Conditional markettiming models for mutual fund performance


 Brittney Fisher
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1 Joanna Olbryś * Conditional markettiming models for mutual fund performance evaluation 1 Introduction erformance evaluation of investment managers is a topic of considerable interest to practitioners and academics alike. Superior performance may be achieved through timing (macroforecasting) and security selection (microforecasting) skills of portfolio managers. Fama suggested that a manager s forecasting ability could be split into two separate activities [Fama, 1972]: microforecasting, macroforecasting. Some researchers have developed models that allow the decomposition of manager performance into markettiming and selectivity skills. This began with the work of Treynor and Mazuy [Treynor, Mazuy, 1966] and since then numerous econometric techniques have been applied to this area ([Henriksson, Merton, 1981], [Jensen, 1968], [Henriksson, 1984], [Romacho, Cortez, 2006], [Ferson, Schadt, 1996], [Ferson, Harvey, 1999]). The main goal of this paper is a performance evaluation using unconditional and conditional models of timing and selectivity. We compare two methods: the unconditional Treynor & Mazuy (TM) model [Treynor, Mazuy, 1966] and the statistical procedure based on the Ferson & Schadt (FS) conditional model [Ferson, Schadt, 1996]. The markettiming and selectivity abilities of 15 equity openend mutual funds have been evaluated for the period January 2003 April For comparison, a bear market period from July 4, 2007 to Feb 17, 2009 has been investigated. The overall index of Warsaw Stock Exchange companies (WIG index) fell from (July 4, 2007) to (Feb 17, 2009). It lost 68.22% during this period. 1. Unconditional and conditional models of timing and selectivity The traditional performance measurement literature has attempted to distinguish security selection, or stockpicking ability, from markettiming, or the ability to predict overall market returns. However, the literature finds that it is not easy to separate ability into two such dichotomous categories. Traditional unconditional TM [Treynor, Mazuy, 1966] or HM [Henriksson, Merton, 1981] models, in addition to their strong assumptions about how managers use their abilities, have taken the view that any information correlated with future market returns is superior information [Ferson, Schadt, 1996, p. 434]. Conditional models of timing and selectivity assume a semistrong form of market effi * dr, Wydział Informatyki, olitechnika Białostocka, 1 Zrealizowano w ramach pracy badawczej statutowej S/WI/3/2008
2 2 Joanna Olbryś ciency. The idea is to distinguish markettiming based on public information from markettiming information that is superior to the lagged information variables (the FS model [Ferson, Schadt, 1996] or the FH model [Ferson, Harvey, 1999]) The TreynorMazuy (TM) unconditional model A classic unconditional markettiming model is the quadratic regression of Treynor and Mazuy (TM model) [Treynor, Mazuy, 1966]: 2 r, t rm, t rm, t, t (1) where: R R is the excess return on the portfolio in period t, r, t, t F, t r M, t RM, t RF, t t is the excess return on market portfolio M in period t, R, is the oneperiod return on the portfolio, R, is the oneperiod return on the market portfolio M, M t R, is the oneperiod return on riskless securities, F t measures selectivity skills of the portfolio s manager, is the systematic risk of the portfolio, measures markettiming skills of the portfolio s manager, is a residual term, with the following standard CAM conditions: E,t ; E 0;, t 0, t, t 1 If the portfolio manager has the ability to forecast security prices, the intercept in equation (1) will be positive. On one hand, a passive strategy (random buyandhold policy) can be expected to yield a zero intercept. On the other hand, if the manager is doing worse than a random selection buyandhold policy, will be negative. If a mutual fund manager increases (decreases) the market exposure of the portfolio prior to a market increase (decrease) then the portfolio return will be a convex function of the market return and will be positive. The size of the estimate ˆ informs about the manager s market skills. Empirical results obtained using the TM technique do not support the hypothesis that mutual fund managers are able to follow an investment strategy that successfully times the return on the market portfolio. The results show neutral or negative performance for mutual fund managers for the period January 2003 January 2008 in the case of 15 olish equity openend mutual funds [Olbryś, 2008a]. In general, the fact that olish managers are not really successful as market timers is consistent with most of the literature on mutual fund performance ([Henriksson, 1984], [Fletcher, 1995], [Kao, Cheng, Chan, 1998], [Romacho, Cortez, 2006]).
