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Jun Wang

Jun Wang

Vice President, MSCI Research

Jun Wang is a Vice President in the Equity Core Research team at MSCI. His focus is on research and development of global, regional and single-country fundamental equity models. Previously, Jun explored experimental physics research in laser and radiofrequency spectroscopy of molecular systems at Yale University. Jun received his Ph.D. in Physics from Yale University, and a Bachelor of Science degree in Physics from the University of Science and Technology of China. Jun is a CFA charterholder.

Research and Insights

Articles by Jun Wang

    Machine Learning Factors Capturing Non Linearities in Linear Factor Models

    Report | Mar 26, 2021 | Howard Zhang , George Bonne , Jun Wang

    It is not etched in stone that relationships between factor exposures and returns must be linear. We found machine-learning algorithms could identify nonlinear relationships and be used to construct a factor showing significant explanatory power. 

    Are Momentum’s Wings Finally Starting to Melt?

    3 mins read Blog | Nov 12, 2020 | George Bonne , Jun Wang

    Positive vaccine news on Nov. 9 caused big moves in industry and style factors. Those hit hardest this year jumped, while previous high performers slumped. Did this mark new factors leadership and a long-awaited rotation from momentum to value?

    Straight Talk on Nonlinearities in Linear Factor Models

    Report | Jun 1, 2020 | Jay Yao , George Bonne , Jun Wang

    Linear regression models have been the workhorses of finance and economics. However, given increasing attention to nonlinear methods, we investigate the extent to which nonlinearities not captured by standard linear models within equity factor risk models are present. Adding nonlinear factors in simple polynomial functions of their linear counterparts contributed some additional explanatory power to the cross-section of security returns. Furthermore, some generated factor returns and...

    The coronavirus market impact spreads globally

    Blog | Mar 4, 2020 | Jun Wang , Jay Yao , George Bonne

    Fear of a coronavirus pandemic and ensuing economic impacts caused sharp drops in global markets after an initially mild response. We look at recent performance from a factor perspective and how quickly factor returns and volatility reverted in past crises.

    The coronavirus epidemic: Implications for markets

    Blog | Feb 7, 2020 | Zhen Wei , Jun Wang , Thomas Verbraken

    The toll from the coronavirus has been felt throughout societies, leading to repercussions on the global economy and financial markets. We examine investor impact through markets’ economic exposures to China and factors and by stress testing portfolios.

    Did FAANG Stocks lead the US Stock Market Drop?

    Blog | Oct 15, 2018 | Andrei Morozov , Jun Wang

    Fears of a global slowdown have sent U.S. stock markets plummeting recently. Given FAANG stocks (Facebook, Apple, Amazon, Netflix and Google) have been a dominant force in driving U.S. market performance higher over the past few years, did these stocks lead the market’s downward trajectory?

    Managing Risk Over Different Investment Horizons

    Blog | Sep 25, 2018 | Jun Wang , Andrei Morozov

    Given high market valuations, some investors worry that a market pullback may be at hand. We saw markets gyrate earlier this year — what if volatility returns? How investors respond to changing market conditions may depend on their time horizons.

    Model Insight - Barra South Africa Equity Model (ZAE4) Empirical Notes - June 2014

    Report | Jul 15, 2014 | Jun Wang , Mehmet Bayraktar , Jay Yao

    This Model Insight summarizes the methodology and empirical results for the fourth-generation Barra South Africa Equity Model (ZAE4). This paper includes extensive information on factor structure, commentary on the performance of select factors, an analysis of the explanatory power of the model, and an examination of its effectiveness in portfolio construction using minimum volatility and index tracking portfolios. It also includes a side-by-side comparison of the forecasting accuracy and...

    Model Insight - Barra Korea Equity Model (KRE3) Empirical Notes - November 2013

    Report | Jul 15, 2014 | Jun Wang , Jay Yao , Mehmet Bayraktar

    This Model Insight provides empirical results for the new Barra Korea Equity Model (KRE3), including detailed information about the structure, the performance, and the explanatory power of the factors. Furthermore, these notes also include backtesting results and a side-by-side comparison of the forecasting accuracy of the KRE3 Model and the KRE2 Model, its predecessor.

    Systematic Equity Strategies: A Test Case Using Empirical Results from the Japan Equity Market

    Report | Jul 15, 2014 | Jun Wang , Jay Yao , Nicolas Meng , Mehmet Bayraktar , Igor Mashtaler , Jyh-huei Lee

    In an introductory paper, we explained Systematic Equity Strategies (SES) and how they can be used as factors in a risk model.  In this paper, we use data from the Japan equity markets to define seven new SES factors and study their empirical behavior.  Our findings illustrate the important role that these factors play in portfolio construction and risk management. Our study also shows problems associated with omitting these factors from a risk model, and explain why models that...

    Model Insight - Barra Japan Equity Model (JPE4) Empirical Notes - October 2013

    Report | Jul 15, 2014 | Jun Wang , Jay Yao , Mehmet Bayraktar , Igor Mashtaler , Nicolas Meng

    This Model Insight provides empirical results for the new Barra Japan Equity Model (JPE4), including detailed information on the structure, the performance, and the explanatory power of the factors. Furthermore, these notes also include backtesting results and a thorough side-by-side comparison of the forecasting accuracy of the JPE4 Model and the JPE3 Model, its predecessor.

    Model Insight - The Barra Europe Equity Model (EUE4) - April 2013

    Report | Jul 15, 2014 | Andrei Morozov , Laszlo Borda , Jun Wang

    This paper provides empirical results for the new Barra Europe Equity Model (EUE4), including details on factor structure, commentary on the performance of select factors, analysis of the explanatory power of the model, and an examination of the statistical significance of the factors. Furthermore, these notes include a side-by-side comparison of forecasting accuracy for EUE4 and EUE3.

    Introducing Multiple Horizon Versions of the Canada Equity Model (CNE4) - Research Notes

    Report | Jul 15, 2014 | Andrei Morozov , Jun Wang

    This report introduces the new multiple horizon versions of the Barra Canada Equity Model (CNE4) - Canada Equity Model Short Term (CNE4S) and Canada Equity Model Long Term(CNE4L). Both versions use daily returns data while accounting for serial correlations in aggregating daily factor returns to longer horizons. The new multiple horizon models provide more responsive risk forecasts than the existing model,CNE4. In addition to using higher frequency returns the new multiple horizon models also...

    GEM2 Factor Returns and Volatilities

    Report | Jul 15, 2014 | Jun Wang , Jose Menchero

    In this Model Insight, we present the volatilities and cumulative returns for every factor and currency in the GEM2 global equity model.  The analysis period runs from January 1997 through August 2009.