Performance evaluation of the Turkish pension fund system

Kuzubas, Tolga Umut; Saltoğlu, Burak; Sert, Ayberk; Yüksel, Ayhan Article Performance evaluation of the Turkish pension fund system Journal of Capital Markets Studies (JCMS) Provided in Cooperation with: Turkish Capital Markets Association Suggested Citation: Kuzubas, Tolga Umut; Saltoğlu, Burak; Sert, Ayberk; Yüksel, Ayhan (2019) : Performance evaluation of the Turkish pension fund system, Journal of Capital Markets Studies (JCMS), ISSN 2514-4774, Emerald, Bingley, Vol. 3, Iss. 1, pp. 18-33, https://doi.org/10.1108/JCMS-03-2019-0013 This Version is available at: https://hdl.handle.net/10419/313263 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Performance evaluation of the Turkish pension fund system Tolga Umut Kuzubas, Burak Saltoğlu, Ayberk Sert and Ayhan Yüksel Department of Economics, Bogazici Universitesi, Istanbul, Turkey Abstract Purpose –The purpose of this paper is to provide an in-depth performance evaluation of funds offered by the Turkish pension system. Design/methodology/approach –This paper compares aggregate fund index returns with the corresponding asset class returns, estimates a factor model to decompose excess returns to factor exposures, i.e., βreturn and excess return originating from residual αand analyzes persistence of fund returns using migration tables and Fama–MacBeth regressions and tests for market timing ability. Findings –Majority of pension funds are unable to generate excess returns. Majority of funds are unable to generate a positive αand fund returns are predominantly driven factor exposures. There is evidence for slight persistence in returns, mainly due to factor exposures and funds do not exhibit market timing ability. Originality/value –In this paper, the authors perform an in-depth analysis of pension fund performance for the Turkish pension fund system. The authors identify weaknesses and strengths of the pension fund industry and provide policy recommendations for a better design of pension fund system. Keywords Fund performance evaluation, Turkish pension system Paper type Research paper Introduction In the last decades, the transformation in the pension fund industry toward defined contribution plans paves the way to the delegation of investment decisions to individuals, i.e., individuals are responsible for their actions such as participation in a plan, the amount and allocation of contributions, portfolio re-balancing and withdrawal of the accumulated sum at retirement. Even though defined contribution plans are flexible, they are prone to uninformed and sub-optimal decisions in complex and uncertain environments. To mitigate this uncertainty on the part of individual investors, Turkish pension fund industry offers a menu of professionally managed funds with different compositions of asset classes. This approach simplifies the portfolio selection problem as it removes complex actions such as security selection from the decision problem, however, limits the universe of pension portfolio construction to a narrowed set of choices, i.e. options provided by the industry. In such an environment, performance of funds offered to the investors is critical for building pension portfolios, which provide sufficient income at retirement. In this paper, we provide a comprehensive analysis of pension funds in terms of design and performance provided by the Turkish pension fund industry. A thorough analysis of fund performance in the domain of pension investing requires a well-defined metric that is suitable for life-cycle investment. The main target of a pension portfolio is to provide sufficient income at retirement, which depends on a number of factors and a complex problem. First, considering cross-section of investors, it depends on time to retirement, income level, education, occupation, life expectancy, target level of spending, etc., Journal of Capital Markets Studies Vol. 3 No. 1, 2019 pp. 18-33 Emerald Publishing Limited 2514-4774 DOI 10.1108/JCMS-03-2019-0013 Received 22 March 2019 Revised 19 April 2019 Accepted 24 April 2019 The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/2514-4774.htm © Tolga Umut Kuzubas, Burak Saltoğlu, Ayberk Sert and Ayhan Yüksel. Published in the Journal of Capital Markets Studies. