Can social microblogging be used to forecast intraday exchange rates?

The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial behavior have challenged the validity of the EMH. Towards this aim, over the last few years, internet-based communication platforms and search engines have been used to extract early indicators of social and economic trends. Here, we used Twitter's social networking platform to model and forecast the EUR/USD exchange rate in a high-frequency intradaily trading scale. Using time series and trading simulations analysis, we provide some evidence that the information provided in social microblogging platforms such as Twitter can in certain cases enhance the forecasting efficiency regarding the very short (intradaily) forex.Comment: This is a prior version of the paper published at NETNOMICS. The final publication is available at http://www.springer.com/economics/economic+theory/journal/1106

Algorithmic optimization and its application in finance

The goal of this thesis is to examine different issues in the area of finance and application of financial and mathematical models under consideration of optimization methods. Prior to the application of a model to its scope, the model results have to be adjusted according to the observed data. For this reason a target function is defined which is being minimized by using optimization algorithms. This allows finding the optimal model parameters. This procedure is called model calibration or model fitting and requires a suitable model for this application. In this thesis we apply financial and mathematical models such as Heston, CIR, geometric Brownian motion, as well as inverse transform sampling, and Chi-square test. Moreover, we test the following optimization methods: Genetic algorithms, Particle-Swarm, Levenberg-Marquardt, and Simplex algorithm. The first part of this thesis deals with the problem of finding a more accurate forecasting approach for market liquidity by using a calibrated Heston model for the simulation of the bid/ask paths instead of the standard Brownian motion and the inverse transformation method instead of compound Poisson process for the generation of the bid/ask volume distributions. We show that the simulated trading volumes converge to one single value which can be used as a liquidity estimator and we find that the calibrated Heston model as well as the inverse transform sampling are superior concerning the use of the standard Brownian motion, resp. compound Poisson process. In the second part, we examine the price markup for hedging or liquidity costs, that customers have to pay when they buy structured products by replicating the payoff of ten different structured products and comparing their fair values with the prices actually traded. For this purpose we use parallel computing, a new technology that was not possible in the past. This allows us to use a calibrated Heston model to calculate the fair values of structured products over a longer period of time. Our results show that the markup that clients pay for these ten products ranges from 0.9%-2.9%. We can also observe that products with higher payoff levels, or better capital protection, require higher costs. We also identify market volatility as a statistically significant driver of the markup. In the third part, we show that the tracking error of an passively managed ETF can be significantly reduced through the use of optimization methods if the correlation factor between Index and ETF is used as target function. By finding optimal weights of a self-constructed bond- and the DAX- index, the number of constituents can be reduced significantly, while keeping the tracking error small. In the fourth part, we develop a hedging strategy based on fuel prices that can be applied primarily to the end users of petrol and diesel fuels. This enables the fuel consumer to buy fuel at a certain price for a certain period of time by purchasing a call option. To price the American call option we use a geometric Brownian motion combined with a binomial model

Agent-Based Models and Human Subject Experiments

This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms

Nature inspired computational intelligence for financial contagion modelling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market

