Which Factors Matter to Investors? Evidence from Brazilian Mutual Funds.
| Autor | Cavalcante-Filho, Elias |
-
Introduction
This paper finds that the capital asset pricing model (CAPM) best represents the performance evaluation practices used by Brazilian mutual fund investors. Moreover, it concludes that more sophisticated investors use more complex models, in addition to being better at detaching risk from skill when it comes to evaluating funds' past returns.
Whenever investors are choosing between an active management fund and a passive management fund, we expect them to prefer to allocate their assets to a skilled manager, who is able to generate higher returns than those attained with passive management. Investors, therefore, reward skilled managers with new allocations while they punish unskilled managers with withdrawals. As their source of information, investors look at funds' past returns and the historical distribution of risk factors. Within this context, fund alphas, which are defined as the excess return not tied to risk factors, are the metric that is capable of determining skilled managers who are able to attain higher returns than those achieved through exposure to risk factors alone (Barber, Huang, & Odean, 2016; Berk & Binsbergen, 2016, 2017).
Thus, investors only increase their investments in one fund to the detriment of another when it yields a higher alpha. As a result, whenever we compare funds' flows to their return, we expect the flows to be sensitive to their alphas, though not to the components of funds' returns tied to risk factors.
We herein reach our conclusions after evaluating the relationship between data on the fund's flow and past performance. Any given fund's high (low) performance is deemed evidence that its manager is skilled (unskilled), and we presume that investors seek skilled managers. The data used herein relates to Brazilian funds with active management in the time window from January 2001 to April 2019.
Our paper follows a methodology akin to the one proposed by Barber et al. (2016), which looks at conflicting scenarios between fund rankings depending on the method employed to calculate the alphas. These situations are interpreted so that, if the inflow (outflow) is more intense, we conclude that the metric that classifies the fund as good (bad) is the most relevant in decision making. For instance, suppose a scenario where a given fund is ranked as one of the best funds according to the CAPM alpha, though when estimated with the three factor model (Fama & French, 1992, 1993) the fund is ranked as one of the worst. Also, suppose we verify intense inflows to that fund. In this case, we conclude that the CAPM alpha is best attuned to investors' behaviors. The opposite applies to outflows.
Furthermore, we break down fund returns into alpha and factor-related returns. We define the factor-related returns as the multiplication of the risk factor and the fund's sensitivity (exposure) to said factor. We deem the factor-related returns as the return explained by risk, whereas unexplained returns are a direct result of skilled managers, in other words, funds' alphas. Finally, we look at how these components are related to funds' flows, as well as how proxies for investor sophistication over time (investor sentiment) and between funds (restricted to sophisticated investors and minimum investment requirement) affect the results.
Next, we carry out an additional decomposition of fund alphas into persistent and random components. The persistent component is the proportion related to the alpha's future realization, and the random component is the remaining portion. This decomposition enables us to examine possible alpha imperfections such as potential failures in being able to fully differentiate returns influenced by skilled managers from returns affected by risk or random effects.
We find that investors pay more attention to market risk (beta) and consider returns tied to risk factors, such as size, value, momentum, illiquidity, and exposure to industry sectors, as alphas. Moreover, we find evidence that more sophisticated investors use more sophisticated performance metrics to distinguish between risk and managers' skill. Finally, we conclude that less sophisticated investors are also more sensitive to alphas. However, when breaking down alphas into persistent and random components, we observe that this greater sensitivity comes from the strong sensitivity to the alpha's random component.
Our paper aims to contribute to research produced by Agarwal, Green, and Ren (2018), Barber et al. (2016), Berk and Binsbergen (2016, 2017), and Blocher and Molyboga (2017), all of which report the same investor patterns for the US market. It also describes how less sophisticated investors' enhanced sensitivity to alphas is a direct result of how their investment flows relate to random alpha variations. Nevertheless, we find that the investment flows of less sophisticated investors do not turn out to be more sensitive to the funds' persistent alphas.
The evidence produced by Barber et al. (2016) and Berk and Binsbergen (2016, 2017) is a standard in the underlying literature, applied to the works of Franzoni and Schmalz (2017), Harvey and Liu (2019), and Polkovnichenko, Wei, and Zhao (2019). However, to the best of our knowledge, our paper is the first one to address this behavior not using data from the United States. It therefore helps to support the documented evidence, in addition to contributing to the financial analysis literature with Brazilian data.
