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Contributions by Jyh-huei Lee

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  1. PAPER

    Research Insight - Attribution Benefits of Aligning a Risk Model to Investment Universe - May 2014 

    May 20, 2014 Jyh-huei Lee , Jose Menchero , Zoltán Nagy

    Factor and Risk Modeling , Investing (Investment Management) , Performance Analysis , Risk Management

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    In this Research Insight, we use the Barra Emerging Markets Model (EMM1) and the Barra Global Equity Model (GEM3) to attribute the returns of a representative set of emerging market portfolios.  We show that by aligning the estimation universe with the investment universe, the EMM1 model provides a more accurate and meaningful description of emerging market portfolios.

  2. Using the lens of the Barra US Equity Model (USE4S), this Research Insight provides a practical guide to constructing investable factor portfolios. This paper begins by discussing the general concept of a factor portfolio. We then explore the role of optimization in making a 'pure factor portfolio' investable. We assess how investability constraints impact the performance of factor-replicating portfolios. Finally, we discuss how MSCI Market Neutral Barra Factor Indexes can be used in an investment process to track factor returns.

  3. PAPER

    Research Insight - Combining Multiple Sources of Alpha in Portfolio Construction - March 2014 

    Mar 6, 2014 Jyh-huei Lee , Jose Menchero

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

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    In this Research Insight, we present a methodology for efficiently combining multiple sources of alpha when constructing a portfolio. The first part of our study shows that the most efficient implementation for a single source of alpha is the minimum-volatility factor portfolio, which has the lowest risk for a given level of expected return and, therefore, the maximum expected information ratio.  &In the second part of our study, we examine how to efficiently combine multiple sources of alpha. We find that the optimal portfolio is a weighted combination of the minimum-volatility factor portfolios for each separate signal. Our paper provides examples throughout to illustrate this 'combining alpha' technique and describes the intuition behind each sample portfolio "Pillar 2"

  4. PAPER

    Research Insight - Benefits of Including Systematic Equity Strategy (SES) Factors - November 2013 

    Nov 20, 2013 Jyh-huei Lee , Jose Menchero

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

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    In the MSCI Japan Equity Model (JPE4), we include some well-known Systematic Equity Strategies as risk factors (SES factors, for short). Incorporating these SES factors can help identify and measure risk in investment strategies typically used by fundamental and quantitative managers.  In this paper, we find that models including the SES factors produced more accurate risk forecasts for portfolios tilted toward those investment strategies. Furthermore, for optimized portfolios tilting on those strategies, we found that models including the SES factors exhibited slightly lower volatility out-of-sample than corresponding portfolios constructed without SES factors included in the risk model.

  5. PAPER

    US Market Report - The Impact of Recent Fed Announcements - July 2013 

    Jul 18, 2013 Frank Vallario , Jose Menchero , Jyh-huei Lee

    Factor and Risk Modeling , Investing (Investment Management)

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    In this Market Report, we analyze the market’s reaction to the Federal Reserve’s recent announcements using the lens of the Barra US Equity Model (USE4). In particular, we find that some industry and style factors experienced very large returns immediately following the Fed announcements. Moreover, we find that in many cases the large moves can be explained by intuitive economic arguments.

  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. 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.

  8. PAPER

    Research Insight - Managing Investments with Fundamental and Stochastic Factor Models - April 2013 

    Apr 17, 2013 Frank Vallario , Jyh-huei Lee , Zoltán Nagy

    Factor and Risk Modeling , Risk Management

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    For years, practitioners have debated the benefits of using fundamental versus statistical models. In this Research Insight, we argue that the two approaches to risk modeling are complementary, not mutually exclusive. To support our reasoning, we provide a case study that demonstrates how the Barra North America Stochastic Factor Model (NAMS1) and the Barra US Equity Model (USE4) can work in concert to uncover hidden sources of risk.

