Academic degree: Master of Economics
Code of the educational program: 7M04106-Finance
For admission, if the profile of the educational program of the master's program coincides with the program of higher education, the results of the previous level of study are recognized automatically; in the event of a mismatch between the profile of the educational program of the master's program and the educational program of higher education, prerequisites for mastering are set for the master's student.
The master's program of the specialty 7M04106-Finance provides training in the scientific and pedagogical direction, where the training period is 2 years.
The unique master's program in the scientific and pedagogical direction is implemented along two educational trajectories for the preparation of undergraduates:
The structure of the master's educational program is built on a modular basis and includes 4 blocks:
General module: Higher Education Pedagogy, Management Psychology, Professional Foreign Language, Pedagogical Practice, Business Analytics: Methods, Technologies and Visualization or Analysis of Company Economic Activities (optional subjects), Project Management Practice or Business Studies (optional subjects).
The research module includes the study of the History and Philosophy of Science, Methodology of Scientific Research or Organization and Planning of Scientific Research (elective courses), Predictive Analytics and Data Modeling or Business Law (elective courses), Research work of a master student, including an internship and implementation master's thesis, also Research practice.
The professional module includes the following courses: Advanced Financial Management, Investments (Fixed Income and Equity), Introduction to Python Programming and Data Preprocessing, Financial Markets and Products, Fundamentals of Risk Management, Mathematical Modeling and Econometric Analysis, Financial Instruments Management, Business Inteligence Technologies in management, economics and finance, Introduction to fintech, Machine learning.
Final certification as a defense of a master's thesis.
Key disciplines
Financial management (advanced course): the goal is to form students' knowledge of the basics of financial management, skills and competencies, the main methods and criteria that determine the effectiveness of managing financial, operational and investment activities and their application in companies in the real sector of the economy and to obtain the following competencies: interpret the content financial statements of the company and analyze the financial condition; apply the effect of financial and operational leverage in making managerial and financial decisions; apply the mathematical foundations of financial management; know and apply the basics of assessing the market value of the company; the ability to make managerial decisions based on the analysis of key financial indicators, risk and return models of the company and financial portfolio, cost and structure of capital, company value; the ability to develop a long-term financial plan of the company for an investment project, as well as evaluate the results and effectiveness of investment projects using various criteria and methods.
Investments (fixed income and capital) (CFA / FRM): the goal is to form knowledge and skills in building and using the basics of the methodology, methods and methods of investment management in terms of assessing their effectiveness, assessing investment risks and financing, fixed income securities. The course includes the study of the fixed income securities market: issuance, trading and financing; corporate bonds. Introduction to Fixed Income Valuation; asset-backed securities. Mortgages and mortgage-backed securities. Understanding the risk and return of fixed income securities. Prices, discount factors and arbitrage. Spot, forward and pair rates. Returns, spreads and returns. Single-factor indicators of risk and hedging. An introduction to alternative investments. The course will allow undergraduates to: evaluate the effectiveness of investments, manage investment risks, calculate the amount of payments for various financing methods, use the mechanism of influence of the choice of financing schemes.
Introduction to programming and data preprocessing in Python: the goal is to develop programming skills in the Python environment. The study of this discipline will allow you to obtain the following competencies: know the basics of programming in the Python language; be able to apply the acquired knowledge in solving practical problems; create programs using the Python programming language; apply structured programming technology to solve problems; apply object-oriented programming technology to solve problems; know the professional language of the subject area; modern technologies for developing reliable program code.
Introduction to fintech: the goal is to form future specialists with modern fundamental knowledge in the field of financial innovations and technologies. Fintech allows the creation of advanced technologies in the field of finance to improve the current financial system through start-up projects, for example, technologies for using online banking. This course will allow you to master modern tools and methods in the field of information technology in the field of fintech; acquire the necessary skills and knowledge for the successful practical application of the acquired tools and methods; get basic knowledge on creating projects in the field of fintech, focused on implementation in domestic banks and investment companies. Contents: Introduction to fintech. Fintech features. Fintech development trends. Blockchains in fintech. Fintech in banking. Fintech in the market of financial instruments. Fintech and regulation. Fintech startups. Cyber security fintech.
