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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
Equity-Factor Commentary: A ‘Magnificent 7’ Factor?
6 mins read Blog | Nov 29, 2023 | George Bonne, Waman Virgaonkar, Jun WangWould adding a Magnificent 7 factor have improved an equity factor model’s performance in the concentrated U.S. equity markets of the last few years? Our analysis shows that the model’s risk-forecast accuracy would not have materially increased.
Machine Learning Factors: Capturing Non Linearities in Linear Factor Models
Research Report | Mar 26, 2021 | Jun Wang, Howard Zhang, George BonneIt 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 13, 2020 | George Bonne, Jun WangPositive 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
Research Report | Jun 1, 2020 | Jun Wang, Jay Yao, George BonneWe investigate the extent to which nonlinearities not captured by standard linear models within equity factor risk models are present. Overall, we found linear models created a robust framework to identify relationships between factor exposures and security returns through simple linear factors or transformed (e.g., polynomial) variants.
The coronavirus market impact spreads globally
Blog | Mar 5, 2020 | Jun Wang, Jay Yao, George BonneFear 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 12, 2020 | Jun Wang, Zhen Wei, Thomas VerbrakenThe 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 | Jun Wang, Andrei MorozovFears 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 MorozovGiven 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
Research Report | Jun 12, 2014 | Jun Wang, Mehmet Bayraktar, Jay YaoThis 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
Research Report | Nov 1, 2013 | Jun Wang, Mehmet Bayraktar, Jay YaoThis 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
Research Report | Jun 19, 2013 | Jun Wang, Mehmet Bayraktar, Jay Yao, Jyh-huei Lee, Igor Mashtaler, Nicolas MengIn 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
Research Report | Jun 18, 2013 | Jun Wang, Mehmet Bayraktar, Jay Yao, Igor Mashtaler, Nicolas MengThis 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
Research Report | Apr 29, 2013 | Jun Wang, Andrei Morozov, Laszlo BordaThis 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.
Model Insight - Barra Global Equity Model (GEM3) Empirical Notes - January 2012
Research Report | Jan 15, 2012 | Jun Wang, Andrei MorozovThe Barra US Equity Model (USE4) - Empirical Notes
Research Report | Sep 20, 2011 | Jun Wang, Yang Liu, Jose Menchero, D.j. OrrGEM2 Factor Returns and Volatilities
Research Report | Jan 14, 2010 | Jun Wang, Jose MencheroIn 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.
Introducing Multiple Horizon Versions of the Canada Equity Model (CNE4) - Research Notes
Research Report | Jan 2, 2010 | Jun Wang, Andrei MorozovThis 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...