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode 18 JCMS 3,1 important determinants of life-cycle investment. Second, given the individual characteristics, as retirement income is evaluated in terms of future purchasing power, investment decisions critically depend on the future path of inflation. Therefore, performance evaluation calls for a strategy based on a risk-adjusted return metric taking into account the effects of inflation. The literature provides alternative paradigms for the appropriate risk return metric for long-term investing. First, paradigm offers an asset-only approach originating from the one-period portfolio optimization of Markowitz and its multi-period extension by Merton. This strategic asset allocation approach requires continuous portfolio re-balancing with an objective of maximizing nominal returns considering the risk penalty related to asset volatility. Second, paradigm is the liability-driven, i.e. considers present value of potential future retirement expenditures as a liability to the investor. Similar to the asset-only approach, portfolio is re-balanced periodically to maximize the economic surplus together with a risk penalty based on the shortfall probability. As mentioned above, Turkish pension fund system provides investor a menu of fund choices and investors try to optimally decide on their portfolio of funds to meet the targets implied by these objectives. This design requires the construction of funds by portfolio managers that is consistent with the long-term targets of investors, i.e. objectives of both parties need to be aligned. However, on the one side, portfolio managers, to a large extent, rely on the time-weighted excess returns over a benchmark fund and on the other side, for the pension fund investors, the relevant risk return metric is the money-weighted excess returns over inflation consistent with their life-cycle investment goals. The literature on the performance evaluation for the pension funds in Turkey is rather thin compared to the literature on mutual funds as the Turkish private pension fund system is established in 2003. Notable exceptions are Dağlar (2007), Ege et al. (2011) and Ayaydin (2013) which analyze the performance of pension funds using a single index model with the market portfolio used as an index. Furthermore, they use different market portfolios as benchmark and find that pension funds usually underperform the market index. Another strand of the literature focuses on the market timing ability of funds. Korkmaz and Uygurtürk (2007) and Gökgöz (2007) aim to capture market timing ability using quadratic and dummy variable regression models and conclude that most funds do not exhibit market timing ability. Another related paper is Apak and Taşciyan (2009) who use Morningstar rating methodology to evaluate performance of pension funds. They find that pension funds generally have a negative Morningstar value indicating an inferior performance relative to their benchmark and attribute it to the outperformance of treasury bonds in their sample period. The closest paper to ours is Gökçen and Yalçin (2015). Their analysis yields a similar conclusion to ours such that funds typically fail to beat their benchmarks and generate positive α. They use a common multi-factor model for all funds and found that the fund industry as a whole does not deliver a positive αand neither does the average fund. The multi-factor model they propose includes eight broad asset class indices, including local and global equity indices, local and global bond indices and USD/TRY exchange rate. Regarding fund return persistence and value of active management, they also test a naive trading strategy that buys the top 10 funds in each year and holds them for the next year and find that this naive strategy earns about the same annual return as a passive strategy of holding a half-and-half blend of Turkish stocks and government bonds. Our paper complements and extends the previous literature in several dimensions. In our analysis, we treat each fund category separately, i.e. for each fund we construct a set of relevant factors rather than using same set of factors for all fund categories. For example, we use size, value (Fama and French, 1993) and momentum (Carhart, 1997) in our factor regressions for equity funds. Similarly, we construct level, slope and curvature factors for Turkish bond market and employ them in our regressions for bond funds. Our approach allows us to refine the search for αin fund returns. We use the bootstrap method recently proposed by Fama and French (2010) and Kosowski et al. (2006) to check the robustness of 19 Turkish pension fund system the role of skill and luck in αgeneration. Furthermore, relying on migration analysis and Fama–MacBeth regressions, we conduct an in-depth