session12 - FINANCIALDATAMINING INSY430:Class#12 Feb 11th...

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FINANCIAL DATA MINING INSY 430: Class # 12 Feb. 11th, 2010
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WHAT IS DATA MINING? A process of discovering useful patterns The exploration of large quantities of data in  order to discover meaningful patterns The extraction of implicit, previously unknown,  and potentially useful information from data Statistics Confirmatory - Small samples Data mining Exploratory - Large samples
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Draws ideas from machine learning/AI, pattern  recognition, statistics, and database systems Traditional Techniques may be unsuitable due to  Enormity of data High dimensionality  of data Heterogeneous,  distributed nature  of data ORIGINS OF DATA MINING Machine Learning/ Pattern Recognition Statistics/ AI Data Mining Database systems
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WHERE IS DATA MINING  USED IN FINANCE? Making predictions and building trading models  are central goals for financial institutions Applications:  Forecasting financial markets (e.g., stock  returns, exchange rate, Financial risks) Trading rules Credit predictions
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DBMS, OLAP, AND DATA  MINING   DBMS OLAP Data Mining Task Extraction of detailed and summary data Summaries, trends and forecasts Knowledge discovery of hidden patterns and insights Type of result Information Analysis Insight and Prediction Method Deduction (Ask the question, verify with data) Multidimensional data modeling, Aggregation, Statistics Induction (Build the model, apply it to new data, get the result) Example question Who purchased mutual funds in the last 3 years? What is the average income of mutual fund buyers by region by year? Who will buy a mutual fund in the next 6 months and why? Source: INSY 331
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TAXONOMY OF DATA MINING  METHODS Data Mining Methods Database Segmentation Predictive Modeling Decision Trees Neural Networks Naive Bayesian Branching criteria Deviation Detection Clustering K-Means Link Analysis Rule Associa tion Visualization Text Mining Semantic Maps
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AI System Expert Systems Problem Type Diagnostic or prescriptive Based On Strategies of experts Starting Information Expert’s know- how Comparison Intelligent Agents Specific and repetitive tasks One or more AI techniques Your preferences Genetic Algorithms Optimal solution Biological evolution Set of possible solutions Neural Networks Identification, classification, prediction Acceptable patterns The human brain
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STOCK PRICE FORECAST  USING NEURAL NETWORKS Inputs can be: Quantities derived from the time series itself  (e.g., moving averages, volatility estimates) Exogenous time series (e.g., interest rates, market  indices) Strength:  For a nonlinear autoregressive model which inputs consist of past values of time series, neural networks outperform linear methods (Compare the linear regression estimation process using Matlab!) Problems: Data-driven approach, therefore, overfitting can be a serious problem
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This note was uploaded on 03/23/2010 for the course INSY INSY434 taught by Professor Oh during the Winter '10 term at McGill.

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session12 - FINANCIALDATAMINING INSY430:Class#12 Feb 11th...

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