Jun Tu

• Singapore Management University

This paper investigates the predictive ability of international volatility risks for the daily Chinese stock market returns. We employ the innovations in implied volatility indexes of seven major international markets as our international volatility risk proxies. We find that international volatility risks are negatively associated with contemporan...

In this paper, we investigate whether the “lotto investor” can benefit from the time-varying skewness of market portfolio and how to capture the gain using skew timing strategies. We find that empirically applying the mean-variance-skewness (M-V-S) rule of Mitton and Vorkink (2007) generates similar performance as that of traditional mean-variance...

We use the adaptive LASSO from the statistical learning literature to identify economically connected industries in a general predictive regression framework. The framework permits complex industry interdependencies, including both direct and indirect sectoral links. Consistent with gradual information diffusion across economically connected indust...

We show that firms with high growth in operating costs achieve substantially low future stock returns after controlling for firm characteristics and risk. High cost growth also predicts significant deterioration in subsequent operating performance. A spread portfolio strategy of long low cost growth stocks and short high cost growth stocks earns ab...

Academic research relies extensively on macroeconomic variables to forecast the U.S. equity risk premium, with relatively little attention paid to the technical indicators widely employed by practitioners. Our paper fills this gap by comparing the predictive ability of technical indicators with that of macroeconomic variables. Technical indicators...

We propose a new investor sentiment index that is aligned with the purpose of predicting the aggregate stock market. By eliminating a common noise component in sentiment proxies, the new index has much greater predictive power than existing sentiment indices both in- and out-of-sample, and the predictability becomes both statistically and economica...

While economic variables have been used extensively to forecast the U.S. bond risk premia, little attention has been paid to the use of technical indicators which are widely employed by practitioners. In this paper, we fill this gap by studying the predictive ability of technical indicators vis-a-vis economic variables. We find that technical indic...

In the last few decades, we observed a significant increase in global economic activities and these activities may have an impact on both China's economy and stock market. Given the potential impact, we empirically examine whether US economic variables are leading indicators of the Chinese stock market. Prior to China joining the World Trade Organi...

Academic research has extensively used macroeconomic variables to forecast the U.S. equity risk premium, with little attention paid to the technical indicators widely employed by practitioners. Our paper fills this gap by comparing the forecasting ability of technical indicators with that of macroeconomic variables. Technical indicators display sta...

We uncover extensive evidence of out-of-sample return predictability for industry portfolios based on a principal component approach that incorporates information from a large number of predictors.Moreover, we find substantial differences in the degree of return predictability across industries. To understand these differences, we propose a decompo...

We analyze return predictability for the Chinese stock market, including the aggregate market portfolio and the components of the aggregate market, such as portfolios sorted on industry, size, book-to-market and ownership concentration. Considering a variety of economic variables as predictors, both in-sample and out-of-sample tests highlight signi...

The stock market displays regime switching between upturns and downturns. This paper provides a Bayesian framework for making portfolio decisions that takes this regime switching into account, together with asset pricing model uncertainty and parameter uncertainty. The findings reveal that the economic value of accounting for regimes is substantial...

We compare the ability of economic fundamentals and technical trading rules to forecast the monthly U.S. equity premium using out-of-sample tests for 1960–2008. Both approaches provide statistically and economically significant out-of-sample forecasting gains, which are highly concentrated in U.S. business-cycle recessions. Nevertheless, fundamenta...

The primary aim of the paper is to place current methodological discussions in macroeconometric modeling contrasting the ‘theory first’ versus the ‘data first’ perspectives in the context of a broader methodological framework with a view to constructively appraise them. In particular, the paper focuses on Colander’s argument in his paper “Economist...

We analyze return predictability for components of the aggregate market, including portfolios sorted on industry, size, and book-to-market. Considering a variety of economic variables and lagged industry returns as predictors, both in-sample and out-of-sample tests highlight substantial differences in return predictability across components. Among...

The modern portfolio theory pioneered by Markowitz (1952) is widely used in practice and extensively taught to MBAs. However, the estimated Markowitz portfolio rule and most of its extensions not only underperform the naive 1/N rule (that invests equally across N assets) in simulations, but also lose money on a risk-adjusted basis in many real data...

The modern portfolio theory pioneered by Markowitz (1952) is widely used in practice and taught in MBA texts. DeMiguel, Garlappi and Uppal (2007), however, show that, due to estimation errors, existing theory-based portfolio strategies are not as good as we once thought, and the estimation window needed for them to beat the naive $1/N$ strategy (th...

Economic objectives are often ignored when estimating parameters, though the loss of doing so can be substantial. This paper proposes a way to allow Bayesian priors to reflect the objectives. Using monthly returns of the Fama-French 25 size and book-to-market portfolios and their three factors from January 1965 to December 2004, we find that invest...

We provide a model-free test for asymmetric correlations in which stocks move more often with the market when the market goes down than when it goes up, and also provide such tests for asymmetric betas and covariances. When stocks are sorted by size, book-to-market, and momentum, we find strong evidence of asymmetries for both size and momentum por...

As the usual normality assumption is firmly rejected by the data, investors encounter a data-generating process (DGP) uncertainty in making investment decisions. In this paper, we propose a novel way to incorporate uncertainty about the DGP into portfolio analysis. We find that accounting for fat tails leads to nontrivial changes in both parameter...

The stock market undergoes regime switching between upturns and downturns. We provide a framework to account for regime switching together with mispricing uncertainty and parameter uncertainty in investment decisions. Once regime switching is incorporated, regardless of the degrees of pricing model uncertainties, the portfolio decisions can deviate...

The stock market undergoes regime shifts with declining (rising) prices and rising (declining) volatilities during bad (good) times. Asset allocation decisions ignoring these patterns are likely far way from optimal if investment opportunities vary across regimes. By extending Pastor and Stambaugh (2000), our framework merges regime shifts into an...

While economic variables have been used extensively to forecast the U.S. bond risk premia, little attention has been paid to the use of technical indicators which are widely employed by practitioners. In this paper, we fill this gap by studying the predictive ability of using a variety of technical indicators vis-a-vis the economic variables. We find...