Subject » BMEVISZM107
Algorithmic Forecasting of Stock Price Processes
Algoritmikus tőzsdei folyamat-előrejelzés
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Algoritmikus tőzsdei folyamat-előrejelzés
Algorithmic Forecasting of Stock Price Processes
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| Subject code | BMEVISZM107 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | vizsga | ||||||||||||
| Credits | 6 | ||||||||||||
| Subject coordinator |
Dr. Telcs András
contact:
telcs@szit.bme.hu
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| Responsible department |
Számítástudományi és Információelméleti Tanszék
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| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | — | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Programme
a Lectures
1. Estimate of the density function, L1 error.
2. Estimate of the density function, histogram.
3. Estimate of the density function, kernel estimates.
4. The regression problem, the regression function, partition method.
5. The regression problem, the regression function, kernel functions.
6. The regression problem, nearest neighbor estimate.
7. The regression problem, empirical error.
8. Pattern recognition, error probability.
9. Pattern recognition, Bayes decision, partitions.
10. Pattern recognition, kernel functions, nearest neighbor estimate.
11. Pattern recognition, empirical error.
12. Optimal portfolio strategies, fixed portfolios.
13. Optimal portfolio strategies, constantly rebalanced portfolios.
14. Optimal portfolio strategies, dynamically rebalanced and empirical portfolios.
b. Labs
1. Data acquisition, database design.
2. Data cleaning and cleansing.
3. Introduction to the software usage.
4. Exploratory data analysis, descriptive statistics.
5. Exploratory data analysis, presentation, graphics.
6. Time series analysis, overview, deterministic models.
7. Test.
8. Time series analysis, overview, application of ARIMA, ARCH, GARCH models.
9. The regression problem, the regression function, elementary, spline, NN.
10. The regression problem, the regression function, kernel functions – Gaussian.
11. The regression problem, nearest neighbor estimate implementation.
12. Test.
13. Log-optimal portfolio, algorithmic implementation static, rebalanced.
14. Log-optimal portfolio, algorithmic implementation combined experts.
a Objectives, learning outcomes and obtained knowledge
The course provide knowledge of methodology of modeling and predicting financial time series and the related portfolio strategies.
b. Acquired skills
By the competition of the course students are enabled to apply tools, techniques to model and forecast financial time series. Will be able to support bank, investment founds in the planning their investment strategies.
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
No learning outcomes recorded.
Skills
No learning outcomes recorded.
Attitudes
No learning outcomes recorded.
Autonomy and responsibility
No learning outcomes recorded.
Oktatási módszertan
Lectures and laboratory
Tanulástámogató anyagok
Online források
1. Száz János: Tőzsdei opciók vételre és eladásra, Tanszék Kft, 1999. ; 2. R. S. Tsay: Analysis of Financial Time Series, Wiley, 2nd edition, 2005. ; 3. L. Györfi, M. Kohler, A. Krzyzak, H. Walk: A Distribution-Free Theory of Nonparametric Regression, Springer-Verlag, 2002. ; 4. L. Györfi, G. Ottucsák: Empirical log-optimal portfolio selections: a survey, http://www.szit.bme.hu/~oti/portfolio/articles/tgyorfi.pdf 2007
Recommended preliminary knowledge for completing the subject
Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
Recommended: Mathematical statistics, Finance, Planning of financial investments
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
Recommended: Mathematical statistics, Finance, Planning of financial investments
General rules
Requirements:
a. Active involvement during lectures, condition of the course signature minimum, completion os lab exercises, 40% score of the two test average. Exam 70%, lab 30% in the final mark. The second test covers the first 11 lab and lecture material.
b. Oral exam
c. Pre exam subject of tutor's agreement
Additional possibilities:
One re-test during the recap-weak and oral presentation 30% of missing lab exercises are possible.
Assessment methods
In-term assessments
No detailed assessments provided.
Weight of in-term assessments
No weights provided.
Exam-period assessments
No detailed assessments provided.
Weight of exam elements
No weights provided.
Grade calculation
No grade thresholds provided.
Attendance requirements
No attendance requirements provided.
Rules for retake and resubmission
Not provided.
Short description
Not provided.
Detailed description
Not provided.
Recommended courses
Not provided.
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
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Requirements valid until:
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Curriculum placement
No curriculum placements recorded for this subject version.