Accessing the material is from Panda or http://www.cse.unsw.edu.au/~fethir/Paderborn2018/
All data for this course is available from
Schedule of activities:
Subject 01: Financial Market Data Analysis
Monday 22 October afternoon
- 01. Introduction to Financial Market Data: 1-2 Reading Lessons, 3 Lecture (IntroFinancialMarketData_Lecture.pptx). 4-5. Further reading.
- 02 Accessing market datasets. 1. Reading Material. 2. Lecture of financial instrument codes. 3. Lecture on accessing financial market datasets. Activities: accessing datasets.
- 03. Daily Market Measures: Lecture: Processing Daily Data. Activities: understanding daily measures.
- 04. Lecture: Processing Daily Data Using HGA. Reading "Building Daily Time Series Using HGA". Install and run the HGA tool. Exercise: "Building Daily Timeseries for German Companies". Activity (optional): Do "Volatility Exercises".
Tuesday morning 09:00 - 13:00
- 05. Intraday (High Frequency) Market Data: Lecture: High Frequency Market Data. Activities.
- 06. Computing Intraday Measures from TR Data. Lecture: Intraday data processing. Trying HGA Tool for Building Intraday series. Activity: Do Exercises.
Tuesday afternoon 14:00 - 17:00
Subject 02: Text Data Analysis
- 01. News in Finance. Lecture: News in Finance.
- 02. Accessing News Sources: Explore Guardian News API, Explore Yahoo News API.
- 03. Computing News Sentiment: Lecture: Sentiment Analysis. Using Aylien API, perform sentiment analysis using Python NLTK, Learn about TRNA (if there is time).
Wednesday morning 09:15 - 13:00
Subject 03: Financial Market Impact Analysis
- 01. Performing a Daily Event Study: Lecture: Performing an Event Study. Event Study Workbench Manual and exercise. Perform News Impact Using Daily Data Exercise.
- 02. Computing Intraday Impact Measures: Lecture. Tutorial: Using HGA to compute price jumps. Perform exercise. Using HGA to compute liquidity measure. Activity: Perform exercise.
- 03. Conducting Intraday News Impact Analysis: Major assignment. Ask tutor check your work when completed.
Wednesday afternoon 14:15 - Open
Subject 04. Analysing Market Information in R
- Lecture on Using R for Market Data Analysis followed by a range of self-learn activities related to R
- Finalising teams. Project Topic Presentations. Teams allocations to topics.
- Working on Project
Thursday 9:15 - 12:00 and 2:00 - 4:00: Open Consultation about the Project. If needed, drop at anytime in Office Q3 140 for help.
For any help, contact Fethi Rabhi (email@example.com)