BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| ECONOMICS AND FINANCE | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
Course Introduction and Application Information
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| IF4316 | Advance Quatitative Analysis using Bloomberg | Fall | 3 | 0 | 3 | 6 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Basic information
| Language of instruction: | Turkish |
| Type of course: | Departmental Elective |
| Course Level: | Bachelor’s Degree (First Cycle) |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Assist. Prof. BAHAR KÖSEOĞLU |
| Course Objectives: | The course provides coverage of important topics in modern Quantitative Finance and Risk Management at the advanced undergraduate level. It is intended for the 4th-year undergraduate students of BAU International Fnance, Economics and Economics Finance. Particular attention is given to the topics such as the Efficient Market Hypothesis, financial markets micro-structure and types of arbitrage, general principles of modelling the price dynamics of financial assets, market risk and other types of financial risks, Value-at-Risk (VaR) approach and applications, modelling of extreme market events, VaR analysis for financial derivatives using bloomberg. The topics covered in this course will enable the students to develop the theoretical knowledge and practical skills required for successful working with multiple types of risks in modern financial markets, both Turkish and international markets. Bloomberg terminal access will be required for any students at BAU Financial Research Center. |
Learning Outcomes
|
The students who have succeeded in this course; At the end of the course, you will be able to: Students, who complete the course successfully, will be able to: 1. Understand the fundementals of the financial models 2. Understand the importance of macroeconomic economic indicators. 4.Understand and apply various approaches for managing credit risk in a portfolio setting 5.Employ qualitative analysis methods to solve busines and finance problems. 6.Synthesize information from multiple disciplines in order to solve financial problems. 7.Understand the rationale, markets and risks of structured finance. 8.Understand the models for future markets and why futures are used, the different uses futures 9.Understand the option markets, option types the transaction idea, 10. How to analyse government securities on Bloomberg and understand their role within the financial system What benchmark bonds are and how their performance can indicate overall market health 11.Understand the exchange rate ratio, analyse the quation strategy, developing a strategy on Exchange rate 12. Analyze the financial statements of a firm. 13.Identify market cap, financial leverage and operating leverage. 14.Understand the pricing of the securities 15. Navigate the available data using the Bloomberg terminal |
Course Content
| Teaching methods of the course are “Lecture, Individual Study, Reading, Discussion, Problem Solving, Case Study, Group Work, Project Preparation, Technology Assisted Learning” |
Weekly Detailed Course Contents
| Week | Subject | Related Preparation |
| 1) | INTRODUCTION AND MOTIVATION AND EXPECTATIONS R Software | |
| 2) | What is quantitative analysis? Types of data, definition of basic statistical concepts, defining variables to R | |
| 3) | Introduction to Basic R for Finance: Logic, Analysis Methods and Packages | |
| 4) | Introduction to Financial analysis using Bloomberg firm data in R | |
| 5) | Technical Analysis using R: Data from Bloomberg | |
| 6) | Technical Analysis using R: Data from Bloomberg | |
| 7) | REGRESSION: T-statistics hypothesis testing, estimation and interpretation of R- square, R hedge model application | |
| 8) | MULTIVARIABLE REGRESSION: Definition of the model and R applications | |
| 9) | TIME-SERIES: Properties of stationary time-series, definition of stationary tests, R ADF test application, definition and R application of autocorrelation and partial autocorrelation, definition of autoregressive model | |
| 10) | AUTOREGRESSIVE MOVING AVERAGE MODEL: Definition of the model, optimum lag selection, Akeike and Bayesian Information Criterias, R price forecasting application | |
| 11) | AUTOREGRESSIVE MOVING AVERAGE MODEL: Model development and price forecasting example on R | |
| 12) | Stock Valuation in R: Using data from Bloomberg and Yahoofinance | |
| 13) | Monte Carlo Simulation in R: Using data from Bloomberg | |
| 14) | Option valuation in R: Using data from Bloomberg and Review |
Sources
| Course Notes / Textbooks: | |
| References: | Books: • Chris, Brooks, Introductory Econometrics for Finance, Second Edition • Clifford S.Ang, Analyzing Financial Data and Implementing Financial Models Using R, Springer Suppmentary Textbooks: Patton, A. (2007). Quantitative Finance, UoL Study Guide. (AP) Diebold, F.X. Elements of Forecasting. (Thomson South-Western, Canada, 2006) fourth edition. Wilmott, P. Paul Wilmott on Quantitative Finance (selected chapters). 2nd ed. Wiley, 2006. Articles will be submitted by instructer . Other required readings will be uploaded for students via Itslearning. Lecture notes: Lecture notes will be given by the instructer. Taking notes during the lectures is students’ responsibility. • Bloomberg Library, Lecture notes, Bloomberg Terminal Sources will be used. |
Evaluation System
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | 1 | % 10 |
| Homework Assignments | 2 | % 50 |
| Final | 1 | % 40 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| Total | % 100 | |
ECTS / Workload Table
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 13 | 3 | 39 |
| Study Hours Out of Class | 14 | 5 | 70 |
| Homework Assignments | 13 | 3 | 39 |
| Final | 1 | 2 | 2 |
| Total Workload | 150 | ||
Contribution of Learning Outcomes to Programme Outcomes
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Build up a body of knowledge in mathematics and statistics, to use them, to understand how the mechanism of economy –both at micro and macro levels – works. | 4 |
| 2) | Understand the common as well as distinctive characters of the markets, industries, market regulations and policies. | 3 |
| 3) | Developing the ability to explain global economic events by understanding different economic perspectives. | 2 |
| 4) | Acquiring the ability to analyze the impact of politics on the economy and vice versa. | 4 |
| 5) | Gaining the competence to propose solutions to economic problems and evaluate opposing policy recommendations. | 1 |
| 6) | Understanding and evaluating new economic developments and approaches. | 1 |
| 7) | Developing the ability to convey economic news and developments through written, oral, and graphical communication. | 4 |
| 8) | Gaining the competence to develop structured solutions for economic issues. | 2 |
| 9) | Acquiring the capability to present findings that support economic assumptions using numerical and verbal skills. | |
| 10) | Gaining the competence to follow economic information and communicate with colleagues using a foreign language. | 3 |