Artificial neural networks applied to option pricing

Master of Science in Engineering - EngineeringArtificial Neural Networks has seen tremendous growth in recent years. It has been applied to various sciences, including applied mathematics, chemistry, physics, and engineering and has also been implemented in various areas of finance. Many researchers have applied them to forecasting of stock prices and other fields of finance. In this study we focus on option pricing. An option is a contract giving the buyer of the contract the right but not the obligation to purchase stock on or before a certain expiration date. Options have become a multi-billion dollar industry in modern times, and there has been a lot of focus on pricing these option contracts. Option pricing data is highly non-linear and its pricing has its basis in stochastic calculus. Since neural networks have excellent non-linear modeling capabilities, it seems obvious to apply neural networks to option pricing. In this thesis, many different methodologies are developed to model the data. The multilayer perceptron and radial basis functions are used in the stand-alone neural networks. Then, the architectures of the stand-alone networks are optimized using particle swarm optimization, which leads to excellent results. Thereafter, a committee of neural networks is investigated. A committee network is an average of a combination of stand-alone neural networks. In contrast to stand-alone networks, a committee network has great generalization capabilities. Many different methods are developed for attaining optimal results from these committee networks. The methods included different forms of weighting the stand-alone networks, a non-linear combination of the committee members using another stand-alone neural network, a two layer committee network where the second layer was used for smoothing the output and a circular committee network. Lastly, genetic algorithm, with the Metropolis-Hastings algorithm, was used to optimize the committee of neural networks. Finally all these methods were analyzed

Exploratory study to explore the role of ICT in the process of knowledge management in an Indian business environment

In the 21st century and the emergence of a digital economy, knowledge and the knowledge base economy are rapidly growing. To effectively be able to understand the processes involved in the creating, managing and sharing of knowledge management in the business environment is critical to the success of an organization. This study builds on the previous research of the authors on the enablers of knowledge management by identifying the relationship between the enablers of knowledge management and the role played by information communication technologies (ICT) and ICT infrastructure in a business setting. This paper provides the findings of a survey collected from the four major Indian cities (Chennai, Coimbatore, Madurai and Villupuram) regarding their views and opinions about the enablers of knowledge management in business setting. A total of 80 organizations participated in the study with 100 participants in each city. The results show that ICT and ICT infrastructure can play a critical role in the creating, managing and sharing of knowledge in an Indian business environment

Study on option pricing based on artificial intelligence

期权理论是20世纪世界经济学领域最伟大的发现之一。由于期权具有良好的规避风险、风险投资和价值发现等功能，且表现出灵活性和多样性特点，故近30年来，特别是上个世纪90年代以来，期权成为最有活力的衍生金融产品，得到了迅速发展和广泛的应用。对于期权价格的正确定价不仅对于学术界而且对于金融市场的实际操作者来说都是十分重要的。目前已经有许多对于欧式期权定价的参数化模型，包括著名的Black-Scholes模型。但是由于有着一些不真实，与真实市场不协调矛盾的隐含参数，所以它们的定价效果并不如我们所期望的那么好。为了避免这些参数化模型的缺陷，基于人工智能的欧式期权定价模型越来越受到关注。同时，如我们所知，美...The option theory is one of the most great discovery in the world economic field in 20th century . Owing to the function of the risk of elusion , venture investment , value discovery and the characteristic of agility and multiplicity , option has been the most great-hearted derivative product and has gained rapid development and broad application since 90th of the last century . It is important to...学位：博士后院系专业：经济学院_金融工程学号：201017003

Predicting Bankruptcy with Support Vector Machines

The purpose of this work is to introduce one of the most promising among recently developed statistical techniques – the support vector machine (SVM) – to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors as financial ratios. A method of adapting it to default probability estimation is proposed. A survey of practically applied methods is given. This work shows that support vector machines are capable of extracting useful information from financial data, although extensive data sets are required in order to fully utilize their classification power.support vector machine, classification method, statistical learning theory, electric load prediction, optical character recognition, predicting bankruptcy, risk classification

