Finding kernel function for stock market prediction with support vector regression

Stock market prediction is one of the fascinating issues of stock market research. Accurate stock prediction becomes the biggest challenge in investment industry because the distribution of stock data is changing over the time. Time series forcasting, Neural Network (NN) and Support Vector Machine (SVM) are once commonly used for prediction on stock price. In this study, the data mining operation called time series forecasting is implemented. The large amount of stock data collected from Kuala Lumpur Stock Exchange is used for the experiment to test the validity of SVMs regression. SVM is a new machine learning technique with principle of structural minimization risk, which have greater generalization ability and proved success in time series prediction. Two kernel functions namely Radial Basis Function and polynomial are compared for finding the accurate prediction values. Besides that, backpropagation neural network are also used to compare the predictions performance. Several experiments are conducted and some analyses on the experimental results are done. The results show that SVM with polynomial kernels provide a promising alternative tool in KLSE stock market prediction

A Neuro-Classification Model for Socio-Technical Systems

This paper presents an original classifier model based on an artificial neural network (ANN) architecture that is able to learn a specific human behavior and can be used in different socio-economic systems. After a training process, the system can identify and classify a human subject using a list of parameters. The model can be further used to analyze and build a safe socio-technical system (STS). A new technique is applied to find an optimal architecture of the neural network. The system shows a good accuracy of the classifications even for a relatively small amount of training data. Starting from a previous result on adaptive forecasting, the model is enhanced by using the retraining technique for an enlarged data set.artificial neural network, training process, classification, socio-technical system

Soft computing techniques applied to finance

Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad

Intellectual technologies and decision support systems for the control of the economic and financial processes

Запропоновано комп'ютерна система підтримки прийняття рішень, основними завданнями якої є побудова адаптивної моделі і прогнозування різних типів процесів, які розвиваються в соціально-економічних системах під впливом фундаментальних структурних змін. Складність і актуальність розв'язуваної проблеми полягає в необхідності забезпечити прийнятні якісні прогнози фінансових і економічних показників для коротких вибірок даних, коли використання ретроспективних даних неможливо або суттєво обмежена. Розробка СППР заснована на принципах системного аналізу, тобто можливості обліку деяких стохастичних та інформаційних невизначеностей, формування альтернатив для моделей і прогнозів і відстеження правильності обчислювальних процедур на всіх етапах обробки даних. Реалізована модульна архітектура, яка забезпечує можливість подальшого розширення і модифікації функціональних можливостей системи за допомогою нових методів прогнозування та оцінки параметрів. Крім того, пропонована система, завдяки модульній архітектурі, може бути поліпшена за рахунок використання програмного забезпечення різних виробників без будь-яких додаткових структурних змін. Висока якість кінцевого результату досягається завдяки належному відстеження обчислювальних процедур на всіх етапах обробки даних в ході обчислювальних експериментів: попередня обробка даних, побудова моделі і оцінка прогнозів. Відстеження виконується з відповідними наборами статистичних параметрів якості. Наведено приклад оцінки фінансового ризику в сфері страхування та споживання електроенергії з точки зору енергозбереження. Наведені приклади показують, що розроблена система має хороші перспективи для практичного використання. Передбачається, що система буде універсальною і знайде своє застосування в якості додаткового інструменту для підтримки прийняття рішень при розробці стратегій для компаній і підприємств різних типів.A computer-based decision support system is proposed the basic tasks of which are adaptive model constructing and forecasting of various types of processes that are developing in socio-economic systems under the influence of fundamental structural changes. The complexity and urgency of the solvable problem is the need to provide acceptable quality forecasts of financial and economic indicators for short data samples, when the usage of retrospective data is impossible or significantly limited. The DSS development is based on the system analysis principles, i.e. the possibility for taking into consideration of some stochastic and information uncertainties, forming alternatives for models and forecasts, and tracking of the computing procedures correctness during all stages of data processing. A modular architecture is implemented that provides a possibility for the further enhancement and modification of the system functional possibilities with new forecasting and parameter estimation techniques. In addition, the proposed system, thanks to the modular architecture, can be improved by using the software of different vendors without any additional structural changes. A high quality of the final result is achieved thanks to appropriate tracking of the computing procedures at all stages of data processing during computational experiments: preliminary data processing, model constructing, and forecasts estimation. The tracking is performed with appropriate sets of statistical quality parameters. The example is given for estimation of financial risk in the insurance sphere and the electricity consumption in terms of energy saving. The examples solved show that the system developed has good perspectives for practical use. It is supposed that the system will be universal and find its applications as an extra tool for support of decision making when developing the strategies for companies and enterprises of various types.Предложена компьютерная система поддержки принятия решений, основными задачами которой являются построение адаптивной модели и прогнозирование различных типов процессов, которые развиваются в социально-экономических системах под воздействием фундаментальных структурных изменений. Сложность и актуальность решаемой проблемы заключается в необходимости обеспечить приемлемые качественные прогнозы финансовых и экономических показателей для коротких выборок данных, когда использование ретроспективных данных невозможно или существенно ограничено. Разработка СППР основана на принципах системного анализа, то есть возможности учета некоторых стохастических и информационных неопределенностей, формирования альтернатив для моделей и прогнозов и отслеживания правильности вычислительных процедур на всех этапах обработки данных. Реализована модульная архитектура, которая обеспечивает возможность дальнейшего расширения и модификации функциональных возможностей системы с помощью новых методов прогнозирования и оценки параметров. Кроме того, предлагаемая система, благодаря модульной архитектуре, может быть улучшена за счет использования программного обеспечения разных производителей без каких-либо дополнительных структурных изменений. Высокое качество конечного результата достигается благодаря надлежащему отслеживанию вычислительных процедур на всех этапах обработки данных в ходе вычислительных экспериментов: предварительная обработка данных, построение модели и оценка прогнозов. Отслеживание выполняется с соответствующими наборами статистических параметров качества. Приведен пример оценки финансового риска в сфере страхования и потребления электроэнергии с точки зрения энергосбережения. Решенные примеры показывают, что разработанная система имеет хорошие перспективы для практического использования. Предполагается, что система будет универсальной и найдет свое применение в качестве дополнительного инструмента для поддержки принятия решений при разработке стратегий для компаний и предприятий различных типов

