The Data Mining Of Finance

Data mining is another concept closely associated with large databases such as clinical data repositories and data warehouses. However data mining like several other IT concepts means different things to different people. Health care application vendors may use the term data mining when referring to the user interface of the data warehouse or data repository. They may refer to the ability to drill down into data as data mining for example. However more precisely used data mining refers to a sophisticated analysis tool that automatically dis­ covers patterns among data in a data store. Data mining is an advanced form of decision support. Unlike passive query tools the data mining analysis tool does not require the user to pose individual specific questions to the database. Instead this tool is programmed to look for and extract patterns, trends and rules. True data mining is currently used in the business community for market­ ing and predictive analysis (Stair & Reynolds, 2012). This analytical data mining is however not currently widespread in the health care community.

by determining their similarity, helping patterns to emerge. The supervised learning is used to classify

The market provides the physical and institutional structure through which the money of one country is exchanged for another.

Abstract - In the Data mining process, we can identify the patterns in the data that is hard to find using normal analysis. Several Mathematical and statistical algorithms are used in this approach to determine the probability of the event or scenario. The main aim of this process in terms of technical representation is to find the correlation amongst the attributes. There is a huge amount of discovery being carried out in this field creating a huge scope and jobs in this area. Several data mining algorithms are present that could determine different features present in the data that could lead in prediction and future analysis. Main Study report would consist of these algorithms that could help us predict and some sample data that we

Companies and organizations all over the world are blasting on the scene with data mining and data warehousing trying to keep an extreme competitive leg up on the competition. Always trying to improve the competiveness and the improvement of the business process is a key factor in expanding and strategically maintaining a higher standard for the most cost effective means in any business in today’s market. Every day these facilities store large amounts of data to improve increased revenue, reduction of cost, customer behavior patterns, and the predictions of possible future trends; say for seasonal reasons. Data

The foreign exchange market is a worldwide decentralized over-the-counter financial market for the trading of currencies. Financial centers around the world function as anchors of trading between a wide range of different types of buyers and sellers around the clock, with the exception of weekends. The foreign exchange market determines the relative values of different currencies. (wiki.org)

Data mining simply explained is automated sorting and analyzing of large amounts of data, searching for patterns and correlations. On a

1.3 Literature Review on Seven Data Mining Techniques . . . . . . . . . . . . . . . . . 6

pouch of values), the currency in all market the world has no central location. Is prostitution of an electronic network full of banks, financial institutions and individual traders, all those involved in the importation and sale of national currencies. Another important property of the Forex market is that it operates twenty-four hours of dawn, which corresponds to the initiation and closing of financial centers in countries around the world, starting each data in Sydney, then Tokyo, London and New York. At any time, in any place, there are buyers and sellers, so the Forex market the most liquid market in the

Regression Analysis is numerical process of determining relationship within different variable. It helps to provide analyzing about outcome of independent variable on dependent variable. The data we use for prediction are dependent by the performance of Regression Analysis. This report is determined if the past data could be used to predict the future change in share price. In this report we used data of share prices of URBN Outfitters Inc. to figure out the future value of shares with Regression Analysis method. Future change will be used as dependent variable on Y axis and other values as independent variables on x axis. According to Regression Analysis we have figured out that the past values are useful to find out the future change in share prices.

As computer storage capacities increased in 1980s, many companies began to collect more transactional information. The resulting record collections, often called data warehouse. These companies used computer science to explode their data warehouses, and attempt to learn the behaviour patterns of their clients. One of the earliest successful applications of data mining was credit-card-fraud detection. Through learning a consumer’s buying behaviour, a typical pattern usually becomes apparent; By a data collection of a long period of time and a large population, it would be not difficult to find distinction between normal and fraudulent behaviour works.

Predictions are an important component of determining the financial progress of a business. Business decisions rely on forecasting techniques to predict things such as price movements or overall success in markets. In the attempt to forecast market predictions, it must be assumed that future occurrences may be partly based on present and past data (Abu-Mostafa, Yaser S 1996). Further assumptions must be made to conclude that there is a predictable pattern in past data. There is evidence for both the idea that financial market forecasting is futile due to the unpredictable nature of finance, as well as for the idea that financial markets are predictable to an extent. The consequences of financial decision-making imply an inherent need for the use of forecasting tools in making predictions about future occurrences. The issue resides in the fact that there is an abundance of data and information that must be organized and interpreted. A number of techniques may be used to manage present and past data in order to create a forecast prediction, though with more research and trials, neural networks have been shown to be superior in performance.

Each step contains multiple effective and efficient techniques. The following table provides an overview of dominant techniques applied in each step and next part further illustrates two of the cutting edge – Expected Monetary Value (decision tree) and Monte Carlo Simulation.

Data mining is an area of data processing in which extraction of useful patterns from pre-existing databases and transformation of extracted information into understandable form is done. Data mining employs various methods like clustering, classification, regression etc.[1] One such method is Association Rule Mining which discovers the dependencies between database variables. Frequent Pattern Mining is an area of data mining which works on this principle and generates

CNX Nifty being an important barometer to indicate country’s growth has always been followed with lots of interest from both academia and industry. Now, CNX Nifty could be predicted or not on a random basis gives rise to many a questions. This sounds redolent with any predictive modeling though with a certain degree of accuracy in built in to the system. The major point of consideration is that predictive modeling could be done by various measures and mechanisms. In predictive modeling Multiple Adaptive Regression (MARS), Classification and Regression Trees (CART), Logistic OLS or Non Linear OLS could be used. Here in this study the researcher has utilized Neural Network as a “Predictive Modeler” to predict CNX Nifty closing on certain random time zones under consideration.