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. In this paper we argue that the stock market state is dynamic and invisible but it will be influenced by some visible stock market information. Existing research on financial time series analysis and stock market volatility prediction can be classified into two categories: in depth study of one market factor on the stock market volatility prediction or prediction by combining historical price fluctuations with either trading volume or news. In this paper we present a service-oriented multi-kernel based learning framework (MKL) for stock volatility analysis. Our MKL service framework promotes a two-tier learning architecture. In the top tier, we develop a suite of data preparation and data transformation techniques to provide a source-specific modelling, which transforms and normalizes a source specific input dataset into the MKL ready data representation. Then we apply data alignment techniques to prepare the datasets from multiple information sources based on the classification model we choose for cross-source correlation analysis. In the next tier, we develop model integration methods to perform three analytic tasks: (i) building one sub-kernel per source, (ii) learning and tuning the weights for sub-kernels through weight adjustment methods and (iii) performing multi-kernel based cross-correlation analysis of market volatility. To validate the effectiveness of our service oriented MKL approach, we performed experiments on HKEx 2001 stock market datasets with three important market information sources: historical prices, trading volumes and stock related news articles. Our experiments show that 1) multi-kernel learning method has a higher degree of accuracy and a lower degree of false prediction, compared to existing single kernel methods; and 2) integrating both news and trading volume data with historical stock price information can significantly improve the effectiveness of stock market volatility prediction, compared to many existing prediction methods.

EXISTING SYSTEM:

Stock market is an important and active part of nowadays financial markets. Since finance market has become more and more competitive, stock price prediction has been a hot research topic in the past few decades. Autoregressive and moving average are some of the popular stock volatility prediction techniques, which have dominated for years in time series analysis. With the advancement of computing technology, data mining techniques have been widely used for the stock price prediction. Several approaches using inductive learning for prediction have been developed using historical stock price data, such as k-nearest neighbour and neural network, which have greatly improved the performance of prediction. However, one major weakness of these existing approaches is that they only rely on the historical price, and neglect some other information and their influences on the market volatility. Many factors observed from other data sources tend to be less or not aligned well to the time-series information of the stock data such as historical prices, such as news about big mergers, bankruptcy of some companies or unexpected election and economic turmoil. However, such sources of information may have significant impacts on the time series behaviour of the market. In recent years, several researchers have engaged in studying stock price prediction by combining historical prices with news to help improve the performance of prediction [4]. Most of them have applied or extended existing data mining techniques to explore how and when the market news may influence the investors’ actions and in-turn affect the stock prices. At the same time, other researchers have argued that the trading volume is an important source of information, which may reflect some actions taken by the investors in stock trading. Some preliminary studies have been conducted to show the impact of stock volume fluctuation on the stock price movements. Some other studies have investigated the impact of combining the trading volume with historical prices on the effectiveness of stock volatility prediction. Surprisingly, none of the existing research, to the best of our knowledge, has explored whether correlations of fluctuations in historical prices, in trading volumes and in news (bad verse good news) may provide more and stronger indicators and improve accuracy of stock market volatility predication.

PROPOSED SYSTEM :

To validate the effectiveness of our service oriented MKL approach, we performed experiments on HK exchange datasets with three important market information sources: historical prices, transaction volumes and stock related news articles. Our experimental results show that the proposed multiple data source prediction system with MKL has better performance, higher degree of accuracy and lower degree of false prediction, compared to single kernel models. Also integrating both news and trading volume data with historical stock price information can significantly increase the prediction accuracy significantly.

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10