Introduction to financial mathematics - BMEGT35M100

The objective of this course is to introduce students to the fundamental quantitative methods required to support financial and economic decision-making. The course covers the basics of statistical inference, hypothesis testing, simple and multiple linear regression, time series analysis, as well as the financial applications of machine learning and big data. A key goal of the course is for students not only to understand the theoretical background of these methods, but also to be able to interpret their results in a financial context, perform simple empirical analyses, and apply them to data-driven decision support tasks.

1. The audience possesses the skills of problem recognition, formulation, and resolution, as well as the modern, theoretically rigorous mathematical-statistical, econometric, and modelling methods of information collection and processing, and are also aware of their limitations. Is familiar with the planning and management regulations, as well as the professional and ethical standards, of the enterprise, economic organisation, and project. (EFMD-ILO connections: Finance - Knowledge (KKK) - T3 - ILO 5: Digital proficiency)
2. Demonstrates a comprehensive understanding of the fundamental concepts, methods, and application possibilities of hypothesis testing, simple and multiple linear regression, time series analysis, as well as machine learning and Big Data analytics.

1. Formulates independent new conclusions, original ideas, and solutions, is capable of applying sophisticated analytical and modelling methods, developing strategies aimed at solving complex problems, making decisions in a changing domestic and international environment, as well as within organisational culture. (EFMD-ILO connections: Finance - Skills (KKK) - K1 - ILO 5: Digital proficiency)
2. Is able to provide high quality analysis, decision preparation and decision-making for companies. (EFMD-ILO connections: Finance - Skills (KKK) - K4 - ILO 1: Analytical thinking)

1. Demonstrates creativity and a proactive approach to identifying and solving problems in their work. (EFMD-ILO connections: Finance - Attitude (KKK) - A6 - ILO 5: Digital proficiency)
2. Is open to learning about new developments in the field of finance and proactive in applying them. (EFMD-ILO connections: Finance - Attitude (Specific competences of the educational programme) - SpecA1 - ILO 1: Analytical thinking)

1. Is open to accepting well-founded constructive criticism,
2. collaborates with fellow students in solving tasks throughout the learning process,
3. is capable of independent decision-making,
4. is capable of making well-informed and balanced judgements in financial decision-making.

Lectures, interactive computational examples, independent problem-solving, as well as the application of digital educational tools and professional software.

• Az oktató által készített előadásdiák és órai jegyzetek. / Lecture slides and class notes prepared by the instructor.
• Az oktató által összeállított gyakorló feladatok és mintapéldák. / Practice problems and sample exercises compiled by the instructor.
• Az órákon bemutatott számítási példák. / Computational examples presented during classes.
• A tantárgy Moodle-felületén közzétett segédanyagok, képlet gyűjtemény. / Supplementary materials and formula sheets published on the course Moodle platform.

Assessment of the learning outcomes described under 2.2. is based on two written midterm tests.

EFMD ILO connection: Two written midterm tests: Supervised application of financial models, computational tasks, and analytical methods. (Finance ILO 1) Online quiz: Formative testing of digital financial tools and ICT skills. (Finance ILO 5)

• 1. written midterm test: 50
• 2. written midterm test: 50
• Total: 100

The two written mid-term tests can be retaken or upgraded together during the retake week; however, in this case, the latest grade will apply. There is no opportunity for a second retake.

Consulted with the Faculty Student Representative Committee, approved by the Vice Dean for Education, valid from: 04.05.2026.

Hypothesis Testing Simple Linear Regression Multiple Linear Regression Time-Series Analysis Fundamentals of Machine Learning Big Data Analytics and Applications