-
Methods and data
2.1 Data source
All data used herein is sorted monthly and cover the time window from January 2001 to April 2019. All financial sums are deflated by the Brazilian Consumer Price Index (IPCA) set for May 2018.
The funds' return, inflows, and outflows series are compiled based on data taken from the Economatica[R] and the Brazilian Securities and Exchange Commission (CVM) online databases. The Brazilian stock market index (Ibovespa) historical rates, as well as the funds' registration information, are collected from the Economatica[R] online platform. The Brazilian risk factors and industry sector returns are gathered from the Brazilian Center for Research in Financial Economics of the University of Sao Paulo NEFIN (www.nefin.com.br).
We remove all funds' observations before said funds' AUM reaches 5.0 million 2018 BRL. Once the funds are included in the dataset, we keep analyzing them until their AUM drops below 1.0 thousand 2018 BRL. Moreover, since the regressions are performed in 30-month rolling windows, funds whose historical performance registers less than 30 months are removed from our evaluation.
Finally, a significant share of the base sample has no information on whether they are open-end or closed-end funds. Thus, to keep only open-end funds in our sample, we dismiss funds with net asset inflows equal to zero in more than 50% of observations.
2.2 Net asset inflows
Our paper's dependent variable, fund flows, entails a fund portfolio's percentage variances resulting from asset inflows and outflows. Consequently, its value for fund p in month t stems from the following equation:
[cap.sub.pt] = ([TNA.sub.p,t]/[TNA.sub.p,t-1] - (1 + [R.sub.p,t])) * 100 (1)
where [TNA.sub.p,t] is the total net assets under management of fund p at the end of month t, and [R.sub.p,t] is the return of fund p in month t. This estimation method follows the standards observed in the literature (Barber et al., 2016; Berk & Binsbergen, 2016; Goldstein, Jiang, & Ng, 2017; Jiang & Yuksel, 2017).
2.3 Return metrics
Investors in actively managed funds are expected to search for funds capable of providing higher returns than anything that can be linked to their exposure to known risk factors (e.g. market risk, size, etc.). That is, they look for funds capable of generating alphas. If investors are only interested in exposing themselves to risk factors, they would only need to allocate their assets to passive management funds (Berk & Binsbergen, 2017).
Even though investors are expected to pursue alphas, how these alphas are measured remains unclear. On the one hand, investors may simply rank funds based on their raw returns. On the other hand, though, they may rank funds based on a multifactor return approach, such as those commonly found in the academic literature on asset pricing.
Based on the scenarios mentioned above, and following the methods suggested by Barber et al. (2016), we proceed to compute six risk-adjusted return metrics (alphas): market adjusted returns (MAR); the capital asset pricing model (CAPM); the Fama-French (1993) three-factor model (M3F), to which size (SMB) and value (HML) factors are added; the Carhart four-factor model (1997), to which the momentum factor (WML) is added; the five-factor model (M5F), to with the Acharya and Pedersen (2005) liquidity factor (IML) is added; and, finally, the eight-factor model (M8F), which also includes three other industry factors measured for the Brazilian market using Nefin industry data, and whose methodology is described by Pastor and Stambaugh (2002a, 2002b).
These models often generate similar rankings for mutual funds. Nevertheless, we choose to examine scenarios where fund rankings established by said measures differ between each other, and we determine which models are best suited to understanding investors' choices based on this discrepancy.
We estimate models with 30-month windows. In the case of the M8F, for instance, we use the following calculation:
[mathematical expression not reproducible] (2)
where [tau] [member of] [t -1, t--30]; [R.sup.e.sub.p,t] stands for excess returns of fund p; [R.sub.m,[tau]] is the market return; [R.sub.f,T] is the risk-free return; and [SMB.sub.[tau]], H [M.sub.L[tau]], [WML.sub.[tau]], I [ML.sub.[tau]], and [IND.sup.k.sub.[tau]] are, respectively, size, value, moment, liquidity, and the k-th industry factor.
Equation (2) leads to [mathematical expression not reproducible], and [[??].sub.pt], which we use to calculate the...
Para continuar a ler
PEÇA SUA AVALIAÇÃOCOPYRIGHT GALE, Cengage Learning. All rights reserved.
