23 July 2023
on course website
Statistical and Machine Learning Methods for Analysis of Financial Data with Ronline course
1. Descriptive statistics and plots, statistical distributions, measures, dependence, tests, etc.
2. Linear and generalized linear models
3. Linear time series models 1: stationarity, autocorrelation, partial correlation, regression, etc.
4. Linear time series models 2: model fitting, selection and prediction
5. Moving average methods and random walk models
6. Volatility models (ARCH and GARCH) and prediction
7. Value at risk modelling and bootstrap methods
8. Classification methods of logistic regression, naïve Bayes, random forest models, etc. for credit rating, bankruptcy prediction, etc.
9. Supervised and unsupervised models and methods
10. Applications and cases studies
WIJAYATUNGA Priyantha, Umea University, Sweden
Master students as well as recent graduates and young professionals who wish to acquire new knowledge in specific areas.
- To give good understanding of different types of financial data that can be utilized for making financial and business decisions
- To provide the students with knowledge and hand-on experience on applicable statistical and machine learning models and methods for analyzing these different types of data to extract useful business knowledge
- To familiarize the students using free software tools to analyze financial data
Learning outcomes (students will have the ability to):
- Do predictions and classifications for financial purposes using related data
- Analyze and model financial Risk, value at risk and volatility
- Interpret results from statistical and machine learning models and methods
Participants can receive the following certificates:
- Certificate of Attendance: awarded to all participants who have completed the full online programme.
- Transcript of Records (with credits and grades): awarded only to participants who complete all course requirements and pass the final examination.
EUR 500: See our website for more information.
None.Register for this course
on course website