LITERATURE REVIEW

1. Harry Markowitz 1 ,The fundamental concept behind MPT is that the assets in an investment portfolio should not be selected individually, each on its own merits. Rather, it is important to consider how each asset changes in price relative to how every other asset in the portfolio changes in price.

Investing is a tradeoff between risk and expected return. In general, assets with higher expected returns are riskier. For a given amount of risk, MPT describes how to select a portfolio with the highest possible expected return. Or, for a given expected return, MPT explains how to select a portfolio with the lowest possible risk (the targeted expected return cannot be more than the highest-returning available security, of course, unless negative holdings of assets are possible.)

Therefore, MPT is a form of diversification. Under certain assumptions and for specific quantitative definitions of risk and return, MPT explains how to find the best possible diversification strategy.

2. Thorp 2 , According to the author PPM techniques are fundamental for getting value from IT projects.

IT spending is the largest single capital investment for most enterprises, ranging from routine productivity improvements to all-out business transformation. A widely-held belief is that increasing IT investment is always desirable. However, the real business benefits achieved by increased IT spending are difficult to identify and measure and sometimes seem impossible to prove—an “information paradox” that often troubles executives when challenged to justify IT spending.

3.Jeffery and Leliveld 3 , IT portfolio management is the application of systematic management to the investments, projects and activities of enterprise Information Technology (IT) departments. Examples of IT portfolios would be planned initiatives, projects, and ongoing IT services (such as application support). The promise of IT portfolio management is the quantification of previously informal IT efforts, enabling measurement and objective evaluation of investment scenarios.

1   Markowitz, H.M. (March 1952). "Portfolio Selection".   The Journal of Finance   7 (1): 77–91.   doi : 10.2307/2975974 .   JSTOR   2975974 .

2 In 1998 Thorp published the “”information paradox” putting ppm in broader framework Called “benefits realization”.

3 Jeffery, M., & Leliveld, I. (2004). Best Practices in IT Portfolio Management.   MIT Sloan Management Review .

4. Haim Shalit 4 , This article presents a methodology for using the Lorenz curve in financial economics. Most of the recent quantitative risk measures that abide by the rules of second-degree stochastic dominance, such as Gini’s mean difference and conditional value at risk, are associated with the Lorenz curve. With financial data, the Lorenz curve is easy to calculate, because it requires sorting asset returns in ascending order. A financial analyst can derive the statistics necessary to carry out a study of risk analysis and establish a set of efficient and most-preferred portfolios for all risk-averse investors.

5. Edward I. Altman, José F. González-Heres, Ping Chen, and Steven S. Shin, 5 This article analyzes the returns of distressed high-yield corporate bond portfolios based on the volatility characteristics of their corresponding option-adjusted spreads (“OAS”). Applying Capital Asset Pricing Model (“CAPM”) theory to a portfolio of distressed bonds generates different results depending on whether overall high-yield market OAS volatility is considered. CAPM expectations (higher risk investments demand higher expected returns) are not generally fulfilled in both time-independent and time-dependent space after normalizing the data for overall high-yield market OAS volatility, i.e. the lowest OAS volatility portfolios outperform the highest volatility portfolios. We provide evidence that this phenomenon is a result of the idiosyncratic characteristics of the securities that comprise the lowest OAS volatility portfolios, which generate higher returns because they experience lower default rates and higher terminal values relative to the securities in the highest OAS volatility portfolios. Alternatively, we find that CAPM generally holds under two conditions:

1) In time-independent space where no consideration is given to the OAS volatility of the overall high-yield corporate debt market; and,

2) In time-dependent space where an investor possesses market timing skills. Because persistent market timing skill is rare, investing based on a buy-and-hold strategy, comprised of portfolios of the lowest OAS volatility distressed bonds, may be a practical solution for the long-term distressed debt investor.

4 Haim Shalit, Portfolio Risk Management Using the Lorenz Curve, The journal of Portfolio management, Spring 2004

5 Edward I. Altman, José F. González-Heres, Ping Chen, and Steven S. Shin, The Return/Volatility Trade-Off of Distressed Corporate Debt Portfolios, The journal of Portfolio management, Winter 2004

6. Jim Clayton, Frank J. Fabozzi, S. Michael Giliberto, Jacques N. Gordon, Youguo Liang, Greg MacKinnon, and Asieh Mansour, 6 Real estate has become more accepted as a basic building block of a well-diversified institutional portfolio over the past decade. The debate around the question “Why Real Estate?” has largely been put to rest. The most relevant research questions about the asset class now revolve around more detailed issues in optimal allocations under different situations and in light of new trends, and also around details of how implementation of a real estate allocation (i.e. the actual investments made) should be done and how it affects the risk/return characteristics of the final portfolio. The relevant new trends in the industry and the associated research questions are discussed.