3 Conditional markettiming models for mutual fund performance evaluation The Ferson Schadt (FS) conditional model Ferson and Schadt derive a conditional version of the TreynorMazuy model [Ferson, Schadt, 1996, p 435]: 2 r, t rm, t ' zt 1 rm, t rm, t, t (2) where: r, is the excess return on the portfolio in period t, t r, is the excess return on market portfolio M in period t, M t measures selectivity skills of the portfolio s manager, is the systematic risk of the portfolio, is the coefficient vector that captures the response of the manager s beta to the public information Z t1, Z t1 is a vector of lagged instrumental variables for the information available at time t 1, zt 1 Z t1 EZ is a vector of the deviations of Z t1 from the unconditional means, is the coefficient that measures the sensitivity of the manager s beta to the private markettiming signal, is a residual term, with the following standard CAM conditions:,t, t 0, t, t1 ; E, 0; E The regression (2) may also be interpreted as an unconditional multiple factor model, where the market index is the first factor and the product of the market and the lagged information variables are additional factors. is a vector with dimension equal to the dimension of Z t1. The elements of are the response coefficients of the conditional beta with respect to the information variables Z t1. The term ' z t 1 rm, t in equation (2) controls for the public information effect, which would bias the coefficients in the original TM model (1). The new term in the model (2) captures the part of the quadratic term in the TM model (1) that is attributed to the public information variables. In the conditional model, the correlation of mutual fund betas with the future market return, which can be attributed to the public information, is not considered to reflect markettiming ability [Ferson, Schadt, 1996, p 435]. 2. The dataset We have studied monthly ordinary excess returns for 15 selected openend equity mutual funds from January 2003 to April 2009 (75 observations). As in the previous studies that used monthly data, we have implicitly assumed that the investors evaluate risk and return, and that mutual fund managers trade using a onemonth horizon. Table 1 records the names of the funds, along with summary statistics for the Jan 2003Apr 2009 period.
4 4 Joanna Olbryś Table 1. Summary statistics for funds excess returns from Jan 2003 to Apr 2009 Equity Funds Standard Mean Minimum Maximum Deviation [%] [%] [%] [%] 1 Arka BZ WBK Akcji FIO BH FIO Akcji Aviva Investors FIO olskich Akcji DWS olska FIO Top 25 Małych Spółek DWS olska FIO Akcji DWS olska FIO Akcji lus ING FIO Akcji Legg Mason Akcji FIO Millennium FIO Akcji ioneer Akcji olskich FIO KO/CREDIT SUISSE Akcji FIO ZU FIO Akcji KRAKOWIAK SEB 3 Akcji FIO Skarbiec Akcja FIO UniKorona Akcja FIO Income Group Average The monthly returns on the index of Warsaw Stock Exchange companies (WIG) are used as the returns on the market portfolio. The returns were obtained from The monthly average of returns on 52week Treasury bills are used as the riskless asset The predetermined information variables Ferson and Schadt use a collection of public information variables that previous studies have shown are useful for predicting security returns and risks over time. The variables are: (1) the lagged level of the onemonth Treasury bill yield, (2) the lagged dividend yield of the CRS valueweighted NYSE and AMEX stock index, (3) a lagged measure of the slope of the term structure, and (4) a lagged quality spread in the corporate bond market [Ferson, Schadt, 1996, p 437]. In oland, the suitable variables are: 1) Z 1, t1  the lagged monthly dividend yield of the WSE stock index (WIG), 