The course analyzes the nature of the firm, and the role of corporate governance and executive compensation in economic organizations. Special attention is paid to analyzing issues related to ownership structure, corporate governance, corporate boards, and executive compensation. Topics covered include the nature of the firm, ownership structure, corporate board structure, executive compensation, and etc.

Financial modeling expertise is of paramount importance to financial professionals, academics and students. Excel and its embedded Visual Basic for Applications (VBA) programming language are therefore preferred tools for them, because with less programming skills complex financial and business models can be developed, solved or simulated therein. This course gives introduction to the Excel and programming techniques of VBA language, and then focuses on the implementation of techniques and financial models (such as data simulation, portfolio optimization, general asset pricing, option pricing, risk management, and corporate finance). The course serves to bridge the gap between financial theory and its implementation.

The purpose of the course is to explain fundamental pricing of risk in competitive financial markets and provide sound frameworks for professional wealth management. The course is mainly built upon rational portfolio theory but covers also common psychological biases in human decision making likely to be important in advising investing clients in private banking and wealth management.

Special focus areas are long-term capital structure and dividend policy decisions, as well as the effect of agency costs and asymmetric information on corporate financial policy. Managing working capital, leasing decisions and financial consequences of mergers and acquisitions are other important topics. Interactions between corporate financial policy and governance are also introduced.

The purpose of the course is to give insights in the functioning of financial markets and the fundamentals of the pricing of various kinds of financial securities and derivatives such as fixed income securities, foreign exchange, forwards and futures, options and swaps.

The main objective of the course is to provide deeper understanding of various advanced topics in asset pricing and corporate finance. Most of these topics will be theoretical in nature, but some relevant empirical analyses will also be discussed. Some of the major issues that will be covered include utility specifications, analysis of optimal portfolio/consumption choices under uncertainty, multifactor models, and other asset pricing models. We will also look more in depth into corporate finance theory, especially capital market efficiency, agency theory, and capital structure. Some special topics related to option pricing, credit risk, political uncertainty, and new financial technologies can also be discussed.

Syllabus_Strategic growth investing_2021_Oct20_.pdfSyllabus_Strategic growth investing_2021_Oct20_.pdf

This course develops a framework for understanding growth from a (i) strategic management perspective and an (ii) investor perspective. Strategic concepts as well as the role of the industry and nonmarket environments are dealt with. The course also explores the role of financial statements as a channel for information from companies to investors and capital markets. Students apply the frameworks by doing investment analyses of business ventures presented at the Slush seminar in Helsinki. Active interaction between students, teachers and guest lecturers play an important role in this course. 

This course covers recent developments in the area of financial innovation (FINTECH), such as Blockchain, digital currencies, peer-to-peer method of identifying ownership, and smart contracts. Its goal is to analyze the emergence of Blockchain and related innovations as highly disruptive technologies for the financial industry, business laws, accounting and monetary economics (central banking).

To fully understand the implications of such technologies, we will cover various related topics such as the nature of money, legacy payment and banking, basics of crypto technology, digital currency systems, peer-to-peer transactions, governance and regulation of emerging technologies, double entry bookkeeping, and financial exchanges. We will also focus on several digital currencies, their “intrinsic value,” the reasons for their recent popularity, and the microstructure of their trading. Finally, we will survey various ventures that begun to capitalize on these innovations.

To enhance students’ understanding of the most recent and most promising financial innovations that will change the nature of the professional jobs that would be available in the future for students majoring in finance, accounting, and other business areas. The course also intends to create a bridge between business, law, and computer science. Students majoring in these areas would be equipped with a basic knowledge in each other’s fields, which should enhance the quality of their communications after entry into their corresponding careers. 

Value_Investing_syllabus_2022_March10.pdfValue_Investing_syllabus_2022_March10.pdf

The course develops a framework for value investing based on a modern treatment of the Graham and Dodd approach to investment management. The course covers the search for undervalued stocks, the valuation of stocks that pass the screening process, and the investment decision to buy a stock if its price is below the intrinsic value by a margin of safety.

The objective of the course is to give students both a broader understanding of research methods and foundational methods-related competences that are required to produce a master’s thesis of high quality in the field of Management and Organization using qualitative and quantitative research methods. The qualitative part of the course focuses on data analytic theories and methods such as grounded theory, data coding, interpretation, displays and connecting strategies, narrative interviewing and analyses, and reporting. The quantitative part of the course will also examine potential data sources for quantitative research projects, and provide an overview different quantitative analysis techniques: machine learning (prediction), estimation of marginal effects (various regression-based techniques), and data description (e.g. visualization, factor analysis, unsupervised learning).