Course syllabus ENC-AFD - Financial Data Analysis (FBE - SS 2019/2020)


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Course code:
ENC-AFD
Course title in Czech:
Financial Data Analysis
Course title in English:
Financial Data Analysis
Semester:
SS 2019/2020
Mode of completion and number of credits:
Exam (5 credits)
Mode of delivery and timetabled classes:
full-time, 2/2 (hours of lectures per week / hours of seminars per week)
Level of course:
master; master continuing
Course type: required
Type of delivery:
usual, consulting
Mode of delivery for our mobility students abroad:
-- item not defined --
Language of instruction:
Czech
Course supervisor:
doc. Ing. Svatopluk Kapounek, Ph.D.
Course supervising department: Department of Finance (FBE)
Faculty: Faculty of Business and Economics
Teachers:
doc. Ing. Svatopluk Kapounek, Ph.D. (examiner, instructor, lecturer, supervisor, tutor)
Prerequisites:
Final Bachelor Exam
 
Timetable in this semester:
Day
From-till
Room
Teacher
Entry
FrequencyCapacity
Wednesday11.00-11.50
Q15
Lecture
Every week
48
Wednesday
13.00-15.50
Q24
Seminar
Every week
24
Wednesday
16.00-18.50
Q24
Seminar
Every week24
 
Aim of the course and learning outcomes:
The course interacts theoretical background with the empirical analyses of real data. Students receive skills to process real data, to employ econometrics methods and interpret results of the empirical analyses. The graduates will be a high skilled financial analyst or consultant of the advisory and consultancy companies, or financial market analyst. The skills are also very useful for the students which plan to emloy empirical analyses in their theses.
 
Course content:
1.
Data sources, data pre-processing and processing, data transformation and its intepretation, interest rate specifics (allowance 1/3)
2.
Identification of capital markets characteristics, graphical analyses, efficient market hypothesis testing, arbitrage identification (allowance 1/3)
3.Basic techniques of prediction (allowance 1/3)
4.
Identification and impact of capital market volatility, capital markets prediction employing conditional volatility (allowance 1/3)
5.
Employing behavioural factors in financial market analyses (allowance 1/3)
6.
Uncertainty in using fat data (allowance 1/3)
7.
Pre-processing and processing big data from companies and financial institutions (allowance 1/3)
8.
Financial analysis of the company employing big data (allowance 1/3)
9.
Financial analysis of banks employing big data (allowance 1/3)
10.Economic policy impact modelling (allowance 1/3)
11.Disequilibrium identification and analysis (allowance 0/0)
12.
Meta-analysis in financial economics (allowance 0/0)
Learning activities and teaching methods:
Type of teaching method
Daily attendance
lecture
12 h
practice
36 h
consultation
12 h
project work
20 h
workshop
20 h
preparation for exam
40 h
Total140 h
 
Assessment methods:
Oral exam verifying knowledges to analyze real data.
 
Assessment criteria ratio:
Requirement type
Daily attendance
Total
0 %
 
Recomended reading and other learning resources:
Basic:
CAMERON, A C. -- TRIVEDI, P K. Microeconometrics : methods and applications. Cambridge: Cambridge University Press, 2005. 1034 p. ISBN 0-521-84805-9.
CAMERON, A C. -- TRIVEDI, P K. Microeconometrics using Stata. College Station, Tex.: Stata Press, 706 p. ISBN 978-1-59718-073-3.
Financial econometrics using Stata. 272 p. ISBN 9781597182140.

Course listed in study plans for this semester:
Track SNAF Applied Finance, full-time form, initial period WS 2019/2020
Track SNAF Applied Finance, full-time form, initial period SS 2019/2020
 
Course listed in previous semesters:
 (and older)
Teaching place: Brno


Last modification made by Ing. Jiří Gruber on 12/24/2019.

Type of output: