The 2016 Capital Market Research Scholarship for Graduate ... · PDF fileในหัวข้อ “Risk and return in equity mutual fund industry: ... Fund Objective Gitman, Joehnk,

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มหาวิทยาลยั อสัสมัชญั ในหวัข้อ “Risk and return in equity mutual fund industry: An unorthodox relationship and its application to new investment strategies”

10 August 2016

The 2016 Capital Market Research Scholarship for Graduate Students

โดย นางสชุญา สยามวาลา..........นกัศกึษาปรญิญาเอก

และผศ.ดร.นพพล ตัง้จติพรหม....เป็นอาจารยท์ีป่รกึษา


Executive summary In 1980, Bowman documented the Bowman paradox, a negative relationship between risk and return of 85 U.S. industries, a contradiction to the high risk-high return doctrine. Examining the open-end equity mutual funds in Thailand, this study documented the negative relationship between risk and return in the industry from time to time during 2003-2012. The study further examined the factors that will affect the probability that a fund will deliver an outstanding low risk-high return performance The results showed that funds with high non-systematic risk, also called idiosyncratic risk, and/or older funds were more likely to deliver a low-risk high return performance and the company who managed a high number of funds was less likely to deliver such performance. This study proposed a new performance evaluation tool called the Risk-Return matrix, which suggested funds with outstanding low risk-high return past performance. It also demonstrated three new investment strategies which delivered return higher than industry average.


Generalities of the study • Modern portfolio theory suggests that we can expect high return

from a high-risk investment. However, in 1980 Bowman conducted a research on correlation between return and standard deviation of return of firms in 85 industries and found that it was negatively correlated within industry. This is known as the Bowman Paradox.

• Recent studies by Brockett, Charnes, Cooper, Kwon and Ruefli (1992) and Cooper, Ruefli and Wilson (2011) examined the paradox using U.S. mutual fund industry.

• They concluded that the paradox could happen in mutual fund industry as well.

• Thai mutual fund industry is growing. In 2014 it grew 23% (Bank of Thailand, 2015).


Research Questions

1

• Does the Thai open-end equity mutual fund industry have a negative relationship between risk and return?

2

• What factors or characteristics of fund that have significant effect on the probability that a fund will deliver a low-risk, high-return performance?

3

• Can the result of this study be used to enhance the investment portfolio return?


Literature Review Bowman (1980) is a strategic management study but used the financial variable such as ROE and standard deviation of ROE, and found that company with high profit had lower profit volatility. According to Andersen, Denrell, and Bettis (2007), subsequence studies can be categorized into 3 groups

The Organizational

Factor

Statistical Artifacts

The Behavioral

Theory

Fiegenbaum (1990), Fiegenbaum and Thomas (1986, 1988),Jegers (1991), Johnson H. J. (1994), and Sinha (1994)

Henkel (2000, 2009) Baucus, Golec, and Cooper (1993)

Andersen et al. (2007), Bettis and Hall (1982), Bettis and Mahajan (1985), and Miller and Chen (2003) Brockett et al. (1992) Cooper et al. (2011)


Literature review Variable Authors Studies Results

Idiosyncratic risk

Amihud and Goyenko (2013) R^2 of fund(1-R^2 = active manage)

Positive

Ang et al. (2008) Co-movement Negative

Fund size Berk and Green (2004); Chen, Hong, Huang, and Kubik (2004)

Fund size drove away performance

Negative

Elton, Gruber, and Blake (2012) Cash flow has negative effect No effect

Pollet & Wilson (2008) Influx of liquidity subsequent to good performance Negative

Fund Objective Gitman, Joehnk, and Smart (2011) Investors in a growth fund want to build up capital rather than expect a regular dividend or income

Growth-neg Value-Pos

Fama and French (1993)

argued that companies with high book-to-market ratios (value stocks) outperformed those with low ones (growth stocks).

