Artificial neural networks to predict share prices on the Johannesburg stock exchange

The use of historical data to build models for stock market prediction has been extensively researched. Artificial Neural Networks (ANNs) bring new opportunities for predicting stock markets, and is now one of the leading techniques used for time series and specifically stock market prediction. This study explored the application of ANNs to predict share prices in the banking sector of the South African Johannesburg Stock Exchange (JSE). This study used three companies, i.e. Standard Bank, Nedbank and First National Bank, listed on the JSE as case studies for the use of ANNs for predicting the closing share price for the next day, week and month. Historical share price data from the JSE was integrated with datasets of external factors that influence market. The external factors considered in this study include index data from NASDAQ, the JSE top 40 and all share indexes, the exchange rate and the business cycle indicator (BCI) values from the South African Reserve Bank. Comparative analysis were conducted between traditional regression models and ANN models using the lagged share price as input variable. The effect on prediction performance of using external factors as additional input variables was also explored. The ANN models using only the share price was found in general to perform better than both traditional models and ANNs that used the external factors as additional input variables. The average next month prediction model produced a noticeably smaller prediction error compared to the next week, and next day prediction models for all three banks. The results showed that the introduction of external factors as additional input variables did not lead to an improved prediction performance, over models that used only the share price. This study also highlights the importance of using an appropriate validation method and evaluating model stability for evaluating and developing ANN models for share price prediction in time series data. The results contribute to existing research that indicate that an ANN is more effective than a regression method for predicting banking share prices, and that these predictive models have potential for supporting investment decision making

Система підтримки прийняття рішень для визначення патернів котирувань на фондових ринках

Тема магістерської дисертації «Система підтримки прийняття рішень для прогнозування цін акцій фінансового ринку на основі рекурентрих нейронних мереж». Актуальність магістерської дисертації обумовлена високим розвитком нейромережевих технологій та штучного інтелекту, потреба в зростанні якості прогнозів фінансових ринків, незавершеність формування цілісного уявлення щодо прогнозування цін на фінансових ринках. Об’єкт дослідження - нейронні мережі на основі рекурентної архітектури, їх можливості та перспективи у сфері фінансового прогнозування. Предмет дослідження - моделі та методи застосування нейронних мереж для задач прогнозування, шляхи покращення існуючих методів та систем прогнозування. Метою магістерської дисертації є підвищення якісних характеристик роботи рекурентних нейромереж для розв’язання задач прогнозування фінансових ринків. Для досягнення мети були поставлення наступні задачі: 1. Огляд предметної області та аналіз існуючих рішень, архітектур нейромереж; 2. Розробка нових підходів до прогнозування на основі використання елементів штучного інтелекту; 3. Розробка програмного комплексу, що забезпечуватиме просте використання існуючих та розроблених методів для вирішення задачі прогнозування часових рядів.The theme of my master's dissertation «Decision making support system for prediction of stock prices in financial area based on recurrent neural networks». The urgency of the Master's dissertation is due to the high development of neural network technologies and artificial intelligence, the need to increase the quality of forecasts of financial markets, the incomplete formation of a holistic view of forecasting prices in financial markets. Object of research - neural networks based on recurrent architecture, their capabilities and prospects in the field of financial forecasting. Subject of research - models and methods of using neural networks for forecasting tasks, ways of improving existing methods and forecasting systems. The purpose of the master's dissertation is to increase the qualitative characteristics of the work of recurrent neural networks for solving tasks of forecasting of financial markets. To achieve the goal were the following tasks: 1. Object review and analysis of existing solutions, neural network architectures; 2. Development of new approaches to forecasting based on the use of elements of 3. artificial intelligence; 4. Development of a software complex that will provide easy use of existing and developed methods for solving the problem of prediction of time series

Using a weightless neural network to forecast stock prices: A case study of Nigerian stock exchange

This research work, proposes forecasting stock prices in the stock market industry in Nigeria using a Weightless Neural Network (WNN). A neural network application used to demonstrate the application of the WNN in the forecasting of stock prices in the market is designed and implemented in Visual Foxpro 6.0. The proposed network is tested with stock data obtained from the Nigeria Stock Exchange. This system is compared with Single Exponential Smoothing (SES) model. The WNN error value is found to be 0.39 while that of SES is 9.78, based on these values, forecasting with the WNN is observed to be more accurate and closer to the real data than those using the SES model

An Improved Stock Price Prediction using Hybrid Market Indicators

In this paper the effect of hybrid market indicators is examined for an improved stock price prediction. The hybrid market indicators consist of technical, fundamental and expert opinion variables as input to artificial neural networks model. The empirical results obtained with published stock data of Dell and Nokia obtained from New York Stock Exchange shows that the proposed model can be effective to improve accuracy of stock price prediction

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001