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

EmC-ICDSST 2019: 5th International Conference on Decision Support System Technology - ICDSST 2019 & EURO Mini Conference 2019 on "Decision Support Systems: Main Developments & Future Trends"

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HEDCOS: High Efficiency Dynamic Combinatorial Optimization System using Ant Colony Optimization algorithm

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDynamic combinatorial optimization is gaining popularity among industrial practitioners due to the ever-increasing scale of their optimization problems and efforts to solve them to remain competitive. Larger optimization problems are not only more computationally intense to optimize but also have more uncertainty within problem inputs. If some aspects of the problem are subject to dynamic change, it becomes a Dynamic Optimization Problem (DOP). In this thesis, a High Efficiency Dynamic Combinatorial Optimization System is built to solve challenging DOPs with high-quality solutions. The system is created using Ant Colony Optimization (ACO) baseline algorithm with three novel developments. First, introduced an extension method for ACO algorithm called Dynamic Impact. Dynamic Impact is designed to improve convergence and solution quality by solving challenging optimization problems with a non-linear relationship between resource consumption and fitness. This proposed method is tested against the real-world Microchip Manufacturing Plant Production Floor Optimization (MMPPFO) problem and the theoretical benchmark Multidimensional Knapsack Problem (MKP). Second, a non-stochastic dataset generation method was introduced to solve the dynamic optimization research replicability problem. This method uses a static benchmark dataset as a starting point and source of entropy to generate a sequence of dynamic states. Then using this method, 1405 Dynamic Multidimensional Knapsack Problem (DMKP) benchmark datasets were generated and published using famous static MKP benchmark instances as the initial state. Third, introduced a nature-inspired discrete dynamic optimization strategy for ACO by modelling real-world ants’ symbiotic relationship with aphids. ACO with Aphids strategy is designed to solve discrete domain DOPs with event-triggered discrete dynamism. The strategy improved inter-state convergence by allowing better solution recovery after dynamic environment changes. Aphids mediate the information from previous dynamic optimization states to maximize initial results performance and minimize the impact on convergence speed. This strategy is tested for DMKP and against identical ACO implementations using Full-Restart and Pheromone-Sharing strategies, with all other variables isolated. Overall, Dynamic Impact and ACO with Aphids developments are compounding. Using Dynamic Impact on single objective optimization of MMPPFO, the fitness value was improved by 33.2% over the ACO algorithm without Dynamic Impact. MKP benchmark instances of low complexity have been solved to a 100% success rate even when a high degree of solution sparseness is observed, and large complexity instances have shown the average gap improved by 4.26 times. ACO with Aphids has also demonstrated superior performance over the Pheromone-Sharing strategy in every test on average gap reduced by 29.2% for a total compounded dynamic optimization performance improvement of 6.02 times. Also, ACO with Aphids has outperformed the Full-Restart strategy for large datasets groups, and the overall average gap is reduced by 52.5% for a total compounded dynamic optimization performance improvement of 8.99 times

The Value of Technics: An Ontogenetic Approach to Money, Markets, and Networks

This thesis investigates the impact of the digitalization of monetary and financial flows on the political-economic sphere in order to provide a novel perspective on the relations between economic and technological forces at the present global juncture. In the aftermath of the Global Financial Crisis and with the rise of the cryptoeconomy, an increasing number of scholars have highlighted the immanence of market logic to cultural and social life. At the same time, speculative practices have emerged that attempt to challenge the political economy through financial experiments. This dissertation complements these approaches by stressing the need to pair the critical study of finance with scholarship in the philosophy of technology that emphasizes the value immanent to technics and technology – i.e. the normative and genetic role of ubiquitous algorithmic networks in the organization of markets and socius. In order to explore these events, I propose an interdisciplinary theoretical framework informed largely by Gilbert Simondon’s philosophy of individuation and technics and the contemporary literature on the ontology of computation, supported by insights drawn from the history of finance and economic theory. This novel framework will provide the means to investigate the ontogenetic processes at work in the techno-cultural ecosystem following the digitalization of monetary and financial flows. Through an exploration of the fleeting materiality and multifaceted character of digital fiat money, the social power of algorithmic financial logic, and the new possibilities offered by the invention of the Bitcoin protocol, this research aims to challenge some of the bedrocks of the economic orthodoxy – economic and monetary value, liquidity, market rationality – in order to move beyond the overarching narrative of capitalism as a monolithic system. The thesis instead foregrounds the techno-historical contingencies that have led to the contemporary power formation. Furthermore, it argues that the ontogenetic character of algorithmic technology ushers in novel possibilities for the speculative engineering of alternative networks of value creation and distribution that have the potential to reverse the current balance of power

Combined Strategic-Tactical Planning for Facility Rehabilitation Using System Dynamics and Optimization

