Stock Prediction Model with Business Intelligence using Temporal Data Mining
نویسندگان
چکیده
The stock market domain is a dynamic and unpredictable environment. Traditional techniques, such as fundamental and technical analysis can provide investors with some tools for managing their stocks and predicting their prices. However, these techniques cannot discover all the possible relations between stocks and thus there is a need for a different approach that will provide a deeper kind of analysis. Data mining can be used extensively in the financial markets and help in stock-price forecasting. We are proposing in this paper a portfolio management solution with business intelligence characteristics. This prototype will serve as a basis for Stock Market Prediction & Portfolio Analysis by Data Mining using Business Intelligence which can benefit users to take informed decisions.
منابع مشابه
A Survey of Stock Price Prediction & Estimation Using Data Mining Techniques
The application of AI techniques for stock price prediction leads to voluminous growth of wealth of investors with the advent of technology. Several prediction and estimations are coming up for almost all sectors of the market. Particularly any kind of stock price prediction is not at all possible without excessive data manipulation which can be done effectively only thru data mining. The syste...
متن کاملStream Time Series Approach for Supporting Business Intelligence
Business intelligence has an important role in effective decision making to improve the business performance and opportunities by understanding the organization’s environments through the systematic process of information. This paper proposes a novel framework based on data mining technologies for making a prediction of business environment. We present a business intelligence model to predict t...
متن کاملPrediction-Based Portfolio Optimization Model for Iran’s Oil Dependent Stocks Using Data Mining Methods
This study applied a prediction-based portfolio optimization model to explore the results of portfolio predicament in the Tehran Stock Exchange. To this aim, first, the data mining approach was used to predict the petroleum products and chemical industry using clustering stock market data. Then, some effective factors, such as crude oil price, exchange rate, global interest rate, gold price, an...
متن کاملPredicting Bankruptcy of Companies using Data Mining Models and Comparing the Results with Z Altman Model
One of the issues helping make investment decisions is appropriate tools and models to evaluate financial situation 0f the organization. By means of these tools, investors can analyze financial situation of the organization and identify financial distress or an ideal condition, they become aware of making decisions to invest in appropriate conditions. The main objective of this study is to ev...
متن کاملA Hybrid Business Success Versus Failure Classification Prediction Model: A Case of Iranian Accelerated Start-ups
The purpose of this study is to reduce the uncertainty of early stage startups success prediction and filling the gap of previous studies in the field, by identifying and evaluating the success variables and developing a novel business success failure (S/F) data mining classification prediction model for Iranian start-ups. For this purpose, the paper is seeking to extend Bill Gross and Robert L...
متن کامل