The role of predictability of financial series in emerging market applications (original) (raw)
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Journal of Intelligent Learning Systems and Applications (JILSA),Scientific Research Publishing (SRP www.scirp.org) , USA, ISSN Print: 2150-8402 , ISSN Online: 2150-8410, Vol. 4, No. 2, Pages 108-119, May 2012, 2012
Stock Market is the market for security where organized issuance and trading of Stocks take place either through ex- change or over the counter in electronic or physical form. It plays an important role in canalizing capital from the in- vestors to the business houses, which consequently leads to the availability of funds for business expansion. In this pa- per, we investigate to predict the daily excess returns of Bombay Stock Exchange (BSE) indices over the respective Treasury bill rate returns. Initially, we prove that the excess return time series do not fluctuate randomly. We are apply- ing the prediction models of Autoregressive feed forward Artificial Neural Networks (ANN) to predict the excess return time series using lagged value. For the Artificial Neural Networks model using a Genetic Algorithm is constructed to choose the optimal topology. This paper examines the feasibility of the prediction task and provides evidence that the markets are not fluctuating randomly and finally, to apply the most suitable prediction model and measure their effi- ciency.