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# QUANTITATIVE METHODS FOR BUSINESS DATA ANALYSIS

Attività formativa integrata
Codice dell'attività formativa:
87173

## Scheda dell'insegnamento

Per studenti immatricolati al 1° anno a.a.:
2020/2021
Insegnamento (nome in italiano):
QUANTITATIVE METHODS FOR BUSINESS DATA ANALYSIS
Insegnamento (nome in inglese):
QUANTITATIVE METHODS FOR BUSINESS DATA ANALYSIS
Tipo di insegnamento:
Opzionale
Anno di corso:
3
2022/2023
Crediti:
6
Moduli
• 3 crediti
Docente/i:
• 3 crediti
Docente/i:

## Altre informazioni sull'insegnamento

Modalità di erogazione:
Didattica Convenzionale
Lingua:
Inglese
Ciclo:
Primo Semestre
Obbligo di frequenza:
No
Ore di attività frontale:
48
Testi e materiali didattici:

#### Course Syllabus

Prerequisites

No prerequisites, but a base knowledge of statistics is preferable.

Educational goals

The course introduces the main statistical, mathematical, and computational tools used for the analysis of business and financial data. We start from an introduction about the data collection process and with the evaluation of data quality. Then, we focus on the main analytical tools for dealing with cross-sectional data (inference, one-way ANOVA, multiple regression analysis). In the following part, we introduce the most common models to analyse time series data, with a particular focus on financial markets (ARMA, ARCH and GARCH models).
Throughout the whole course, we will emphasize the practical implementation of the introduced techniques by means of some of the most common software (SAS and R). In our laboratories we will work on real datasets highlighting how to interpret the obtained output.

Course content

MODULE 1

• Data collection: survey methods, sample selection, questionnaire design.
• Data quality: how to detect and treat data issues.
• Base inference: main statistical distributions and base of hypothesis testing, mean and proportion testing, testing group differences.
• One-way ANOVA for comparing group means and for experimental studies.
• Multiple regression analysis: setting and running the analysis, interpreting the output; introduction of logistic regression.

MODULE 2

• Introduction to financial markets: stocks, bonds and other financial instruments.
• Time series analysis: univariate and multivariate random variables, random walk processes, efficient market hypothesis.
• Random processes for stock prices: ARMA processes for non-stationary time series, GARCH processes for heteroskedastic time series, statistical tests for stationarity and heteroskedasticity.
• Risk measures: conditional and unconditional Value at Risk (VaR) and Conditional Value at Risk (CVaR), risk backtesting.

Teaching methods

The course consists of traditional lectures and laboratory sessions where the introduced techniques are practically implemented and interpreted using a software.

Assessment and Evaluation

The exam is made by two parts: one for each module. Each exam awards up to 30 points.
The students have to reach a positive (i.e., at least 18) score for both modules. The final full course evaluation is computed as simple average of the two scores.
For both modules, the exam is written and includes an evaluation about the ability acquired by the student in working with the introduced software. There are no differences for attending and for non-attending students.

Further information

The course 87173 (6 university credits) is made by two Modules (each one is worth 3 university credits).

"In case of provisions of the competent authority on containment and management of epidemiological emergency, the teaching may be subject to changes from what is declared in the syllabus in order to make the course and examinations in line with the regulations."