CONCLUSION INDONESIAN MANAGED FUNDS PERFORMANCE EVALUATION AFTER GLOBAL ECONOMIC RECOVERY BY USING DATA ENVELOPMENT ANALYSIS WITH NEW RISK MEASURES (2011 – MID 2015).

CHAPTER V CONCLUSION

5.1. Conclusions

The objectives of this research are to assess the attractiveness and efficiency of Indonesian managed funds and to underline outperformed and underperformed Indonesian managed funds after global economic recovery where markets and investments have already passed the “redemption era” after the global economic crisis. Thus, based on the previous chapter’s data collection and analysis, we can draw following statements of conclusion:

1. Indonesian managed funds averagely performed below than their weighted benchmark return as seen by their lower average of expected return. However, selecting best performers of these funds can be still profitable, even compared to the weighted benchmark return where the top 10 performers based on the covariance can generate 9.16%.annually. Among the top best performers, Kresna Flexima shines by generating 15.3% annually after the global economic recovery. Table 4.4. also presents the importance of syariah managed funds that generate 6.3% of return, compared to 4.45% of conservative managed funds, even though conservative managed funds’ covariance is better.

2. 47 out of 50 funds are experiencing an increase in total risk measurement when wee apply the root of semi lower variance. This means that skeewness and kurtosis are able to be taken into account for most of Indonesian managed funds. Aberdeen Dana Handal and MNC

Dana Kombinasi stand out from others by significantly improve their covariance from such predicament and they are even among top 10 performers of Indonesian managed funds.

3. Equity Prima, Aberdeen Dana Handal, BNP Paribas Equitra, Cipta Dinamika, and Schroder Dana Kombinasi are those which are the closest to zero (random) influence from the systematic risk with all respective values less than 0.09. Overall, from only 2 among 50 funds which have invalid βj, we can see that Indonesian managed funds are positively influenced by the market. However, the market influence is still inelastic to the managed funds. Thus, this signifies that Indonesian managed funds are safely composed without exaggerating effect from the financial market.

4. There are only 16 Indonesian managed funds that are able to overcome the weighted benchmark return, risk-free rate asset, total risk, systematick risk, or the CAPM betha at the same time with 9.62% annualized expected return and the top performers based on merely covariance cannot measure this. Even more, 3 of them are syariah managed funds. Thus, the 16 managed funds from Table 4.9. are important, even though their covariances are not the best.

5. All Indonesian managed funds possess kurtosis and only 1 fund does not have the skewness. This proves that what Chen and Lin (2006) discuss is true to include more sophisticated performance evaluation to include variables which measure skewness and kurtosis.

6. VaR and CVaR are among the sophisticated evaluation tool that take the skewness and kurtosis into account for investors to realize the maximum possible loss that can be suffered in a period of time that is realistically affect investors’ emotion as emphasized by Chen and Lin (2006). With different angle of perspective, Indonesian managed funds investors have to be ready for maximum possible loss up to 1.2% (VaR) and 9.5% (CVaR).

7. 24 Indonesian managed funds are efficient with their own characterics among the 50 observed funds with 11 variables (7 inputs and 4 outputs). Banker et al., (1984) in Chen and Lin (2006) also emphasizes the possibility of having efficient Decision Making Units (DMUs) with negative outputs. Thus, understanding the nature of DEA model in a research is essential. Obviously, no negative output is desirable. Thus, 8 Indonesian managed outperformed others in terms of performance in the variables and efficiency among variables and DMUs. They are: (1) Semesta Dana Maxima, (2) TRIM Kombinasi II, (3) TRIM Syariah Berimbang, (4) Minna Padi Keraton, (5) Krena Flexima, (6) Nikko BUMN Plus, (7) SAM Syariah Berimbang, and (8) Schroder Syariah Balanced Fund, where three of them are syariah managed funds and on the other hand, except, for kurtosis, VaR, and Sharpe, Indonesian managed funds should have performed better. Jensen alpha, followed by Treynor ratio, βj, return, and the root of semi lower variance are among the most inefficient variables of Indonesian managed funds after global economic recovery.

5.2. Research Limitations and Suggestions

This research has limitations due to several predicaments. Thus, several suggestions are also given for future research:

1. The inavailability of Indonesian managed funds’ transaction cost. As suggested by Chen and Lin (2006), providing transaction cost information is necessary to describe more variables that directly affect investors’ choice of investment. Subsciption and redemption costs are some examples of transaction costs that are going to so meaningful to be applied in DEA model.

2. STABLE program software. The expensive STABLE program software is also suggested by Chen and Lin (2006) to conduct the DEA fully. There are three advantages of having the STABLE program: (1) Standardizing the VaR and CVaR variables to include the number of skewness and kurtosis directly, (2) Conduct the DEA at weighted-standardized more than 1 runs, applicable to the types of variables, and (3) Overcoming the negative outputs in models by applying Translation Invariance Property.

3. For future researches, the author suggests several things concerning the topics that are:

a. Using more DEA models along with model comparison in use because of the ability of OSDEA-GUI to comprise various DEA models such as global returns to scale (GRS), increasing and decreasing global returns to scale (IRS and DRS), non-controllable models (NC), non-discretionary models (NC), and variations that

combine them at the same time. Compared to OSDEA-GUI’s ability, well known international journals are still about CCR and BCC approach.

b. Utilizing several other constraints of the linear program of DEA to analyze and comprehend the characteristics and results among decision making units. The DEA’s linear programming is rich enough to be analyzed deeper in its each constraints. Lambdas, returns to scale, and variables’ weights are several examples of variables inside the DEA linear programming that can be analyzed comprehensively based on research’s necessity.

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4 ; : / 34 / 31!5 / 22 3 32 / 1 !02 !4

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105

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107

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0 7 ; /5 5 !!1 / 542!4 4 3/ 511 4!5! 5 /2443! 44 ' @ 7 ,

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4 : ? % .8 6 ; .8 /1015 43 3/! 3053 2 / 1 !55 21/2 04 4 ' @ 7 ,

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2 7 .8 /!2 2134 4 1/!104 35! 1/ 3 250! 0 43/51 !43 ' @ 7 ,

2 7 , /230 40 1 /4!1 32!4 !/ 3 5 !2/3541 !2 ' @ 7 ,

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! , = : / 2 350 10 /!0 5 224

5 , ; .8 / !3 042 /354 12525

1 ; , . /205 0540 /25244 0

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4 ; 8 + , / 3!5 455 /2!!5 414

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9, ; : / 5031 42 /502 ! 4!

2 9, . /0 !23 /! 50 3 1

0 9, '- 6 ; : /20 50235! /4 2253 52

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1 , ', , ; : / 5!03 0! /!12 23440

3 , ', , ' / 5 51 /3!45441!5

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2 7 : . /!0 5! 0 5 / 2! 0 30

0 7 ; /04 35 2 / 134 1

! 7 : < / 5!042!!5 /114222444

5 , , ? / 015423 /1!5 55022

1 : ? .9 / 4 42 /!15 133 1

3 : ? ', 8 : ; .8 / !05!3 23 /! 5 40!22

4 : ? % .8 6 ; .8 / !044!4 2 /3 10142

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20 , : / 0 1430 /53!0 332

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24 9 ; : /51 41302 /01044401

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