2) Z 2, t1  the lagged monthly level of the 1M WIBOR, 3) Z 3, t1  the lagged monthly measure of the slope of the term structure; the term spread is a difference between the average of 2year Treasury bond yield and the average of 10year Treasury bond yield. We assume that the lagged variables are readily available, public information over our entire sample period. Table 2 presents summary statistics for the lagged information variables. Note that all variables demonstrate high values of variation coefficients. Table 2. Summary statistics for lagged variables from Jan 2003 to Apr 2009
5 1 2 3 Conditional markettiming models for mutual fund performance evaluation 5 Variable Mean Standard Deviation Variation Coefficient Minimum Maximum Z 2.49% 1.07% 42.89% 1.16% 5.83% 1, t1 Z 0.44% 0.07% 16.99% 0.31% 0.56% 2, t1 Z 0.014% 0.043% % 0.09% 0.09% 3, t1 Figure 1. The lagged monthly dividend yield of the WSE stock index (WIG) from Jan 2003 to Apr 2009 Figure 2. The lagged monthly level of the 1M WIBOR from Jan 2003 to Apr 2009
6 6 Joanna Olbryś Figure 3. The lagged monthly measure of the slope of the term structure from Jan 2003 to Apr 2009 In fact, the additional exogenous variables used in the conditional model (2) are: r Z E Z r M t rm t 3, t 1 3 rm t z 1,, z 2,,, t 1 M, t 1, t 1 1, t 1 rm, t Z2, t 1 EZ 2 r Z EZ z 3, t 1 M, t,. Fig. 4, Fig. 5 and Fig.6 present this data in the form of charts, respectively. We have detected (based on Dickey Fuller test) that the analysed series are stationary. Figure 4. The lagged exogenous variable z 1, t 1 r M, t from Jan 2003 to Apr 2009
7 Conditional markettiming models for mutual fund performance evaluation 7 Figure 5. The lagged exogenous variable z 2, t 1 r M, t from Jan 2003 to Apr 2009 Figure 6. The lagged exogenous variable z 3, t 1 r M, t from Jan 2003 to Apr 2009
8 8 Joanna Olbryś 3. Empirical results Table 3 presents the results of the OLS estimates for the TM parametric tests. The DWstatistic values indicate that we have encountered some autocorrelation problems. Autocorrelated disturbances are present in the case of the following funds: BH FIO Akcji, Aviva Investors FIO olskich Akcji, DWS olska FIO Top 25 Małych Spółek and Skarbiec Akcja FIO. The critical values of the DWtest are: d L 1.571, du To detect for heteroskedasticity we have used White s test. The results show that the residuals are heteroskedastic only in the case of SEB 3 Akcji FIO. The LMstatistic of this fund LM is higher than the critical value 2 * Table 3. Unconditional TM model (1) (period from Jan 2003 to Apr 2009) Equity Funds ˆ ˆ ˆ 2 R DW LM AIC 1 Arka BZ WBK Akcji FIO 0.006* 0.943* BH FIO Akcji * Aviva Investors FIO olskich Akcji 0.004* 0.908* * DWS olska FIO Top * Małych Spółek 5 DWS olska FIO Akcji * * DWS olska FIO Akcji * * lus 7 ING FIO Akcji * Legg Mason Akcji FIO 0.005* 0.830* * Millennium FIO Akcji * ioneer Akcji olskich * 1.005*
9 Conditional markettiming models for mutual fund performance evaluation 9 FIO 11 KO/CREDIT SUISSE * * Akcji FIO ZU FIO Akcji KRAKO WIAK * * SEB 3 Akcji FIO * * Skarbiec Akcja FIO * UniKorona Akcja FIO * *Significant at 5% (using Gretl) The new, improved models have been estimated using the Cochrane Orcutt procedure [Osińska, 2005], [Kufel, 2004]. The new model for SEB 3 Akcji FIO has been evaluated using the WLS procedure [Kufel, 2004, p.121] to receive heteroskedasticity corrected estimates. We have tested the normality of the residuals in this case. Table 4 presents final results of the TM parametric tests. The evidence is that all of the funds present significant estimates of the systematic risk ( ˆ ) at 5% level. Almost every coefficient (except for that of ioneer Akcji olskich FIO) lies