Evolutionary computation for trading systems

2007/2008Evolutionary computations, also called evolutionary algorithms, consist of several heuristics, which are able to solve optimization tasks by imitating some aspects of natural evolution. They may use different levels of abstraction, but they are always working on populations of possible solutions for a given task. The basic idea is that if only those individuals of a population which meet a certain selection criteria reproduce, while the remaining individuals die, the population will converge to those individuals that best meet the selection criteria. If imperfect reproduction is added the population can begin to explore the search space and will move to individuals that have an increased selection probability and that hand down this property to their descendants. These population dynamics follow the basic rule of the Darwinian evolution theory, which can be described in short as the “survival of the fittest”. Although evolutionary computations belong to a relative new research area, from a computational perspective they have already showed some promising features such as: • evolutionary methods reveal a remarkable balance between efficiency and efficacy; • evolutionary computations are well suited for parameter optimisation; • this type of algorithms allows a wide variety of extensions and constraints that cannot be provided in traditional methods; • evolutionary methods are easily combined with other optimization techniques and can also be extended to multi-objective optimization. From an economic perspective, these methods appear to be particularly well suited for a wide range of possible financial applications, in particular in this thesis I study evolutionary algorithms • for time series prediction; • to generate trading rules; • for portfolio selection. It is commonly believed that asset prices are not random, but are permeated by complex interrelations that often translate in assets mispricing and may give rise to potentially profitable opportunities. Classical financial approaches, such as dividend discount models or even capital asset pricing theories, are not able to capture these market complexities. Thus, in the last decades, researchers have employed intensive econometric and statistical modeling that examine the effects of a multitude of variables, such as price- earnings ratios, dividend yields, interest rate spreads and changes in foreign exchange rates, on a broad and variegated range of stocks at the same time. However, these models often result in complex functional forms difficult to manage or interpret and, in the worst case, are solely able to fit a given time series but are useless to predict it. Parallelly to quantitative approaches, other researchers have focused on the impact of investor psychology (in particular, herding and overreaction) and on the consequences of considering informed signals from management and analysts, such as share repurchases and analyst recommendations. These theories are guided by intuition and experience, and thus are difficult to be translated into a mathematical environment. Hence, the necessity to combine together these point of views in order to develop models that examine simultaneously hundreds of variables, including qualitative informations, and that have user friendly representations, is urged. To this end, the thesis focuses on the study of methodologies that satisfy these requirements by integrating economic insights, derived from academic and professional knowledge, and evolutionary computations. The main task of this work is to provide efficient algorithms based on the evolutionary paradigm of biological systems in order to compute optimal trading strategies for various profit objectives under economic and statistical constraints. The motivations for constructing such optimal strategies are: i) the necessity to overcome data-snooping and supervisorship bias in order to learn to predict good trading opportunities by using market and/or technical indicators as features on which to base the forecasting; ii) the feasibility of using these rules as benchmark for real trading systems; iii) the capability of ranking quantitatively various markets with respect to their profitability according to a given criterion, thus making possible portfolio allocations. More precisely, I present two algorithms that use artificial expert trading systems to predict financial time series, and a procedure to generate integrated neutral strategies for active portfolio management. The first algorithm is an automated procedure that simultaneously selects variables and detect outliers in a dynamic linear model using information criteria as objective functions and diagnostic tests as constraints for the distributional properties of errors. The novelties are the automatic implementation of econometric conditions in the model selection step, making possible a better exploration of the solution space on one hand, and the use of evolutionary computations to efficiently generate a reduction procedure from a very large number of independent variables on the other hand. In the second algorithm, the novelty is given by the definition of evolutionary learning in financial terms and its use in a multi-objective genetic algorithm in order to generate technical trading systems. The last tool is based on a trading strategy on