Master of Business Administration Executive
Faculty / School
Faculty of Business Administration (FBA)
Year of Award
MBA Executive Research Project
Timely forecasting of stock market may serve as an early recommendation for novice and short term investors as well as the early financial distress warning for seasoned/long term investors. Most of the stock predicting studies focus the macroeconomic indicators such as CP! and GDP to harness the prediction forecasting model. However, the daily data of these macroeconomic indicators is practically impossible to obtain, thus making it difficult for the model deployment.
In this research report, an extensive empirical study of the available forecasting methodologies have been reviewed and proposed a method for forecasting using business intelligence tools. The methodology adopted for preparing time series forecasting model comprises of Holt Winter Model and ARIMA model coupled with technical indicators so that the results can be compared for greater profit returns. The research is primarily focused on cement sector of Pakistan as this is most rapid growing sector of economy and less likely to be affected by economic anomalies. The research study on this topic will be extremely beneficial in identifying profit taking strategies using business analytics as compared to the conventional buy-and-hold strategy.
The forecasting accuracy of ARIMA model for Attock Cement script using MAPE (Mean Absolute Percentage Error) is calculated as 6.42%, forecasted values of Lucky Cement for ARIMA Model is calculated to be 8.1% andfor D.G. Stock script shows an error of 1 %. The MAPE of Holt Winter forecasting for Attock Cement is calculated to be 7.2%, for Lucky Cement script is 6.5% and for D.G. Cement script shows an error of 14.6%. This research further determined the constraints and fear factors for novice and seasoned traders which were evaluated through survey with sample size (n=140) predominantly male, working and educated individuals investing in Pakistan stock exchange. The major barriers for new investors are fear of loss of their investments, relying of trader’s suggestion for stock selection and lack of automated platform for short term trading.
The results of this research would be valuable in providing thorough insights of different forecasting strategies used for higher profit returns along with other crucial aspects related to profitability of cement industry in Pakistan.
Hasnie, M. F. (2018). Business intelligence tools for predicting stock prices of cement companies listed at Pakistan Stock Exchange (Unpublished graduate research project). Institute of Business Administration, Pakistan. Retrieved from https://ir.iba.edu.pk/research-projects-emba/83
Available for download on Tuesday, December 31, 2030
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