The document summarizes a study examining the performance of actively managed global equity funds in Central and Eastern Europe between 2005-2019. The study uses a bootstrap methodology to separate fund manager skill from luck. Key findings include: - Approximately 5% of funds showed skill in outperforming their benchmarks gross of fees, with one fund in particular outperforming factor returns net and gross of fees. - Most funds that underperformed did so due to lack of skill rather than bad luck. - Fees were too high relative to the abnormal performance added by many mutual funds. - While some fund managers possessed skill, it was generally not enough to cover their fees, suggesting fees may be too high or competition

1. Luck and skill in the performance of global
equity funds in Central and Eastern Europe
Triinu Tapver
Department of Economics and Finance
Tallinn University of Technology
triinu.tapver@taltech.ee
16 December 2022
Eesti Panga avatud seminar
*Published as:
Tapver, T. (2022), "Luck and skill in the performance of global equity funds in Central and Eastern Europe", Managerial
Finance, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MF-01-2022-0051

2. Background
• Active versus passive fund management
• Do actively managed funds deliver alpha (nonzero risk-adjusted return)?
• Actively managed funds generally underperform their benchmarks (e.g. Malkiel, 1995; Pástor and
Vorsatz, 2020), but superior performance has been detected at some actively managed funds
(e.g. Berk and van Binsbergen, 2015; Fortin and Michelson, 2002; Guercio and Reuter, 2014; Wermers, 2000).
• Do these fund managers have good skills? Or are they just lucky?
𝛼 > 0 or 𝛼 < 0 or 𝛼 = 0

3. Background
• Active versus passive fund management
• Do actively managed funds deliver alpha (nonzero risk-adjusted return)?
• Actively managed funds generally underperform their benchmarks (e.g. Malkiel, 1995; Pástor and
Vorsatz, 2020), but superior performance has been detected at some actively managed funds
(e.g. Berk and van Binsbergen, 2015; Fortin and Michelson, 2002; Guercio and Reuter, 2014; Wermers, 2000).
• Do these fund managers have good skills? Or are they just lucky?
• Central and Eastern Europe (CEE)
• Relatively young region with expanding stock markets (+8.5% pa) and investment fund
industry (no of funds 1.6x as fast as in the rest of EU).
• The ratio of stock market capitalisation to GDP, average size and the total number of
funds is still low -> room for future growth.
• Household savings rate is higher than in the US and the EU average -> growth potential for
further mutual fund demand.
• Financial literacy rate remains among the low -> potential shortage of skilful managers.
• Research gap: The separation of luck and skill is uncovered in most emerging markets including
CEE. Research on fund performance in CEE has been sparse to date.
𝛼 > 0 or 𝛼 < 0 or 𝛼 = 0

4. This study
• Examining the performance of individual global equity funds in CEE and separating their
fund manager skill from luck.
• Monthly net and gross returns of 175 open-ended equity mutual funds incorporated in
CEE countries in the EU.
• Measured in US dollars gross on all distributions passed to investors.
• Actively managed equity funds with globally invested portfolios.
• At least 24 monthly returns during the sample period.
• CEE countries that are represented: The Czech Republic, Estonia, Hungary, Latvia, Lithuania,
Poland, Slovakia, and Slovenia.
• Period September 2005 to December 2019.
• After-crisis period May 2009 to December 2019 (163 funds).
• Fund and fund benchmark data from Eikon. Risk-free fate, market return and factor
returns from K. French database.
• Cross-sectional bootstrap methodology that separates fund manager skill from luck,
following Kosowski et al. (2006).
• Allows complex non-normalities in the idiosyncratic risk of the funds and for non-normalities
in individual fund alpha distributions.

5. • Step 1. Evaluate true alpha for each fund using:
(1) Fama-French (1993) three-factor model (w/o MOM);
(2) Carhart’s (1997) four-factor model (incl MOM);
(3) Fama-French (2015) five-factor model (w/o MOM; incl RMW, CMA);
(4) Benchmark regressions i.e., regressions relative to fund-specific benchmark indexes denoted in fund
prospectus.
• Rit – return of fund i for month t,
• RFt – risk-free rate (the 1-month U.S. Treasury bill rate),
• RMt – market return,
• SMBt – size return,
• HMLt – value-growth return,
• MOMt – momentum return,
• RMWt; CMAt – profitability and investment factors extending the three-factor model,
• RBMit – return of a fund-specific benchmark index denoted in the fund prospectus,
• 𝜶𝒊 – abnormal return, performance that did not have to do with the market i.e. average return left un-explained by the
factor model (the estimate of 𝜶𝒊), added by fund manager,
• 𝜀𝑖𝑡 – regression residual.
How? – Step 1. Evaluating true alpha
𝑅𝑖𝑡 − 𝑅𝐹𝑡 = 𝛼𝑖 + 𝛽𝑖 𝑅𝑀𝑡 − 𝑅𝐹𝑡 + 𝛾𝑖𝑆𝑀𝐵𝑡 + 𝛿𝑖𝐻𝑀𝐿𝑡 + 𝜀𝑖𝑡 (1)
+ 𝜑𝑖𝑀𝑂𝑀𝑡 + 𝜀𝑖𝑡 (2)
+ 𝜂𝑖𝑅𝑀𝑊𝑡 + 𝜃𝑖𝐶𝑀𝐴𝑡 + 𝜀𝑖𝑡 (3)
+ 𝛽𝑖 𝑅𝐵𝑀𝑖𝑡 − 𝑅𝐹𝑡 + 𝜀𝑖𝑡 (4)

