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Title: Predicting Stock Price Volatility
Authors: Singhal, Prashant
Singh, Sanjana [Guided by]
Keywords: Stock market
Machine learning
Data mining
Issue Date: 2015
Publisher: Jaypee University of Information Technology, Solan, H.P.
Abstract: Stock market is an important and active part of nowadays financial markets. Stock time series volatility analysis is regarded as one of the most challenging time series forecasting due to the hard-to-predict volatility observed in worldwide stock markets. Stock market state is dynamic and invisible but it will be influenced by some visible stock market information. Current models for predicting volatility do not incorporate information flow and are solely based on historical volatilities. The future stock volatility is better predicted by our method than the conventional models. Forecasting accuracy is the most important factor in selecting any forecasting methods. Research efforts in improving the accuracy of forecasting models are increasing since the last decade. The appropriate stock selections those are suitable for investment is a difficult task. The key factor for each investor is to earn maximum profits on their investments. Numerous techniques used to predict stocks in which fundamental and technical analysis are one among them. In this project, prediction algorithms and functions are used to predict future share prices and their performance is compared. The results from analysis shows that M5P algorithm offers the ability to predict the stock prices more accurately than the other existing algorithms.
Appears in Collections:B.Tech. Project Reports

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