# EXC 3672 Analyses of Financial Data

## EXC 3672 Analyses of Financial Data

The course aims at taking advantage of the information contained in data for decision-making. We are all prone to different kind of biases such as overconfidence or recency. Quantitative empirical methods help us to discipline decision making process. More specifically, they allow to test the existence of a relation between variables (e.g., does inflation affect nominal interest rates?), quantify this relation (e.g., a one percent increase in inflation should lead to how much increase in nominal interest rate?) and forecast the evolution of variables (e.g, which interest rate should we expect in six month from now?).

The aims of this course are to:

- Introduce students to important empirical quantitative techniques that are used in finance and more generally in business.
- Make students able to apply them appropriately.
- Prepare students for subsequent course work in finance and business.

More specifically, on completion of the course the students acquired knowledge and skills should be as follows:

- Understand basic probability theory
- Understand basic measures of location, tendency and dispersion such as the expectation, median, variance, standard deviation, skewness, kurtosis.
- Understand what is meant by correlation and regression analysis - and the difference between them
- Understand what is meant by Ordinary Least Squares (OLS) - the estimation technique used in order to estimate our econometric model.
- Limits and assumptions of regression analysis
- Be familiar with basic R syntax.

On completion of the course students should be able to use software like R in order to:

- Perform basic data handling
- Estimate financial models formulated as linear regression models
- Test the statistical assumptions underlying OLS.

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This course introduces students to empirical techniques that are relevant for finance and business in general. More specifically, the outline of the course is as follows:

*Foundations for empirical methods in finance.*

- Probability basics
- When and why econometric can work
- Econometric basics

**Introduction to programming for data analysis**

- Data and computer basics: data in finance, what is a programming language
- Introduction to R: Basic data manipulation with R
- Introduction to programming with R: Control structures in R, Monte Carlo simulation

**Linear regression analysis**

- Simple regression analysis
- Regression analysis with multiple explanatory variables
- Limits and assumptions of regression analysis

Each topic will be accompanied by a hands-on practical application of an empirical finance topic.

The software package R will be an integral part of the coursework. R is a software program that has become a standard for data analysis inside academia and corporations, especially in the finance industry. It is an open source software available free of charge on internet. The use of R will introduce students to some of the basics of programming. Programming is a skill typically required in the financial industry.

If a student misses a class, it is her/his responsibility to obtain any information provided in class that is not included on the course homepage/itslearning or in the text book.

Computer-based tools: Google, Yahoo finance, Quandl, and itslearning.

This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course start.

At re-sit all exam components must, as a main rule, be retaken during next scheduled course.

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The specialisation requires two years of university education in Business Administration or equivalent.

EXC 2910 Mathematics or EXC 2904 Statistics

Exam category | Weight | Invigilation | Duration | Support materials | Grouping | Comment exam |
---|---|---|---|---|---|---|

Exam category:Submission Form of assessment:Written submission Exam code:EXC36721 Grading scale:Point scale Grading rules:Internal examiner Resit:All components must, as a main rule, be retaken during next scheduled course | 10 | No | 2 Week(s) | Group/Individual ( 1 - 4) | R-assignments. | |

Exam category:Submission Form of assessment:Written submission Exam code:EXC36721 Grading scale:Point scale Grading rules:Internal examiner Resit:All components must, as a main rule, be retaken during next scheduled course | 20 | No | 2 Week(s) | Group/Individual ( 1 - 4 ) | Home assignment | |

Exam category:Submission Form of assessment:Written submission Exam code:EXC36721 Grading scale:Point scale Grading rules:Internal and external examiner Resit:All components must, as a main rule, be retaken during next scheduled course | 70 | Yes | 3 Hour(s) | - BI-approved exam calculator
- Simple calculator
- Bilingual dictionary
| Individual |

Activity | Duration | Comment |
---|---|---|

Teaching | 39 Hour(s) | |

Other in classroom | 3 Hour(s) | Computer sessions. |

Student's own work with learning resources | 133 Hour(s) | Review of the slides every evening after the lecture. |

Group work / Assignments | 5 Hour(s) | |

Student's own work with learning resources | 20 Hour(s) |

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.