6. How? – Step 2. Cross-sectional bootstrap simulations
• Regression coefficients መ
𝛽𝑖, ො
𝛾𝑖, መ
𝛿𝑖, ො
𝜑𝑖, Ƹ
𝜂𝑖, ෠
𝜃𝑖;
• Draw random samples (time series) of saved estimated fund residuals Ƹ
𝜀𝑖𝑡;
• Generate pseudo excess returns (𝑟𝑖𝑡
𝑏
), assuming H0: real abnormal performance is
zero i.e. real alpha and real alpha t-statistic are equal to zero ( ො
𝛼𝑖 = 0 and Ƹ
𝑡ෝ
𝛼𝑖
= 0);
• Estimate new pseudo alphas and t-statistics (pure „luck“) using models (1)-(4);
• x 1000 simulation runs (b = 1, …, 1000):
• 1000 pseudo alphas and t(𝛼) for each fund;
• Compare the distribution of real and pseudo t(𝛼) to distinguish skill from luck:
• t(𝛼) has better properties for controlling for heterogeneous risk-taking than 𝛼
• If actual t(𝛼) > 95% of pseudo t(𝛼) → POSITIVE SKILL
• If actual -t(𝛼) < 95% of pseudo -t(𝛼) → NEGATIVE SKILL
𝑟𝑖𝑡
𝑏
= 𝑅𝑖𝑡 − 𝑅𝐹𝑡
𝑏 = 0 + መ
𝛽𝑖 𝑅𝑀𝑡 − 𝑅𝐹𝑡 + ො
𝛾𝑖𝑆𝑀𝐵𝑡 + መ
𝛿𝑖𝐻𝑀𝐿𝑡 + ො
𝜑𝑖𝑀𝑂𝑀𝑡 + Ƹ
𝜂𝑖𝑅𝑀𝑊𝑡 + መ
𝜃𝑖𝐶𝑀𝐴𝑡 + Ƹ
𝜀𝑖𝑡
𝑏

7. Results

8. Bootstrap results: whole period (Sept 2005 – Dec 2019)

9. Bootstrap results: post-crisis period

10. Regression results of individual funds:
Performance relative to fund-specific benchmarks

11. Bootstrap results, post-crisis period:
• Proportion of skilful funds is considerably higher than in the multifactor model results.
• Skill is absorbed by the charged fees.
1. Fees may be too high given the abnormal performance mutual funds add.
2. While some mutual fund managers in CEE do possess skill, they may not have enough skill to cover
their fund fees.
• Low competition?
Performance relative to fund-specific benchmarks
v
v

12. Performance relative to fund-specific
benchmarks: economic value-added
Economic value-added = gross alpha from
benchmark regressions ∙ fund size
(Berk and van Binsbergen, 2015)
• Yearly economic value-added:
• 3.5 bn US dollars in the whole period
• 7.5 bn US dollars after the crisis.
• Fund size seems to matter:
• Large funds drive the value-added up.
This contradicts with previous
literature (e.g., Chen et al., 2004;
Zhu, 2018).
→Funds in CEE are still small and
have not reached to the point
of being oversized?
→Many of the small funds in CEE
are sub-scale?

13. Conclusion:
• One CEE global equity fund has the skill to beat factor returns, net and gross of fees.
• Most of the negative alphas are delivered by poor skills, not bad luck.
• Less skill in contrast with the studies on US and UK data (e.g. Cuthbertson, Nitzsche, and O'Sullivan
2008; Fama and French 2010; Kosowski et al. 2006).
• About 5% of the funds have the skill to beat their benchmark indexes, gross of fees.
• This skill is absorbed by the fund management fees, except for the top fund.
→Majority of the funds fail to deliver sufficient alpha to cover fees, they underperform the
market.
• Market-tracking passive indexes are the most reliable choice for investors.
• Economic value added > value destroyed, gross of fees.
• Driven by large funds.
• Fund size groups:
• If high risk appetite: small funds when market conditions are stable or growing.
• If less risk tolerant: large funds at any stage of the market cycle.

14. Thank You!
Questions?
This work has received funding from the Estonian Ministry of Education and Research, project “Efficiency in the Financial Sector in the Light
of a Changing Regulatory Environment” under Grant No B57; from the European Regional Development Fund, Tallinn University of
Technology ASTRA project, TTÜ Development Program 2016-2022 under project 2014-2020.4.01.16-0032; from the European Union’s
Horizon 2020 Research and Innovation Programme under Grant Agreement No 952574; from the European Economic Area (EEA) Financial
Mechanism 2014-2021 Baltic Research Program under project S-BMT-21-8 (LT08-2-LMT-K-01-073); and from the European Regional
Development Fund, Dora Plus Programme under activity T1.1 and T1.2.

15. Regression results for the equally weighted (EW) portfolio. (t-statistics in parentheses)