analysis of return persistence for each fund category, which allows us to identify performance persistence purely due to fund management by eliminating the role of factor exposures. First, we compare aggregate fund index returns with the corresponding asset class returns. Our analysis reveals that, after considering fees and dividends, majority of pension funds are unable to generate excess returns. Second, we estimate a factor model by defining the set of factors separately for each fund category; we decompose excess returns to factor exposures, i.e. βreturn and excess return originating from residual α. We show that majority of funds are unable to generate a positive αand fund returns are predominantly driven factor exposures. Third, in order to test robustness of our results and investigate skill and luck components in αgeneration, we employ a bootstrap test and conclude that αgeneration is not distinguishable from a pure random outcome. Fourth, we analyze persistence of fund returns using migration tables and Fama–MacBeth regressions and find evidence for a slight persistence in returns, mainly due to factor exposures. Finally, we conduct a test for market timing ability for different fund categories. Our analysis indicate that majority of funds do not exhibit a significant market timing ability. Data and empirical analysis We obtain funds’specific data from the “Financial Information News Network”(FINNET), a private up-to-date data provider about capital and financial markets (accessed: 2018). All pension funds in Turkey are required to report their net asset value in daily basis to be in compliance with the regulations. As FINNET sources the data from regulatory filings, the data we use are free from reporting bias. Our sample starts with the launch date of the new private pension system in Turkey, i.e. October 2003, and ends in December 2018. Our funds’ specific data contain price, asset under management (AUM), fee, their own benchmark details (return, weights, indices, etc.) and asset weights. Furthermore, funds’descriptive information like their managers, founder, category, foundation date, closing date if the fund is closed during the sample period are available. We include closed funds to our analysis to be free of survivorship bias. We define main fund categories as money market, local currency bond (LC Bond), foreign currency bond (FC Bond), equity, balanced and gold. BIST Market Indices used to calculate asset class indices/returns and in factor regressions are obtained from Borsa Istanbul. As data on daily returns are noisy, we calculate monthly returns using the first and last day of the month. We also observe that some funds switch their category without changing their name. Each category has different obligations about market operations; thus, to such funds, we behave as a different fund after the date they switched their category. Do pension funds generate excess returns? First, we investigate whether pension funds are able to generate excess returns. To this end, we construct a data set consisting of aggregate fund index returns and the corresponding asset class returns. Index returns are calculated as the asset (AUM) weighted average of daily fund returns for each fund category. Asset class indices used in the analysis are money market, LC Bond, FC Bond, equity, balanced and gold. Detailed information on the composition of asset class indices is provided in Table I. We define excess return as the difference between the average of annual geometric returns (i.e. cumulative average growth rate (CAGR)) for the fund index and the corresponding asset class index. We also use tracking error (TE), information ratio (IR), calculated as the ratio of excess returns to the TE and relative maximum drawdown. Table II presents performance metrics regarding excess returns for each fund category. We observe that for all fund 20 JCMS 3,1 categories excess returns are negative implying portfolios formed using pension funds in each category underperform the corresponding asset class indices. Next, we explore performance heterogeneity among pension funds by repeating the same analysis at the fund level to understand whether the conclusion for the index returns carry over to individual fund returns. To this end, we calculate two versions of excess returns, one using the corresponding asset class as in the previous exercise and second, using the benchmark index constructed by the fund. We present our results in Table III. We define a “typical”fund in each category as the one