between 0 and 1. The mean estimate of this coefficient is During the period investigated, the mean value of Rsquared was quite high: Table 4 provides the evidence of negative markettiming ( ˆ 0 ). The mean value of this coefficient is The empirical results show no statistical evidence that olish equity funds managers have outguessed the market. We have also observed that only three funds present significantly positive estimates of selectivity ( ˆ 0 ).The mean value of this coefficient is Table 4. Unconditional TM model (1); heteroskedasticity and autocorrelationcorrected estimates using the observations from the period Jan Apr 2009 Equity Funds ˆ ˆ ˆ 2 R 1 Arka BZ WBK Akcji FIO 0.006* 0.943* BH FIO Akcji * Aviva Investors FIO olskich Akcji * * DWS olska FIO Top 25 Małych Spółek * DWS olska FIO Akcji * * DWS olska FIO Akcji lus * * ING FIO Akcji * Legg Mason Akcji FIO 0.005* 0.830* * Millennium FIO Akcji * ioneer Akcji olskich FIO * 1.005* KO/CREDIT SUISSE Akcji FIO * * ZU FIO Akcji KRAKOWIAK * * SEB 3 Akcji FIO * Skarbiec Akcja FIO 0.004* 0.777* UniKorona Akcja FIO * The Group Average
10 10 Joanna Olbryś *Significant at 5% (using Gretl) Table 5 presents summary results received based on the conditional model ' are the response coefficients of the (2). The elements of conditional beta with respect to the lagged regressors: X 1 z1, t 1 r M, t, X 2 z 2, t 1 r M, t, X 3 z3, t 1 r M, t. Table 5. Conditional FS model (2); heteroskedasticity and autocorrelationcorrected estimates using the observations from the period Jan Apr 2009 Equity Funds ˆ ˆ ˆ 1 ˆ 2 ˆ ˆ 2 AIC 3 R DW (OLS) 1 Arka BZ WBK Akcji FIO 0.006* 0.940* BH FIO Akcji * , * Aviva Investors 3 FIO olskich * * * Akcji DWS olska 4 FIO Top * * Małych Spółek 5 DWS olska FIO Akcji * * DWS olska FIO Akcji lus * * ING FIO Akcji * Legg Mason Akcji FIO 0.006* 0.805* * Millennium FIO Akcji * ioneer Akcji olskich FIO * 0.957* 12.64* 161.9* 187.6* 0.328* KO/CREDIT 11 SUISSE Akcji * 7.241* 136.7* FIO 12 ZU FIO Akcji KRAKOWIAK * * SEB 3 Akcji FIO * 6.311* * Skarbiec Akcja FIO 0.004* 0.776* UniKorona Akcja FIO * The Group Average *Significant at 5% (using Gretl) The evidence regarding the mean value of Rsquared is similar to that from the model (1) in Table 4. During the period investigated, the mean value of the Rsquared was slightly higher and equal to All of the funds present sig
11 Conditional markettiming models for mutual fund performance evaluation 11 nificant estimates of the systematic risk ( ˆ ) at 5% level. Each coefficient lies between 0 and 1. The mean value of this coefficient is equal to We have used Cochrane Orcutt procedure to correct autocorrelated error terms in the case of eight funds, and Table 5 reports final empirical results from the conditional FS models. To detect for heteroskedasticity we have used White s test. Although not reported in the paper, the results show that for all of the funds the LMstatistics have been lower than the critical value 2 * , so we have no grounds for rejecting the null hypothesis that the residuals are homoskedastic. Additionally, we have used the VIF test to detect for multicollinearity. The major undesirable consequence of multicollinearity is that the variances of the OLS estimates of the parameters of the collinear variables are quite large. The inverse of the correlation matrix is used in detecting for multicollinearity. The diagonal elements of this matrix are called variance inflation factors VIF i. One interpretation is that it is a measure of the amount by which the variance of the ith coefficient estimate is increased (relative to no collinearity) due to its linear association with the other explanatory variables. As a rule of thumb, for