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 methods in the time domain (moving average, exponential smoothing)
4. Understand the structural decomposition in components of time series data
5. The use of classic time series models for forecasting
6. The use of the Kalman Filters and state space methods for modelling time series (If time allows)

- Essential Prerequisites

Mathematics:
Matrix Algebra
Series and Sequences

Statistics and Probability:
Random Variables and Distribution Theory
Conditional and Unconditional Expectation
Multivariate Linear Regression

- Preferable Prerequisites

Mathematics:
Differential Calculus

Statistics and Probability:
Point and Interval Estimation
Maximum Likelihood Estimation
Hypothesis Testing

1. Introduction to signal extraction and Forecasting
2. Forecasts Evaluation
3. Basic smoothing and filtering methods.
4. Time Series Regression and Distributed Lags Models.
5. Time series models: ARIMA models.
6. State Space Models and the Kalman Filter. (If time allows)

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
Harvey, A. C. (1990): Forecasting, Structural Time Series Models and the Kalman Filter. https://books.google.it/books/about/Forecasting_Structural_Time_Series_Model.html?id=Kc6tnRHBwLcC&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) for the students sitting the first exam session of the year, that examines students' capability in developing a solution to a problem without limiting themselves to the information given in class. As a consequence the course grade will be based on the homework and the final examination for the first exam session in the year, and only on a final examination for all the other exam sessions. The final grade for the first exam session will be determined using the following weights: 30\% Homework, 70\% final written exam.

written

The final grade is based on a written exam. The following grading system is applied:
The grades are based on a 30 points scale. The points from 18 to 20 are allocated for having correctly answered or solved fully or partially from 60% to 69% of the questions in the exam. The points from 21 to 23 are allocated for having correctly answered or solved fully or partially from 70% to 79% of the questions in the exam. The points from 24 to 26 are allocated for having correctly answered or solved fully or partially from 80% to 89% of the questions in the exam. The points from 27 to 30 are allocated for having correctly answered or solved fully or partially from 90% to 100% of the questions in the exam.

Series of lectures on the various topics

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.