Growth-neg Value-Pos


Literature review Variable Authors Studies Results

Fund Age Pastor, Stambaugh, and Taylor (2014)

Industry growth Negative

Switzer and Huang (2007) Managerial human capital Positive

Type of parent company

Nathaphan (2010) Skill pf parent company Bank – negative Non Bank- Positive

Ferris and Yan (2009) Agency cost theory Public-Neg Private-Pos

Total number of fund

Pollet & Wilson (2008) Fewer funds in the family diversified more

Negative

Bogle (2010) Put more resources in increasing number of funds

Negative

Total AUM Chen, Hong, Huang, and Kubik (2004)

Economies of scale, a greater power of negotiation, and a better access to analysts

Positive


Methodology and Findings


Question 1

Test

Methodology

• Does the Thai open-end equity mutual fund industry have a negative relationship between risk and return?

• Pearson product moment correlation

9

Research Question 1


• Select all Thai open-end equity mutual funds, exclude LTF, RMF, and Target fund

• Study period = 2003-2014, when the industry has become materialized

• Funds must have at least 36 months historical record, for meaningful regression result

• Samples

10

Sampling method and variable measurement

• Return =

• Risk =

Geometric Mean Return

Annualized standard deviation of monthly returns


The Correlation Result Panel A. Yearly 2005 2006 2007 2008 2009 2010 2011 2012

-.428*** 64

-.326*** 70

.794*** 78

-.613*** 80

.637*** 80

.256** 83

-.671*** 83

-.337*** 83

Panel B. (3Y) 2003-05 2004-06 2005-07 2006-08 2007-09 2008-10 2009-11 2010-12

.188

64

-.409*** 64

-.054

64

-.541*** 70

.052

78

.223** 80

.256** 80

-.503*** 83

Panel C. (5Y) 2003-07 2004-08 2005-09 2006-10 2007-11 2008-12 - - 0.15

64

-.482*** 64

-.260** 64

.259** 70

0.151

78

-.402*** 80

- -

Panel D. (10 Y) 2003-12 - - - - - - - -.299**

64

- - - - - - -

Although, the results show mix types of correlations, there are more negative-correlation years(red) than the positive ones(blue) in all kind of period tested, To answer the 1st research question : This study rejected the null hypothesis and concluded that there is not always a positive relationship between risk and return in Thai open-end equity mutual fund industry. There could be a negative risk-return relationship in the industry from time to time.


Question 2

Test

Methodology

• What factors or characteristics of fund that have significant effect on the probability that a fund will deliver a low-risk, high-return performance?

• Unbalanced panel data logistic regression to capture both time and space effects

12

Research Question 2


Research framework


Risk Return

Low risk (0-30th )

Medium risk (30th -70th )

High risk (70th-100th )

High return (70th 100th )

Low-risk, High-return Med risk, High return High-risk ,High-return

Medium return (30th -70th )

Low-risk, Med- return Med-risk, Med-return High-risk, Med- return

Low return (0-30th )

Low-risk, Low-return Med- risk, Low-return High- risk, Low- return

14

Mutual Fund Performance Measurement

Sweet Spot Umami-2 Hot

Umami-1 Salty

Sour-1

Sour-2

Bland Bitter

Risk-Return Matrix


Measurements of independent variables Variables Name Measurement

Idiosyncratic risk Standard deviation of regression residual

Fund Size Log of Net asset value of fund j at the end

of year T

Fund Objective As stated in

www.morningstarthailand.com.

Type of parent

company Company website

Fund Age Age from the inception date to the

measurement date

Number of Fund www.aimc.or.th

Total asset under

management Total assets under the management of the

company k at the end of year T

http://www.morningstarthailand.com/

http://www.aimc.or.th/


Research model

Risk Return

Low risk (0-30th )