six assets, where future movements of each variable are obtained by an evolutionary procedure that integrates various types of financial variables. The contribution is given by the introduction of a genetic algorithm to optimize trading signals parameters and the way in which different informations are represented and collected. In order to compare the contribution of this work to “classical” techniques and theories, the thesis is divided into three parts. The first part, titled Background, collects Chapters 2 and 3. Its purpose is to provide an introduction to search/optimization evolutionary techniques on one hand, and to the theories that relate the predictability in financial markets with the concept of efficiency proposed over time by scholars on the other hand. More precisely, Chapter 2 introduces the basic concepts and major areas of evolutionary computation. It presents a brief history of three major types of evolutionary algorithms, i.e. evolution strategies, evolutionary programming and genetic algorithms, and points out similarities and differences among them. Moreover it gives an overview of genetic algorithms and describes classical and genetic multi-objective optimization techniques. Chapter 3 first presents an overview of the literature on the predictability of financial time series. In particular, the extent to which the efficiency paradigm is affected by the introduction of new theories, such as behavioral finance, is described in order to justify the market forecasting methodologies developed by practitioners and academics in the last decades. Then, a description of the econometric and financial techniques that will be used in conjunction with evolutionary algorithms in the successive chapters is provided. Special attention is paid to economic implications, in order to highlight merits and shortcomings from a practitioner perspective. The second part of the thesis, titled Trading Systems, is devoted to the description of two procedures I have developed in order to generate artificial trading strategies on the basis of evolutionary algorithms, and it groups Chapters 4 and 5. In particular, chapter 4 presents a genetic algorithm for variable selection by minimizing the error in a multiple regression model. Measures of errors such as ME, RMSE, MAE, Theil’s inequality coefficient and CDC are analyzed choosing models based on AIC, BIC, ICOMP and similar criteria. Two components of penalty functions are taken in analysis- level of significance and Durbin Watson statistics. Asymptotic properties of functions are tested on several financial variables including stocks, bonds, returns, composite prices indices from the US and the EU economies. Variables with outliers that distort the efficiency and consistency of estimators are removed to solve masking and smearing problems that they may cause in estimations. Two examples complete the chapter. In both cases, models are designed to produce short-term forecasts for the excess returns of the MSCI Europe Energy sector on the MSCI Europe index and a recursive estimation- window is used to shed light on their predictability performances. In the first application the data-set is obtained by a reduction procedure from a very large number of leading macro indicators and financial variables stacked at various lags, while in the second the complete set of 1-month lagged variables is considered. Results show a promising capability to predict excess sector returns through the selection, using the proposed methodology, of most valuable predictors. In Chapter 5 the paradigm of evolutionary learning is defined and applied in the context of technical trading rules for stock timing. A new genetic algorithm is developed by integrating statistical learning methods and bootstrap to a multi-objective non dominated sorting algorithm with variable string length, making possible to evaluate statistical and economic criteria at the same time. Subsequently, the chapter discusses a practical case, represented by a simple trading strategy where total funds are invested in either the S&P 500 Composite Index or in 3-month Treasury Bills. In this application, the most informative technical indicators are selected from a set of almost 5000 signals by the algorithm. Successively, these signals are combined into a unique trading signal by a learning method. I test the expert weighting solution obtained by the plurality voting committee, the Bayesian model averaging and Boosting procedures with data from the the S&P 500 Composite Index, in three market phases, up-trend, down- trend and sideways-movements, covering the period 2000–2006. In the third part, titled Portfolio Selection, I explain how portfolio optimization models may be constructed on the basis of evolutionary algorithms and on the signals produced by artificial trading systems. First, market neutral strategies from an economic point of view are introduced, highlighting their risks and benefits and focusing on their quantitative formulation. Then, a description of the GA-Integrated Neutral tool, a MATLAB set of functions based on genetic algorithms for active portfolio management, is given. The algorithm specializes in the parameter optimization of trading signals for an integrated market neutral strategy. The chapter concludes showing an application of the tool as a support to decisions in the Absolute Return Interest