European exchange trading funds trading with locally weighted support vector regression

In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series

Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review

Recent crises, recessions and bubbles have stressed the non-stationary nature and the presence of drastic structural changes in the financial domain. The most recent literature suggests the use of conventional machine learning and statistical approaches in this context. Unfortunately, several of these techniques are unable or slow to adapt to changes in the price-generation process. This study aims to survey the relevant literature on Machine Learning for financial prediction under regime change employing a systematic approach. It reviews key papers with a special emphasis on technical analysis. The study discusses the growing number of contributions that are bridging the gap between two separate communities, one focused on data stream learning and the other on economic research. However, it also makes apparent that we are still in an early stage. The range of machine learning algorithms that have been tested in this domain is very wide, but the results of the study do not suggest that currently there is a specific technique that is clearly dominant

Market and Economic Modelling of the Intelligent Grid: End of Year Report 2009

The overall goal of Project 2 has been to provide a comprehensive understanding of the impacts of distributed energy (DG) on the Australian Electricity System. The research team at the UQ Energy Economics and Management Group (EEMG) has constructed a variety of sophisticated models to analyse the various impacts of significant increases in DG. These models stress that the spatial configuration of the grid really matters - this has tended to be neglected in economic discussions of the costs of DG relative to conventional, centralized power generation. The modelling also makes it clear that efficient storage systems will often be critical in solving transient stability problems on the grid as we move to the greater provision of renewable DG. We show that DG can help to defer of transmission investments in certain conditions. The existing grid structure was constructed with different priorities in mind and we show that its replacement can come at a prohibitive cost unless the capability of the local grid to accommodate DG is assessed very carefully.Distributed Generation. Energy Economics, Electricity Markets, Renewable Energy

Exchange-Rate Economics for the Resources Sector

The paper provides an account of aspects of exchange-rate economics that are of particular relevance to the resources sector. The issues discussed include exchange-rate volatility and risk management practices used to deal with it, the role of productivity differences across countries, the impact of a booming resources sector on the country’s exchange rate and approaches to forecasting exchange rates. The discussion is organised around a simple stylised model that emphasises the quantity theory of money and purchasing power parity as a long-run link between prices and exchange rates.