The 2016 Capital Market Research Scholarship forGraduate Students

งานวิจยัจากคณะบริหารธรุกิจและเศรษฐศาสตร์

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

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

ผศ.ดร.นพพล ตัง้จติพรหม..........อาจารยท์ีป่รกึษา

17 พฤศจิกายน 2559

Executive summaryIn 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 Bowmanconducted a research on correlation between return andstandard deviation of return of firms in 85 industries and foundthat it was negatively correlated within industry. This is knownas the Bowman Paradox.

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

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

• Thai mutual fund industry is growing. For the past 4-5 years since 2012, it grew 123% (Bank of Thailand, 2016).

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 ReviewBowman (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 reviewVariable 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-negValue-Pos

Fama and French (1993) argued that companies with high book-

to-market ratios (value stocks)

outperformed those with low ones (growth stocks).

Growth-negValue-Pos

Literature reviewVariable 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 – negativeNon Bank- Positive

Ferris and Yan (2009) Agency cost theory Public-NegPrivate-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?

• 𝑯𝒂𝟏: There is a significant negative relationship between risk and return in the Thai open-end equity mutual funds industry.

• 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 Return𝑟𝑇𝑌𝑗 = 𝑡=1

𝑇 (1 + 𝑟𝑦𝑡𝑗)1/𝑇

−1

Annualized standard deviation of monthly returns

= 1

𝑛−1 𝑛=1𝑛 𝑟𝑛

𝑗− 𝑟𝑗𝑇

2* 12

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

𝑟𝑌𝑗and STD -.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

𝑟3𝑌𝑗 and 𝑆𝑇

𝑗 .18864

-.409***64

-.05464

-.541***70

.05278

.223**80

.256**80

-.503***83

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

𝑟5𝑌𝑗 and 𝑆𝑇

𝑗 0.1564

-.482***64

-.260**64

.259**70

0.15178

-.402***80

- -

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

𝑟10𝑌𝑗 and 𝑆𝑇

𝑗 -.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?

• 𝑯𝒂𝟐 to 𝑯𝒂𝟖

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

12

Research Question 2

Research framework

RiskReturn

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

Sweet Spot Matrix

Source: Developed for this study

Measurements of independent variablesVariables Name Measurement

𝜓𝑇𝑗 Idiosyncratic risk Standard deviation of regression residual

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

year T

𝐷𝑂𝑏𝑗𝑗 Fund Objective As stated in www.morningstarthailand.com.

𝐷𝑇𝑦𝑝𝑒 − 𝑝𝑎𝑟𝑒𝑛𝑡 Type of parent company

Company website

𝐴𝑔𝑒𝑇𝑗 Fund Age Age from the inception date to the

measurement date

𝑁𝑜. 𝐹𝑢𝑛𝑑𝑘,𝑇 Number of Fund www.aimc.or.th

𝐿𝑜𝑔 𝐴𝑈𝑀𝑘,𝑇 Total asset under management

Total assets under the management of the company k at the end of year T

Research modelPr(𝑆𝑆 = 1) = 𝐿𝑜𝑔𝑖𝑡( 𝜇0 + 𝜇1𝜓𝑇

𝑗+𝜇2 𝐿𝑜𝑔 𝑆𝑖𝑧𝑒𝑇

𝑗+ 𝜇3𝐷𝑂𝑏𝑗

𝑗 + 𝜇4 𝐴𝑔𝑒𝑘,𝑇 +

𝜇5 𝐷𝑇𝑦𝑝𝑒 − 𝑝𝑎𝑟𝑒𝑛𝑡𝑘 + 𝜇6𝑁𝑜. 𝐹𝑢𝑛𝑑𝑘,𝑇 + 𝜇7 𝐿𝑜𝑔 𝐴𝑈𝑀𝑘,𝑇 )

𝑆𝑆 = The binary variable represent the performance of mutual fund j at time T. It takes value equal to one if the fund deliver a low-risk, high-return performance and zero otherwise.

RiskReturn

Low risk(0-30th )

Medium risk (30th -70th )

High risk(70th-100th )

High return (70th 100th )

Low Risk High Return Med risk High Return Umami-2

High Risk High ReturnHot

Medium return (30th -70th )

Low Risk Med ReturnUmami-1

Med risk Med ReturnSalty

High Risk Med ReturnSour-2

Low return(0-30th )

Low Risk Low ReturnBland

Med risk Low Return Sour-1

High Risk Low ReturnBitter

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 SpotMedium 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-2Medium 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 HotMedium 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

Sweetspot

Umami-1 Bland Umami-2 Salty Sour-1 Hot Sour-2BitterSpot

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 ResultsLogit regression Wilcoxon Rank Sum test

Finding and conclusionHypotheses 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 questionThis 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

22

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%

Both on Standard Deviation and Beta

23

Ls 30

Hs 30

Ls 15

Hs 15

Ls 10

Hs 10

Avg sample

15.00%

15.50%

16.00%

16.50%

17.00%

17.50%

18.00%

18.50%

19.00%

19.50%

20.00%

18.50% 19.00% 19.50% 20.00% 20.50% 21.00% 21.50% 22.00%

Ave

rage

Ret

urn

Average Standard Deviation

STD Strategy- Average Return and Standard deviation

Lb 30

Hb 30

Lb 15

H…

Lb 10

Hb 10

Avg Sample

16.00%

16.50%

17.00%

17.50%

18.00%

18.50%

19.00%

19.50%

20.00%

20.50%

21.00%

0.8 0.85 0.9 0.95 1 1.05

Avera

ge R

etu

rn

Average Beta

Beta strategy- Average Return

and Beta

THE SCATTER PLOT BETWEEN THE AVERAGE RETURNS AND RISK OF PORTFOLIOS DURING 2006-2012

Result of Simulation 1(STD)

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 outperformedthat 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 )

Medium risk (30th -70th )

High risk(70th-100th )

High return (70th 100th )

Low Risk High ReturnSweet Spot

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

Sweet Spot Matrix ( 7 years :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

• This study construct portfolio S which was the set of funds that had the following condition

𝑆 = 𝐹𝑢𝑛𝑑𝑠| 𝑋𝑛 > 𝑋𝑛, 𝑖𝑓 𝜇𝑛 > 0, 𝑋𝑛 < 𝑋𝑛, 𝑖𝑓 𝜇𝑛 < 0,

𝑋𝑛 = Significant explanatory variables from the Logistic Regression

𝜇𝑛 = Regression coefficient of 𝑋𝑛

𝑋𝑛 = Industry average value of variable 𝑋𝑛

• From regression result, we had 3 significant variables. So we construct the following portfolio.

𝑆 = 𝐹𝑢𝑛𝑑𝑠|ѱ:ѱ > ѱ,𝐴𝑔𝑒: 𝐴𝑔𝑒 > 𝐴𝑔𝑒 ,𝑁𝑜. 𝐹𝑢𝑛𝑑:𝑁𝑜. 𝐹𝑢𝑛𝑑 < 𝑁𝑜. 𝐹𝑢𝑛𝑑

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 resultsStrategy Strategy 1 Strateg

y 2Strategy 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 Sweetspot

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

31

Implication of Study