Quoting the movie Pi:
1. Mathematics is the language of nature
2. Everything can be represented and understood using numbers
3. If you graph the numbers of any system, patterns emerge
Predicting the stock prices is a part of the human curiosity that wants to know the future. The central idea behind this need is to invest for profit.
When I casually turned to search for papers and patents on this topic, I stumbled across some interesting publications.
Firstly, it is important to know that stock prices depends largely on two things - Performance of the company and the stock buyers.
Sentiments of the buyers affects other buyers and affects the total buying of the stock, which in turn affects the demand-supply of the stock and affects the pricing of the stock.
Searching for patents for 'stock market prediction' on google patents gives us US8285619. This patent is assigned to Fred Herz Patents, LLC, and it discloses a method of using natural language processing (NLP) techniques to extract information from online news feeds and then using the information so extracted to predict changes in stock prices or volatilities. These predictions can be used to make profitable trading strategies.
Many papers [1] [2] disclose such analysis of news to predict the state of the market and company performance.
Some papers, such as [3], also suggest use of micro-blogging sites (such as Twitter) to analyse sentiments of buyers and the overall 'perceived' state of the market. Other papers, such as [4], also examine the effectiveness of analysing web traffic, i.e. online community traffic to predict the stock prices.
There are many papers out there showing great interest among researchers to tackle this problem of knowing the future.
References:
[1] Text Mining of News Articles for Stock Price Predictions, Kim-Georg Aase, Norwegian University of Science and Technology. Link
[2] Textual Analysis of Stock Market Prediction Using Financial News Articles, Robert P. Schumaker and Hsinchun Chen, The University of Arizona. Link
[3] Some experiments on modeling stock market behavior using investor sentiment analysis and posting volume from Twitter, Nuno Oliveira, Paulo Cortez and Nelson Areal, University of Minho. Link
[4] Stock Market Prediction Without Sentiment Analysis: Using a Web-Traffic Based Classifier and User-Level Analysis, Pierpaolo Dondio, Dublin Institute of Technology. Link
1. Mathematics is the language of nature
2. Everything can be represented and understood using numbers
3. If you graph the numbers of any system, patterns emerge
Predicting the stock prices is a part of the human curiosity that wants to know the future. The central idea behind this need is to invest for profit.
When I casually turned to search for papers and patents on this topic, I stumbled across some interesting publications.
Firstly, it is important to know that stock prices depends largely on two things - Performance of the company and the stock buyers.
Sentiments of the buyers affects other buyers and affects the total buying of the stock, which in turn affects the demand-supply of the stock and affects the pricing of the stock.
Searching for patents for 'stock market prediction' on google patents gives us US8285619. This patent is assigned to Fred Herz Patents, LLC, and it discloses a method of using natural language processing (NLP) techniques to extract information from online news feeds and then using the information so extracted to predict changes in stock prices or volatilities. These predictions can be used to make profitable trading strategies.
Many papers [1] [2] disclose such analysis of news to predict the state of the market and company performance.
Some papers, such as [3], also suggest use of micro-blogging sites (such as Twitter) to analyse sentiments of buyers and the overall 'perceived' state of the market. Other papers, such as [4], also examine the effectiveness of analysing web traffic, i.e. online community traffic to predict the stock prices.
There are many papers out there showing great interest among researchers to tackle this problem of knowing the future.
References:
[1] Text Mining of News Articles for Stock Price Predictions, Kim-Georg Aase, Norwegian University of Science and Technology. Link
[2] Textual Analysis of Stock Market Prediction Using Financial News Articles, Robert P. Schumaker and Hsinchun Chen, The University of Arizona. Link
[3] Some experiments on modeling stock market behavior using investor sentiment analysis and posting volume from Twitter, Nuno Oliveira, Paulo Cortez and Nelson Areal, University of Minho. Link
[4] Stock Market Prediction Without Sentiment Analysis: Using a Web-Traffic Based Classifier and User-Level Analysis, Pierpaolo Dondio, Dublin Institute of Technology. Link