Essays On Optimal Portfolio And Resource Allocation
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| Author | : Sergiy Pysarenko |
| Publisher | : |
| Total Pages | : |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
This paper proposes a micro-foundation for Contest-Success Functions (CSF) in a principal/agents setting. We characterize the principal's utility function so that the Ratio Form CSF results as the unique optimal allocation rule. The literature assumes a particular CSF without considering that the principal has her own preferences and that the optimal allocation, after having observed efforts, may differ from the allocation stipulated by the CSF. In this case, the CSF is not the principal's best response strategy; thus, the contest is not strongly credible. To ensure strong credibility, we consider sufficient conditions for a non-monotonic utility function, as well as for a larger family of monotonic utility functions compared to the literature. This paper proposes a novel approach of portfolio allocation. The fundamental indexing (FI) and Markowitz mean-variance optimization (MVO) approaches are complementary but have been considered separately in the portfolio choice literature. Using data on S&P 500 constituents, we evaluate a portfolio construction technique that utilizes the benefits of both approaches. The out-of-sample results of the blended portfolios attest to their superior performance compared to common benchmarks, and to portfolios constructed solely based on the FI or MVO methods. In pursuit of the optimal blend between the MVO and FI, we find that the ratio of market capitalization to GDP, being a leading indicator for an overpriced market, demonstrates remarkably advantageous properties. This paper proposes a Price-Adjusted Fundamental Index (PAFI) portfolio to improve on the Arnott Fundamental Index (FI) portfolio construction methodology. We adjust the Arnott fundamentals with a measure of under- or overpricing. We use data on S&P 500 constituents, and separate industries to compute Sharpe ratios that test the performance of the Arnott FI, the Global Minimum Variance (GMV), and PAFI portfolios against appropriate benchmarks. We test an alternative way to blend FI and GMV portfolios, based on Markowitz mean variance optimization. We find that PAFI and some of PAFI-based portfolios outperform FI and FI-based portfolios for Oil & Gas, Health Care, Technology and Telecommunications industries, and for defensive and sensitive super-industries.
| Author | : Dimitrios Bisias |
| Publisher | : |
| Total Pages | : 188 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
This thesis revolves around applications of optimal portfolio theory. In the first essay, we study the optimal portfolio allocation among convergence trades and mean reversion trading strategies for a risk averse investor who faces Value-at-Risk and collateral constraints with and without fear of model misspecification. We investigate the properties of the optimal trading strategy, when the investor fully trusts his model dynamics. Subsequently, we investigate how the optimal trading strategy of the investor changes when he mistrusts the model. In particular, we assume that the investor believes that the data will come from an unknown member of a set of unspecified alternative models near his approximating model. The investor believes that his model is a pretty good approximation in the sense that the relative entropy of the alternative models with respect to his nominal model is small. Concern about model misspecification leads the investor to choose a robust optimal portfolio allocation that works well over that set of alternative models. In the second essay, we study how portfolio theory can be used as a framework for making biomedical funding allocation decisions focusing on the National Institutes of Health (NIH). Prioritizing research efforts is analogous to managing an investment portfolio. In both cases, there are competing opportunities to invest limited resources, and expected returns, risk, correlations, and the cost of lost opportunities are important factors in determining the return of those investments. Can we apply portfolio theory as a systematic framework of making biomedical funding allocation decisions? Does NIH manage its research risk in an efficient way? What are the challenges and limitations of portfolio theory as a way of making biomedical funding allocation decisions? Finally in the third essay, we investigate how risk constraints in portfolio optimization and fear of model misspecification affect the statistical properties of the market returns. Risk sensitive regulation has become the cornerstone of international financial regulations. How does this kind of regulation affect the statistical properties of the financial market? Does it affect the risk premium of the market? What about the volatility or the liquidity of the market?
