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150
2005), How Useful is Bagging in Forecasting Economic Time Series? A Case Study
 Department of Economics, University of Michigan
"... This article explores the usefulness of bagging methods in forecasting economic time series from linear multiple regression models. We focus on the widely studied question of whether the inclusion of indicators of real economic activity lowers the prediction meansquared error of forecast models of ..."
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Cited by 41 (0 self)
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This article explores the usefulness of bagging methods in forecasting economic time series from linear multiple regression models. We focus on the widely studied question of whether the inclusion of indicators of real economic activity lowers the prediction meansquared error of forecast models of U.S. consumer price inflation. We study bagging methods for linear regression models with correlated regressors and for factor models. We compare the accuracy of simulated outofsample forecasts of inflation based on these bagging methods to that of alternative forecast methods, including factor model forecasts, shrinkage estimator forecasts, combination forecasts and Bayesian model averaging. We find that bagging methods in this application are almost as acccurate or more accurate than the best alternatives. Our empirical analysis demonstrates that large reductions in the prediction mean squared error are possible relative to existing methods, a result that is also suggested by the asymptotic analysis of some stylized linear multiple regression
Asset pricing models and financial market anomalies
 Review of Financial Studies
"... This article develops a framework that applies to single securities to test whether asset pricing models can explain the size, value, and momentum anomalies. Stock level beta is allowed to vary with firmlevel size and booktomarket as well as with macroeconomic variables. With constant beta, none ..."
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Cited by 32 (4 self)
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This article develops a framework that applies to single securities to test whether asset pricing models can explain the size, value, and momentum anomalies. Stock level beta is allowed to vary with firmlevel size and booktomarket as well as with macroeconomic variables. With constant beta, none of the models examined capture any of the market anomalies. When beta is allowed to vary, the size and value effects are often explained, but the explanatory power of past return remains robust. The past return effect is captured by model mispricing that varies with macroeconomic variables. The capital asset pricing model (CAPM) of Sharpe (1964) and Lintner (1965) has long been a basic tenet of finance. However, subsequent work
The term structure of the riskreturn tradeoff
 Financial Analysts Journal
, 2005
"... Recent research in empirical finance has documented that expected excess returns on bonds and stocks, real interest rates, and risk shift over time in predictable ways. Furthermore, these shifts tend to persist over long periods of time. This paper has two objectives. First, we propose an empirical ..."
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Cited by 32 (5 self)
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Recent research in empirical finance has documented that expected excess returns on bonds and stocks, real interest rates, and risk shift over time in predictable ways. Furthermore, these shifts tend to persist over long periods of time. This paper has two objectives. First, we propose an empirical model that is able to capture the complex dynamics of expected returns and risk, yet is simple to apply in practice. Second, we explore the implications for asset allocation. Changes in investment opportunities have the important implication that the riskreturn tradeoff of bonds, stocks, and cash may be significantly different across investment horizons, thus creating a “term structure of the riskreturn tradeoff. ” We show how one can easily extract this term structure using our parsimonious model of return dynamics, and illustrate our approach using data from the U.S. stock and bond markets. We find that asset return predictability has important effects on the variance and correlation structure of returns on stocks, bonds and Tbills across investment horizons. Recent research in empirical finance has documented that expected excess returns on bonds and stocks, real interest rates, and risk shift over time in predictable ways. Furthermore, these shifts tend to persist over long periods of time. Starting at least
Asset allocation with a highdimensional latent factor stochastic volatility model,” The Review of Financial Studies
, 2006
"... This paper investigates implications of both timevarying expected return and volatility on the asset allocation problem in a high dimensional setting. We propose a dynamic latent factor multivariate stochastic volatility (DFMSV) model that, for the first time, allows for both timevarying expected ..."
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Cited by 32 (2 self)
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This paper investigates implications of both timevarying expected return and volatility on the asset allocation problem in a high dimensional setting. We propose a dynamic latent factor multivariate stochastic volatility (DFMSV) model that, for the first time, allows for both timevarying expected return and stochastic volatility for a large number of assets, and evaluate its economic significance by examining the portfolio performance of various dynamic strategies constructed based on the DFMSV model. With funds allocated among 36 stocks, we conduct conditional meanvariance portfolio analysis for shorthorizon investors and find that the DFMSVbased dynamic strategies significantly outperform various benchmark strategies both insample and outofsample. In addition, the outperformance is robust to different performance measures, perturbations in the investor’s objective functions, transaction costs and investment horizons.
