the 2016 capital market research scholarship for graduate ... · pdf...
TRANSCRIPT
University Logo 2.18 * 3.37 cm
งานวิจยัจากคณะบริหารธรุกิจและเศรษฐศาสตร ์
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
University Logo 2.18 * 3.37 cm
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.
University Logo 2.18 * 3.37 cm
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).
University Logo 2.18 * 3.37 cm
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?
University Logo 2.18 * 3.37 cm
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)
University Logo 2.18 * 3.37 cm
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
University Logo 2.18 * 3.37 cm
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
University Logo 2.18 * 3.37 cm
Methodology and Findings
University Logo 2.18 * 3.37 cm
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
University Logo 2.18 * 3.37 cm
• 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
University Logo 2.18 * 3.37 cm
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.
University Logo 2.18 * 3.37 cm
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
University Logo 2.18 * 3.37 cm
Research framework
University Logo 2.18 * 3.37 cm
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
University Logo 2.18 * 3.37 cm
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
University Logo 2.18 * 3.37 cm
Research model
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 Umami-2
High Risk High Return Hot
Medium return (30th -70th )
Low Risk Med Return Umami-1
Med risk Med Return Salty
High Risk Med Return Sour-2
Low return (0-30th )
Low Risk Low Return Bland
Med risk Low Return Sour-1
High Risk Low Return Bitter
Sweet Spot
University Logo 2.18 * 3.37 cm
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
University Logo 2.18 * 3.37 cm
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
University Logo 2.18 * 3.37 cm
Statistical Results Logit regression Wilcoxon Rank Sum test
University Logo 2.18 * 3.37 cm
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.
University Logo 2.18 * 3.37 cm
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
University Logo 2.18 * 3.37 cm 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%
University Logo 2.18 * 3.37 cm
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
University Logo 2.18 * 3.37 cm
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 Return Sweet 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
Risk-Return Matrix (2005-2011)
University Logo 2.18 * 3.37 cm
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)%
University Logo 2.18 * 3.37 cm
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.
University Logo 2.18 * 3.37 cm
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
University Logo 2.18 * 3.37 cm
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.
University Logo 2.18 * 3.37 cm
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.
University Logo 2.18 * 3.37 cm
• 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