scispace - formally typeset
Search or ask a question

Showing papers by "M. Thenmozhi published in 2006"


Journal ArticleDOI
TL;DR: Empirical experimentation suggests that the SVM outperforms the other classification methods in terms of predicting the direction of the stock market movement and random forest method outperforms neural network, discriminant analysis and logit model used in this study.
Abstract: There exists vast research articles which predict the stock market as well pricing of stock index financial instruments but most of the proposed models focus on the accurate forecasting of the levels (i.e. value) of the underlying stock index. There is a lack of studies examining the predictability of the direction/sign of stock index movement. Given the notion that a prediction with little forecast error does not necessarily translate into capital gain, this study is an attempt to predict the direction of S&P CNX NIFTY Market Index of the National Stock Exchange, one of the fastest growing financial exchanges in developing Asian countries. Random forest and Support Vector Machines (SVM) are very specific type of machine learning method, and are promising tools for the prediction of financial time series. The tested classification models, which predict direction, include linear discriminant analysis, logit, artificial neural network, random forest and SVM. Empirical experimentation suggests that the SVM outperforms the other classification methods in terms of predicting the direction of the stock market movement and random forest method outperforms neural network, discriminant analysis and logit model used in this study.

166 citations


Journal ArticleDOI
TL;DR: In this paper, the authors developed an appropriate model for forecasting the short-term interest rates i.e., commercial paper rate, implicit yield on 91 day treasury bill, overnight MIBOR rate and call money rate.
Abstract: Forecasting interest rates is of great concern for financial researchers, economists and players in the fixed income markets. The purpose of this study is to develop an appropriate model for forecasting the short-term interest rates i.e., commercial paper rate, implicit yield on 91 day treasury bill, overnight MIBOR rate and call money rate. The short-term interest rates are forecasted using univariate models, Random Walk, ARIMA, ARMA-GARCH and ARMA-EGARCH and the appropriate model for forecasting is determined considering six-year period from 1999. The results show that interest rates time series have volatility clustering effect and hence GARCH based models are more appropriate to forecast than the other models. It is found that for commercial paper rate ARIMA-EGARCH model is most appropriate model, while for implicit yield 91 day Treasury bill, overnight MIBOR rate and call money rate, ARIMA-GARCH model is the most appropriate model for forecasting.

14 citations


Journal ArticleDOI
TL;DR: In this article, the stock market's impact to public announcement of strategic decisions of Indian companies is studied and the authors have used the event study methodology to assess whether there is an increase in firm value surrounding the days of such public announcements.
Abstract: In this paper, we have studied the stock market's impact to public announcement of strategic decisions of Indian companies. We have used the event study methodology to assess whether there is an increase in firm value surrounding the days of such public announcements. The corporate strategic announcements include proposed acquisitions, merger announcements, joint venture creations, alliance formations and capital expenditure and R&D expenditure intents. We have further analyzed the strategic announcements into different categories and have tried to capture the effects of size of announcement, the term of the announcement, whether the announcement relates to strategic activities within the country or outside the country and whether the announcement relates to 'Government' owned or 'Privately' owned companies. The results indicate that the stock market reacts negatively to public announcements of corporate strategic decisions.

1 citations


Journal Article
TL;DR: In this paper, the effect of the September 11, 2001 terrorist attack on the Indian stock market has been examined by comparing the means and variances of the excess returns in the 100 day pre-event and 100 day post-event periods.
Abstract: Individuals, institutional investors, and management companies, are interested in tracking stock prices as they represent a company's value, which is affected by many micro and macroeconomic variables. The effect of these variables is more visible for companies that have had a cross-listing. In this study, we attempt to analyze the effects of ADR-listed stocks in the Indian market around a specific event – the September 11, 2001 attack. This event is chosen as it had a significant impact on the American stock exchanges with the Dow dropping by 14.2%, which in turn was expected to have a trickling effect on the domestic stock markets. The impact of this event on the risk and excess returns of the stock prices has been examined by comparing the means and variances of the excess returns in the 100 day pre-event and 100 day post-event. The risk parameter ‘beta’ was also compared for both pre and post-event periods. The results show that the domestic price returns of the companies’ floating ADRs are not significantly affected by this specific event.

1 citations