The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

An Evolutionary Approach to Multistage Portfolio Optimization

Portfolio optimization is an important problem in quantitative finance due to its application in asset management and corporate financial decision making. This involves quantitatively selecting the optimal portfolio for an investor given their asset return distribution assumptions, investment objectives and constraints. Analytical portfolio optimization methods suffer from limitations in terms of the problem specification and modelling assumptions that can be used. Therefore, a heuristic approach is taken where Monte Carlo simulations generate the investment scenarios and' a problem specific evolutionary algorithm is used to find the optimal portfolio asset allocations. Asset allocation is known to be the most important determinant of a portfolio's investment performance and also affects its risk/return characteristics. The inclusion of equity options in an equity portfolio should enable an investor to improve their efficient frontier due to options having a nonlinear payoff. Therefore, a research area of significant importance to equity investors, in which little research has been carried out, is the optimal asset allocation in equity options for an equity investor. A purpose of my thesis is to carry out an original analysis of the impact of allowing the purchase of put options and/or sale of call options for an equity investor. An investigation is also carried out into the effect ofchanging the investor's risk measure on the optimal asset allocation. A dynamic investment strategy obtained through multistage portfolio optimization has the potential to result in a superior investment strategy to that obtained from a single period portfolio optimization. Therefore, a novel analysis of the degree of the benefits of a dynamic investment strategy for an equity portfolio is performed. In particular, the ability of a dynamic investment strategy to mimic the effects ofthe inclusion ofequity options in an equity portfolio is investigated. The portfolio optimization problem is solved using evolutionary algorithms, due to their ability incorporate methods from a wide range of heuristic algorithms. Initially, it is shown how the problem specific parts ofmy evolutionary algorithm have been designed to solve my original portfolio optimization problem. Due to developments in evolutionary algorithms and the variety of design structures possible, a purpose of my thesis is to investigate the suitability of alternative algorithm design structures. A comparison is made of the performance of two existing algorithms, firstly the single objective stepping stone island model, where each island represents a different risk aversion parameter, and secondly the multi-objective Non-Dominated Sorting Genetic Algorithm2. Innovative hybrids of these algorithms which also incorporate features from multi-objective evolutionary algorithms, multiple population models and local search heuristics are then proposed. . A novel way is developed for solving the portfolio optimization by dividing my problem solution into two parts and then applying a multi-objective cooperative coevolution evolutionary algorithm. The first solution part consists of the asset allocation weights within the equity portfolio while the second solution part consists 'ofthe asset allocation weights within the equity options and the asset allocation weights between the different asset classes. An original portfolio optimization multiobjective evolutionary algorithm that uses an island model to represent different risk measures is also proposed.Imperial Users onl

Applications of biased randomised algorithms and simheuristics to asset and liability management