  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

    Manipulating Correlations Through Latent Drivers 

    May 25, 2010 Jennifer Bender , Jyh-huei Lee

    Asset Allocation and Asset Liability Management

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    The analysis of a possible positive relationship between economic growth and stock market returns is interesting both theoretically and practically. Investors often wonder if they should assign higher weight to countries with higher economic performance, hoping that economic growth will eventually show up in equity returns. Although this relationship seems quite intuitive, historically long-run stock price growth has fallen short of GDP growth in many countries. In this bulletin, we use long-term equity data to analyze the steps leading from GDP to stock prices, and point out several factors that could explain why GDP growth is diluted before it can reach shareholders.

  15. In this study, our goal is to adapt mean-variance optimization to produce active portfolios with less exposure to extreme losses than normal optimized portfolios. Specifically, we illustrate how extreme risk can be incorporated into portfolio construction in a straightforward way by constraining the shortfall beta of the optimal portfolio. Our simple empirical examples suggest that constraining shortfall beta may offer some downside protection in turbulent periods without sacrificing performance over longer periods.

  16. 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.

  17. PAPER

    Decomposing the Impact of Portfolio Constraints 

    Aug 1, 2009 Jennifer Bender , Jyh-huei Lee , Dan Stefek

    Portfolio Construction and Optimization

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    This paper analyzes the impact of constraints on portfolio return and risk, extending the insights of previous research in this area. We show that constraints move a manager's portfolio away from the optimal unconstrained portfolio in two ways. First, they may rein in or increase the risk of the portfolio without impairing its information ratio. Second, they may force the portfolio to take unwanted bets that incur risk but yield no return. As a result, a constrained portfolio consists of positions that are aligned with the manager's alphas and positions that are orthogonal to the alphas but are adopted to satisfy the constraints. We illustrate how to measure the risk and return arising from each of these sources and how to drill down to examine the contributions of individual constraints.

  18. PAPER

    Refining Portfolio Construction by Penalizing Residual Alpha - Empirical Examples 

    Jun 1, 2009 Jennifer Bender , Jyh-huei Lee , Dan Stefek

    Portfolio Construction and Optimization

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    Misalignment between alpha and risk factors may create unintended bets in optimized portfolios, as shown analytically in Lee and Stefek (2008).  In a March research insight, we introduced a way to mitigate this issue by penalizing the portion of the alpha not related to the risk factors, the 'residual alpha.' Here, we further illustrate this method with two signals commonly used by portfolio managers. The potential improvement from this method depends on the strategy in question, in particular the degree to which the misalignment of alpha and risk factors erodes information in optimization.

  19. PAPER

    Refining Portfolio Construction When Alphas and Risk Factors Are Misaligned 

    Mar 1, 2009 Jennifer Bender , Jyh-huei Lee , Dan Stefek

    Portfolio Construction and Optimization

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    The misalignment of alpha and risk factors may result in inadvertent and unwanted bets that may hamper performance. Lee and Stefek (2008) show that better aligning risk factors with alpha factors may improve the information ratio of optimized portfolios. They propose four ways of modifying a risk model to reduce misalignment. Here, we discuss one way to mitigate these problems by modifying the optimization process, itself. The objective function is modified to include a penalty term on the residual alpha. In our examples, the method proposed helps to mitigate the mismatch between alpha and risk by assigning a suitable penalty to the residual alpha.

  20. PAPER

    Do Risk Factors Eat Alphas? 

    Apr 1, 2008 Jyh-huei Lee , Dan Stefek

    Investing (Investment Management)

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    Portfolio managers worry that discrepancies between risk and alpha factors may create unintended biases in their optimized portfolios. We analyze the ramifications of using different factor models of risk and alpha in portfolio optimization and show that aligning risk factors with alpha factors may improve the information ratio of optimized portfolios, even if doing so lowers the overall accuracy of risk forecasts. We discuss ways of modifying a risk model that may help remedy these problems.

  21. PAPER

    Robust Portfolio Optimization: A Closer Look 

    Jun 14, 2006 Jyh-huei Lee , Alexander Zheleznyak , Dan Stefek

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