Stock return predictability and asset pricing models
, 2001
"... Asset pricing models based on rational timevarying expected returns or on equity characteristics imply restrictions on stock return predictability. This paper develops a framework for investigating these pricing restrictions through the use of an economic metric that is based on asset allocation wi ..."
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Cited by 31 (3 self)
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Asset pricing models based on rational timevarying expected returns or on equity characteristics imply restrictions on stock return predictability. This paper develops a framework for investigating these pricing restrictions through the use of an economic metric that is based on asset allocation with estimation risk. The evidence shows that when portfolio weights are unconstrained, the deviations from the pricing models are economically significant. Incorporating constraints on leverage and short equity positions results in a sharp reduction in these deviations, which disappear in some cases, yet they remain economically significant in most cases. Finally, imposing factor model restrictions on predictive regressions generate smaller outofsample Sharpe ratios and larger mean square forecast errors. The results carry implications for various applications in financial economics using risk factors or equity characteristics as benchmarks.
Forecasting Inflation Using Dynamic Model Averaging
, 2009
"... We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coefficients to change over time, but also allow for the entire forecasting model to change over time. We find that dynamic ..."
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Cited by 28 (15 self)
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We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coefficients to change over time, but also allow for the entire forecasting model to change over time. We find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coefficient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.
Predictable returns and asset allocation: Should a skeptical investor time the market
 Journal of Econometrics
, 2009
"... are grateful for financial support from the Aronson+Johnson+Ortiz fellowship through the Rodney L. White Center for Financial Research. This manuscript does not reflect the views of the Board of Governors of the Federal Reserve System. Predictable returns and asset allocation: Should a skeptical inv ..."
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Cited by 27 (0 self)
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are grateful for financial support from the Aronson+Johnson+Ortiz fellowship through the Rodney L. White Center for Financial Research. This manuscript does not reflect the views of the Board of Governors of the Federal Reserve System. Predictable returns and asset allocation: Should a skeptical investor time the market? We investigate optimal portfolio choice for an investor who is skeptical about the degree to which excess returns are predictable. Skepticism is modeled as an informative prior over the R 2 of the predictive regression. We find that the evidence is sufficient to convince even an investor with a highly skeptical prior to vary his portfolio on the basis of the dividendprice ratio and the yield spread. The resulting weights are less volatile and deliver superior outofsample performance as compared to the weights implied by an entirely modelbased Are excess returns predictable, and if so, what does this mean for investors? In classic studies of rational valuation (e.g. Samuelson (1965, 1973), Shiller (1981)), risk premia are constant over time and thus excess returns are unpredictable. 1
Simple forecasts and paradigm shifts
 Journal of Finance
, 2007
"... Abstract: We study the implications of learning in an environment where the true model of the world is a multivariate one, but where agents update only over the class of simple univariate models. If a particular simple model does a poor job of forecasting over a period of time, it is eventually disc ..."
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Cited by 24 (1 self)
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Abstract: We study the implications of learning in an environment where the true model of the world is a multivariate one, but where agents update only over the class of simple univariate models. If a particular simple model does a poor job of forecasting over a period of time, it is eventually discarded in favor of an alternative—yet equally simple—model that would have done better over the same period. This theory makes several distinctive predictions, which, for concreteness, we develop in a stockmarket setting. For example, starting with symmetric and homoskedastic fundamentals, the theory yields forecastable variation in the size of the value/glamour differential, in volatility, and in the skewness of returns. Some of these features mirror familiar accounts of stockprice bubbles.
InSample vs. OutofSample Tests of Stock Return Predictability
 in the Context of Data Mining,”, Journal of Empirical Finance, forthcoming
, 2005
"... In this paper, we undertake an extensive analysis of insample and outofsample tests of stock return predictability in an effort to better understand the nature of the empirical evidence on return predictability. We show that a number of financial variables appearing in the literature display both ..."
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Cited by 23 (2 self)
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In this paper, we undertake an extensive analysis of insample and outofsample tests of stock return predictability in an effort to better understand the nature of the empirical evidence on return predictability. We show that a number of financial variables appearing in the literature display both insample and outofsample predictive ability with respect to stock returns in annual data covering most of the twentieth century. In contrast to the extant literature, we demonstrate that there is little discrepancy between insample and outofsample test results once we employ recently developed outofsample tests with good power properties. While conventional wisdom holds that outofsample tests help guard against data mining, Inoue and Kilian (2004) recently argue that insample and outofsample tests are equally susceptible to data mining biases. With this in mind, we test for return predictability using a bootstrap procedure that explicitly accounts for data mining when calculating critical values, and we still find that certain financial variables display significant insample and outofsample predictive ability with respect to stock returns.