Global Portfolio Optiomization Revisted: A Least Discrimination Alternative to Black-Litterman

Global portfolio optimization models rank among the proudest achievements of modern finance theory, but practitioners are still struggling to put them to work. In 1992, Black and Litterman put the problem down to difficulties portfolio managers have in extrapolating views about some expected asset returns into full probabilistic forecasts about all asset returns and proposed a method to alleviate this problem. We propose a more general method based on a least discrimination (LD) principle. It produces a probabilistic forecast that remains true to personal views but is otherwise as close as possible to the forecast implied by a reference portfolio. The LD method produces optimal portfolios for a variety of views, including views on volatility and correlation, in which case optimal portfolios include option-like pay-offs. It also justifies a simple linear interpolation between market and personal forecasts, should a compromise be reached.Global portfolio optimization, black-litterman model, least discrimination, utility theory, mean-variance analysis, relative entropy, generalized relative entropy, non-linear pay-offs

Strategic fast supply demand-chains in a network context: opportunistic practices that can destroy supply chain systems

This paper has a conceptual character and explores an approach between transaction cost analysis theory and network theory when applied to supply chains in a broader context: industrial management research. This approach raises the assumptions that fast supply chains, i.e., supply chains made of short time relationships and multiple partners can contribute to destroying trust and collaboration between companies, ending up by stressing actual systems’ arrangements in somehow stable supply chains/network chains. As a consequence, transforming them in distrust arrangements and thus giving birth to new (old) approaches based only on transaction cost analysis theory: opportunism and limited rationality as the continuum for relationships between companies in a globalized world with numerous potential agents/companies that can play several roles. Too high levels of entropy can show this reality: the number of potential players (suppliers, customers or complementors) with theoretically equal probability of establishing partnerships with one focal company in a supply chain or network arrangement is excessive in relation to the number of current suppliers, customers and complementors, and for that reason, the focal company is somehow dissipating energy in identifying several potential players and in a state of giving one way or another equal importance to them all, situation that can affect stable relations with current partners. Theoretically, this will create what looks like strategic fast supply—demand chains or network chains: fast because they are rapidly settle down and fast as they are also rapidly dismantled. Those arrangements are the ones responsible for several possible and fast relations (internalizing resources from the environment and/or externalizing resources to the environment) but, anyway, contributing to loose trust, credibility and running against profitable games with partners already involved with focal companies in stable supply chains

Financial predictions using intelligent systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents a collection of practical techniques for analysing various market properties in order to design advanced self-evolving trading systems based on neural networks combined with a genetic algorithm optimisation approach. Nonlinear multivariate statistical models have gained increasing importance in financial time series analysis, as it is very hard to fmd statistically significant market inefficiencies using standard linear modes. Nonlinear models capture more of the underlying dynamics of these high dimensional noisy systems than traditional models, whilst at the same time making fewer restrictive assumptions about them. These adaptive trading systems can extract information about associated time varying processes that may not be readily captured by traditional models. In order to characterise the fmancial time series in terms of its dynamic nature, this research employs various methods such as fractal analysis, chaos theory and dynamical recurrence analysis. These techniques are used for evaluating whether markets are stochastic and deterministic or nonlinear and chaotic, and to discover regularities that are completely hidden in these time series and not detectable using conventional analysis. Particular emphasis is placed on examining the feasibility of prediction in fmancial time series and the analysis of extreme market events. The market's fractal structure and log-periodic oscillations, typical of periods before extreme events occur, are revealed through recurrence plots. Recurrence qualification analysis indicated a strong presence of structure, recurrence and determinism in the fmancial time series studied. Crucial fmancial time series transition periods were also detected. This research performs several tests on a large number of US and European stocks using methodologies inspired by both fundamental analysis and technical trading rules. Results from the tests show that profitable trading models utilising advanced nonlinear trading systems can be created after accounting for realistic transaction costs. The return achieved by applying the trading model to a portfolio of real price series differs significantly from that achieved by applying it to a randomly generated price series. In some cases, these models are compared against simpler alternative approaches to ensure that there is an added value in the use of these more complex models. The superior performance of multivariate nonlinear models is also demonstrated. The long-short trading strategies performed well in both bull and bear markets, as well as in a sideways market, showing a great degree of flexibility and adjustability to changing market conditions. Empirical evidence shows that information is not instantly incorporated into market pnces and supports the claim that the fmancial time series studied, for the periods analysed, are not entirely random. This research clearly shows that equity markets are partially inefficient and do not behave along lines dictated by the efficient market hypothesis

Pricing European stock options using stochastic and fuzzy continuous time processes

Over the past 40 years, much of mathematical finance has been built on the premise that stocks tend to move according to continuous-time stochastic processes, particularly geometric Brownian Motion. However, fuzzy set theory has recently been shown to hold promise as a model for financial uncertainty as well, with continuous time fuzzy processes used in place of Brownian Motion. And, like Brownian Motion, fuzzy processes also cannot be measured using a traditional Lebesque integral. This problem was solved on the stochastic side with the development of Ito's calculus. Likewise, the Liu integral has been developed to measure fuzzy processes. In this paper I will describe and compare the theoretical underpinnings of these models, as well as "back-test" several variations of them on historical market data

