schumpeter2 – Creative Destruction.

The actual cost of developing behavioral models is very high, starting from educating a new crop

of investors to use behavioral strategies, to much larger issues such as actually creating a model

for price discrepancies that are occurring due to behavioral or we could say psychological reasons.

One good example of this cost would be the research papers done by the famous due of Terrance

Odean and co-author Brad Barber, who had to obtain trading records of 10,000 different

accounts and 78,000 different brokerage records of households just to put a statistical stamp on

the existence of behavioral principles such as overconfidence and mean reversion among others.

Now imagine, a mutual fund going through great lengths and large costs for their research and

development of these models only to have come out blank, i.e. their research lead them to

nothing of significance, and even if they were to obtain meaningful results and create a model

worthy of profiting from, their sunk research and development costs would increase their

management fees to such a large amount that the fund would not have any sort of capital to

work with at all. Hence, such risks are very difficult to undertake, even for the sake of innovation,

which frankly the financial sector does not enjoy much off. Therefore, we believe that one of the

main issues regarding lack of more widespread usage of behavioral finance is cost and difficulty

of implementation.

The second major issue that is a road block to behavioral finance implementation is creative

destruction. All theories rise and fall, all trees grow and die, every buyer has to sell eventually –

basically – every Humpty Dumpy has to fall off the wall. In the realm of science or just plain ideas,

structural change is most often referred as a paradigm shift. Classic examples of these revolutions

1 Are the disciples profiting from the doctrine? (2006)

-http://papers.ssrn.com/sol3/papers.cfm?abstract_id=930400 2 Joseph Schumpeter - http://en.wikipedia.org/wiki/Joseph_Schumpeter

in paradigm would be Einstein, Newton, and Galileo. Change is not always welcomed with open

arms, therefore every paradigm change is accompanied by “revolting regimes” which rallies

around seemingly radical and crazy ideas, while the “old school” having already invested their

physical, intellectual, and psychological capital in their “old” regime, be damned if they change

their beliefs or belittle their bottom lines by recognizing the errors of their paradigm. And just like

the old story of Humpty Dumpy, the fall of the wall never ends up beautiful, more likely it is going

to be splattered. In short, Efficient Market Hypothesis believers do not want to let go of their

beliefs, hence the underinvestment in the new fields, such as behavioral finance.

Drawbacks

A research is always accompanied by drawbacks, something the researcher could have done

better, or the situation could have been different. Hence, it is always normal and common to not

have 100 percent satisfaction with one owns’ report. Likewise, we have ours.

The definition of behavioral funds is a very iffy situation, not only are we fighting for

trustworthiness of the Mutual Funds claim of usage of behavioral funds, but we have to also take

their whole strategy portfolio with a tablespoon of salt. The issue of investment strategy

dilution is reasonably important, a Mutual Fund may have over 100 strategies they actually

implement, and it happens that only one is considered a behavioral strategy, hence quickly the

question arises, do we consider this as a Behavioral Mutual Fund, or due to its majority of

strategies being non-behavioral we consider them “normal”.

The funds that we identified as behavioral were either explicitly expressing their direct

involvement of behavioral investment strategies, or were identified doing so by media outlets,

but disclosure of what percentage of the strategies are behavioral, or whether they are truly

behavioral and not some similar strategy we are not able to conclude.

As always with almost any quantitative research analysis, the larger the sample size the better

the outcome and the happier the research team, equally so, our team was not fully satisfied with

the size of our sample population. So in addition to battling the transparency war with the

mutual funds, we would have definitely loved to have a larger sample to research upon.

Unfortunately, most funds do not disclose their strategies, and especially the ones that are

profiting from it greatly, which could easily be a plethora of behavioral strategies.

Lastly, we believe that the actual models that have been used throughout, not only our research

but all analyses of behavioral strategies and funds are outdated and more importantly were built

in an era of domination of the Efficient Market Hypothesis and Investor Rationality, but as

behavioral finance has taught us, this is not the case. Therefore, we believe to be 100 percent

true to the nature of innovation, new models identifying behavioral risk has to be invented and

only then can we truly measure the success and failure of behavioral funds and their strategies.

But due to our limitations as of today, we are forced to use models a little outdated and not

adjusted for behavior.

These are our drawbacks, and hopefully in the near to long term future we will be able to address

each issue and take our research to perfection.

Conclusion

We learnt a lot during the whole process of research, analysis and compiling the results, from the

media surrounding the field of behavioral finance, to its measuring up with other major and

minor funds and indexes. But we came here for the pure purpose of comparing the performance

of behavioral mutual funds to large market indices and their matched mutual funds. We can

proudly conclude three points:

• Based on raw returns, behavioral funds generally beat both S&P 500 index funds and

their matched non-behavioral counterparts.

• Based on risk-adjusted returns, behavioral funds mostly, but not significantly outperform

their matched non-behavioral counterparts.

• Behavioral mutual funds are essentially value funds, trading the aggregate risk of value

stocks for their aggregate excess raw return. There is no evidence that these funds

generate excess raw returns in other ways.

Brought to you by:

2008568632 Furuzonfari Zekhni

2010973023 Huang Yuren

2010973011 Wang Ningxin

Note:

We have added the Excel empirical analysis sheet and the pictures of our matched funds

comparisons for your revision.