Low Risk High Return

Med risk High Return Umami-2

High Risk High Return Hot


Low Risk Med Return Umami-1

Med risk Med Return Salty

High Risk Med Return Sour-2


Low Risk Low Return Bland

Med risk Low Return Sour-1

High Risk Low Return Bitter

Sweet Spot


The Distribution of Fund Performance during 2005-2012

Risk Return 2005 2006 2007 2008 2009 2010 2011 2012 Total Performance

Low Risk

High 10 5 7 13 0 9 17 10 71 Sweet Spot Medium 8 7 3 9 9 8 7 14 65 Umami-1

Low 2 9 14 2 15 8 1 1 52 Bland

Medium Risk

High 3 7 4 7 13 2 5 12 53 Umami-2 Medium 7 10 18 23 11 17 22 16 124 Salty

Low 14 11 8 2 8 14 6 5 68 Sour-1

High Risk

High 7 9 13 4 11 14 3 3 64 Hot Medium 9 11 9 0 12 8 4 3 56 Sour-2

Low 4 1 2 20 1 3 18 19 68 Bitter

Total 64 70 78 80 80 83 83 83 621


Outstanding Performance of Sweet Spot funds during 2005-2012

Sweet spot

Umami-1 Bland Umami-2 Salty Sour-1 Hot Sour-2 Bitter Spot

Return 25.46% 17.66% 11.39% 25.59% 17.53% 11.92% 25.77% 17.55% 11.66%

Risk 15.97% 16.21% 16.52% 17.56% 17.55% 17.62% 18.68% 19.05% 18.74%

Low Risk Medium Risk High Risk

14.00%

14.50%

15.00%

15.50%

16.00%

16.50%

17.00%

17.50%

18.00%

18.50%

19.00%

19.50%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

Average Performance of Each Group

Medium Return

High Return

Low Return

Risk


Statistical Results Logit regression Wilcoxon Rank Sum test


Finding and conclusion Hypotheses Variable Expected Results

H2 Idiosyncratic risk Positive Positive significant

H3 Fund size Negative Failed to reject

H4 Fund objective Growth neg, others-pos Failed to reject

H5 Type of parent company Bank-neg, Non Bank-pos Failed to reject

H6 Fund age Positive Positive significant

H7 Number of fund Negative Negative significant

H8 Total asset under management Positive Failed to reject

This study rejected the null hypotheses H2, H6, and H7. To answer the 2nd research question This study concluded that the idiosyncratic risk of fund and the age of fund have significant positive effects on the probability that a fund will deliver a low-risk, high-return performance. The total number of funds managed by the asset management company has the adverse effect to such fund performance. The rest shows no significant roles.


Question 3

Test

Methodology

• Can the result of this study be used to enhance the investment portfolio return?

• Propose three strategies

• Simulations of strategies and compare return

21

Research Question 3

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Strategy 1: Low vs High risk portfolio

Each year the funds were ranked based on its risk. The funds ranked from the 30th percentile downward were selected to invest in the next calendar year as “ Low risk portfolio”

Fund# STD

F1 11.05

F2 11.85

F3 12.33

: :

: :

F20 15.18

: :

: :

:

:

:

F44 16.15

F45 16.16

F46 16.22

: :

: :

F64 17.66

2005 Low risk portfolio Average (2006-12) Cumulative (2006-12)

0-30th percentile 18.64 % 147 %

0-15th percentile 19.26 % 158 %

Lowest 10 funds 19.40 % 159 %

High risk portfolio Average (2006-12) Cumulative (2006-12)

70-100th percentile 16.79% 103%

85-100th percentile 15.66% 89%

Highest 10 funds 15.43% 87%


Result of Simulation 1

18.64% 19.26% 19.40% 16.79%

15.66% 15.43%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30th percentile 15th percentile 10 funds

Average return during 2006-2012

Low risk port High risk port

147% 158% 159%

103% 89% 87%

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

30th percentile 15th percentile 10 funds

Cumulative return from 2006-2012

Low risk port High risk port

Conclusion: Average Return and cumulative return of low risk portfolio outperformed that of the high risk portfolio


Strategy 2 Investing in “Sweet Spot” Winner

43

8

4

4

5

0 10 20 30 40 50

0

1

2

3

4

Number of Funds

Num

ber o

f Tim

es

Funds with "Sweet spot" performance during 2005-2011

Risk Return

Low risk (0-30th )




Low Risk High Return Sweet Spot

Med risk High Return

High Risk High Return


Low Risk Med Return

Med risk Med Return

High Risk Med Return


Low Risk Low Return

Med risk Low Return

High Risk Low Return

Risk-Return Matrix (2005-2011)


Findings Fund Code

2012-2014 Return

2012-2014 Risk

(1) Fund # 4 79.43% 17.24% (2) Fund # 5 71.87% 12.43% (3) Fund # 7 73.32% 12.33% (4) Fund # 8 72.58% 12.25% (5) Fund # 11 64.46% 12.79% (6) Equally-weighted of the selected five

72.33%

13.41%

(7) Industry average 58.11% 15.16% (8) = (6)-(7) 14.22% (1.75)%


Strategy 3-Funds with high probability to be sweet spot

2005 2006 2007 2008 2009 2010 2011

10 14 14 12 12 6 16

• After using 3 filters, we had the selected funds as shown in the table below and the performance in the next slide.


Findings

Portfolio 2006 2007 2008 2009 2010 2011 2012 Average Cumulative

return

(1) Funds with Idio

higher than average

-3.70% 33.93% -42.17% 54.63% 35.44% -1.66% 36.59% 16.15% 110%

(17.06%) (18.63%) (33.60%) (18.72%) (17.37%) (22.70%) (13.59%) (20.24%)

28 18 30 32 30 26 44

(2) Funds with Idio

and Age higher than

average.

-2.68% 33.60% -41.20% 50.25% 32.47% -2.06% 43.08% 16.21% 113%

(16.81%) (18.36%) (32.86%) (17.94%) (16.33%) (21.89%) (13.18%) (19.62%)

18 14 14 14 14 10 21

(3) Funds with Idio

and Age higher than

average and No.Fund

lower than average

-0.19% 33.60% -41.20% 48.27% 31.45% 3.04% 46.14% 17.30% 130%

(16.95%) (18.36%) (32.86%) (17.56%) (16.15%) (20.34%) (13.05%) (19.32%)

10 14 14 12 12 6 16

(4) Industry average -5.21% 33.16% -43.45% 57.70% 41.62% -3.38% 36.65% 16.73% 110%

Table 12: Portfolio Emphasizing the Characteristics of the “Sweet Spot” Fund


Summary simulation results Strategy Strategy 1 Strateg

y 2 Strategy 3

Return Average(2006-2012) Cumulative(2006-2012) Average 2012-14

Average (2006-12)

Cumulative (2006-12)

Description 30th 15th 10 Funds 30th 15th 10 Funds 5 Sweet spot

3 Filtered

Proposed Portfolios

18.6 % (19.4%

19.3 % (18.9%)

19.4 % (18.9%)

147 % 158 % 159 % 72.3 % (13.4%)

17.3 % (19.3%)

130%

Hi-Risk 16.8 % (21.5%)

15.7 % (21.6%)

15.4 % (21.5%)

103% 89% 86 %

Average industry

58.1 % (15.2%)

16.7 % (20.5%)

110%

Difference 1.9 % 3.6% 4% 44 % 69 % 73 % 14.2% 0.60% 20 %

To answer 3rd research question; Based on three simulations, this study concluded that the results of this study may be used to enhance the investment portfolio return.


Conclusion • 1. The study provides another empirical support that in the mutual fund industry, there is

not always a positive risk-return tradeoff, funds with high risk does not always deliver high return. There can be a negative risk-return paradox in the industry.

• 2. It also provides a new performance evaluation technique, which is easy to prepare, use data that is accessible by general investors, and yet is proved to be useful in helpings investors evaluate fund performance.

• 3. Using the regression method, the result shows that the level of specific risk of funds, fund age, and number of funds managed by a company can indicate the probability of the winner funds.

• 4. The study also shows how new investment strategies, which is not necessarily follow high risk high expected return notion, can assist investor in obtaining higher return while exposing to lower risk.


• Instead of evaluate performance using only 2 dimensions,

• Perhaps the 3rd dimension should be considered (Gibson and Sidoni, 2013)

Idiosyncratic risk is significant to fund’s

performance

• Fundamental factor model could be explored further to see whether it will have better predictive power than the single factor model.

Significant of three variables

• We may have a performance persistence in Thai equity mutual fund industry

Superiority of sweet spot performers in

Simulation 2

30

Implication of Study

Documents

The 2016 Capital Market Research Scholarship for Graduate ... · PDF fileในหัวข้อ “Risk and return in equity mutual fund industry: ... Fund Objective Gitman, Joehnk,