Market anomalies and tactical asset allocation: Utilising market anomalies in multiple asset class portfolios with the Black−Litterman model

This thesis discusses the basic problem of the modern portfolio theory about how to optimise the perfect allocation for an investment portfolio. The theory provides a solution for an efficient portfolio, which minimises the risk of the portfolio with respect to the expected return. A central feature for all the portfolios on the efficient frontier is that the investor needs to provide the expected return for each asset. Market anomalies are persistent patterns seen in the financial markets, which cannot be explained with the current asset pricing theory. The goal of this thesis is to study whether these anomalies can be observed among different asset classes. Finally, if persistent patterns are found, it is investigated whether the anomalies hold valuable information for determining the expected returns used in the portfolio optimization Market anomalies and investment strategies based on them are studied with a rolling estimation window, where the return for the following period is always based on historical information. This is also crucial when rebalancing the portfolio. The anomalies investigated within this thesis are value, momentum, reversal, and idiosyncratic volatility. The research data includes price series of country level stock indices, government bonds, currencies, and commodities. The modern portfolio theory and the views given by the anomalies are combined by utilising the Black-Litterman model. This makes it possible to optimise the portfolio so that investor’s views are taken into account. When constructing the portfolios, the goal is to maximise the Sharpe ratio. Significance of the results is studied by assessing if the strategy yields excess returns in a relation to those explained by the threefactormodel. The most outstanding finding is that anomaly based factors include valuable information to enhance efficient portfolio diversification. When the highest Sharpe ratios for each asset class are picked from the test factors and applied to the Black−Litterman model, the final portfolio results in superior riskreturn combination. The highest Sharpe ratios are provided by momentum strategy for stocks and long-term reversal for the rest of the asset classes. Additionally, a strategy based on the value effect was highly appealing, and it basically performs as well as the previously mentioned Sharpe strategy. When studying the anomalies, it is found, that 12-month momentum is the strongest effect, especially for stock indices. In addition, a high idiosyncratic volatility seems to be positively correlated with country indices on stocks.

PERFORMANCE ANALYSIS OF A TACTICAL ASSET ALLOCATION – Evidence from the Black–Litterman model based fixed income portfolio

Over time the demand for quantitative portfolio management has increased among financial institutions but there is still a lack of practical tools. In 2008 EDHEC Risk and Asset Management Research Centre conducted a survey of European investment practices. It revealed that the majority of asset or fund management companies, pension funds and institutional investors do not use more sophisticated models to compensate the flaws of the Markowitz mean-variance portfolio optimization. Furthermore, tactical asset allocation managers employ a variety of methods to estimate return and risk of assets, but also need sophisticated portfolio management models to outperform their benchmarks. Recent development in portfolio management suggests that new innovations are slowly gaining ground, but still need to be studied carefully. This thesis tries to provide a practical tactical asset allocation (TAA) application to the Black–Litterman (B–L) approach and unbiased evaluation of B–L models’ qualities. Mean-variance framework, issues related to asset allocation decisions and return forecasting are examined carefully to uncover issues effecting active portfolio management. European fixed income data is employed in an empirical study that tries to reveal whether a B–L model based TAA portfolio is able outperform its strategic benchmark. The tactical asset allocation utilizes Vector Autoregressive (VAR) model to create return forecasts from lagged values of asset classes as well as economic variables. Sample data (31.12.1999–31.12.2012) is divided into two. In-sample data is used for calibrating a strategic portfolio and the out-of-sample period is for testing the tactical portfolio against the strategic benchmark. Results show that B–L model based tactical asset allocation outperforms the benchmark portfolio in terms of risk-adjusted return and mean excess return. The VAR-model is able to pick up the change in investor sentiment and the B–L model adjusts portfolio weights in a controlled manner. TAA portfolio shows promise especially in moderately shifting allocation to more risky assets while market is turning bullish, but without overweighting investments with high beta. Based on findings in thesis, Black–Litterman model offers a good platform for active asset managers to quantify their views on investments and implement their strategies. B–L model shows potential and offers interesting research avenues. However, success of tactical asset allocation is still highly dependent on the quality of input estimates.

Economic capital allocation

For my Licentiate thesis, I conducted research on risk measures. Continuing with this research, I now focus on capital allocation. In the proportional capital allocation principle, the choice of risk measure plays a very important part. In the chapters Introduction and Basic concepts, we introduce three definitions of economic capital, discuss the purpose of capital allocation, give different viewpoints of capital allocation and present an overview of relevant literature. Risk measures are defined and the concept of coherent risk measure is introduced. Examples of important risk measures are given, e. g., Value at Risk (VaR), Tail Value at Risk (TVaR). We also discuss the implications of dependence and review some important distributions. In the following chapter on Capital allocation we introduce different principles for allocating capital. We prefer to work with the proportional allocation method. In the following chapter, Capital allocation based on tails, we focus on insurance business lines with heavy-tailed loss distribution. To emphasize capital allocation based on tails, we define the following risk measures: Conditional Expectation, Upper Tail Covariance and Tail Covariance Premium Adjusted (TCPA). In the final chapter, called Illustrative case study, we simulate two sets of data with five insurance business lines using Normal copulas and Cauchy copulas. The proportional capital allocation is calculated using TCPA as risk measure. It is compared with the result when VaR is used as risk measure and with covariance capital allocation. In this thesis, it is emphasized that no single allocation principle is perfect for all purposes. When focusing on the tail of losses, the allocation based on TCPA is a good one, since TCPA in a sense includes features of TVaR and Tail covariance.