7. Will Kinlaw, Mark Kritzman, and David Turkington 7 , The authors propose a simple analytical construct for incorporating liquidity into portfolio choice. In cases where investors deploy liquidity to raise a portfolio’s expected utility beyond its original expected utility, the authors attach a shadow asset to tradable assets. In cases where investors deploy liquidity to prevent a portfolio’s expected utility from falling, the authors attach a shadow liability to assets that are not tradable. This construct lets investors determine the optimal allocation to illiquid assets. Alternatively, investors can use this construct to estimate the premium an illiquid asset requires, or the degree to which they must benefit from liquidity in order to justify forgoing investment in illiquid assets. This approach improves on other methods of incorporating liquidity into portfolio choice in four fundamental ways. First, it mirrors what actually occurs within a portfolio. Second, it maps units of liquidity onto units of expected return and risk, so that investors can analyze liquidity within the same context as other portfolio decisions. Third, it distinguishes absolute illiquidity from partial illiquidity and lets investors address these attributes within a single, unifying framework. Fourth, it recognizes that liquidity serves not only to meet demands for capital, but to exploit opportunities as well, thus revealing that investors bear an illiquidity cost to the extent that any fraction of a portfolio is immobile.

6 Jim Clayton, Frank J. Fabozzi, S. Michael Giliberto, Jacques N. Gordon, Youguo Liang, Greg MacKinnon, and Asieh Mansour Portfolio Strategy and Structure Take Center Stage: “How, What, Where, and When?” Replace “Why?” ,The journal of Portfolio management, JPM RE 2013.

7 Will Kinlaw, Mark Kritzman, and David Turkington , Liquidity and Portfolio Choice:   A Unified Approach, The Journal of Portfolio Management,   Winter 2013 .

8. Kenneth Froot, John Arabadjis, Sonya Cates, and Stephen Lawrence 8 , Currency investors exhibit a tendency to cut risk by pairing both longs and shorts following losses and a weaker tendency to add risk following gains. By differentiating between position-level, portfolio-level, and aggregate cross portfolio losses in currency investments, the authors demonstrate that this dynamic loss aversion spans multiple frames of reference. Losses are not compartmentalized, but rather a loss in one currency may impact trading in another. The authors also show that while the impact of a loss on subsequent trading decisions does linger, the effect declines sharply after a losing position is closed.

9. Maximilian A. Vermorken, Francesca R. Medda, and Thomas Schröder 9 , The concept of diversification is central in finance and has become even more so since the 2008 financial crisis. In this article, the authors introduce a new measure for diversification. The measure, referred to as “diversification delta,” is nonparametric, based on higher moments, easily interpretable due to its mathematical formulation, and incorporates the advantages of the present measures of diversification while extending them. The measure is applied to infrastructure returns data in order to understand the benefits of diversifying across various infrastructure classes, gaining useful insights for infrastructure fund managers and investors.

10. Sebastián Ceria, Anureet Saxena, and Robert A. Stubbs 10 , Quantitative equity portfolio management has evolved into an interdisciplinary activity that draws expertise from the fields of finance, statistics, econometrics, accounting, and optimization. Each one of these streams is a mature discipline in itself, having its own body of knowledge, and operates under assumptions that are usually well accepted within the respective communities. But when concepts from these diverse fields are applied in a common setting, there is bound to be friction among various assumptions that get further magnified due to the use of an optimizer. In this article, Ceria, Saxena, and Stubbs focus on the interaction of three key elements that are part of the quantitative portfolio management process, namely, the expected returns model, the risk model, and the constraints that are used to formulate the portfolio construction problem. They generally refer to the issues caused by this interaction as factor alignment problems. The authors present a detailed investigation of these alignment problems, survey some of their common sources, analyze and

8 Kenneth Froot, John Arabadjis, Sonya Cates, and Stephen Lawrence, Evidence on Dynamic Loss Aversion from Currency Portfolios, the journal of Portfolio management, Fall 2011.

9 Maximilian A. Vermorken, Francesca R. Medda, and Thomas Schröder, The Diversification Delta:   A Higher-Moment Measure for Portfolio Diversification, The Journal of Portfolio Management,   Fall 2012.

10 Sebastián Ceria, Anureet Saxena, and Robert A. Stubbs, Factor Alignment Problems and Quantitative Portfolio Management, The Journal of Portfolio Management,   Winter 2012.

document their effects on the ex post performance of optimized portfolios, and conclude with a practical and effective remedy in the form of augmented risk models.