SSRN Author: Andrew AngAndrew Ang SSRN Content
http://www.ssrn.com/author=94010
http://www.ssrn.com/rss/en-usThu, 16 Nov 2017 01:07:25 GMTeditor@ssrn.com (Editor)Thu, 16 Nov 2017 01:07:25 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: Estimating Time-Varying Factor ExposuresWe develop a methodology to estimate dynamic factor loadings using cross-sectional risk characteristics, which is especially useful when factor loadings significantly vary over time. In comparison, standard regression approaches assume the factor loadings are constant over a particular window. Applying the methodology to a dataset of U.S.-domiciled mutual funds we distinguish the components of active returns attributable to (1) constant factor exposures, for example, a tilt to value stocks; (2) time-varying factor exposures; and (3) security selection. The decomposition of active returns into these three components yields valuable insight into how managers generate excess returns. We show that there is diversity in factor concentration across managers and styles. For example, large-cap growth funds show the greatest concentration in two factors, momentum and quality, whereas large-cap blend funds have the most factor diversity. Finally, common measures to gauge manager skill may be ...
http://www.ssrn.com/abstract=2879071
http://www.ssrn.com/1643115.htmlWed, 15 Nov 2017 18:07:10 GMTREVISION: Using Individual Stocks or Portfolios in Tests of Factor ModelsWe examine the efficiency of using individual stocks or portfolios as base assets to test asset pricing models using cross-sectional data. The literature has argued that creating portfolios reduces idiosyncratic volatility and allows more precise estimates of factor loadings, and consequently risk premia. We show analytically and empirically that smaller standard errors of portfolio beta estimates do not lead to smaller standard errors of cross-sectional coefficient estimates. Factor risk premia standard errors are determined by the cross-sectional distributions of factor loadings and residual risk. Portfolios destroy information by shrinking the dispersion of betas, leading to larger standard errors.
http://www.ssrn.com/abstract=1106463
http://www.ssrn.com/1633103.htmlThu, 12 Oct 2017 02:39:35 GMTNew: What's in Your Benchmark? A Factor Analysis of Major Market IndexesWe examine the factor exposures of several popular market capitalization indexes and how they vary over time. We find that most market capitalization weight indexes are effectively exposed to only two or three factors, with value and momentum being increasingly dominant. We find that the proportion of the index movements explained by factors has materially increased in recent years, which is consistent with a more top-down, macro-driven environment or the increasing importance of economy-wide risks for financial markets.
http://www.ssrn.com/abstract=3036108
http://www.ssrn.com/1625554.htmlFri, 15 Sep 2017 17:18:24 GMTUpdate: Estimating Private Equity Returns from Limited Partner Cash FlowsWe introduce a methodology to estimate the historical time-series of returns to investment in private equity funds. The approach requires only an unbalanced panel of cash contributions and distributions accruing to limited partners, and is robust to sparse data. We decompose private equity returns into a component due to traded factors and a time-varying private equity premium. We find strong cyclicality in private equity returns that differs according to fund type. The time-series estimates allow us to directly test theories about private equity cyclicality, and we find evidence that capital market segmentation helps to determine private equity returns.<br/><i>New PDF Uploaded</i>
http://www.ssrn.com/abstract=2460789
http://www.ssrn.com/1608457.htmlSun, 16 Jul 2017 17:01:04 GMTNew: Portfolio Structuring and the Value of ForecastingOn 8 October 2015, CFA Montréal hosted its annual Asset Allocation Forum under the theme “Portfolio Structuring and the Value of Forecasting.” Two asset management approaches were compared:
• The factor investing approach, which relies on identifying common factors in security returns determining which factors represent compensated risks, and then extracting returns from a larger and more balanced set of compensated risks than traditional cap-weighted indices do
• The traditional approach, which relies on explicit forecasts of security - or industry-specific expected returns made by asset managers
Traditional asset management has sustained much criticism in recent years. Few active managers outperform their benchmark after fees over longer time horizons, such as 5 to 10 years. There has been much empirical evidence supporting the view that professional forecasters cannot predict or that their predictions explain only a very small part of the variability of asset ...
http://www.ssrn.com/abstract=2978148
http://www.ssrn.com/1595825.htmlWed, 31 May 2017 12:43:47 GMTREVISION: Capacity of Smart Beta Strategies: A Transaction Cost PerspectiveUsing a transaction cost model, and an assumption for the smart beta premium observed in data, we estimate the capacity of a particular implementation of momentum, quality, value, size, minimum volatility, and a multi-factor combination. For a given trading horizon, we can find the fund size where the transaction costs from flows into these strategies negate the smart beta premium. For a one-day trading horizon, momentum is the strategy with the smallest assets under management (AUM) capacity of $65 billion, and size is the largest with an AUM capacity of $5 trillion. At five days, momentum and size capacity rise to $320 billion and over $10 trillion, respectively.
http://www.ssrn.com/abstract=2861324
http://www.ssrn.com/1572671.htmlThu, 09 Mar 2017 14:10:52 GMTNew: Estimating Private Equity Returns from Limited Partner Cash FlowsWe introduce a methodology to estimate the historical time-series of returns to investment in private equity funds. The approach requires only an unbalanced panel of cash contributions and distributions accruing to limited partners, and is robust to sparse data. We decompose private equity returns into a component due to traded factors and a time-varying private equity premium. We find strong cyclicality in private equity returns that differs according to fund type. The time-series estimates allow us to directly test theories about private equity cyclicality, and we find evidence that capital market segmentation helps to determine private equity returns.
http://www.ssrn.com/abstract=2460789
http://www.ssrn.com/1567539.htmlSat, 18 Feb 2017 05:13:50 GMTREVISION: Estimating Time-Varying Factor Exposures with Cross-Sectional Characteristics with Application to Active Mutual Fund ReturnsWe develop a methodology to estimate dynamic factor loadings using cross-sectional risk characteristics, which is especially useful when factor loadings significantly vary over time. In comparison, standard regression approaches assume the factor loadings are constant over a particular window. Applying the methodology to a dataset of U.S.-domiciled mutual funds we distinguish the components of active returns attributable to (1) constant factor exposures, for example, a tilt to value stocks; (2) time-varying factor exposures; and (3) security selection. The decomposition of active returns into these three components yields valuable insight into how managers generate excess returns. We show that there is diversity in factor concentration across managers and styles. For example, large-cap growth funds show the greatest concentration in two factors, momentum and quality, whereas large-cap blend funds have the most factor diversity. Finally, common measures to gauge manager skill may be ...
http://www.ssrn.com/abstract=2879071
http://www.ssrn.com/1552649.htmlWed, 21 Dec 2016 09:23:33 GMTNew: Factor Risk Premiums and Invested Capital: Calculations with Stochastic Discount FactorsPortfolios with positive exposures to rewarded risk premiums have historically exhibited high average returns adjusting for their market betas. As capital allocated to such strategies increases, the excess returns of these portfolios should decrease. We compute the flows from low-return to high-return portfolios required so that the factor risk premiums are equal to zero. We also estimate the factor premiums resulting when all capital from the bottom 30% of stocks ranked by common risk factors—value, size, momentum, and idiosyncratic volatility—is transferred to the top 30% of stocks. We find that size is the least robust factor and in fact reverses under this scenario. The value, momentum, and volatility factor premiums are reduced by at most half from their historical premiums.
http://www.ssrn.com/abstract=2879049
http://www.ssrn.com/1548865.htmlMon, 05 Dec 2016 18:14:10 GMT