计量经济分析 = Econometric analysis

We study how investor sentiment affects the cross-section of stock returns. We predict that a wave of investor sentiment has larger effects on securities whose valuations are highly subjective and difficult to arbitrage. Consistent with this prediction, we find that when beginning-of-period proxies for sentiment are low, subsequent returns are relatively high for small stocks, young stocks, high volatility stocks, unprofitable stocks, non-dividend-paying stocks, extreme growth stocks, and distressed stocks. When sentiment is high, on the other hand, these categories of stock earn relatively low subsequent returns.

/pdf/investor-sentiment-and-the-cross-section-of-stock-returns-3pbeoidk6o.pdf

Investor sentiment and the cross-section of stock returns

This paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling and forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility constructed from high-frequency intraday returns, in contrast, permits the use of traditional time series procedures for modeling and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic variation, we formally develop the links between the conditional covariance matrix and the concept of realized volatility. Next, using continuously recorded observations for the Deutschemark/Dollar and Yen /Dollar spot exchange rates covering more than a decade, we find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatitilies perform admirably compared to popular daily ARCH and related models. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically grounded assumption of normally distributed standardized returns, gives rise to well-calibrated density forecasts of future returns, and correspondingly accurate quintile estimates. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation and financial risk management applications.

Modeling and Forecasting Realized Volatility

We examine how investor sentiment affects the cross-section of stock returns. Theory predicts that a broad wave of sentiment will disproportionately affect stocks whose valuations are highly subjective and are difficult to arbitrage. We test this prediction by studying how the cross-section of subsequent stock returns varies with proxies for beginning-of-period investor sentiment. When sentiment is low, subsequent returns are relatively high on smaller stocks, high volatility stocks, unprofitable stocks, non-dividend-paying stocks, extreme-growth stocks, and distressed stocks, consistent with an initial underpricing of these stocks. When sentiment is high, on the other hand, these patterns attenuate or fully reverse. The results are consistent with predictions and appear unlikely to reflect an alternative explanation based on compensation for systematic risk.

Investor Sentiment and the Cross-Section of Stock Returns

This paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility constructed from high-frequency intraday returns, in contrast, permits the use of traditional time series procedures for modeling and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic variation, we formally develop the links between the conditional covariance matrix and the concept of realized volatility. Next, using continuously recorded observations for the Deutschemark / Dollar and Yen / Dollar spot exchange rates covering more than a decade, we find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably compared to popular daily ARCH and related models. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically grounded assumption of normally distributed standardized returns, gives rise to well-calibrated density forecasts of future returns, and correspondingly accurate quantile estimates. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation and financial risk management applications.

/pdf/modeling-and-forecasting-realized-volatility-2mqo5gygak.pdf

Modeling and forecasting realized volatility

We estimate parameters of standard stock selection and market timing models using daily mutual fund returns and quarterly measurement periods. We then rank funds quarterly by abnormal return and measure the performance of each decile the following quarter. The average abnormal return of the top decile in the post-ranking quarter is 39 basis points. The post-ranking abnormal return disappears when funds are evaluated over longer periods. These results suggest that superior performance is a short-lived phenomenon that is observable only when funds are evaluated several times a year. The net new cash flow invested in U.S. mutual funds in 2000 was $229.2 billion, which exceeded the 2000 gross domestic product of all but 19 countries. 1 Not surprisingly, a large industry exists to provide investors with information to help them choose from among the thousands of available mutual funds. The existence of the mutual fund selection industry is predicated on the assumptions that some mutual fund managers possess significant ability and that this ability persists, allowing the astute investor to predict future performance based on past results. From an academic perspective, assessing the existence and persistence of mutual fund managerial ability is an important test of the efficient market hypothesis; evidence of persistent ability would support a rejection of its semi-strong form. What should we expect in equilibrium? Grossman and Stiglitz (1980) argue that we should not expect that security prices fully reflect the information of informed individuals; otherwise, there would be no reward for the costly endeavor of seeking new information. In the context of mutual fund performance, we should expect some fund