which provides an excess return at the mean/median level. We observe that only funds investing to a large extent in LC Bond, equity and balanced categories are able to generate excess returns before fees. As a matter of fact, these are the fund categories portfolio managers in Turkey predominantly invest as evident from the high shares in total fund flows. Furthermore, in these categories majority of funds outperform their corresponding asset class before fees. Percentage of positive excess returns are 96.2 percent for LC Bond, 89.4 percent in balanced funds and 86.4 percent in equity. On the other hand, considering fees, only the typical fund in the balanced category can generate a positive excess return against the corresponding asset class index. Figures 1 and 2 show that distributions of excess returns after subtracting fund fees have mean/median values close to 0 in equity and balanced funds and approximately −80 basis points for LC Bonds. Furthermore, typical funds in money market, FC Bond and gold categories, we do not observe a positive excess return before or after fees. Comparing fund returns to the corresponding benchmark returns, all categories except the FC Bond generate positive excess returns; however, after fees taken into account, only equity funds exhibit an outperformance. For the equity funds, price indices do not account or the dividend returns, which are approximately 2.5 percent per annum in Borsa Istanbul, can be collected by pension funds through stock investments. Furthermore, for a typical equity fund, after-fee excess return is around 1.78 percent not covering the dividend return yielding to a negative excess return. Thus, we conclude that after considering the effects of fees and dividends, typical pension funds are not able to generate excess returns compared to their corresponding asset class indices or benchmarks. For the remainder of our analysis, we focus on the excess returns using the corresponding asset classes rather than fund’s own benchmarks. The main reason is that differences in own Category Asset class index Money market BIST-KYD 91 Day Bond Index LC Bond BIST-KYD All Bonds Index FC Bond 50% BIST-KYD USD Eurobond Index +50% BIST-KYD EUR Eurobond Index Equity BIST All Total Return Index Balanced 10% 91 Day Bond +25% BIST All Total Return +65% BIST-KYD All Bonds Gold BIST-KYD Gold Index Table I. Definition of asset class indices Category Start date CAGR (%) TE (%) IR RMDD (%) Money market October 24, 2003 −1.5 0.9 −1.67 23.7 LC Bond October 24, 2003 −1 1.6 −0.62 20.1 FC Bond October 24, 2003 −1.9 3.9 −0.49 29.6 Equity October 24, 2003 −1.5 10.7 −0.14 28.6 Balanced October 24, 2003 −0.7 5.9 −0.12 28.1 Gold April 15, 2013 −2.2 8 −0.28 16.6 Table II. Pension fund index performance vs asset class indices 21 Turkish pension fund system benchmarks are driven by the strategies to overcome investment constraints thus fund managers typically compare fund performances with the relevant asset class index since the design of the benchmark is at fund managers discretion. Next, we explore on the underlying factors leading to positive and negative performances. To this end, we decompose excess returns in two parts: positive/negative excess return due to factor exposures, i.e. βreturn, and the excess return originating from residual α. This decomposition allows us to identify the difference between security selection ability of fund managers and static exposure to traditional return factors. For the analysis, we estimate a factor model, i.e. regress fund returns to several factor returns, defined separately for each fund category. For equity funds, we use BIST-KYD 91 Day Bond Index, BIST ALL Stocks Total Return Index that includes dividends together with size, value and momentum factor returns. We employ factor construction framework by Eugene F. Fama and French (1993) and Carhart (1997) with two modifications. First, we remove any stock with negative book value from our calculations. Second, we use monthly re-balancing relying on balance sheet data rather than annual re-balancing with end-of-year balance sheet data as in the study of Fama and French (1993). Clifford and Frazzini (2013) show that monthly re-balancing with timely balance sheet data yields better results. For LC Bonds, we use BIST-KYD 91 Bond Index and BIST-KYD All bonds index to capture aggregate market return along with long-short factor indices. Litterman and Scheinkman (1991) show that “level,”“slope”and “curvature”factors generated from the yield curve data capture most of the variation in bond returns. Following a similar path, we Category Balanced Equity FC Bond Gold LC Bond Money market Fee 1.81 2.06 1.71 1.38 1.72 1.39 N85 26 26 12 33 23 Asset class (gross) PP 89.4 84.6 15.4 8.3 84.8 47.8 Mean 3.84 2.13 −1.43 −0.56 0.80 −0.02 Q25 1.03 1.18 −2.19 −0.83 0.34 −0.36 Q50 2.32 2.09 −1.25 −0.46 0.89 −0.04 Q75 3.70 3.21 −0.26 −0.29 1.32 0.46 Asset class (net) PP 62.4 50.0 0.0 0.0 6.1 4.3 Mean 1.79 −0.19 −3.39 −2.20 −1.13 −1.58 Q25 −1.00 −1.48 −4.11 −2.41 −1.47 −1.98 Q50 0.38 −0.18 −3.49 −2.26 −0.87 −1.70 Q75 1.84 1.06 −2.32 −1.94 −0.61 −0.76 Benchmark (gross) PP 85.5 100.0 24.0 50.0 75.8 78.3 Mean 1.64 4.21 −0.60 0.06 0.38 0.53 Q25 0.62 3.86 −0.70 −0.52 0.02 0.04 Q50 1.32 4.29 −0.33 0.06 0.28 0.53 Q75 2.66 4.94 −0.03 0.38 0.72 0.83 Benchmark (net) PP 37.3 80.8 0.0 0.0 3.0 8.7 Mean −0.38 1.87 −2.57 −1.58 −1.53 −1.01 Q25 −1.81 0.77 −2.84 −2.15 −1.90 −1.42 Q50 −0.56 1.82 −2.51 −1.65 −1.59 −1.01 Q75 0.51 2.85 −2.01 −1.23 −0.92 −0.85 Table III. Fund excess return statistics 22 JCMS 3,1 construct a “slope”factor based on the difference between BIST-KYD All Bonds Index and BIST-KYD 182 Day Bond Index. “Curvature”factor is generated as the return of a portfolio taking a unit long position in BIST-KYD All Bonds Index and BIST-KYD 182 day index and simultaneously holding two short positions in BIST-KYD 365 day bond index. Finally, we Excess Return Gold LC Bond Money Market Balanced Equity FC Bond −0.03 −0.02 −0.01 0.00 −0.03 −0.02 −0.01 0.00 0.01 0.02 −0.04 −0.02 0.00 0.02 0.0 0.1 0.2 −0.03 0.00 0.03 0.06 −0.06 −0.04 −0.02 0.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 Distribution of Gross and Net Excess Returns vs Asset Class Index Figure 1. Excess return distributions vs asset class index Gold LC Bond Money Market Balanced Equity FC Bond −0.02 −0.01 0.00 0.01 −0.02 0.00 0.02 −0.03 −0.02 −0.01 0.00 0.01 0.02 −0.05 0.00 0.05 0.10 0.00 0.03 0.06 0.09 −0.050 −0.025 0.000 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 Excess Return Distribution of Gross and Net Excess Returns vs Benchmark Index Figure 2. Excess return distributions vs fund benchmarks 23 Turkish pension fund system include the return difference BIST-KYD All Bonds Index and BIST-KYD Inflation Linked Bonds Index as our final factor. For foreign currency funds, we use BIST-KYD 91 Day Bond Index and BIST-KYD USD Eurobond Index (in Turkish lira), together with two exchange rates, i.e. USD/TRY, EUR/ USD. Furthermore, we include “slope”and “curvature”factors in our analysis. For balanced funds, we employ all factors described above. For each fund category, we regress asset-weighted fund indices on the constructed factors. First, in our baseline regression, we regress fund indices on the short-term bond return index –BIST-KYD 91 Day Bond Index –and main market indices. Baseline regression model is specified as follows: Yi t¼aiþbi 1BAlli tþbi 2BISTi tþbi 3Bond91i tþbi 4EBondi tþEi t; where BAlli tis the BIST-KYD All Bonds Index; BISTi tthe BIST All Stocks Total Return Index that includes dividends; Bond91i tthe BIST-KYD 91 day bond index; and EBondi tthe BIST-KYD USD Eurobond Index (in Turkish lira). The set of covariates changes depending on fund category. We present our results in Table IV. Second, we extend our baseline model by including other factors described above and perform regressions for each fund category and present results in Table V. Our main focus is to identify funds that generate a positive α. Our results suggest that for all pension fund categories, in both baseline and extended regressions, asset-weighted aggregate fund portfolio are not able to generate a statistically significant α. Next, we focus on the individual fund returns and estimate similar factor models separately for each fund to uncover the heterogeneity in fund performances. We present our regression results in Table VI and Figure 3. In Table VI, we show the number of funds with statistically significant αvalues for each fund category and in Figure 3, we provide the distribution of estimated αvalues for each fund category. Our results confirm our average prediction as majority of funds are not able to generate a positive and statistically significant αwhich is consistent with our prediction that fund returns predominantly are due to factor/style exposures rather that stock selection ability. Is it luck or skill? In this section, we further explore the significance of αby using bootstrap tests suggested by Kosowski et al. (2006) and extended by Eugene F. Fama and French (2010)[1]. The aim of Variable Coef All Balanced Bond