| Author | : Janke, Oliver |
| Publisher | : Lehmanns Media |
| Total Pages | : 244 |
| Release | : |
| Genre | : Business & Economics |
| ISBN | : 396543506X |
In this thesis, we analyze various problems of dynamic portfolio optimization as well as green capital requirements under risk constraints and incomplete information. First, we examine the problem of optimal expected utility under the constraint of a utility-based shortfall risk measure in an incomplete market. The existence and uniqueness of an optimal solution to the problem are shown using a Lagrange multiplier and duality methods. Second, we consider the optimization problem under various levels of the investor’s information. By using martingale representation theorems, we demonstrate the existence and uniqueness of optimal solutions, which differ in their market dynamics. Third, we analyze the effects of green- and brownwashing on banks’ lending to firms, on the regulator’s deposit insurance subsidy, and on carbon emissions under different green capital requirement functions. Furthermore, we show that green capital requirements may compromise financial stability.
| Author | : Yihong Xia |
| Publisher | : |
| Total Pages | : 418 |
| Release | : 2000 |
| Genre | : Asset allocation |
| ISBN | : |
| Author | : John Y. Campbell |
| Publisher | : OUP Oxford |
| Total Pages | : 272 |
| Release | : 2002-01-03 |
| Genre | : Business & Economics |
| ISBN | : 019160691X |
Academic finance has had a remarkable impact on many financial services. Yet long-term investors have received curiously little guidance from academic financial economists. Mean-variance analysis, developed almost fifty years ago, has provided a basic paradigm for portfolio choice. This approach usefully emphasizes the ability of diversification to reduce risk, but it ignores several critically important factors. Most notably, the analysis is static; it assumes that investors care only about risks to wealth one period ahead. However, many investors—-both individuals and institutions such as charitable foundations or universities—-seek to finance a stream of consumption over a long lifetime. In addition, mean-variance analysis treats financial wealth in isolation from income. Long-term investors typically receive a stream of income and use it, along with financial wealth, to support their consumption. At the theoretical level, it is well understood that the solution to a long-term portfolio choice problem can be very different from the solution to a short-term problem. Long-term investors care about intertemporal shocks to investment opportunities and labor income as well as shocks to wealth itself, and they may use financial assets to hedge their intertemporal risks. This should be important in practice because there is a great deal of empirical evidence that investment opportunities—-both interest rates and risk premia on bonds and stocks—-vary through time. Yet this insight has had little influence on investment practice because it is hard to solve for optimal portfolios in intertemporal models. This book seeks to develop the intertemporal approach into an empirical paradigm that can compete with the standard mean-variance analysis. The book shows that long-term inflation-indexed bonds are the riskless asset for long-term investors, it explains the conditions under which stocks are safer assets for long-term than for short-term investors, and it shows how labor income influences portfolio choice. These results shed new light on the rules of thumb used by financial planners. The book explains recent advances in both analytical and numerical methods, and shows how they can be used to understand the portfolio choice problems of long-term investors.
| Author | : Maria Esther Mayorga |
| Publisher | : |
| Total Pages | : 228 |
| Release | : 2006 |
| Genre | : |
| ISBN | : |
| Author | : S.K. Tamotia And Vinayshil Gautam |
| Publisher | : Concept Publishing Company |
| Total Pages | : 280 |
| Release | : 2005 |
| Genre | : Executives |
| ISBN | : 9788180691911 |
| Author | : Ahti Salo |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 410 |
| Release | : 2011-08-12 |
| Genre | : Business & Economics |
| ISBN | : 1441999434 |
Portfolio Decision Analysis: Improved Methods for Resource Allocation provides an extensive, up-to-date coverage of decision analytic methods which help firms and public organizations allocate resources to 'lumpy' investment opportunities while explicitly recognizing relevant financial and non-financial evaluation criteria and the presence of alternative investment opportunities. In particular, it discusses the evolution of these methods, presents new methodological advances and illustrates their use across several application domains. The book offers a many-faceted treatment of portfolio decision analysis (PDA). Among other things, it (i) synthesizes the state-of-play in PDA, (ii) describes novel methodologies, (iii) fosters the deployment of these methodologies, and (iv) contributes to the strengthening of research on PDA. Portfolio problems are widely regarded as the single most important application context of decision analysis, and, with its extensive and unique coverage of these problems, this book is a much-needed addition to the literature. The book also presents innovative treatments of new methodological approaches and their uses in applications. The intended audience consists of practitioners and researchers who wish to gain a good understanding of portfolio decision analysis and insights into how PDA methods can be leveraged in different application contexts. The book can also be employed in courses at the post-graduate level.
