http://ir.lib.ruh.ac.lk/handle/iruor/7392 2026-05-12T14:34:21Z 2026-05-12T14:34:21Z Nandani, E.J.K.P. Wedagedara, J.R. http://ir.lib.ruh.ac.lk/handle/iruor/9767 2022-12-12T06:16:57Z 2009-01-01T00:00:00Z

Decision Support System for Bank Loan Classification Using Neural Network Nandani, E.J.K.P.; Wedagedara, J.R. We report the designing and application of an Artificial Neural Network (ANN) to classify the consumer loan application in banking sector. The system can be used as a “Second Level” filter, upon which shall be supplied with data of loans that already had been approval process. In particular, this shall be useful in identifying potential risks associated with these loan applications before giving the final approval by the management so can be integrated into the bank’s information management system.

2009-01-01T00:00:00Z Nandani, E.J.K.P. Wedagedara, J.R. http://ir.lib.ruh.ac.lk/handle/iruor/9764 2022-12-12T05:57:02Z 2011-01-01T00:00:00Z

A comparative study of the forecasting bility of Backpropagation Artificial Neural Network Models with Learning Rate Adaptation for Colombo Stock Market Nandani, E.J.K.P.; Wedagedara, J.R. A stock market is one of the fundamental types of financial markets. The stock market can also be thought of as a highly complex and adaptive system. Financial indices defined on stock prices are used as indicators of the economical trend of a country. Forecasting the behavior of the stock market is at primary concern not only of the business community but also of policy makers of a country.

2011-01-01T00:00:00Z Nandani, E.J.K.P. Mahinda, M.K. Wedagedara, J.R. http://ir.lib.ruh.ac.lk/handle/iruor/9763 2022-12-12T05:39:39Z 2015-01-23T00:00:00Z

Comparison of ARIMA and Neural Network Models for S&P SL(20) Index Nandani, E.J.K.P.; Mahinda, M.K.; Wedagedara, J.R. In the current financial world, prediction of stock prices has become a vital task. Predicting the future is important for the organizations to plan or adopt the necessary policies. Forecasting can assist them to make a better development and decision making for the country in the academic literature. The main aim of this study is to compare the forecasting performance for future values of Standard and Poor Sri Lanka 20 (S&P Regressive Integrated Moving Average (ARIMA) models and Artificial Neural Networks (ANN) which are based on statistical and artificial intelligence based techniques by fitting the data and calculating computational errors. We used daily S&P SL 20 Stock Exchange from the period 27th July 2012 to 28th December 2013 to forecast the future values of S&P SL 20. The best architectures for forecasting nth future day of S&P SL 20 were 30 model and ARIMA (1, 1, 1) model. The suitable parameters of each model are selected by using training data set together with trial and error technique. The forecasting performance of each model was compared by using Absolute error, Absolute fraction of variance (R2), Mean Absolu Mean Square Error (MSE) and Root Mean Square Error (RMSE). The results show that ANN forecasting is more accurate in forecasting for an increased number of days than ARIMA model.

2015-01-23T00:00:00Z Nandani, E.J.K.P. http://ir.lib.ruh.ac.lk/handle/iruor/9755 2024-10-21T07:13:09Z 2015-08-01T00:00:00Z

Effect of External Factor on Share Price Forecasting Nandani, E.J.K.P. Generally, stock markets represent the economic status of a country. Different stock markets have different characteristics based on the economics of the countries they represent [1]. Stock markets are complex, nonlinear, dynamic and chaotic [2]. Prediction of stock prices is one of the major challenges in the hands of business analysts for the interest of the financial and economic personnel and in general the policy makers and planners of a country. Techniques of prediction vary greatly according to the availability of information, quality of modeling and the underlying assumptions used. Neural networks are regarded as more suitable for stock prediction than other techniques mainly due to its ability to adaptation to the nature of observations (data); for instance in situation like financial markets in general it is impossible to incorporate the effects of some socio-economic factors directly in the models (e.g. Exchange rate of other non-western currencies, GDP, Consumer indices, etc.) [1]. We used the S&P SL20 index of Colombo Stock Exchange (CSE) in Sri Lanka and exchange rate between US $ and LKR data from 02nd January 2013 to 31st December 2013. The best feed forward ANN model and suitable parameters of each model are selected by using training data set with trial and error technique. The accuracy of each model was compared via Absolute fraction of variance (R2), Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE) and visually. The results examined that the exchange rates which is an external factor affected the share price forecasting in ANNs.

2015-08-01T00:00:00Z