Economic Uncertainty and the Cross-Section of Hedge Fund Returns

Transcription

1 Economic Uncertainty and the Cross-Section of Hedge Fund Returns Turan Bali, Georgetown University Stephen Brown, New York University Mustafa Caglayan, Ozyegin University

2 Introduction Knight (1921) draws a distinction between risk and true uncertainty and argues that uncertainty is more common in decision-making process. Risk occurs where the future is unknown, but the probability of all possible outcomes is known. Uncertainty occurs where the probability distribution is itself unknown. In empirical asset pricing literature, not much attention has been paid to this distinction so far.

3 Introduction This paper investigates whether standard measures of risk or economic uncertainty is a more powerful determinant of the cross-sectional differences in hedge fund returns. Knight (1921) draws the important distinction between risk, in the sense of a measurable probability, and uncertainty, which cannot be measured and by that fact is uninsurable. While uncertainty of its nature cannot be measured, economic change which he argues is the source of this uncertainty can indeed be measured.

4 Introduction Economic uncertainty, as calibrated by the the time-varying conditional volatility of macroeconomic variables, is associated with business cycle fluctuations: Default spread, term spread, TED spread Short-term interest rate changes Aggregate dividend yield Equity market index Inflation rate, unemployment rate, growth rate of real GDP per capita, and the Chicago Fed National Activity (CFNAI) index.

5 Introduction Alternative measures of economic uncertainty are generated by estimating time-varying volatility of 10 economic indicators based on the GARCH model. Monthly uncertainty betas are estimated for each fund from the time-series regressions on the basis of 36-month rolling regressions of hedge fund excess returns on these uncertainty factors. We examine the performance of these uncertainty betas in predicting the cross-sectional variation in hedge fund returns.

6 Introduction Portfolio level analyses and cross-sectional regressions indicate a positive and significant link between uncertainty beta and future hedge fund returns. Funds in the highest uncertainty beta quintile generate 5.5% to 7.5% higher average annual returns than do funds in the lowest uncertainty beta quintile. The positive relation between uncertainty beta and risk-adjusted returns (9- factor alpha) remains economically and statistically significant. We control for a large set of fund characteristics and risk attributes, and find that the average slope on uncertainty beta remains positive and highly significant.

7 Introduction Why hedge funds with higher exposure to economic uncertainty generate higher returns? Is there a theoretical framework supporting this finding? The positive relation between uncertainty beta and expected returns is justified in Merton s (1973) intertemporal capital asset pricing model.

8 Conditional ICAPM with Risk and Uncertainty Merton s ICAPM: We provide a time-series and cross-sectional investigation of the conditional ICAPM with time-varying covariances: In Merton s (1973) ICAPM, investors are concerned not only with the terminal wealth that their portfolio produces, but also with the investment and consumption opportunities that they will have in the future.

9 Conditional ICAPM with Risk and Uncertainty Hence, when choosing a portfolio at time t, ICAPM investors consider how their wealth at time t+1 might vary with future state variables. ICAPM investors prefer high expected return and low return variance, but they are also concerned with the covariances of portfolio returns with state variables that affect future investment opportunities. ICAPM investors are concerned with hedging more specific state-variable (consumption-investment) risks.

10 Conditional ICAPM with Risk and Uncertainty Bloom (2009) and Bloom et al. (2007) introduce a theoretical model linking economic uncertainty shocks to aggregate output, employment and investment dynamics. Chen (2010) introduces a model that shows how business cycle variation in economic uncertainty and risk premia influences firms financing decisions. Stock and Watson (2012) indicate that the decline in aggregate output and employment during the recent crisis period are driven by financial and economic uncertainty shocks. Allen, Bali, and Tang (2012) show that systemic/downside risk in the financial sector predicts future economic downturns.

11 Conditional ICAPM with Risk and Uncertainty Hence, economic uncertainty is a relevant state variable that affects investors expectations about future consumption and investment opportunities. Hence, the state variable is proxied by economic uncertainty. ICAPM in terms of conditional betas:

12 Why do we investigate hedge funds? Hedge funds use a wide variety of dynamic trading strategies, and make extensive use of derivatives, short-selling, and leverage. Hedge fund managers actively vary their exposures to changes in macroeconomic conditions and to fluctuations in financial markets. Hedge fund managers have heterogeneous expectations and different reactions to changes in the state of the economy.

13 Why do we investigate hedge funds? Disagreement among professional forecasters and investors on expectations about macroeconomic fundamentals (e.g., Kandel and Pearson (1995), Lamont (2002), and Mankiw et al. (2004)). Hence, economic uncertainty plays a critical role in generating crosssectional differences in fund managers expectations about the level and volatility of economic indicators.

14 Number of Hedge Funds:

15 Total Assets Under Management:

16 Literature Review Sadka (2010) Liquidity risk Bali, Brown, and Caglayan (2011) Default premium and inflation beta Titman and Tiu (2011) Manager skill Bali, Brown, and Caglayan (2012) Systematic risk vs. residual risk Cao, Chen, Liang, and Lo (2012) Exposure to market illiquidity Patton and Ramadorai (2013) Time-varying exposures

17 Data Lipper TASS hedge fund database as of March 2012 included information on 17,534 defunct and live hedge funds with close to $1.4 trillion under management 10,805 are defunct funds remaining 6,729 are lives funds net monthly returns of individual hedge funds (net of fees) monthly AUM for each individual hedge funds Fund characteristics: management and incentive fee structures, redemption period, minimum investment amount, lockup and leverage information.

