TAILIEUCHUNG - Machine Learning Techniques for Stock Prediction

To assess the required fleet numbers, we commissioned a consultant, Chris Kinchin-Smith, to engage with TOCs, ROSCOs 2 , manufacturers and the wider supply chain. We developed a base position for April 2014, the end of Control Period 4, and then identified likely developments over the course of Control Period 5 (CP5) based on the options put forward in the IIP. The outcome of this process is attached as Appendix 1. These forecast numbers for CP5 are estimates based around our understanding of the position at the end of March 2012. They are highly dependent upon decisions taken. | Machine Learning Techniques for Stock Prediction Vatsal H. Shah 1 1. Introduction An informal Introduction to Stock Market Prediction Recently a lot of interesting work has been done in the area of applying Machine Learning Algorithms for analyzing price patterns and predicting stock prices and index changes. Most stock traders nowadays depend on Intelligent Trading Systems which help them in predicting prices based on various situations and conditions thereby helping them in making instantaneous investment decisions. Stock Prices are considered to be very dynamic and susceptible to quick changes because of the underlying nature of the financial domain and in part because of the mix of known parameters Previous Days Closing Price P E Ratio etc. and unknown factors like Election Results Rumors etc. An intelligent trader would predict the stock price and buy a stock before the price rises or sell it before its value declines. Though it is very hard to replace the expertise that an experienced trader has gained an accurate prediction algorithm can directly result into high profits for investment firms indicating a direct relationship between the accuracy of the prediction algorithm and the profit made from using the algorithm. Motivation behind the Project In this paper we discuss the Machine Learning techniques which have been applied for stock trading to predict the rise and fall of stock prices before the actual event of an increase or decrease in the stock price occurs. In particular the paper discusses the application of Support Vector Machines Linear Regression Prediction using Decision Stumps Expert Weighting and Online Learning in detail along with the benefits and pitfalls of each method. The paper introduces the parameters and variables that can be used in order to recognize the patterns in stock prices which can be helpful in the future prediction of stocks and how Boosting can be combined with other learning algorithms to improve the accuracy of .

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