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Jay Yao

Jay Yao

Vice President, MSCI Research

Jay is a Vice President in the Equity Core Research team at MSCI. His focus is on developing equity risk models for the use in portfolio construction and risk management. Prior to MSCI, Jay was a Quantitative Researcher at Money Management Group. He also worked as a software engineer at Oracle Inc. Jay received a Bachelor of Science degree in Mechanical Engineering from Shanghai Jiao Tong University and a Ph.D. in Industrial Engineering and Operations Research from the University of California at Berkeley.

Research and Insights

Articles by Jay Yao

    MSCI Security Crowding Model

    Report | Feb 4, 2022 | George Bonne , Jay Yao

    The MSCI Security Crowding Model extends the framework of the MSCI Factor Crowding Model to individual securities. Our primary motivation was to identify crowded stocks, which we assumed would underperform, on average, and be more susceptible to crashes. If the model is working as intended, we also assume uncrowded stocks would outperform. This would be consistent with our factor-crowding research. Because any security-level characteristic could potentially be a factor, we evaluated our...

    Eyeing the Crowds from Multiple Perspectives

    6 mins read Blog | Jul 8, 2021 | George Bonne , Jay Yao , Howard Zhang

    We observed historically notable crowding across factors, industries and stocks through the first half of 2021. Examining crowding from multiple perspectives and incorporating multiple data elements provides investors a more holistic view. 

    Are (Stock) Bubbles Rising?

    6 mins read Blog | Feb 11, 2021 | George Bonne , Howard Zhang , Jay Yao

    How does one identify or quantify a bubble? We propose a framework for assessing the “bubbliness” of stocks and portfolios, rooted in the idea that bubbles are driven by the same forces, and share characteristics with crowded trades.

    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.

    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.

    Research Insight - Constructing Quality Risk Models - June 2013

    Report | Jul 15, 2014 | Oleg Ruban , Jyh-huei Lee , Jay Yao

    In this Research Insight, we outline the building blocks essential to constructing an effective standard risk model. We then turn to how risk models are used in the investment process — that is, constructing efficient portfolios and attributing their risk and return. Finally, we describe the best practices of proprietary model construction, including an empirical investigation of the economic impact of using proprietary models in portfolio optimization.

    Is Your Risk Model Letting Your Optimized Portfolio Down?

    Report | Jul 15, 2014 | Oleg Ruban , Dan Stefek , Jyh-huei Lee , Jay Yao

    Many portfolio managers use multi-factor models, but not all factor models are equally effective in forecasting risk. Flawed model construction can result in optimized portfolios that are not efficient.  This paper addresses the concern of portfolio managers that some risk models used in optimization may not be forecasting risk accurately, or may be creating suboptimal portfolios. We review pitfalls in portfolio construction and explain how MSCI’s best practices in model building are...

    Manager Crowding and Portfolio Construction

    Report | Jul 15, 2014 | Oleg Ruban , Dan Stefek , Jyh-huei Lee , Jay Yao

    Following the “quant meltdown” of August 2007, market observers became concerned that quant strategies were leading to crowded trades. This paper analyzes the impact that a risk model used in portfolio construction has on manager crowding by identifying the drivers of crowding and by illustrating their impact.  A risk model’s effect on manager crowding depends, in part, on how alphas used by different managers are related to each other, and to the risk model factors. We...

    Alpha-Risk Factor Misalignment

    Report | Jul 15, 2014 | Oleg Ruban , Dan Stefek , Jyh-huei Lee , Jay Yao

    Portfolio managers have long worried that discrepancies between risk and alpha factors may somehow detract from the performance of their optimized portfolios. This paper presents a comprehensive overview of alpha-risk factor alignment and its consequences, showing how penalizing the residual alpha may help reduce the unintended bets resulting from misalignment. However, we also illustrate that correcting for misalignment may not always be necessary and can sometimes be...

    Forecast Risk Bias in Optimized Portfolios

    Report | Jul 15, 2014 | Dan Stefek , Jay Yao , Jennifer Bender , Jyh-huei Lee

    When there is noise in a covariance matrix, portfolio optimization tends to produce portfolios for which the risk forecasts are underestimates of the true risk. In this paper, we take a closer look at the connection between estimation error and the underestimation of the risk of optimized portfolios. We pay special attention to the case in which returns have a known factor structure. There, the bias in optimization can be reduced dramatically by using a covariance matrix based on a factor...

    Risk Forecast Biases of Optimized Portfolios - A Quantitative Analysis

    Report | Jul 15, 2014 | Dan Stefek , Rong Xu , Jennifer Bender , Jyh-huei Lee , Jay Yao

    Portfolio managers have long suspected that the risk forecast of an optimized portfolio tends to be optimistic. Many have identified the culprit as estimation error in the covariance matrix. Forecasts based on historical asset covariance matrices are particularly sensitive to this error. The bias is reduced dramatically by using a factor model. Even so, factor models still tend to under-forecast the risk of optimized portfolios, especially the risk coming from factors. In this paper, we show...

    Mitigating Risk Forecast Biases of Optimized Portfolios

    Report | Jul 15, 2014 | Dan Stefek , Rong Xu , Jyh-huei Lee , Jay Yao

    Portfolio managers have long suspected that the risk forecast of an optimized portfolio tends to be optimistic. Many have identified the culprit as estimation error in the covariance matrix. Forecasts based on historical asset covariance matrices are particularly sensitive to this error. The bias is reduced dramatically by using a factor model. Even so, factor models still tend to under-forecast the risk of optimized portfolios, especially the risk coming from factors. In this paper, we show...