Rating Companies with Support Vector Machines

The goal of this work is to introduce one of the most successful among recently developed statistical techniques - the support vector machine (SVM) - to the field of corporate bankruptcy analysis. The main emphasis is done on implementing SVMs for analysing predictors in the form of financial ratios. A method is proposed of adapting SVMs to default probability estimation. A survey of practically and commercially applied methods is given. This work proves that support vector machines are capable of extracting useful information from financial data although extensive data sets are required in order to fully utilise their classification power.Support vector machines; Company rating; Default probability estimation

The enablers and implementation model for mobile KMS in Australian healthcare

In this research project, the enablers in implementing mobile KMS in Australian regional healthcare will be investigated, and a validated framework and guidelines to assist healthcare in implementing mobile KMS will also be proposed with both qualitative and quantitative approaches. The outcomes for this study are expected to improve the understanding the enabling factors in implementing mobile KMS in Australian healthcare, as well as provide better guidelines for this process

Low power and high performance heterogeneous computing on FPGAs

L'abstract è presente nell'allegato / the abstract is in the attachmen

Learning Machines Supporting Bankruptcy Prediction

In many economic applications it is desirable to make future predictions about the financial status of a company. The focus of predictions is mainly if a company will default or not. A support vector machine (SVM) is one learning method which uses historical data to establish a classification rule called a score or an SVM. Companies with scores above zero belong to one group and the rest to another group. Estimation of the probability of default (PD) values can be calculated from the scores provided by an SVM. The transformation used in this paper is a combination of weighting ranks and of smoothing the results using the PAV algorithm. The conversion is then monotone. This discussion paper is based on the Creditreform database from 1997 to 2002. The indicator variables were converted to financial ratios; it transpired out that eight of the 25 were useful for the training of the SVM. The results showed that those ratios belong to activity, profitability, liquidity and leverage. Finally, we conclude that SVMs are capable of extracting the necessary information from financial balance sheets and then to predict the future solvency or insolvent of a company. Banks in particular will benefit from these results by allowing them to be more aware of their risk when lending money.Support Vector Machine, Bankruptcy, Default Probabilities Prediction, Profitability

Non Linear Modelling of Financial Data Using Topologically Evolved Neural Network Committees

Most of artificial neural network modelling methods are difficult to use as maximising or minimising an objective function in a non-linear context involves complex optimisation algorithms. Problems related to the efficiency of these algorithms are often mixed with the difficulty of the a priori estimation of a network's fixed topology for a specific problem making it even harder to appreciate the real power of neural networks. In this thesis, we propose a method that overcomes these issues by using genetic algorithms to optimise a network's weights and topology, simultaneously. The proposed method searches for virtually any kind of network whether it is a simple feed forward, recurrent, or even an adaptive network. When the data is high dimensional, modelling its often sophisticated behaviour is a very complex task that requires the optimisation of thousands of parameters. To enable optimisation techniques to overpass their limitations or failure, practitioners use methods to reduce the dimensionality of the data space. However, some of these methods are forced to make unrealistic assumptions when applied to non-linear data while others are very complex and require a priori knowledge of the intrinsic dimension of the system which is usually unknown and very difficult to estimate. The proposed method is non-linear and reduces the dimensionality of the input space without any information on the system's intrinsic dimension. This is achieved by first searching in a low dimensional space of simple networks, and gradually making them more complex as the search progresses by elaborating on existing solutions. The high dimensional space of the final solution is only encountered at the very end of the search. This increases the system's efficiency by guaranteeing that the network becomes no more complex than necessary. The modelling performance of the system is further improved by searching not only for one network as the ideal solution to a specific problem, but a combination of networks. These committces of networks are formed by combining a diverse selection of network species from a population of networks derived by the proposed method. This approach automatically exploits the strengths and weaknesses of each member of the committee while avoiding having all members giving the same bad judgements at the same time. In this thesis, the proposed method is used in the context of non-linear modelling of high-dimensional financial data. Experimental results are'encouraging as both robustness and complexity are concerned.Imperial Users onl