The Genetic Algorithm

The portfolio optimization is an important research field of the financial sciences. In portfolio optimization problems, it is aimed to create portfolios by giving the best return at a certain risk level from the asset pool or by selecting assets that give the lowest risk at a certain level of return. The diversity of the portfolio gives opportunity to increase the return by minimizing the risk. As a powerful alternative to the mathematical models, heuristics is used widely to solve the portfolio optimization problems. The genetic algorithm (GA) is a technique that is inspired by the biological evolution. While this book considers the heuristics methods for the portfolio optimization problems, this chapter will give the implementing steps of the GA clearly and apply this method to a portfolio optimization problem in a basic example.

Role of Metaheuristic Optimization in Portfolio Management for the Banking Sector

In this chapter, the importance of optimization technique, more specifically metaheuristic optimization in banking portfolio management, is reviewed. Present work deals with interactive bank marketing campaign of a specific Portugal bank, taken from UCI dataset archive. This dataset consists of 45,211 samples with 17 features including one response/output variable. The classification work is carried out with all data using decision tree (DT), support vector machine (SVM), and k-nearest neighbour (k-NN), without any feature optimization. Metaheuristic genetic algorithm (GA) is used as a feature optimizer to find only 5 features out of the 16 features. Finally, the classification work with the optimized feature shows relatively good accuracy in comparison to classification with all feature set. This result shows that with a smaller number of optimized features better classification can be achieved with less computational overhead.

Metaheuristic-Based Feature Optimization for Portfolio Management

In the last two to three decades, use of credit cards is increasing rapidly due to fast economic growth in developing countries and worldwide globalization issues. Financial institutions like banks are facing a very tough time due to fast-rising cases of credit card loan payment defaulters. The banking institution is constantly searching for the perfect mechanisms or methods to identify possible defaulters among the whole set of credit card users. In this chapter, the most important features of a credit card holder are identified from a considerably large set of features using metaheuristic algorithms. In this work, a standard data set archived in UCI repository of credit card payments of Taiwan is used. Metaheuristic algorithms like particle swarm optimization, ant colony optimization, and simulated annealing are used to identify the significant sets of features from the given data set. Support vector machine classifier is used to identify the class in this two-class (loan defaulter or not) problem.

Heuristic Optimization of Portfolio Considering Sharpe's Single Index Model

Selection of weights of the selected securities in the portfolio is a cumbersome job for any investor. The famous nonlinear Sharpe's single index model has been simplified with a linear solution and the risk-taking propensity of the investors have been taken into consideration in the simplified formulation. The coefficient of optimism is included to observe the effect of risk-taking propensity in the portfolio selection. After the empirical analysis it is found that heuristically an investor can reach near to the optimum solution. For empirical analysis 126 months data have been considered of NSE Bank Index. To reduce the volatility of the data the whole period again has been divided into two parts each of 63 months duration, and separately the data pertaining to the three periods have been considered for calculation. The city block distance is used to calculate the nearness between the optimum solutions and the heuristic solutions.

Optimizing Social Media Data Using Genetic Algorithm

Twitter-based research for sentiment analysis is popular for quite some time now. This is used to represent documents in a corpus usually. This increases the time of classification and also increases space complexity. It is hence very natural to say that non-redundant feature reduction of the input space for a classifier will improve the generalization property of a classifier. In this approach, the researchers have tried to do feature selection using Genetic Algorithm (GA) which will reduce the set of features into a smaller subset. The researchers have also tried to put forward an approach using Genetic Algorithm to reduce the modelling complexity and training time of classification algorithm for 10k Twitter data based on GST. They aim to improve the accuracy of the classification that the researchers have obtained in a preface work to this work and achieved an accuracy of 87% through this work. Hence the Genetic Algorithm will do the feature selection to reduce the complexity of the classifier and give us a better accuracy of the classification of the tweet.

Bayesian Networks and Evolutionary Algorithms as a Tool for Portfolio Simulation and Optimization

This chapter will propose solution how to recognise important factors within portfolio, how to derive new information from existing data and evaluate its importance factor. The chapter will also propose methodology for sensitivity evaluation between factors recognised as important. This information has valuable factors for Bayesian network construction. Such created Bayesian network can be used as simulation tool, as well as tool for portfolio optimization. As a simulation tool, such Bayesian network can be for output analysis regarding potential decisions via decision graphs. Also, as an optimization tool, Bayesian network can be used in way of finding optimal value of decision factors upon expected outputs from portfolio. For achieving this aim, evolutionary algorithms will be used as optimization tool.

Portfolio Optimization and Asset Allocation With Metaheuristics

Portfolio optimization stands to be an issue of finding an optimal allocation of wealth to place within the obtainable assets. Markowitz stated the problem to be structured as dual-objective mean-risk optimization, pointing the best trade-off solutions within a portfolio between risks which is measured by variance and mean. Thus the major intention was nothing else than hunting for optimum distribution of wealth over a specific amount of assets by diminishing risk and maximizing returns of a portfolio. Value-at-risk, expected shortfall, and semi-variance measures prove to be complex for measuring risk, for maximization of skewness, liquidity, dividends by added objective functions, cardinality constraints, quantity constraints, minimum transaction lots, class constraints in real-world constraints all of which are incorporated in modern portfolio selection models, furnish numerous optimization challenges. The emerging portfolio optimization issue turns out to be extremely tough to be handled with exact approaches because it exhibits nonlinearities, discontinuities and high-dimensional, efficient boundaries. Because of these attributes, a number of researchers got motivated in researching the usage of metaheuristics, which stand to be effective measures for finding near optimal solutions for tough optimization issues in an adequate computational time frame. This review report serves as a short note on portfolio optimization field with the usage of Metaheuristics and finally states that how multi-objective metaheuristics prove to be efficient in dealing with portfolio selection problems with complex measures of risk defining non-convex, non-differential objective functions.

Conditional Value-at-Risk-Based Portfolio Optimization

Over the past few decades, an extensive research on the multi-objective decision making and combinatorial optimization of real world's financial transactions has taken place. The modern capital market theory problem of portfolio optimization stands to be a multi-objective problem aiming at the maximization of the expected return of the portfolio in turn minimizing portfolio risk. The conditional value-at-risk (CVaR) is a widely used measure for determining the risk measures of a portfolio in volatile market conditions. A heuristic approach to portfolio optimization problem using ant colony optimization (ACO) technique centering on optimizing the conditional value-at-risk (CVaR) measure in different market conditions based on several objectives and constraints has been reported in this paper. The proposed ACO approach is proved to be reliable on a collection of several real-life financial instruments as compared to its value-at-risk (VaR) counterpart. The results obtained show encouraging avenues in determining optimal portfolio returns.

Particle Swarm Algorithm

Optimization is discovering an alternative with the most cost-effective or highest-achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. Portfolio optimization in finance depends on selecting assets from an opportunity set which yields highest expected return on each level of portfolio risk. Optimization algorithms based on natural events are called heuristic algorithms. The particle swarm optimization (PSO) is a population-based heuristic optimization technique. The technique is inspired by the ability of animals such as birds and fish to adapt to their environment by applying a “sharing of knowledge” approach, to find rich food sources and to avoid hunting. This chapter focuses on portfolio selection problems and shows how to manage financial portfolios using a particle swarm optimization (PSO) technique which is a heuristic algorithm. In order to better understand the subject, the technique has been evaluated in Istanbul Stock Exchange for three transportation sector stocks.