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Jay Yao

Jay Yao
Vice President, MSCI Research

About the Contributor

Jay Yao 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.

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Contributions by Jay Yao

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  1. BLOG

    Are (Stock) Bubbles Rising? 

    Feb 12, 2021 George Bonne , Howard Zhang , Jay Yao

    Factor Investing

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    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.

  2. 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 information ratios higher than corresponding linear factors. 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.

  3. BLOG

    The coronavirus market impact spreads globally 

    Mar 5, 2020 Jun Wang , Jay Yao , George Bonne

    Factor Investing , Global Investing

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    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.

  4. PAPER

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

    Jun 12, 2014 Mehmet Bayraktar , Jay Yao , Jun Wang

    Factor and Risk Modeling

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    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 backtesting performance of the ZAE4 Model and its predecessor, the Barra SAE3 Model.  The new Barra South Africa Equity Model captures the dynamics of the South African market through the latest advances in MSCI research methodology and a comprehensive factor set, including the expanded Systematic Equity Strategy (SES) factors.

  5. PAPER

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

    Nov 1, 2013 Jun Wang , Mehmet Bayraktar , Jay Yao

    Factor and Risk Modeling

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    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.

  6. PAPER

    Research Insight - Systematic Equity Strategies - A Test Case Using Empirical Results from the Japan Equity Market - June 2013 

    Jun 19, 2013 Jun Wang , Jay Yao , Jyh-huei Lee , Mehmet Bayraktar , Igor Mashtaler , Nicolas Meng

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    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 include SES risk factors should lead to improved portfolio risk forecasts.

  7. PAPER

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

    Jun 18, 2013 Jun Wang , Jay Yao , Mehmet Bayraktar , Igor Mashtaler , Nicolas Meng

    Factor and Risk Modeling

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    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.

  8. 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.

  9. 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 counterproductive—the need for corrective action depends on the information content of residual alpha. Improvement in Information Ratios was achieved in just over half of the cases we examined and the average magnitude of improvement was small.

  10. PAPER

    Research Insight - Manager Crowding and Portfolio Construction - October 2012 

    Oct 10, 2012 Oleg Ruban , Jyh-huei Lee , Jay Yao , Dan Stefek

    Investing (Investment Management) , Portfolio Construction and Optimization , Risk Management

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    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 explain how this works with some simple, intuitive examples, and with the aid of a well established analytical framework.

  11. PAPER

    Research Insight - Is Your Risk Model Letting Your Optimized Portfolio Down? - August 2012 

    Aug 23, 2012 Oleg Ruban , Jyh-huei Lee , Jay Yao , Dan Stefek

    Factor and Risk Modeling , Investing (Investment Management) , Portfolio Construction and Optimization

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    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 designed to overcome these challenges.

  12. PAPER

    Mitigating Risk Forecast Biases of Optimized Portfolios 

    Sep 26, 2011 Dan Stefek , Jyh-huei Lee , Jay Yao , Rong Xu

    Portfolio Construction and Optimization

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    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 how estimation error may lead to under-forecasting the risk of optimized portfolio. The degree of under-forecasting depends on several factors including the investment style of the portfolio as well as the size of the investment universe. We review MSCI’s new Optimization Bias Adjustment for reducing this forecasting bias and illustrate its effectiveness on portfolios tilting on commonly used styles.

  13. PAPER

    Risk Forecast Biases of Optimized Portfolios - A Quantitative Analysis 

    Sep 20, 2011 Dan Stefek , Rong Xu , Jennifer Bender , Jyh-huei Lee , Jay Yao

    Portfolio Construction and Optimization

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    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 how estimation error may lead to under-forecasting the risk of optimized portfolio. The degree of under-forecasting depends on several factors including the investment style of the portfolio as well as the size of the investment universe. These affects have a  mathematical basis. We quantify them and explain why they occur.  Lastly, we review MSCI’s new Optimization Bias Adjustment for reducing this forecasting bias and illustrate its effectiveness on portfolios tilting on commonly used styles.

  14. PAPER

    Barra Hedge Fund Model (HFM2) Research Notes 

    Jul 2, 2011 Jay Yao , Michael Levinson

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  15. PAPER

    Forecast Risk Bias in Optimized Portfolios 

    Oct 1, 2009 Dan Stefek , Jennifer Bender , Jyh-huei Lee , Jay Yao

    Portfolio Construction and Optimization

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    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 model, rather than one computed from historical asset covariances. Moreover, our analysis reveals that for many active portfolios, the bias in factor-model forecasts is less than previously thought. Lastly, we discuss the role of constraints in mitigating risk forecasting bias.

  16. PAPER

    What Does Hedge Fund Ownership Tell Us About Stock Return and Risk? 

    Jan 2, 2009 Jay Yao , Dan Stefek

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