Essays on Corporate Finance

This thesis is comprised of three chapters. In the first chapter, I exploit the 2012 French introduction of a financial transaction tax (FTT) levied on stock purchases to examine its impact on corporate investment. Investment may decrease due to the increased cost of capital. The FTT, however, may encourage investment by reducing short-termism. I find an overall positive effect of the FTT on corporate investments. I also find that the FTT causes a shift from short-term to long-term ownership, an improvement in investment sensitivity to changes in growth opportunities, and an increase in likelihood and quality of acquisitions. These results are in line with the prediction that the FTT encourages investment by inducing long-term ownership and alleviating short-termism. In the second chapter, coauthored with Beatriz Garc´ıa Osma, Anna Toldr`a-Simats and Fengzhi Zhu (UC3M), we examine whether voting requirements in M&As induce disclosure, lowering information asymmetry. We find that acquirers subject to share-holder voting provide more 8-K disclosure during the transaction period, and are more likely to provide timely disclosure of the merger agreement, information on expected synergies and post-merger earnings forecasts. For acquirers subject to voting, we docu-ment a more negative association between disclosure and bid-ask spread than in other acquirers, and a more positive association between disclosure and transient institutional sales. Lower bid-ask spread and higher transient institutional sales are associated with higher voting support and likelihood of deal completion. These results suggest that the induced disclosure is informative and it can affect voting outcomes through changing the deal valuation and the shareholder base. Evidence from falsification tests and a regression-discontinuity design supports the causal interpretation of the positive effect of shareholder voting on disclosure. In the third chapter, a joint work with Faiza Majid (UNSW), we examine the effect of tariff changes on the market for corporate assets. On the one hand, the intensified competition from foreign entry due to tariff reductions can induce firms to divest its less productive assets. On the other hand, tariff reductions can alter foreign firms’ entry mode to export instead of acquiring assets and producing locally. Using detailed information about corporate asset sales, we find that tariff changes can affect both supply and demand sides of the market for corporate assets. Our study highlights that the lower demand can limit some firms’ ability to divest assets to become more efficient. Some firms, however, are able to divest assets that are less important and less affected by the lower demand to cope with increased competition.Programa de Doctorado en Empresa y Finanzas / Business and Finance por la Universidad Carlos III de MadridPresidente: Miguel Duro Rivas.- Secretaria: María Gutiérrez Urtiaga.- Vocal: Alberta Di Giul

Financial predictions using intelligent systems : the application of advanced technologies for trading financial markets

This thesis presents a collection of practical techniques for analysing various market properties in order to design advanced self-evolving trading systems based on neural networks combined with a genetic algorithm optimisation approach. Nonlinear multivariate statistical models have gained increasing importance in financial time series analysis, as it is very hard to fmd statistically significant market inefficiencies using standard linear modes. Nonlinear models capture more of the underlying dynamics of these high dimensional noisy systems than traditional models, whilst at the same time making fewer restrictive assumptions about them. These adaptive trading systems can extract information about associated time varying processes that may not be readily captured by traditional models. In order to characterise the fmancial time series in terms of its dynamic nature, this research employs various methods such as fractal analysis, chaos theory and dynamical recurrence analysis. These techniques are used for evaluating whether markets are stochastic and deterministic or nonlinear and chaotic, and to discover regularities that are completely hidden in these time series and not detectable using conventional analysis. Particular emphasis is placed on examining the feasibility of prediction in fmancial time series and the analysis of extreme market events. The market's fractal structure and log-periodic oscillations, typical of periods before extreme events occur, are revealed through recurrence plots. Recurrence qualification analysis indicated a strong presence of structure, recurrence and determinism in the fmancial time series studied. Crucial fmancial time series transition periods were also detected. This research performs several tests on a large number of US and European stocks using methodologies inspired by both fundamental analysis and technical trading rules. Results from the tests show that profitable trading models utilising advanced nonlinear trading systems can be created after accounting for realistic transaction costs. The return achieved by applying the trading model to a portfolio of real price series differs significantly from that achieved by applying it to a randomly generated price series. In some cases, these models are compared against simpler alternative approaches to ensure that there is an added value in the use of these more complex models. The superior performance of multivariate nonlinear models is also demonstrated. The long-short trading strategies performed well in both bull and bear markets, as well as in a sideways market, showing a great degree of flexibility and adjustability to changing market conditions. Empirical evidence shows that information is not instantly incorporated into market pnces and supports the claim that the fmancial time series studied, for the periods analysed, are not entirely random. This research clearly shows that equity markets are partially inefficient and do not behave along lines dictated by the efficient market hypothesis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Symmetric and Asymmetric Data in Solution Models

This book is a Printed Edition of the Special Issue that covers research on symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multicriteria decision-making (MCDM) problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the book

Regulating Complexity in Financial Markets

As the financial crisis has tragically illustrated, the complexities of modern financial markets and investment securities can trigger systemic market failures. Addressing these complexities, this Article maintains, is perhaps the greatest financial-market challenge of the future. The Article first examines and explains the nature of these complexities. It then analyzes the regulatory and other steps that should be considered to reduce the potential for failure. Because complex financial markets resemble complex engineering systems, and failures in those markets have characteristics of failures in those systems, the Article‟s analysis draws on chaos theory and other approaches used to analyze complex engineering systems