18 Descriptive Statistics

19 Descriptive Statistics

20 Risk Factors MKT: Excess return on the value-weighted market index SMB: Fama-French size factor HML: Fama-French book-to-market factor MOM: Carhart momentum factor 10Y: Fung-Hsieh long-term interest rate factor CrdSpr: Fung-Hsieh credit spread factor BDTF: Fung-Hsieh bond trend-following factor FXTF: Fung-Hsieh currency trend-following factor CMTF: Fung-Hsieh commodity trend-following factor IRTF: Fung-Hsieh short-term interest rate trend-following factor SKTF: Fung-Hsieh stock index trend-following factor

21 Correlation between Risk Factors

22 Economic Uncertainty Factors DEF_U: Uncertainty about default premium TERM_U: Uncertainty about term spread TED_U: Uncertainty about credit risk RREL_U: Uncertainty about short-term interest changes DIV_U: Uncertainty about aggregate dividend yield MKT_U: Uncertainty about the equity market INF_U: Uncertainty about the inflation rate UNEMP_U: Uncertainty about the unemployment rate GDP_U: Uncertainty about real GDP per capita CFNAI_U: Uncertainty about macroeconomic activity

23 Economic Uncertainty Factors Alternative measures of economic uncertainty are estimated using the timevarying conditional volatility of the state variables based on the Asymmetric GARCH model with AR(1) process:

24 Economic Uncertainty Factors

25 Economic Uncertainty Factors

26 Correlation between Uncertainty Factors

27 Correlation between Risk and Uncertainty Factors

28 Uncertainty Beta We use a monthly rolling regression approach with a fixed estimation window of 36 months to generate the risk factor and uncertainty betas:

29 Correlation between Uncertainty Betas

30 Univariate Portfolios of Risk Factor Betas

31 Portfolios of Uncertainty Betas: Returns and Alphas

32 Portfolios of Uncertainty Betas: Average Beta

33 Univariate Fama-MacBeth Regressions

34 Multivariate Fama-MacBeth Regressions

35 Subsample Analysis

36 Subsample Analysis

37 Subsample Analysis

38 Uncertainty Betas by Three Hedge Fund Style Categories

39 Portfolios of Uncertainty Betas by Three Hedge Fund Styles

40 Portfolios of Uncertainty Betas by Three Hedge Fund Styles

41 Canonical Correlation Analysis We construct univariate measures of hedge fund-related economic uncertainty by considering the portfolio returns of 11 hedge fund investment styles, and the 10 measures of economic uncertainty. We then generate a linear combination of the 11 hedge fund portfolio investment style returns and a linear combination of the 10 economic uncertainty factors which leads to the highest correlation between these two. Economic Uncertainty Index: A univariate index of hedge fund-related economic uncertainy (the linear combination of economic uncertainty factors) Hedge Fund Index: A univariate index of economic uncertainty-related hedge fund investment style portfolio returns (the linear combination of hedge fund style index returns)

42 Economic Uncertainty Index

43 Hedge Fund Index

44 Canonial Correlation Analysis

45 Economic Uncertainty Index Beta After building the two univariate indices of economic uncertainty, we test their performance in predicting the cross-sectional variation in hedge fund returns. The results indicate a positive and significant relation between exposures to the newly proposed economic uncertainty index and future fund returns. Funds in the highest economic uncertainty index beta quintile generate 6% higher annual returns and alphas than do funds in the lowest economic uncertainty index beta quintile.

46 Survey of Professional Forecasters The Federal Reserve Bank of Philadelphia releases measures of crosssectional forecast dispersion for the Survey of Professional Forecasters. The cross-sectional forecast dispersion measures the degree of disagreement among the expectations of different forecasters. We use measures of cross-sectional dispersion for quarterly forecasts for the U.S. gross domestic product (GDP), industrial production (IP), and inflation rate (INF).

47 Survey of Professional Forecasters These measures are the percent difference between the 75th percentile and the 25th percentile (the interquartile range) of the projections for the quarterly level:

48 Survey of Professional Forecasters

49 Survey of Professional Forecasters

50 Conclusion Earlier studies have so far paid no attention to the distinction between risk and uncertainty in the cross-sectional pricing of individual hedge funds. We contribute to the literature by examining the relative performance of hedge funds exposures to risk and uncertainty factors in terms of their ability to explain cross-sectional differences in hedge fund returns. In the literature, this is the first sensitivity analysis of expected future hedge fund returns to loadings on economic uncertainty.

51 Conclusion Consistent with the market-timing ability of hedge funds, our results suggest that by predicting fluctuations of financial and economic variables, fund managers can adjust their portfolio exposures up or down in a timely fashion to generate superior returns. We find that hedge funds following directional and semi-directional trading strategies correctly adjust their aggregate exposure to economic uncertainty, and hence there exists a positive and stronger link between their uncertainty beta and future returns. However, the cross-sectional relation between uncertainty beta and future returns is relatively weaker or insignificant for the funds following non-directional strategies.

52 3-month ahead predictability

53 Controlling for Default Premium and Inflation Betas