Rate Strategies sub-fund of Generali Investments.Gli “algoritmi evolutivi”, noti anche come “evolutionary computations” comprendono varie tecniche di ottimizzazione per la risoluzione di problemi, mediante alcuni aspetti suggeriti dall’evoluzione naturale. Tali metodologie sono accomunate dal fatto che non considerano un’unica soluzione alla volta, bens`ı trattano intere popolazioni di possibili soluzioni per un dato problema. L’idea sottostante `e che, se un algoritmo fa evolvere solamente gli individui di una data popolazione che soddisfano a un certo criterio di selezione, e lascia morire i restanti, la popolazione converger`a agli individui che meglio soddisfano il criterio di selezione. Con una selezione non ottimale, cio`e una che ammette pure soluzioni sub-ottimali, la popolazione rappresenter` a meglio l’intero spazio di ricerca e sar`a in grado di individuare in modo pi`u consistente gli individui migliori da far evolvere. Queste dinamiche interne alle popolazioni seguono i principi Darwiniani dell’evoluzione, che si possono sinteticamente riassumere nella dicitura “la sopravvivenza del più adatto”. Sebbene gli algoritmi evolutivi siano un’area di ricerca relativamente nuova, dal punto di vista computazionale hanno dimostrato alcune caratteristiche interessanti fra cui le seguenti: • permettono un notevole equilibrio tra efficienza ed efficacia; • sono particolarmente indicati per la configurazione dei parametri in problemi di ottimizzazione; • consentono una flessibilit`a nella definizione matematica dei problemi e dei vincoli che non si trova nei metodi tradizionali; • possono facilmente essere integrati con altre tecniche di ottimizzazione ed essere essere modificati per risolvere problemi multi-obiettivo. Dal un punto di vista economico, l’applicazione di queste procedure pu`o risultare utile specialmente in campo finanziario. In particolare, nella mia tesi ho studiato degli algoritmi evolutivi per • la previsione di serie storiche finanziarie; • la costruzione di regole di trading; • la selezione di portafogli. Da un punto di vista pi`u ampio, lo scopo di questa ricerca `e dunque l’analisi dell’evoluzione e della complessit`a dei mercati finanziari. In tal senso, dal momento che i prezzi non seguono andamenti puramente casuali, ma sono governati da un insieme molto articolato di eventi correlati, i modelli e le teorie classiche, come i dividend discount model e le varie capital asset pricing theories, non sono pi`u sufficienti per determinare potenziali opportunit`a di profitto. A tal fine, negli ultimi decenni, alcuni ricercatori hanno sviluppato una vasta gamma di modelli econometrici e statistici in grado di esaminare contemporaneamente le relazioni e gli effetti di centinaia di variabili, come ad esempio, price-earnings ratios, dividendi, differenziali fra tassi di interesse e variazioni dei tassi di cambio, per una vasta gamma di assets. Comunque, questo approccio, che fa largo impiego di strumenti di calcolo, spesso porta a dei modelli troppo complicati per essere gestiti o interpretati, e, nel peggiore dei casi, pur essendo ottimi per descrivere situazioni passate, risultano inutili per fare previsioni. Parallelamente a questi approcci quantitativi, si `e manifestato un grande interesse sulla psicologia degli investitori e sulle conseguenze derivanti dalle opinioni di esperti e analisti nelle dinamiche del mercato. Questi studi sono difficilmente traducibili in modelli matematici e si basano principalmente sull’intuizione e sull’esperienza. Da qui la necessit` a di combinare insieme questi due punti di vista, al fine di sviluppare modelli che siano in grado da una parte di trattare contemporaneamente un elevato numero di variabili in modo efficiente e, dall’altra, di incorporare informazioni e opinioni qualitative. La tesi affronta queste tematiche integrando le conoscenze economiche, sia accademiche che professionali, con gli algoritmi evolutivi. Pi`u pecisamente, il principale obiettivo di questo lavoro `e lo sviluppo di algoritmi efficienti basati sul paradigma dell’evoluzione dei sistemi biologici al fine di determinare strategie di trading ottimali in termini di profitto e di vincoli economici e statistici. Le ragioni che motivano lo studio di tali strategie ottimali sono: i) la necessit`a di risolvere i problemi di data-snooping e supervivorship bias al fine di ottenere regole di investimento vantaggiose utilizzando indicatori di mercato e/o tecnici per la previsione; ii) la possibilità di impiegare queste regole come benchmark per sistemi di trading reali; iii) la capacit`a di individuare gli asset pi`u vantaggiosi in termini di profitto, o di altri criteri, rendendo possibile una migliore allocazione di risorse nei portafogli. In particolare, nella tesi descrivo due algoritmi che impiegano sistemi di trading artificiali per predire serie storiche finanziarie e una procedura di calcolo per strategie integrate neutral market per la gestione attiva di portafogli. Il primo algoritmo `e una procedura automatica che seleziona le variabili e simultaneamente determina gli outlier in un modello dinamico lineare utilizzando criteri informazionali come funzioni obiettivo e test diagnostici come vincoli per le caratteristiche delle distribuzioni degli errori. Le novit`a del metodo sono da una parte l’implementazione automatica di condizioni econometriche nella fase di selezione, consentendo una migliore analisi dello EVOLUTIONARY COMPUTATIONS FOR TRADING SYSTEMS 3 spazio delle soluzioni, e dall’altra parte, l’introduzione di una procedura di riduzione evolutiva capace di riconoscere in modo efficiente le variabili pi`u informative. Nel secondo algoritmo, le novità sono costituite dalla definizione dell’apprendimento evolutivo in termini finanziari e dall’applicazione di un algoritmo genetico multi-obiettivo per la costruzione di sistemi di trading basati su indicatori tecnici. L’ultimo metodo proposto si basa su una strategia di trading su sei assets, in cui le dinamiche future di ciascuna variabile sono ottenute impiegando una procedura evolutiva che integra diverse tipologie di variabili finanziarie. Il contributo è dato dall’impiego di un algoritmo genetico per ottimizzare i parametri negli indicatori tecnici e dal modo in cui le differenti informazioni sono presentate e collegate. La tesi `e organizzata in tre parti. La prima parte, intitolata Background, comprende i Capitoli 2 e 3, ed è intesa a fornire un’introduzione alle tecniche di ricerca/ottimizzazione su base evolutiva da una parte, e alle teorie che si occupano di efficienza e prevedibilit`a dei mercati finanziari dall’altra. Più precisamente, il Capitolo 2 introduce i concetti base e i maggiori campi di studio della computazione evolutiva. In tal senso, si dà una breve presentazione storica di tre dei maggiori tipi di algoritmi evolutivi, ciò e le strategie evolutive, la programmazione evolutiva e gli algoritmi genetici, evidenziandone caratteri comuni e differenze. Il capitolo si chiude con una panoramica sugli algoritmi genetici e sulle tecniche classiche e genetiche di ottimizzazione multi-obiettivo. Il Capitolo 3 affronta nel dettaglio la problematica della prevedibilit`a delle serie storiche finanziarie mettendo in luce, in particolare, quanto il paradigma dell’efficienza sia influenzato dalle pi`u recenti teorie finanziarie, come ad esempio la finanza comportamentale. Lo scopo è quello di dare una giustificazione su basi teoriche per le metodologie di previsione sviluppate nella tesi. Segue una descrizione dei metodi econometrici e di analisi tecnica che nei capitoli successivi verrano impiegati assieme agli algoritmi evolutivi. Una particolare attenzione è data alle implicazioni economiche, al fine di evidenziare i loro meriti e i loro difetti da un punto di vista pratico. La seconda parte, intitolata Trading Systems, raggruppa i Capitoli 4 e 5 ed è dedicata alla descrizione di due procedure che ho sviluppato per generare sistemi di trading artificiali sulla base di algoritmi evolutivi. In particolare, il Capitolo 4 presenta un algortimo genetico per la selezione di variabili attraverso la minimizzazione dell’errore in un modello di regressione multipla. Misure di errore, quali il ME, il RMSE, il MAE, il coefficiente di Theil e il CDC sono analizzate a partire da modelli selezionati sulla scorta di criteri informazionali, come ad esempio AIC, BIC, ICOMP. A livello di vincoli diagnostici, ho considerato una funzione di penalità a due componenti e la statistica di Durbin Watson. Il programma impiega variabili finanziarie di vario tipo, come rendimenti di titoli, bond e prezzi di indici composti ottenuti dalle economie Statunitense ed Europea. Nel caso le serie storiche 4 MASSIMILIANO KAUCIC considerate presentino outliers che distorcono l’efficienza e la consistenza degli stimatori, l’algoritmo `e in grado di individuarle e rimuoverle dalla serie, risolvendo il problema di masking and smearing. Il capitolo si conclude con due applicazioni, in cui i modelli sono progettati per produrre previsioni di breve periodo per l’extra rendimento del settore MSCI Europe Energy sull’indice MSCI Europe e una procedura di tipo recursive estimation-window è utilizzata per evidenziarne le performance previsionali. Nel primo esempio, l’insieme dei dati `e ottenuto estraendo le variabili di interesse da un considerevole numero di indicatori di tipo macro e da variabili finanziarie ritardate rispetto alla variabile dipendente. Nel secondo esempio ho invece considerato l’intero insieme di variabili ritardate di 1 mese. I risultati mostrano una notevole capacità previsiva per l’extra rendimento, individuando gli indicatori maggiormente informativi. Nel Capitolo 5, il concetto di apprendimento evolutivo viene definito ed applicato alla costruzione di regole di trading su indicatori tecnici per lo stock timing. In tal senso, ho sviluppato un algoritmo che integra metodi di apprendimento statistico e di boostrap con un particolare algoritmo multi-obiettivo. La procedura derivante è in grado di valutare contemporaneamente criteri economici e statistici. Per descrivere il suo funzionamento, ho considerato un semplice esempio di trading in cui tutto il capitale è investito in un indice (che nel caso trattato è l’indice S&P 500 Composite) o in un titolo a basso rischio (nell’esempio, i Treasury Bills a 3 mesi). Il segnale finale di trading `e il risultato della selezione degli indicatori tecnici pi`u informativi a partire da un insieme di circa 5000 indicatori e la loro conseguente integrazione mediante un metodo di apprendimento (il plurality voting committee, il bayesian model averaging o il Boosting). L’analisi è stata condotta sull’intervallo temporale dal 2000 al 2006, suddiviso in tre sottoperiodi: il primo rappresenta l’indice in un