Asset and Liability Management (ALM) has captured the attention of academics and financial researchers over the last few decades. On the one hand, we need to try to maximise our wealth by taking advantage of the financial market and, on the other hand, we need to cover our payments (liabilities) over time. The purpose of ALM is to give investors a series of resources or techniques to select the appropriate assets on the financial market that respond to the aforementioned two key factors: cover our liabilities and maximise our wealth. This thesis presents a set of techniques that are capable of tackling realistic financial problems without the usual requirement of considerable computational resources. These techniques are based on heuristics and simulation. Specifically, a biased randomised metaheuristic model is developed that has a direct application in the way insurance companies usually operate. The algorithm makes it possible to efficiently select the smallest number of assets, mainly fixed income, on the balance sheet while guaranteeing the company's obligations. This development allows for the incorporating of the credit quality of the issuer of the assets used. Likewise, a portfolio optimisation model with liabilities is developed and solved with a genetic algorithm. The portfolio optimisation problem differs from the usual one in that it is multi-period, and incorporates liabilities over time. Additionally, the possibility of external financing is included when the entity does not have sufficient cash. These conditions give rise to a complex problem that is efficiently solved by an evolutionary algorithm. In both cases, the algorithms are improved with the incorporation of Monte Carlo simulation. This allows the solutions to be robust when considering realistic market situations. The results are very promising. This research shows that simheuristics is an ideal method for this type of problem.La gestión de activos y pasivos (asset and liability management, ALM) ha acaparado la atención de académicos e investigadores financieros en las últimas décadas. Por un lado, debemos tratar de maximizar nuestra riqueza aprovechando el mercado financiero, y por otro, debemos cubrir nuestros pagos (pasivos) a lo largo del tiempo. El objetivo del ALM es dotar al inversor de una serie de recursos o técnicas para seleccionar los activos del mercado financiero adecuados para obedecer a los dos factores clave mencionados: cumplir con nuestros pasivos y maximizar nuestra riqueza. Esta tesis presenta un conjunto de técnicas que son capaces de abordar problemas financieros realistas sin la necesidad habitual de considerables recursos computacionales. Estas técnicas se basan en la heurística y la simulación. En concreto, se desarrolla un modelo metaheurístico sesgado que tiene una aplicación directa en la operación habitual de inmunización de las compañías de seguros. El algoritmo permite seleccionar eficientemente el menor número de activos, principalmente de renta fija, en el balance y garantizar las obligaciones de la compañía. Este desarrollo permite incorporar la calidad crediticia del emisor de los activos utilizados. Asimismo, se desarrolla un modelo de optimización de la cartera con el pasivo y se resuelve con un algoritmo genético. El problema de optimización de la cartera difiere del habitual en que es multiperiodo e incorpora los pasivos a lo largo del tiempo. Además, se incluye la posibilidad de financiación externa cuando la entidad no tiene suficiente efectivo. Estas condiciones dan lugar a un problema complejo que se resuelve eficientemente mediante un algoritmo evolutivo. En ambos casos, los algoritmos se mejoran con la incorporación de la simulación de Montecarlo. Esto permite que las soluciones sean robustas cuando consideramos situaciones de mercado realistas. Los resultados son muy prometedores. Esta investigación demuestra que la simheurística es un método ideal para este tipo de problemas.La gestió d'actius i passius (asset and liability management, ALM) ha acaparat l'atenció d'acadèmics i investigadors financers les darreres dècades. D'una banda, hem de mirar de maximitzar la nostra riquesa aprofitant el mercat financer, i de l'altra, hem de cobrir els nostres pagaments (passius) al llarg del temps. L'objectiu de l'ALM és dotar l'inversor d'una sèrie de recursos o tècniques per seleccionar els actius del mercat financer adequats per obeir als dos factors clau esmentats: complir els passius i maximitzar la nostra riquesa. Aquesta tesi presenta un conjunt de tècniques que són capaces d'abordar problemes financers realistes sense la necessitat habitual de recursos computacionals considerables. Aquestes tècniques es basen en l'heurística i la simulació. En concret, es desenvolupa un model metaheurístic esbiaixat que té una aplicació directa a l'operació habitual d'immunització de les companyies d'assegurances. L'algorisme permet seleccionar eficientment el menor nombre d'actius, principalment de renda fixa, al balanç i garantir les obligacions de la companyia. Aquest desenvolupament permet incorporar la qualitat creditícia de l'emissor dels actius utilitzats. Així mateix, es desenvolupa un model d'optimització de la cartera amb el passiu i es resol amb un algorisme genètic. El problema d'optimització de la cartera difereix de l'habitual en el fet que és multiperíode i incorpora els passius al llarg del temps. A més, s'inclou la possibilitat de finançament extern quan l'entitat no té prou efectiu. Aquestes condicions donen lloc a un problema complex que es resol eficientment mitjançant un algorisme evolutiu. En tots dos casos, els algorismes es milloren amb la incorporació de la simulació de Montecarlo. Això permet que les solucions siguin robustes quan considerem situacions de mercat realistes. Els resultats són molt prometedors. Aquesta recerca demostra que la simheurística és un mètode ideal per a aquesta mena de problemes.Tecnologías de la información y de rede

