MFIN 6607 Financial Forecasting
This course aims to provide you with a solid theoretical foundation in time series techniques and their practical application in financial studies. You'll learn to analyze financial data, understand its time series characteristics (e.g., trends, seasonality, andstationarity), and forecast future data using econometric models such as Autoregressions (AR), Moving Average (MA), and ARIMA.You will gain advanced data analytics and coding skills in software R. STEM-designated
Course overview
- Department
- Finance
- School
- CSOM
- Credits
- 3
- Level
- Both
- Offered
- Periodically
Catalog details
Requirements fulfilled
Course evaluations
- Course rating
- 3.63 / 5 · Very Good
- Instructor rating
- 3.80 / 5 · Very Good
- Terms evaluated
- 6
| Term | Instructor | Course overall | Instructor overall | Well organized | Clear learning objectives | Intellectually challenging | Helpful assignments | Applicable beyond course | Attendance necessary | Weekly effort | Responses |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Spring 2026 | Cheng Jiang | 4.3 | 4.6 | 4.7 | 4.7 | 4.5 | 4.5 | 4.5 | 3.7 | 1–3 hrs/week | 33 of 39 |
| Spring 2025 | Cheng Jiang | 4.0 | 4.3 | 4.6 | 4.4 | 4.0 | 4.2 | 4.2 | 4.0 | 1–3 hrs/week | 23 of 26 |
| Spring 2024 | Cheng Jiang | 4.1 | 4.3 | 4.4 | 4.3 | 4.3 | 4.2 | 4.2 | 4.0 | 1–3 hrs/week | 19 of 24 |
| Spring 2023 | Richard Mc Gowan | 3.1 | 3.1 | 3.7 | 3.9 | 4.3 | 3.9 | 3.8 | 4.0 | 1–3 hrs/week | 34 of 39 |
| Fall 2022 | Richard Mc Gowan | 2.7 | 3.0 | 3.7 | 3.6 | 4.0 | 3.9 | 3.4 | 3.6 | 4–6 hrs/week | 26 of 31 |
| Spring 2022 | Haydar Evren | 4.2 | 3.9 | 4.4 | 4.7 | 4.6 | 4.4 | 4.7 | 4.1 | 4–6 hrs/week | 9 of 18 |
| Spring 2022 | Richard Mc Gowan | 3.3 | 3.5 | 3.8 | 4.0 | 3.7 | 4.2 | 3.8 | 3.8 | — | 19 of 25 |