The doctoral research abstract. Vol:9 2016 / Institute of Graduate Studies, UiTM

FOREWORD: Seventy three doctoral graduands will be receiving their scroll today signifying their achievements in completing their PhD journey. The novelty of their research is shared with you through The Doctoral Abstracts on this auspicious occasion, UiTM 84th Convocation. We are indeed proud that another 73 scholarly contributions to the world of knowledge and innovation have taken place through their doctoral research ranging from Science and Technology, Business and Administration, and Social Science and Humanities. As we rejoice and celebrate your achievement, we would like to acknowledge dearly departed Dr Halimi Zakaria’s scholarly contribution entitled “Impact of Antecedent Factors on Collaborative Technologies Usage among Academic Researchers in Malaysian Research Universities”. He has left behind his discovery to be used by other researchers in their quest of pursuing research in the same area, a discovery that his family can be proud of. Graduands, earning your PhD is not the end of discovering new ideas, invention or innovation but rather the start of discovering something new. Enjoy every moment of its discovery and embrace that life is full of mystery and treasure that is waiting for you to unfold. As you unfold life’s mystery, remember you have a friend to count on, and that friend is UiTM. Congratulations for completing this academic journey. Keep UiTM close to your heart and be our ambassador wherever you go. / Prof Emeritus Dato’ Dr Hassan Said Vice Chancellor Universiti Teknologi MAR

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Development of a framework for configuring fractal supply networks and logistics capabilities

The contemporary, dynamic marketplace requires a flexible supply network capable of achieving an effective and efficient logistics operation in order to provide a high level of logistical service and customer satisfaction. A fractal supply network is a reconfigurable supply network which has the ability to present many different problem-solving methods under the terms of the various situations. It has been only proposed and studied recently in the academic literature. However, when the overall number of research works available on this topic is taken into consideration, more work is still needed to, holistically, cover some of the related issues. Therefore, this research presents a framework for configuring/reconfiguring a fractal supply network and its logistical capabilities, with the aim to provide a systematic approach which enables practitioners to measure and optimise the logistics capabilities within the network. Configuration/reconfiguration is started by developing conceptual models based on changes in the environments with respect to the capabilities of the fractal supply network. Conceptual models for measurement or optimisation problems are developed. A multi-criteria decision-making model is, then, developed to prioritise the logistics capability in the fractal supply network where also questionnaire is used. Quantitative models and simulations with regards to the selected problems are developed and tested hypothetically. A simulation is used for verification and validation. Experimental factorial design and statistical techniques are used to generate and analyse the results. The research results proved that the proposed framework and developed models in this thesis provide systematic methods through which practitioners should be able to specify high-priority logistics capabilities for further investment planning, introducing a unique dynamic sustainability control system and an inventory control system to increase both collaboration and integration and improve the process of sharing information across the network, which have proven to be a problematic area for industrialists and provides a foundation for further research development

TRADING NATURAL GAS FUTURES THROUGH SIMULATION PREDICTIVE MODELING AND OPTIMIZATION

For many years, natural gas prices were strongly correlated with those of crude oil. Recently, natural gas prices started to show an independent trend. Natural gas prices are driven by the law of supply and demand which is reflected by the weather and inventory levels among other factors. In the last decade, electronic trading platforms took over the exchanges. With the advent of algorithm trading (AT) and in particular high-frequency trading (HFT), trading commodities, which include energy trading, became riskier due to their extremely volatile nature. This dissertation presents a novel framework that provides insight into the use of HFT in natural gas futures markets. Since there are no publicly disclosed data on such practices, the objective is to develop a comprehensive model for natural gas futures trading. A new heuristic simulation, predictive modeling and optimization algorithm that automates trading natural gas futures is proposed and evaluated. Simulation is used to reconstruct the order book using top of the book natural gas futures historical data. Predictive modeling techniques based on multi-class support vector machines are used to predict the occurrence and the amplitude of spread crossings. Finally, an inventory optimization model is used to determine optimal trading volumes for each trading period. Two types of trading strategies are derived: a strategy using Immediate-Or-Cancel orders where an order is totally or partially executed while the remaining is cancelled, and a strategy that limits orders’ cancellation. Both strategies are tested with real and synthetic data. In this setting, both strategies can lead to profit. This could be used by policymakers and market regulators to implement order cancellation restrictions on commodity futures trading to prevent harmful speculation