Course Details

FINANCIAL DATA MODELING

DSBE519

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
2DSBE519FINANCIAL DATA MODELING3+0+037,5

Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program DATA SCIENCE
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Introducing portfolio modeling.
Providing working knowledge of applications of R libraries and MS Excel into
portfolio models.
Improving students’ understanding of risk and hedging.
Introducing construction of hedging strategies with options.
Course Content The course is aimed at presenting financial models that are widely used by finance
professionals and showing how these models can be implemented by using Excel and
R. Subjects such as modern portfolio theory, capital asset pricing model, options
trading, interest rate risk, stochastic finance and risk measurement are examined.
Thus, the course is designed to encourage students to analyze financial asset
dynamics and to model associated risks through applied models by using different
softwares. After the course is completed, students are expected to be able to structure
their own portfolios, measure risks and develop their own hedging strategies.
Course Methods and Techniques -
Prerequisites and co-requisities None
Course Coordinator Associate Prof.Dr. Umut Türk umut.turk@agu.edu.tr
Name of Lecturers None
Assistants Research Assist. Semih Peker semih.peker@agu.edu.tr
Work Placement(s) No

Recommended or Required Reading
Resources -
-


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Yarıyıl İçi Çalışmalarının Başarı Notunun Katkısı 1 % 40
Final examination 1 % 60
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Belirsiz 5 30 150
Sözlü Sınav 1 15 15
Derse Devam 1 30 30
Final Sınavı 1 30 30
Total Work Load   Number of ECTS Credits 7,5 225

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Explain the portfolio optimization with factor models.
2 Measure risks of assets.
3 Determine efficient evaluation criteria for performances of different portfolios.
4 Explain the mechanics of derivative products that underly other financial assets.
5 Structure financial data appropriately by identifying its specifications for analysis.
6 Evaluate the outcomes of financial econometrics tools provided by computer softwares.


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Week 1: Introduction to Financial Data and Modeling Tools • Types of financial data (time series, cross-section, panel) • Data sources (Yahoo Finance, Quandl, Bloomberg API) • Overview of R and Excel environments • Assignment: Download, clean, and visualize price data of stocks - -
2 Week 2: Basics of Portfolio Theory • Mean-variance optimization • Efficient frontier and investor preferences • Lab: Implement a basic portfolio in Excel and R • Reading: Markowitz (1952) model and extensions - -
3 Week 3: Capital Asset Pricing Model (CAPM) • Derivation and assumptions of CAPM • Beta estimation and alpha interpretation • Lab: Regressions in R and Excel (CAPM-based performance) • Assignment: Apply CAPM to real asset data - -
4 Week 4: Multifactor Models and Fama-French Extensions • Arbitrage Pricing Theory (APT) • Fama-French 3-factor and 5-factor models • Factor loadings estimation in R • Group work: Compare factor model fit for selected portfolios - -
5 Week 5: Portfolio Performance Evaluation • Sharpe, Treynor, Jensen’s alpha, information ratio • Active vs passive management • Lab: Performance comparison in R • Assignment: Construct a dashboard of metrics in Excel - -
6 Week 6: Risk Measurement: Volatility and Value-at-Risk (VaR) • Historical and parametric VaR • Conditional VaR (CVaR) • Rolling window volatility, standard deviation, GARCH introduction • Lab: VaR estimation in Excel and R - -
7 Week 7: Introduction to Derivatives and Options • Options terminology: call, put, moneyness, expiration • Payoff diagrams, intrinsic vs. time value • Greeks and sensitivities • In-class activity: Option payoff plotting with Excel - -
8 Week 8: Midterm Exam + Case Review • Midterm Exam: Closed-book or take-home case • Optional Lab: Recap and Q&A on portfolio modeling & factor regressions - -
9 Week 9: Options Pricing Models • Binomial trees and Black-Scholes model • Pricing assumptions and limitations • Lab: Implement Black-Scholes in Excel and R • Assignment: Option valuation using market data - -
10 Week 10: Interest Rate Risk and Fixed Income Modeling • Bond math: duration, convexity • Yield curves and term structure • Excel Lab: Calculate duration, convexity, and simulate rate changes - -
11 Week 11: Stochastic Processes in Finance • Brownian motion, geometric Brownian motion • Ito’s Lemma (intuitively) and stochastic differential equations • R Lab: Simulate asset paths and returns • Reading: Chapter from Baxter & Rennie or similar - -
12 Week 12: Hedging Strategies and Financial Engineering • Delta and gamma hedging • Portfolio insurance and protective puts • Building dynamic hedge portfolios in Excel • Case: Hedging a portfolio with index options - -
13 Week 13: Introduction to Risk Management Frameworks • Risk aggregation, stress testing, scenario analysis • Basel framework and regulatory context • R Lab: Stress testing a hypothetical portfolio - -
14 Week 14: Student Project Presentations + Course Wrap-Up • Final project presentations: Each team presents their portfolio, risk models, and hedging strategy • Feedback and discussion • Review of major concepts + advice on industry applications and certifications (e.g., CFA, FRM) - -


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10
All 5 3 4 4 3 4 5 2 5 5
C1 4 1 3 4 4 2 5 5 4
C2 4 3 5 3 5 3 5 1 5 5
C3 4 4 4 5 5 3 5 4 5 5
C4 5 5 3 3 3 4 5 2 5 5
C5 5 5 5 5 4 4 1 5 3
C6 5 5 5 5 4 5 2 5 5

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant


https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=75108&lang=en
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