| Author | : Lukas Plachel |
| Publisher | : |
| Total Pages | : |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
Modern Portfolio Theory (MPT) provides an elegant mathematical framework for the efficient portfolio allocation problem. Despite its exceptional popularity, MPT poses a number of well-documented problems in practical applications. Especially the fact that it generates notoriously extreme and non-robust allocations which may seriously impair the out-of-sample performance. This thesis introduces three methods with the common objective to remedy those shortcomings. Chapter 1 addresses the problems of traditional mean-variance optimization originating from model- and estimation errors. In order to simultaneously tackle both error sources, a joint method for covariance regularization and robust optimization is proposed which exploits the inherent complementarity between the two concepts. An application of the method to equity markets reveals similarly attractive behaviour as pure covariance regularization during normal times and improved performance as measured by out-of-sample volatility if a jump in systematic risk occurs. Chapter 2 introduces a covariance estimation approach which is based solely on characteristic company information. In contrast to traditional, time series based estimation procedures which typically lead to extreme and unreliable estimates, the proposed method produces stable covariance matrices which can be used if no time series data is available, or complementary to traditional methods. We derive characteristics-based covariance matrices for a US stock universe and use them as shrinkage targets in a minimum variance optimization example. The resulting strategies clearly dominate the benchmark case of identity shrinkage in terms of out-of-sample volatility. Chapter 3 bridges the gap between MPT and one of the most vivid fields of contemporary research: Artificial Intelligence. A model is introduced which uses a Neural Network to learn the relation between portfolio weights and arbitrary measures of portfolio.
| Author | : Xin Gao |
| Publisher | : |
| Total Pages | : |
| Release | : 2016 |
| Genre | : Commodity exchanges |
| ISBN | : |
This dissertation consists of two essays in asset allocation. In the first essay, I explore the question of how investors should optimally incorporate commodities in their multi-asset portfolios, or even if they should at all. To tackle this problem, I conduct a comprehensive out-of-sample assessment on the economic value of commodities in multi-asset investment strategies for both mean-variance and non-mean-variance investors who exploit the predictability of time-varying asset return moments. With both monthly and quarterly rebalancing frequencies, I find that predictability makes the addition of commodities profitable even when short-selling and high leverage are not permitted. For instance, a mean-variance (non mean-variance) investor rebalancing quarterly, with moderate risk aversion and leverage, would be willing to pay up to 108 (155) basis points per year after transaction cost for adding commodities into her stock, bond and cash portfolio. In the second essay, I study the economic value generated by active equity mutual funds from an investor’s perspective. I employ an optimization-based portfolio approach to construct a composite investment strategy of U.S. active equity mutual funds. The strategy jointly exploits the conditioning information conveyed by multiple fund characteristics and macroeconomic variables about the cross-section of fund performance. Based on an extensive out-of-sample performance evaluation, I find that the proposed strategy consistently outperforms a large set of passive investments that rely on index funds as well as the strategies that exploit the fund characteristics on an individual basis. The outperformance is net of fees and expenses and after precluding short-sales and leverage. I further show that the proposed strategy’s superior performance derives from effectively exploiting the predictive power of distinct fund characteristics to shift portfolio allocation toward (away from) funds with future outperformance (underperformance) as market conditions evolve over time. The findings indicate that investing in active equity mutual funds can add significant economic value for investors if the time-varying predictability in fund performance is properly taken into account and if an optimal portfolio approach, as opposed to simpler strategies based on sorting or on equal-weighted schemes, is adopted.
