Our course "Data Mining in Finance and Financial Forecasting" provides an introduction into data mining methods and demonstrates how data mining can be employed in various tasks in the financial sector by banks, investment funds, hedge funds, insurance companies, and other financial institutions and companies in the finance sector as well as sophisticated private investors and traders.

Financial data often comes in the form of time series, e.g. stock market prices, commodity prices, utility prices, or currency exchange rates observed over time. The analysis of financial time series is often based on indicators derived from such uni- or multi-variate time series and on linear or non-linear combinations of such indicators. Data mining techniques can be used to analyse financial time series data, to find patterns, to detect anomalies and outliers, to recognize situations of chance and risk, to detect temporal changes in the correlation patterns and structures, to predict future demand, prices, and rates, to determine the most successful indicators, to optimally combine such indicators to achieve strong predictive power. Hence data mining can support analysts, investors, and traders in their decisions when trading stocks, options, commodities, utilities, or currencies.

This course describes data mining techniques for financial time series data including data pre-processing methods, time window based approaches and the automated optimization of the time window sizes, indicator or feature weighting and selection methods, and statistical and machine learning techniques to automatically find weighted linear and non-linear combination of an optimized set of indicators or features when automatically learning a prediction or forecasting model (trade decision (support) system). The topics include necessary preprocessing steps for numerical data transformations, an introduction into linear and non-linear statistical regression methods, neural networks, and support vector machines (SVM), and a discussion of validation methods in order to measure the goodness of the predictions, i.e. how to thoroughly evaluate such models on historic data (backtesting). These methods are especially useful for numerical predictions from series data as they often occur in financial markets.

In addition to time series data, transactional data is also very frequent in the financial sector. Transactional and accounting data can be analyzed and leads to models for detecting patterns and irregularities, improper trading or accounting practices, dubios transactions, potential fraud, money laundering, and other undesired activities as well as for transaction monitoring, credit scoring,´ credit default prediction, risk assessment and minimization, finding risk factors, and portfolio management.

This course also describes the practical steps necessary to create such models with the open-source data mining software RapidMiner. The large amount of practical examples and exercises enables the participants to design appropriate data mining processes and apply the gained knowledge to their data mining problems and to solve them efficiently and successfully.

Details

• Course ID: 1202
• Number of days: 2 days
• Location: Dortmund, Germany
• Target audience: financial analysts, investors, traders, bank accountants, employees of accounting, controlling, auditing, or internal revision departments, auditors, decision makers, fund managers, software developers
• Previous knowledge: basic knowledge of computer programs
• Methods: lectures, discussions, individual and group work, exercises on realistic data. Participants may introduce own work and project specific questions in order to find particular solutions together with the trainer and other participants.
• Content: this course includes a compact introduction into the foundations of data mining and statistical learning for predictions and forecasting as well as into the software RapidMiner. It addresses beginners and intermediate learners. Topics of this course are
  • Introduction to data mining methods for data cleaning, finding reliable indicators and key factors, feature selection, pattern and outlier detection, anomaly detection, automated classification, regression, forecasting, predictive analytics
  • Basics of cost-sensitive machine learning and data mining with RapidMiner
  • Definition of prediction / forecasting of series data
  • Statistical Learning: Regression Methods, Neural Networks, Support Vector Machines (SVM)
  • Preprocessing for series data: from series to data points
  • Forecasting with regression methods
  • Influence of the prediction horizon
  • Validation of forecasting: introduction into performance criteria, cross validation, bootstrapping, back testing
  • Visualization of series data and predictions: high-dimensional data visualizations
  • Identifying changes – detecting opportunities and risks
  • Detecting patterns, irregularities, and inidcators for identifying and preventing improper accounting practices, dubious transactions, potential fraud, money laundering, or other undesired activities
  • Credit scoring, credit default prediction, and risk factors for risk assessment, mitigation, and minimization
  • Predicting future demand, prices, and sales; quantitative stock market predictions, forecasting of foreign currency exchange rates or commodity prices
  • Mining structured and unstructured data, i.e. database tables as well as e.g. textual information using statistical analysis, data and text mining – an outlook to sentiment classification based on e.g. news texts or web blogs
  Extensive exercises on different data sets will be performed for all topics. For advanced topics like text mining and web mining including sentiment analysis, introductions and application examples are provided here, but for an in-depth coverage and understanding, special courses on these topics are offered and recommended.

Prices

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