Predicting stock price movement in Pakistan: Comparison between artificial neural network models and traditional linear models: A case of KSE

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dc.contributor.authorMuhammad Abubakr Naeem
dc.date.accessioned2016-04-20T06:28:11Z
dc.date.available2016-04-20T06:28:11Z
dc.date.issued2015
dc.descriptionSupervisor: Prof. Dr. Ahmed Faisal Siddiqien_US
dc.description.abstractSince the establishment of the Karachi Stock Exchange (KSE) in 1993, Pakistan's stock markets have expanded rapidly. Although this rapid growth has attracted considerable academic interest, few studies have examined the ability of conventional financial models to predict the share price movements of stocks. This gap in the literature is significant, given the volatility of the Pakistani stock market and the additional risk arising from the political and regulatory environs of Pakistan, we examine the relative ability of Hybrid Artificial Neural Networks models to forecast stock returns. In this research, the gap is attended by comparing the predictive ability of three linear models i.e. Univariate ARIMA model, Multivariate CAPM model and Multivariate three factor model. Furthermore, we compared these three linear models with the equally specified artificial neural network models containing the same predictor variables. One of the advantages of artificial neural networks is that they relax the model linearity assumption. The analysis conducted is based on the data from Karachi Stock Exchange, containing data for companies from the period of 2010-2014. The results of the research designate that the multivariate models i.e. the dynamic CAPM and the dynamic three factor model surpass the univariate model, that only incorporate lagged returns of stock prices, in forecasting future returns. Additionally, using the artificial neural networks notably enhance the predictive ability using the same predictor variables. Also, irrespective of the linear or non-linear model used, there is significant difference in the forecasting accuracy of univariate ARIMA, multivariate CAPM and multivariate FF3 model.en_US
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/1822
dc.publisherUNIVERSITY OF MANAGEMENT AND TECHNOLOGYen_US
dc.subjectMS Thesisen_US
dc.subjectArtificial neural networken_US
dc.subjectAutoregressive moving average modelen_US
dc.subjectCapital asset pricing modelen_US
dc.subjectThree-factor modelen_US
dc.subjectStock price predictionen_US
dc.titlePredicting stock price movement in Pakistan: Comparison between artificial neural network models and traditional linear models: A case of KSEen_US
dc.titlePredicting stock price movement in Pakistan: comparison between artificial neural network models and traditional linear models: a case of kseen_us
dc.typeThesisen_US
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