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 the Analysis of Time Series Data
2. Time Series Decomposition and Signal Extraction
3. Simple Forecasting Methods and Model Evaluation
4. Exponential Smoothing Filters
5. Time Series Linear Regression
6. ARIMA models for Time Series
7. 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
Harvey, A. C. (1993): Time Series Models (2nd Edition). https://books.google.ge/books/about/Time_Series_Models.html?id=s1ScQgAACAAJ&redir_esc=y
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. (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

Assessment will consist of a main examination covering both the theoretical aspects and the applications of the concepts developed during the course. Students taking the first examination session of the academic year will also be required to complete a final project (take-home assignment), designed to assess their ability to develop a solution to a problem without relying solely on the information provided in class. Accordingly, for the first examination session of the academic year only, the final course grade will be based on both the take-home assignment and the final written examination. For all subsequent examination sessions, the grade will be determined exclusively by the final examination. For the first examination session, the final grade will be calculated using the following weights: 30% take-home assignment and 70% final written examination.

written

The instructor is responsible for ensuring the authenticity and originality of all examinations and coursework. In cases of suspected academic misconduct, an additional on-site assessment may be required during the exams, which may differ from the standard format.

For marking the 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.