Modeling the Use of Nonrenewable Resources Using a Genetic Algorithm

This paper shows, how a genetic algorithm (GA) can be used to model an economic process: the interaction of profit-maximizing oil-exploration firms that compete with each other for a limited amount of oil. After a brief introduction to the concept of multi-agent-modeling in economics, a GA-based resource-economic model is developed. Several model runs based on different economic policy assumptions are presented and discussed in order to show how the GA-model can be used to gain insight into the dynamic properties of economic systems. The remainder outlines deficiencies of GA-based multi-agent approaches and sketches how the present model can be improved.

Genetic algorithms applied to asset & liability management

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementEffective asset liability management is at the core of what a life insurance company must do, particularly in what concerns defined benefits pension fund products. The life insurer faces a complex problem whereby multiple and sometimes conflicting objectives must be addressed at the same time, such as achieving higher returns while reducing the portfolio’s exposure to a plethora of risks. To achieve these goals, pension fund managers must then carefully choose asset allocation strategies for their portfolios from an infinite pool of asset combinations and weights. Given the nature of this problem, the use of genetic algorithms seems to be adequate, as this method is particularly well suited to deal with very large and multi-modal solution spaces. The main purpose of this dissertation is to assess how well the genetic algorithm method performs in solving this specific problem, and compare the results with other simpler methods. The results of Genetic Algorithms application were satisfactory and the results of this study suggests that Genetic Algorithms are a useful tool to solve ALM problems

Dynamic changes and multi-dimensional evolution of portfolio optimization

Although there has been an increasing number of studies investigate portfolio optimization from different perspectives, few attempts could be found that focus on the development trend and hotspots of this research area. Therefore, it motivates us to comprehensively investigate the development of portfolio optimization research and give some deep insights into this knowledge domain. In this paper, some bibliometric methods are utilized to analyse the status quo and emerging trends of portfolio optimization research on various aspects such as authors, countries and journals. Besides, ‘theories’, ‘models’ and ‘algorithms’, especially heuristic algorithms are identified as the hotspots in the given periods. Furthermore, the evolutionary analysis tends to presents the dynamic changes of the cutting-edge concepts of this research area in the time dimension. It is found that more portfolio optimization studies were at an exploration stage from mean-variance analysis to consideration of multiple constraints. However, heuristic algorithms have become the driving force of portfolio optimization research in recent years. Multidisciplinary analyses and applications are also the main trends of portfolio optimization research. By analysing the dynamic changes and multi-dimensional evolution in recent decades, we contribute to presenting some deep insights of the portfolio optimization research directly, which assists researchers especially beginners to comprehensively learn this research field

Genetic programming optimization for a sentiment feedback strength based trading strategy

This study is motivated by the empirical findings that news and social me- dia Twitter messages (tweets) exhibit persistent predictive power on financial market movement. Based on the evidence that tweets are faster than news in revealing new market information, whereas news is regarded broadly a more reliable source of information than tweets, we propose a superior trading strat- egy based on the sentiment feedback strength between the news and tweets using generic programming optimization method. The key intuition behind this feedback strength based approach is that the joint momentum of the two sentiment series leads to significant market signals, which can be exploited to generate superior trading profits. With the trade-off between information speed and its reliability, this study aims to develop an optimal trading strategy us- ing investors' sentiment feedback strength with the objective to maximize risk adjusted return measured by the Sterling ratio. We find that the sentiment feed- back based strategies yield superior market returns with low maximum draw- down over the period from 2012 to 2015. In comparison, the strategies based on the sentiment feedback indicator generate over 14.7% Sterling ratio compared with 10.4% and 13.6% from the technical indicator-based strategies and the ba- sic buy-and-hold strategy respectively. After considering transaction costs, the sentiment indicator based strategy outperforms the technical indicator based strategy consistently. Backtesting shows that the advantage is statistically significant. The result suggests that the sentiment feedback indicator provides support in controlling loss with lower maximum drawdown