standardized data, a VIF i 10 indicates harmful collinearity. In the investigated sample of equity funds FS models, no multicollinearity has been found. For selected equity funds, the factors z, X 2 z 2, t 1 r M, t X 1 1, t 1 r M, t and X 3 z3, t 1 r M, t included in equation (2), do not seem to have an important role in explaning mutual fund excess returns. In fact, only one fund (ioneer Akcji olskich FIO) exhibits significant estimates of ˆ 1, ˆ 2, ˆ 3 coefficients. We have used Akaike Information Criterion (AIC) to compare TM models (see Table 3) and FS models (see Table 5). Lower values of the AIC index indicate the preferred model, that is, the one with the fewest parameters that still provides an adequate fit to the data. The evidence is that only in the case of seven funds, the regressors addition caused a little decrease in the AIC index. To sum up, the lagged variables Z 1, t1, Z 2, t1, Z 3, t1 are not very useful for improving the quality of the markettiming models for olish equity openend mutual funds. 4. Empirical results in a bear market period The period from July 4, 2007 to Feb 17, 2009 was a bear market period. The overall WIG index fell from (July 4, 2007) to (Feb 17, 2009). It lost 68.22% during this period (Fig. 7). We have studied monthly ordinary excess returns for 15 selected openend equity mutual funds in this period (20 observations). Figure 7. The WIG index in the period from July 4, 2007 to Feb 17, 2009
12 12 Joanna Olbryś Table 6 reports final empirical results of the unconditional TM tests in the bear market period. Although not reported in the paper, the values of DWstatistic show evidence that residuals are autocorrelated in the case of five funds: Arka BZ WBK Akcji FIO, BH FIO Akcji, DWS olska FIO Akcji, ZU FIO Akcji KRAKOWIAK and SEB 3 Akcji FIO. To detect for heteroskedasticity we have used White s test. The results have shown that the residuals are heteroskedastic in the case of BH FIO Akcji and Millennium FIO Akcji. Hence, we have used the Cochrane Orcutt procedure to correct autocorrelated error terms and the WLS procedure to receive heteroskedasticity corrected estimates. We tested the normality of the residuals in this case. Table 6 provides the evidence of negative, but not significant markettiming ( ˆ 0 ), in the case of 14 funds (except BH FIO Akcji). The mean value of this coefficient is All of the funds present significant estimates of the systematic risk ( ˆ ) at 5% level. The mean estimate of this coefficient is and it is almost equal to this in Table 4. Note that the poor quality of the models can be attributed to small sample size. Table 6. Unconditional TM model (1) in a bear market period from July 2007 to Feb 2009 Equity Funds ˆ ˆ ˆ 2 R 1 Arka BZ WBK Akcji FIO * BH FIO Akcji * 0.955* 0.740* Aviva Investors FIO olskich Akcji * DWS olska FIO Top 25 Małych Spółek * 0.694* DWS olska FIO Akcji * DWS olska FIO Akcji lus * 0.828* ING FIO Akcji * 0.629* Legg Mason Akcji FIO *
13 Conditional markettiming models for mutual fund performance evaluation 13 9 Millennium FIO Akcji * 0.848* ioneer Akcji olskich FIO * 1.053* KO/CREDIT SUISSE Akcji FIO * ZU FIO Akcji KRAKOWIAK * 0.878* SEB 3 Akcji FIO * Skarbiec Akcja FIO * UniKorona Akcja FIO * The Group Average *Significant at 5% (using Gretl) Conditional FS models (2) have not been estimated in the bear market period from July 2007 to Feb 2009 because of their low quality (see Table 5) and small sample size. Misspecifying the timing function may cause violations of regression assumptions in unknown and possibly timevarying ways, so that standard corrections for heteroskedasticity and serial correlation may not fully capture the effect of these violations