/pdf/short-term-persistence-in-mutual-fund-performance-4ovc1pbgzo.pdf

Short-Term Persistence in Mutual Fund Performance

Existing studies of mutual fund market timing analyze monthly returns and find little evidence of timing ability. We show that daily tests are more powerful and that mutual funds exhibit significant timing ability more often in daily tests than in monthly tests. We construct a set of synthetic fund returns in order to control for spurious results. The daily timing coefficients of the majority of funds are significantly different from their synthetic counterparts. These results suggest that mutual funds may possess more timing ability than previously documented.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID245790_code001012130.pdf?abstractid=245790&mirid=1

On the Timing Ability of Mutual Fund Managers

Existing studies of mutual fund market timing analyze monthly returns and find little evidence of timing ability. We show that daily tests are more powerful and that mutual funds exhibit significant timing ability more often in daily tests than in monthly tests. We construct a set of synthetic fund returns in order to control for spurious results. The daily timing coefficients of the majority of funds are significantly different from their synthetic counterparts. These results suggest that mutual funds may possess more timing ability than previously documented. THE PERFORMANCE OF MUTUAL FUNDS RECEIVES a great deal of attention from both practitioners and academics. Almost 50 percent of U.S. households invest in mutual funds, with an aggregate investment of over five trillion dollars (Investment Company Institute, 2000). Given the size of their stake, the investing public's interest in identifying successful fund managers is understandable, especially in light of mounting evidence that the returns of most actively managed funds are lower than index fund returns.1 From an academic perspective, the goal of identifying superior fund managers is interesting because it challenges the efficient market hypothesis. In this paper, we examine the ability of mutual fund managers to time the market, that is, to increase a fund's exposure to the market index prior to market advances and to decrease exposure prior to market declines. Most existing studies find little evidence that fund managers possess market timing ability. Treynor and Mazuy ((1966), hereafter referred to as TM), for example, develop a test of market timing and find significant ability in only 1 fund out of 57 in their sample. Henriksson (1984) uses the market timing test of Henriksson and Merton ((1981), hereafter referred to as HM) and finds that only 3 funds out of 116 exhibit significant positive market timing ability. Graham and Harvey (1996) analyze investment newsletters' suggested allocations between equity and cash, thereby measuring explicitly the

/pdf/on-the-timing-ability-of-mutual-fund-managers-1ohi1jvohx.pdf

I use daily mutual fund returns to shed new light on the question of whether or not mutual fund managers are successful market timers. Previous studies find that funds are unable to time the market return. I study the funds' ability to time market volatility. I show that volatility timing is an important factor in the returns of mutual funds and has led to higher risk-adjusted returns. The returns of surviving funds are especially sensitive to market volatility; those of nonsurvivors are not. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

Volatility Timing in Mutual Funds: Evidence from Daily Returns

Using new, survivorship bias-free data, we examine the performance and persistence in performance of 4,617 active domestic equity institutional products managed by 1,448 investment management firms between 1991 and 2008. Controlling for the Fama‐French (1993) three factors and momentum, aggregate and average estimates of alphas are statistically indistinguishable from zero. Even though there is considerable heterogeneity in performance, there is only modest evidence of persistence in three-factor models and little to none in four-factor models. THE TWIN QUESTIONS OF WHETHER INVESTMENT MANAGERS generate superior riskadjusted returns (“alpha”) and whether superior performance persists are central to our understanding of efficient capital markets. Academic opinion on these issues revolves around the most recent evidence incorporating either new data or improved measurement technology. Although Jensen’s (1968) original examination of mutual funds concludes that funds do not have abnormal performance, later studies provide evidence that relative performance persists over both short and long horizons. 1 Carhart (1997), however, reports that accounting for momentum in individual stock returns eliminates almost all evidence of persistence among mutual funds (with one exception, the continued underperformance of the worst performing funds (Berk and Xu (2004)). More recently, Bollen and Busse (2005), Cohen, Coval, and P´

/pdf/performance-and-persistence-in-institutional-investment-3lhz7b70fu.pdf

Jeffrey A. Busse

Papers

Short-Term Persistence in Mutual Fund Performance

On the Timing Ability of Mutual Fund Managers

On the Timing Ability of Mutual Fund Managers

Volatility Timing in Mutual Funds: Evidence from Daily Returns

Performance and Persistence in Institutional Investment Management