EBond Equity αEstimate −0.0019 −0.0014 −0.0021 −0.0009 0.001 αt-stat −2.1705 −0.6021 −4.3941 −0.7283 0.4485 BAll Estimate 0.2282 −0.4141 0.7838 BAll t-stat 9.659 −6.5398 65.7824 BIST Estimate 0.133 0.2991 0.9131 BIST t-stat 24.8574 20.1607 70.0354 Bond91 Estimate 0.5505 1.1752 0.3297 0.082 −0.0807 Bond91 t-stat 6.7889 5.2033 7.1541 0.7571 −0.4183 EBond Estimate 0.1304 0.8319 EBond t-stat 11.5179 52.2845 R 2 0.8727 0.7044 0.9711 0.9382 0.9648 Notes: Dependent variable is AUM weighted fund category indices. BAll stands for BIST-KYD All Bonds index. BIST is BIST ALL Stocks Total Return Index that includes dividends. Bond91 is the short-term bond return index which is BIST-KYD 91 Day Bond Index and EBond is BIST-KYD USD Eurobond Index (in Turkish lira) Table IV. Fund index factor regression results 24 JCMS 3,1 this analysis is to identify for each fund category a significant positive value of estimated αis due to the skill of fund manager or luck. To this end, in our bootstrap test, for each fund category, we estimate a factor model and store αvalues, corresponding t-statistics, factor βs and residuals. For each bootstrap iteration, we randomly select Tdates from historical data with replacement where Tis the number of months in the original analysis. For each T, we obtain factor returns and fund residuals. For each fund category, we generate a counterfactual monthly return by combining original factor βs, resampled factor returns and residuals. Note that by construction, counterfactual returns have 0 true α. Using the Variable Coef All Balanced Bond EBond Equity αEstimate −0.0017 −0.003 −0.0015 −0.0011 0.0007 αt-stat −2.1016 −1.5376 −3.15 −1.2319 0.3017 BAll Estimate −0.1036 −1.3757 0.7933 BAll t-stat −0.5496 −3.0844 7.7201 BIST Estimate 0.1249 0.2721 0.9004 BIST t-stat 22.2208 20.457 67.6843 Bond91 Estimate 0.9115 2.2122 0.2531 0.1245 −0.0419 Bond91 t-stat 3.9098 4.0101 1.9589 1.5525 −0.2219 Curvature Estimate −0.1501 −0.5728 −0.1053 −0.1025 Curvature t-stat −3.2755 −5.2836 −3.9824 −2.4319 EBond Estimate 0.0713 0.1203 0.7252 EBond t-stat 3.8228 2.725 34.7642 EURUSD Estimate 0.027 0.0445 0.1797 EURUSD t-stat 2.0547 1.4318 11.9445 Momentum Estimate 0.0006 −0.048 −0.0564 Momentum t-stat 0.0653 −2.0976 −2.0225 Size Estimate −0.0085 −0.0563 0.0224 Size t-stat −0.7921 −2.2134 0.7239 Slope Estimate 0.6353 1.7749 0.0247 0.027 Slope t-stat 3.0551 3.6074 0.2085 0.5714 TIPS Estimate 0.1427 0.2575 −0.0145 TIPS t-stat 4.262 3.2508 −0.7518 USDTRY Estimate 0.0605 0.0686 0.1238 USDTRY t-stat 3.09 1.4789 6.0373 Value Estimate 0.0039 −0.0283 0.0565 Value t-stat 0.3358 −1.0269 1.6772 R 2 0.8992 0.8314 0.9747 0.9697 0.9667 Notes: Dependent variable is AUM weighted fund category indices. Slope is defined as difference between BIST-KYD All Bonds Index and BIST-KYD 182 Day Bond Index. Curvature is generated as the return of a portfolio taking a unit long position in BIST-KYD All bonds index and BIST-KYD 182 day index and simultaneously holding two short positions in BIST-KYD 365 day bond index. USDTRY and EURUSD stand for exchange rates. TIPS is the return difference BIST-KYD All Bonds Index and BIST-KYD Inflation Linked Bonds Index. Size, Value and Momentum are factors from Fama and French (1993) and Carhart (1997) with two modifications. We remove any stock with negative book value, and use monthly re-balancing. Other covariates are defined in Table V Table V. Fund index multi-factor regression results Category NPos Neg Perc_Pos Perc_Neg Balanced 85 13 7 0.1529412 0.0823529 Bond 33 0 11 0.0000000 0.3333333 EBond 26 1 3 0.0384615 0.1153846 Equity 26 1 0 0.0384615 0.0000000 Table VI. Funds with statistically significant α 25 Turkish pension fund system Conclusion Introduction of defined contribution plans in the Turkish pension system lead to the delegation of investment decisions to individual investors. Despite the flexibility defined contribution plans provide, they are prone uninformed and sub-optimal decisions in complex and uncertain environments. Turkish pension fund system offers professionally managed funds to pension investors to mitigate this uncertainty, however, leave investors with a narrow set of choices. In such an environment, performance of funds offered to the investors is crucial for building pension portfolios, which provide sufficient income at retirement. Our goal in this paper is to present an in-depth performance evaluation of funds offered by the Turkish pension system. Our analysis reveal that majority of pension funds are not able to generate excess returns when compared to