Using genetic algorithms for the single allocation hub location problem

Hub location problem is an NP-hard problem that frequently arises in the design of transportation and distribution systems, postal delivery networks, and airline passenger flow. This work focuses on the Single Allocation Hub Location Problem (SAHLP). Genetic Algorithms (GAs) for the capacitated and uncapacitated variants of the SAHLP based on new chromosome representations and crossover operators are explored. The GAs is tested on two well-known sets of real-world problems with up to 200 nodes. The obtained results are very promising. For most of the test problems the GA obtains improved or best-known solutions and the computational time remains low. The proposed GAs can easily be extended to other variants of location problems arising in network design planning in transportation systems.

Multi-Objective Genetic Algorithms for the Single Allocation Hub Location Problem

Hub Location Problems play vital economic roles in transportation and telecommunication networks where goods or people must be efficiently transferred from an origin to a destination point whilst direct origin-destination links are impractical. This work investigates the single allocation hub location problem, and proposes a genetic algorithm (GA) approach for it. The effectiveness of using a single-objective criterion measure for the problem is first explored. Next, a multi-objective GA employing various fitness evaluation strategies such as Pareto ranking, sum of ranks, and weighted sum strategies is presented. The effectiveness of the multi-objective GA is shown by comparison with an Integer Programming strategy, the only other multi-objective approach found in the literature for this problem. Lastly, two new crossover operators are proposed and an empirical study is done using small to large problem instances of the Civil Aeronautics Board (CAB) and Australian Post (AP) data sets.

The Role of Experience in the Organization and Refinement of Face Space

Adults code faces in reference to category-specific norms that represent the different face categories encountered in the environment (e.g., race, age). Reliance on such norm-based coding appears to aid recognition, but few studies have examined the development of separable prototypes and the way in which experience influences the refinement of the coding dimensions associated with different face categories. The present dissertation was thus designed to investigate the organization and refinement of face space and the role of experience in shaping sensitivity to its underlying dimensions. In Study 1, I demonstrated that face space is organized with regard to norms that reflect face categories that are both visually and socially distinct. These results provide an indication of the types of category-specific prototypes that can conceivably exist in face space. Study 2 was designed to investigate whether children rely on category-specific prototypes and the extent to which experience facilitates the development of separable norms. I demonstrated that unlike adults and older children, 5-year-olds rely on a relatively undifferentiated face space, even for categories with which they receive ample experience. These results suggest that the dimensions of face space undergo significant refinement throughout childhood; 5 years of experience with a face category is not sufficient to facilitate the development of separable norms. In Studies 3 through 5, I examined how early and continuous exposure to young adult faces may optimize the face processing system for the dimensions of young relative to older adult faces. In Study 3, I found evidence for a young adult bias in attentional allocation among young and older adults. However, whereas young adults showed an own-age recognition advantage, older adults exhibited comparable recognition for young and older faces. These results suggest that despite the significant experience that older adults have with older faces, the early and continuous exposure they received with young faces continues to influence their recognition, perhaps because face space is optimized for young faces. In Studies 4 and 5, I examined whether sensitivity to deviations from the norm is superior for young relative to older adult faces. I used normality/attractiveness judgments as a measure of this sensitivity; to examine whether biases were specific to norm-based coding, I asked participants to discriminate between the same faces. Both young and older adults were more accurate when tested with young relative to older faces—but only when judging normality. Like adults, 3- and 7-year-olds were more accurate in judging the attractiveness of young faces; however, unlike adults, this bias extended to the discrimination task. Thus by 3 years of age children are more sensitive to differences among young relative to older faces, suggesting that young children's perceptual system is more finely tuned for young than older adult faces. Collectively, the results of this dissertation help elucidate the development of category-specific norms and clarify the role of experience in shaping sensitivity to the dimensions of face space.

Le financement des municipalités par le gouvernement fédéral au Canada et aux États-Unis: transferts et facteurs d'allocation.

Rapport de recherche

Fair Production and Allocation of an Excludable Nonrival Good

We study fairness in economies with one private good and one partially excludable nonrival good. A social ordering function determines for each profile of preferences an ordering of all conceivable allocations. We propose the following Free Lunch Aversion condition: if the private good contributions of two agents consuming the same quantity of the nonrival good have opposite signs, reducing that gap improves social welfare. This condition, combined with the more standard requirements of Unanimous Indifference and Responsiveness, delivers a form of welfare egalitarianism in which an agent's welfare at an allocation is measured by the quantity of the nonrival good that, consumed at no cost, would leave her indifferent to the bundle she is assigned.

Efficient Strategy-Proof Allocation Functions in Linear Production Economies

In a linear production model, we characterize the class of efficient and strategy-proof allocation functions, and the class of efficient and coalition strategy-proof allocation functions. In the former class, requiring equal treatment of equals allows us to identify a unique allocation function. This function is also the unique member of the latter class which satisfies uniform treatment of uniforms.

Resource-Monotonicity for House Allocation Problems

We study a simple model of assigning indivisible objects (e.g., houses, jobs, offices, etc.) to agents. Each agent receives at most one object and monetary compensations are not possible. We completely describe all rules satisfying efficiency and resource-monotonicity. The characterized rules assign the objects in a sequence of steps such that at each step there is either a dictator or two agents who “trade” objects from their hierarchically specified “endowments.”

Consistent House Allocation

In practice we often face the problem of assigning indivisible objects (e.g., schools, housing, jobs, offices) to agents (e.g., students, homeless, workers, professors) when monetary compensations are not possible. We show that a rule that satisfies consistency, strategy-proofness, and efficiency must be an efficient generalized priority rule; i.e. it must adapt to an acyclic priority structure, except -maybe- for up to three agents in each object's priority ordering.