PREDICTIVE BUSINESS AND FINANCE

This course is one of the teaching activities of the Master's Degree Programme in "Data Analytics for Business and Society". In tandem with the educational objectives of this course, students will be exposed to data analytic techniques and methods for handling economic-financial prediction related problems. Precisely, this activity seeks to present the main mathematical and statistical tools necessary for forecasting.

1. Visualize time series data
2. Specify appropriate metrics to assess forecasting models
3. Introduction to basic filtering in the time domain (moving average, exponential smoothing)
4. Understand the structural decomposition in components of time series data
5. The use of time series models for forecasting

Mathematical Tools:
Matrix Algebra
Differential Calculus
Integral Calculus

Statistical Tools:
Random Variables and Distribution Theory
Point and Interval Estimation
Hypothesis Testing
Least Squares and Standard Linear Model

1. Introduction to signal extraction.
2. Basic smoothing and filtering methods.
3. Regression models
4. Time series models: ARIMA models.
5. Dynamic regression models
6. Advanced forecasting methods

Hyndman, R. J. and G. Athanasopoulos (2021): Forecasting: Principles and Practice (3rd Edition). https://otexts.com/fpp3/
Shumway, R. H. and Stoffer, D. S. (2017): Time Series Analysis and Its Applications, With R Examples. https://link.springer.com/book/10.1007/978-3-319-52452-8
Bee Dagum, E. and Bianconcini, S. (2016): Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation. (Ch.2-5) https://link.springer.com/book/10.1007/978-3-319-31822-6
Harvey, A. C. (1993): Time Series Models (2nd Edition). https://books.google.ge/books/about/Time_Series_Models.html?id=s1ScQgAACAAJ&redir_esc=y

By way of evaluation, a main examinations covering both the theory and application of the concepts developed in class will be conducted. However, I will also propose an end-of-course project (homework) that examines students' capability in developing a solution to a problem without limiting themselves to the information given in class. The course grade will be based on the homework and a the final examination. The final grade will be determined using the following weights: 30\% Homework, 70\% final written exam.

Series of lectures on the various topics

English

The course is carried out in collaboration with the extended partnership GRINS - Growing Resilient, INclusive and Sustainable, code PE0000018, CUP H73C22000930001, public notice no. 341/2022 of the National Recovery and Resilience Plan ("NRRP"), Mission 4 - Component 2 - Investment 1.3, funded by the European Union - NextGenerationEU.
As part of the course, meetings with companies’ testimonials involved in the project may be offered, focusing on the development of practical knowledge in the subject matter, as well as the results of the project itself.
This course covers topics related to Spoke 4 Sustainable Finance - Work Package No. 3.

written

This subject deals with topics related to the macro-area "Climate change and energy" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development