A machine learning approach in financial markets

A machine learning approach in financial markets

Abstract

Stock market prediction with data mining techniques is one of the most important issues to be investigated. We intend to present a system that predicts the changes of stock trend by analyzing the influence of news articles.

Chapter One

Introduction Efficient Market Hypothesis The efficient-market hypothesis (EMH) asserts that financial markets are "informational efficient", or that prices on traded assets (e.g., stocks, bonds, or property) already reflect all known information, and instantly change to reflect new information. Therefore, according to theory, it is impossible to consistently outperform the market by using any information that the market already knows, except through luck. Information or news in the EMH is defined as anything that may affect prices that is unknowable in the present and thus appears randomly in the future. Stock market prediction brings with it the challenge of proving whether the financial market is predictable or not, since there has been no consensus on the validity of Efficient Market Hypothesis (EMH). Stock market prediction has been an important issue in the field of finance, engineering and mathematics due to its potential financial gain. As a vast amount of capital is traded through the stock market, the stock-market is seen as a peak investment outlet. Researchers have strived for proving the predictability of the financial market. Henceforth, Stock Market prediction has always had a certain appeal for researchers. While numerous scientific attempts have been made, no method has been discovered to accurately predict stock price movement. Even with a lack of consistent prediction methods, there have been some mild successes. Autoregressive and moving average are some of the famous stock trend prediction techniques which have dominated the time series prediction for several decays. With the help of data mining, several approaches using inductive learning for prediction have also been developed, such as k-nearest neighbour and neural network. However, their major weakness is that they rely heavily on structural data, in which they neglect the influence of non-quantifiable information such as news articles. With the advent of faster computers and vast information over the Internet, stock markets have become more accessible to either strategic investors or the general public. Information from quarterly reports or breaking news stories can dramatically affect the share price of a security. As the Internet provides a primary source of event information which has a significant impact on stock markets, the techniques to extract and use information to support decision making have become a critical task. To predict the stock market accurately, various prediction algorithms and models have been proposed by many researchers in both academics and industry. In this paper, recent development in prediction algorithms and models will be introduced and their performance will be compared. In addition, for accurate stock market prediction, we investigate various global events and their issues on predicting stock markets.

Topic: A machine learning approach in financial markets

Chapters: 1 - 5

Delivery: Email

Number of Pages: 75

Price: 5000 NGN

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