Financial Markets and Products (FRM): The goal is to generate knowledge on the functioning of the financial market. The course will evaluate strategies for analyzing customer needs and collecting feedback; set out the main information requirements required from clients; distinguish between financial products and distinguish between their advantages and disadvantages; describe a number of methods for evaluating information about financial market products; describe the key features of organizational policies and procedures related to the analysis of financial market products; know the full range of products and services of the financial market; outline the main risks and implications associated with financial products and services, taxation issues in relation to financial products and markets.
Fundamentals of risk management: the goal is to obtain basic knowledge and develop basic skills in applying modern mathematical methods of risk management, which form an idea of a competent approach to the problem of risk management. Development of the conceptual base and formation of the level of thinking necessary to solve complex problems that arise in the process of investment and banking and financial activities. The study of this course will allow you to know the theoretical and practical aspects of modern mathematical methods of risk management and financial and economic models based on them; be able to apply exact and approximate methods of risk analysis and forecasting; build original mathematical models adequate to a specific economic problem; interpret the obtained analytical and mathematical results to predict possible risks, explain economic effects. Own: the technique of probabilistic methods of risk assessment and risk management, the technique of creating mathematical models that adequately describe the risk.
Machine learning: the goal is to give an idea of the theoretical foundations and algorithms of machine learning, their practical implementation. The discipline forms practical visualization skills in data processing and skills in applying various machine learning models to solve problems. This course is aimed at studying the following concepts as a feature, feature vector, object, class, classification, classifier. Training, types of training "with a teacher" and "without a teacher". Linear classifiers. Linear classification model, stochastic gradient method, Perceptron algorithm. Support vector machine. Fundamentals of the support vector machine. The case of a linearly separable sample. The case of a linearly inseparable sample. Kernels and rectifying spaces. Regression recovery methods.
Business Intelligence technologies in management, economics and finance: the goal is to prepare undergraduates who professionally use the capabilities of MS Excel to solve business problems. The discipline allows you to study the rules of data organization, professional methods of data processing and calculations in the MS Excel environment; apply the logic and technologies for developing analytical panels in the Microsoft Power BI environment from the formation of requirements to the full development of dashboards of various levels of complexity and tool saturation; master the skills of working with Power Query add-ins for collecting data from various sources and creating templates for processing them, creating reports, and Power Pivot, which allows you to create data models and complex reports based on them; skills in creating measures using DAX formulas.
Mathematical modeling and econometric analysis: the goal is to analyze and evaluate the existing financial and economic risks, to make and justify the forecast of the dynamics of the main financial and economic indicators at the micro-, macro-mesolevel; analyze and recognize the essence of derivative financial instruments; knowledge of the main institutions that organize the movement of derivative financial instruments; knowledge of methods and strategies and tactics of hedging through financial futures and options contracts; the ability to hedge with various underlying assets of futures contracts; to identify the circle of participants in the market of derivative financial instruments with the definition of the advantages and disadvantages of their positions, to distinguish the opportunity provided by various platforms for the circulation of instruments, to form and implement hedging strategies using the example of insurance of risks of agricultural producers, to find price anomalies that can be used to build arbitrage operations. possession of methods of economic analysis of the behavior of economic agents and markets in a global environment.
Management of financial instruments: the goal is to develop the ability of undergraduates to analyze the market of financial instruments. The study of this course will allow you to know: types of investments, content and participants in investment activities; concept, types, stages of the life cycle of an investment project; the content of the business plan of the investment project; criteria of economic efficiency of investment projects; methods of analysis of economic efficiency of investments; methods of analysis of investment projects in terms of inflation and risk; methods for estimating the cost of various sources of investment financing. Be able to: apply the methods of analysis of the effectiveness of investment projects to solve specific problems; use the results of investment analysis in the development of management decisions; identify the impact of various factors on the economic efficiency of the investment project. Own: methods for evaluating the effectiveness of investments and investment projects; tools for determining simple and compound interest; skills in using information tools that provide automation of analytical calculations; own the Microsoft Office software package in order to draw up documentation on a computer.
Catalog of elective disciplines EP "7M04106 - Finance", 2022