the corresponding asset class returns. Furthermore, they do not generate significant αvalues and excess returns are predominantly driven by factor exposures indicating a minuscule role of active fund management. This observation is confirmed by our bootstrap results, i.e. αgeneration is not distinguishable from a pure random outcome. We analyze return persistence for pension funds using migration tables for different return quartiles and Fama–MacBeth regressions. We find evidence for a slight return persistence. As a further analysis, we evaluate the performance of a fund selecting strategy based on past six month’s performance. We observe that for some fund categories, it is feasible to achieve above average excess returns by selecting funds based on recent past performance, however, this outperformance is limited and again mainly due to factor exposures. Our analysis provides several policy recommendations regarding the design of pension funds in Turkey. First of all, in a volatile economic environment like Turkey, finding an excess return in general is a difficult task. This performance should in addition could not find a persistence in fund performance. Therefore, a typical pension fund investor who is completely different from a mutualfundinvestorshouldbeofferedalessriskyfundwithaplainbenchmark.Generous government support to the pension system might produce an excess return had a better financial asset been chosen. For instance, an inflation protected bond could have been a much better alternative than that of a long-term government bond as a government contribution tool. The generous government support has a relatively more conservative portfolio, therefore the choice of asset universe and investor profile is needed to be studied further. Furthermore, in order to use inflation as a benchmark, inflation hedging instruments such as swaps should be allowed in portfolio construction process. Our common conclusion is that it is essential to improve the longer term risk/return ratio for the pension fund managers. This can be obtained by using more ETF alternatives, less risky government contributions and using more inflation hedging instruments. As a conclusion, pension fund system in a volatile macroeconomic environment, different tools should be utilized to design a more sustainable system. Note 1. They run a bootstrap simulation mimicking the properties of the actual fund returns, and set the value of αto 0 in the population distribution, i.e. these simulations provide the distribution of αs when there are no abnormal returns. Finally, they compare the distribution of αs obtained from simulations with αestimates for actual fund returns and infer the existence of skilled managers. References Apak,S.andTaşciyan, K.A. 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(1993), “Common risk factors in the returns on stocks and bonds”,Journal of Financial Economics, Vol. 33 No. 1, pp. 3-56. Fama, E.F. and French, K.R. (2010), “Luck versus skill in the cross-section of mutual fund returns”, The Journal of Finance, Vol. 65 No. 5, pp. 1915-1947. Gökçen, U. and Yalçin, A. (2015), “The case against active pension funds: evidence from the Turkish private pension system”,Emerging Markets Review, Vol. 23, June, pp. 46-67. Gökgöz, F. (2007), “Bireysel Emeklilik Fonlarinin Performans Değerlendirmesi”,Hacettepe Üniversitesi İktisadi ve İdari Bilimler Dergisi, Vol. 25 No. 1, pp. 259-291. Korkmaz, T. and Uygurtürk, H. (2007), “Türkiye’deki Emeklilik Fonlarinin Performans Ölçümü Ve Fon Yöneticilerinin Zamanlama Yeteneği”,Akdeniz Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, Vol. 7 No. 14, pp. 66-93. Kosowski, R., Timmermann, A., Wermers, R. and White, H. (2006), “Can mutual fund ‘stars’really pick stocks? New evidence from a bootstrap analysis”,The Journal of Finance, Vol. 61 No. 6, pp. 2551-2595. Litterman, R.B. and Scheinkman, J. (1991), “Common factors affecting bond returns”,The Journal of Fixed Income, Vol. 1 No. 1, pp. 54-61. Treynor, J. and Mazuy, K. (1966), “Can mutual funds outguess the market”,Harvard Business Review, Vol. 44 No. 4, pp. 131-136. Further reading Fama, E.F. and MacBeth, J.D. (1973), “Risk, return, and equilibrium: empirical tests”,Journal of Political Economy, Vol. 81 No. 3, pp. 607-636. Swamy, P.A.V.B. (1970), “Efficient inference in a random coefficient regression model”,Econometrica, Vol. 38 No. 2, pp. 311-323. Corresponding author Tolga Umut Kuzubas can be contacted at: [email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] 33 Turkish pension fund system