on the standard errors of regression coefficients. Such models may generate false evidence of markettiming abilities. Conclusion In this paper we have examined the usefulness of the unconditional TM and the conditional FS models for the investment managers performance evaluation. While Ferson s and Schadt s empirical investigations of conditional markettiming models are adequate to illustrate that the use of conditioning information is important, they do not advocate using them to evaluate managers in practice [Ferson, Schadt, 1996, p.453]. The evidence on olish market shows that the quality of the conditional models is rather low (Table 5). robably the selected lagged variables are not very appropriate for timing and selectivity modelling and it seems to be the main reason why these models are not better in comparison with the unconditional versions. References 1. Admati A., Bhattacharya S., fleiderer., Ross S., (1986), On timing and selectivity, The Journal of Finance, 41 (July), pp Becker C., Ferson W.E., Myers D., Schill M. (1999), Conditional market timing with benchmark investors, Journal of Financial Economics, 52, pp Bollen N.. B., Busse J. A. (2001), On the timing ability of mutual fund managers, The Journal of Finance, Vol. LVI, No. 3, pp Brown S.J., Goetzmann W.N. (1995), erformance persistence, Journal of Finance, 50, pp Chang E., Lewellen W. (1984), Market timing and mutual fund investment performance, Journal of Business, 57, pp Chen C.R., Stockum S. (1986), Selectivity, market timing and random beta behaviour of mutual funds: a generalized model, The Journal of Financial Research, Vol. IX, No. 1, Spring 1986.
14 14 Joanna Olbryś 7. Chengfew Lee, Rahman S. (1990), Market timing, selectivity, and mutual fund performance: an empirical investigation, Journal of Business, 63, No. 2, pp Czekaj J., Woś M., Żarnowski J. (2001) Efektywność giełdowego rynku akcji w olsce, WN, Warszawa. 9. Fama E., (1972), Components of investment performance, The Journal of Finance, 27, No. 2, pp Ferson W.E., Harvey C.R. (1999), Conditioning variables and the cross section of stock returns, The Journal of Finance, 54 (August), pp Ferson W.E., Schadt R.W. (1996), Measuring fund strategy and performance in changing economic conditions, The Journal of Finance, 51 (June), pp Fletcher J. (1995), An examination of the selectivity and market timing performance of UK unit trusts, Journal of Business Finance & Accounting, 22, pp Grinblatt M., Titman S. (1994), A study of monthly mutual fund returns and performance evaluation techniques, Journal of Financial and Quantitative Analysis, 29, pp Henriksson R., Merton R. (1981), On market timing and investment performance. II. Statistical procedures for evaluating forecasting skills, Journal of Business, 54, No. 4, pp Henriksson R. (1984), Market timing and mutual fund performance: an empirical investigation, Journal of Business, 57, pp Jensen M. (1968), The performance of mutual funds in the period , Journal of Finance, 23, pp Kao G., Cheng L., Chan K. (1998), International mutual fund selectivity and market timing during up and down market conditions, The Financial Review, 33, pp Kufel T. (2004), Ekonometria. Rozwiązywanie problemów z wykorzystaniem programu Gretl, WN, Warszawa. 19. Lehmann B., Modest D.M. (1987), Mutual fund performance evaluation: A comparison of benchmarks and benchmark comparisons, Journal of Finance, 42 (June), pp Maddala G.S. (2008) Ekonometria, WN, Warszawa. 21. Merton R. (1981), On market timing and investment performance. I. An equilibrium theory of value for market forecasts, Journal of Business, 54, No. 3, pp Olbryś J. (2008a), arametric tests for timing and selectivity in olish mutual fund performance, Optimum. Studia Ekonomiczne, Wydawnictwo Uniwersytetu w Białymstoku, 3(39)/2008, str Olbryś J. (2008b), arametryczne testy umiejętności wyczucia rynku porównanie wybranych metod na przykładzie OFI akcji, [w:] Z. Binderman
