• Shilpa Amit Verma
• G.T.Thampi
• Madhuri Rao

Precision financial market forecasting plays an important role in financial prediction and investing. There are various global, local and psychological factors that affect financial market forecasting making it a non-trivial, complex problem. Numerous soft computing techniques based on application of Artificial Neural Network (ANN), Fuzzy Systems (FS) and hybrid models, employing a combination of varied models have been applied by various researchers for forecasting stock prices, with modest results. In the present work, the time-series data, from ICICI Bank Ltd, obtained from the National Stock Exchange (NSE), India is considered as a reference data to demonstrate precise prediction on application of varied ANN and fuzzy algorithms. A thorough and detailed comparison of these algorithms under the ANN & Fuzzy domains, on widely varying time scales - daily price forecasting on one-year data and minute to minute forecasting on intraday data, has been attempted. Forecasting accuracies on the optimized methods has resulted in better than 99% accuracy.

Stock Market, Forecasting, Efficacy Parameters, ANN, ANFIS

"Precision forecasting for intraday and daily stock prices with neural networks and fuzzy models.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.529-540, February-2019, Available :http://www.jetir.org/papers/JETIR1902965.pdf

"Precision forecasting for intraday and daily stock prices with neural networks and fuzzy models.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp529-540, February-2019, Available at : http://www.jetir.org/papers/JETIR1902965.pdf