Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms

This paper reproduces the performance of an international market capitalization shipping stock index and two physical shipping indexes by investing only in US stock portfolios. The index-tracking problem is addressed using the differential evolution algorithm and the genetic algorithm. Portfolios are constructed by a subset of stocks picked from the shipping or the Dow Jones Composite Average indexes. To test the performance of the heuristics, three different trading scenarios are examined: annually, quarterly and monthly rebalancing, accounting for transaction costs where necessary. Competing portfolios are also assessed through predictive ability tests. Overall, the proposed investment strategies carry less risk compared to the tracked benchmark indexes while providing investors the opportunity to efficiently replic ate the performance of both the stock and physical shipping indexes in the most cost-effective way

A survey on financial applications of metaheuristics

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program (E-2015-36)

Solving cardinality constrained portfolio optimisation problem using genetic algorithms and ant colony optimisation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn this thesis we consider solution approaches for the index tacking problem, in which we aim to reproduces the performance of a market index without purchasing all of the stocks that constitute the index. We solve the problem using three different solution approaches: Mixed Integer Programming (MIP), Genetic Algorithms (GAs), and Ant-colony Optimization (ACO) Algorithm by limiting the number of stocks that can be held. Each index is also assigned with different cardinalities to examine the change to the solution values. All of the solution approaches are tested by considering eight market indices. The smallest data set only consists of 31 stocks whereas the largest data set includes over 2000 stocks. The computational results from the MIP are used as the benchmark to measure the performance of the other solution approaches. The Computational results are presented for different solution approaches and conclusions are given. Finally, we implement post analysis and investigate the best tracking portfolios achieved from the three solution approaches. We summarise the findings of the investigation, and in turn, we further improve some of the algorithms. As the formulations of these problems are mixed-integer linear programs, we use the solver ‘Cplex’ to solve the problems. All of the programming is coded in AMPL

Technical and Fundamental Features Analysis for Stock Market Prediction with Data Mining Methods

Predicting stock prices is an essential objective in the financial world. Forecasting stock returns and their risk represents one of the most critical concerns of market decision makers. This thesis investigates the stock price forecasting with three approaches from the data mining concept and shows how different elements in the stock price can help to enhance the accuracy of our prediction. For this reason, the first and second approaches capture many fundamental indicators from the stocks and implement them as explanatory variables to do stock price classification and forecasting. In the third approach, technical features from the candlestick representation of the share prices are extracted and used to enhance the accuracy of the forecasting. In each approach, different tools and techniques from data mining and machine learning are employed to justify why the forecasting is working. Furthermore, since the idea is to evaluate the potential of features in the stock trend forecasting, therefore we diversify our experiments using both technical and fundamental features. Therefore, in the first approach, a three-stage methodology is developed while in the first step, a comprehensive investigation of all possible features which can be effective on stocks risk and return are identified. Then, in the next stage, risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, based on some filters and function-based clustering; and re-predicted the risk and return of stocks. In the second approach, instead of using single classifiers, a fusion model is proposed based on the use of multiple diverse base classifiers that operate on a common input and a meta-classifier that learns from base classifiers’ outputs to obtain a more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting, and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes are determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. Finally, in the third approach, a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) – ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick is presented. Two approaches of Raw-based and Signal-based are devised to extract the model’s input variables and buy and sell signals are considered as output variables. To illustrate the methodologies, for the first and second approaches, Tehran Stock Exchange (TSE) data for the period from 2002 to 2012 are applied, while for the third approach, we used General Motors and Dow Jones indexes.Predicting stock prices is an essential objective in the financial world. Forecasting stock returns and their risk represents one of the most critical concerns of market decision makers. This thesis investigates the stock price forecasting with three approaches from the data mining concept and shows how different elements in the stock price can help to enhance the accuracy of our prediction. For this reason, the first and second approaches capture many fundamental indicators from the stocks and implement them as explanatory variables to do stock price classification and forecasting. In the third approach, technical features from the candlestick representation of the share prices are extracted and used to enhance the accuracy of the forecasting. In each approach, different tools and techniques from data mining and machine learning are employed to justify why the forecasting is working. Furthermore, since the idea is to evaluate the potential of features in the stock trend forecasting, therefore we diversify our experiments using both technical and fundamental features. Therefore, in the first approach, a three-stage methodology is developed while in the first step, a comprehensive investigation of all possible features which can be effective on stocks risk and return are identified. Then, in the next stage, risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, based on some filters and function-based clustering; and re-predicted the risk and return of stocks. In the second approach, instead of using single classifiers, a fusion model is proposed based on the use of multiple diverse base classifiers that operate on a common input and a meta-classifier that learns from base classifiers’ outputs to obtain a more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting, and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes are determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. Finally, in the third approach, a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) – ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick is presented. Two approaches of Raw-based and Signal-based are devised to extract the model’s input variables and buy and sell signals are considered as output variables. To illustrate the methodologies, for the first and second approaches, Tehran Stock Exchange (TSE) data for the period from 2002 to 2012 are applied, while for the third approach, we used General Motors and Dow Jones indexes.154 - Katedra financívyhově

Artificial Intelligence in Asset Management

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways

Banking Environment, Agency Costs, and Loan Syndication : A Cross-Country Analysis

Bank loan syndicate structure can be considered as an organizational response to agency problems stemming from the syndication process. The banking environment also influences the syndication process. We investigate how syndicate structure is influenced by the characteristics of the banking environment, such as banking market structure, financial development, banking regulation and supervision, and legal risk. The results of a cross-country analysis performed on a sample of 15,586 syndicated loan facilities from 24 countries over a period of 15 years confirm that syndicate structure is influenced by banking environments consistent with agency costs minimization and efficient re-contracting objectives.Banking environment, Agency costs, Loan syndication, Syndicate structure.