15 Conditional markettiming models for mutual fund performance evaluation 15 (red.) Metody ilościowe w badaniach ekonomicznych IX, Wydawnictwo SGGW w Warszawie, Warszawa, str Olbryś J. (2008c), Ocena umiejętności stosowania strategii markettiming przez zarządzających portfelami funduszy inwestycyjnych a częstotliwość danych, Studia i race Wydziału Nauk Ekonomicznych i Zarządzania Nr 10, Uniwersytet Szczeciński, Szczecin, str Olbryś J., Karpio A. (2009), Markettiming and selectivity abilities of olish openend mutual funds managers, [w:].chrzan, T.Czernik (red.) Metody matematyczne, ekonometryczne i komputerowe w finansach i ubezpieczeniach 2007, Wydawnictwo AE im. K. Adamieckiego w Katowicach, str Osińska M. (2006) Ekonometria finansowa, WE, Warszawa. 27. Osińska M. (red.) (2005) Wybrane zagadnienia z ekonometrii, Wydawnictwo WSIiE TW w Olsztynie. 28. rather L.J., Middleton K.L. (2006), Timing and selectivity of mutual fund managers: An empirical test of the behavioral decisionmaking theory, Journal of Empirical Finance, 13, pp Rao S. (2000), Market timing and mutual fund performance, American Business Review, 18, pp Rao S. (2001), Mutual fund performance during up and down market conditions, Review of Business, 22, pp Romacho J. C., Cortez M. C. (2006), Timing and selectivity in ortuguese mutual fund performance, Research in International Business and Finance, 20, pp Sharpe W. (1992), Asset allocation: Management style and performance measurement, The Journal of ortfolio Management Winter 1992, pp Treynor J., Mazuy K. (1966), Can mutual funds outguess the market?, Harvard Business Review, 44, pp Zastosowanie warunkowych modeli markettiming do oceny wyników funduszy inwestycyjnych Streszczenie ierwszy parametryczny model markettimng (tzw. wyczucia rynku) zaproponowali w 1966 roku Treynor i Mazuy (model TM). Technika markettiming zarządzania portfelem polega na wyborze momentu dokonania inwestycji oraz czasu jej trwania w oparciu o krótkoterminowe oczekiwania cenowe, na podstawie obserwacji całego rynku (przewidywanie w skali makro). W odpowiedzi na zapotrzebowanie praktyków pojawiły się w literaturze przedmiotu modele wspomagające ocenę jakości zarządzania portfelem pod kątem analizy umiejętności w zakresie stosowania technik markettiming. Celem artykułu jest porównawcza analiza empiryczna umiejętności wyczucia rynku przez zarządzających portfelami OFI akcji z wykorzystaniem parametrycznego modelu TM oraz warunkowego modelu FS Ferson a i Schadt a (1996). Badaniem objęto grupę 15 funduszy akcji na rynku polskim w okresie styczeń 2003 kwiecień 2009.
16 16 Joanna Olbryś Conditional markettiming models for mutual fund performance evaluation erformance evaluation of investment managers is a topic of considerable interest to practitioners and academics alike. Superior performance may be achieved through timing (macroforecasting) and security selection (microforecasting) skills of portfolio managers. The main goal of this paper is a performance evaluation using unconditional and conditional models of timing and selectivity. We compare two methods: the unconditional Treynor & Mazuy (TM) model [Treynor, Mazuy, 1966] and the statistical procedure based on the Ferson & Schadt (FS) conditional model [Ferson, Schadt, 1996]. The markettiming and selectivity abilities of 15 equity openend mutual funds have been evaluated for the period January 2003 April 2009.
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