Rehabilitation programs are essential for efficiently managing large networks of infrastructure assets and sustaining their safety and operability. While numerous studies in the literature have focused on various aspects of infrastructure rehabilitation, such as rehabilitation processes, deterioration modeling, life cycle cost analysis, project financing, etc., limited efforts have investigated the overall dynamics among these functions and the development of holistic models that can analyze the long-term effect of different strategic policies and their impact on tactical rehabilitation decisions. To support strategic level of decision-making and long-term policy analysis, this research utilized System Dynamics (SD) to study the dynamic interactions among the deterioration, rehabilitation, and budgeting feedback loops. Model performance and suggested policies were also checked against reference modes and verified using various model testing methods to ensure adequacy. The proposed System Dynamics model was then expanded to incorporate four main modules including policy, physical condition, life cycle cost, and sustainability, for the purpose of backlog accumulation analysis. School building facilities were used as the focused asset domain of this study. After identification of key variables based on literature analysis, previous researches on school building facilitates, and experts’ opinion, the dynamic interactions were studied using causal loop diagraming (CLD) methods. The developed CLD was then mapped into a stock-and-flow simulation model incorporating the four integrated modules with all the underlying mathematical relationships. Numerous experiments with different policy scenarios were conducted to investigate the impact of various policies related to rehabilitation, budget distribution, government investment, and private financing. The simulation results clearly indicated that some of the commonly used policies such as condition-based prioritization methods can lead to significant long-term problems in terms of backlog, and showed that equal distribution of budget can be more effective. Simulation results also indicated that the use of private financing for backlog elimination need to be carefully analyzed to determine a proper payback scheme without a negative effect on long-term backlog projections. The SD Model was also adopted to provide optimum policy solutions in terms of the level of budget allocated to rehabilitation of exiting school buildings and construction of new facilities to accommodate future enrolment. The proposed model used facility condition index (FCI) as an industry standard to investigate facility performance and also a utilized a facility risk index (FRI) to account for the risk of failure. The model was used to investigate and compare the effect of using enrolment-based budgeting policies versus an optimized policy solution on a network of 438 elementary school buildings. Results clearly showed that the enrolment-based approach, which has been used by education ministries for a long time, could be significantly improved with the used of policy optimization. The policy solutions form the strategic-level analysis were used to create detailed tactical rehabilitation plans. To support the tactical level of decision-making this research investigated the performance of mathematical mixed integer programming and genetic algorithm (GA) optimization models to handle the large-scale tactical problems. First, various model formulations including an integer, a one-shot binary, and a year-by-year binary formulation were examined for their performance on large-scale problems. A year-by-year formulation was then selected for the network-level analysis and was used with GA-based optimization. To improve the performance of the GA-based model, a segmentation approach was used that was able to eliminate performance degradation, yet exhibited long processing time. Subsequently, an integer programming model was developed on the GAMS/CPLEX optimization tool that resulted in the best solution quality and fast processing time for very large-scale problems (e.g., 50,000 building components). The promising result of the proposed mathematical model was mainly attributed to the formulation of the optimization model, advancements in the used optimization tools, and the separation between project and network level analysis. Combination of the strategic and tactical models developed in this research provides a comprehensive and systematic framework for a combined analysis of rehabilitation plans at both strategic and tactical levels of facility management

Computational methodology for modelling the dynamics of statistical arbitrage

Recent years have seen the emergence of a multi-disciplinary research area known as "Computational Finance". In many cases the data generating processes of financial and other economic time-series are at best imperfectly understood. By allowing restrictive assumptions about price dynamics to be relaxed, recent advances in computational modelling techniques offer the possibility to discover new "patterns" in market activity. This thesis describes an integrated "statistical arbitrage" framework for identifying, modelling and exploiting small but consistent regularities in asset price dynamics. The methodology developed in the thesis combines the flexibility of emerging techniques such as neural networks and genetic algorithms with the rigour and diagnostic techniques which are provided by established modelling tools from the fields of statistics, econometrics and time-series forecasting. The modelling methodology which is described in the thesis consists of three main parts. The first part is concerned with constructing combinations of time-series which contain a significant predictable component, and is a generalisation of the econometric concept of cointegration. The second part of the methodology is concerned with building predictive models of the mispricing dynamics and consists of low-bias estimation procedures which combine elements of neural and statistical modelling. The third part of the methodology controls the risks posed by model selection and performance instability through actively encouraging diversification across a "portfolio of models". A novel population-based algorithm for joint optimisation of a set of trading strategies is presented, which is inspired both by genetic and evolutionary algorithms and by modern portfolio theory. Throughout the thesis the performance and properties of the algorithms are validated by means of experimental evaluation on synthetic data sets with known characteristics. The effectiveness of the methodology is demonstrated by extensive empirical analysis of real data sets, in particular daily closing prices of FTSE 100 stocks and international equity indices

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace