Part-Time Specialization in Data Driven Financial Management

The aim of the course is to introduce students to Financial Reporting. Key components of financial reporting (SFP,OCI,P&L accounts) and accounting record keeping (journal, ledger) are discussed. The knowledge presented is necessary both for students who have not been taught accounting courses at the undergraduate level and for students that need additional knowledge to cover the requirements of the program.

The aim of the course is to introduce students to the basic concepts of Finance. The course discusses the time value of money, the notion of compound interest, and the basic principles of valuation of the financial instruments in the capital markets. The knowledge presented is necessary both for students that have not been taught finance courses at the undergraduate level and for students that need additional knowledge to cover the requirements of the program.

This course can be considered as an introduction to Econometrics. Its aim is to present the basic theory of Econometrics and how this can be rigorously applied to a variety of problems arising from Economics, Finance and Business Administration. In this course, econometric theory is combined with econometric practice by showing its use with software package EViews and programming language R.

The aim of the course is to introduce students to the main principles and techniques of programming, and teach them how to design, structure and implement simple programs in Python. The course describes the setup and philosophy of the programming environment and of the different methods of interacting with the language kernel (command windows/scripts/notebooks). It introduces the student to data types, operands, and control flow commands. Functional programming techniques are also presented. Finally, it familiarizes the student with the use of the core Python libraries used for Finance applications (Numpy, Scipy, Pandas).

The objective of this course is to describe the theory and practice of the main topics in financial management at the postgraduate level. The course is organized into three parts. The course starts with an overview of the time value of money, valuing bonds, equity valuation, and cost of capital. The second part pertains to capital budgeting and the financial criteria used for making investment decisions. Finally, we consider issues related to payout policy, leverage and capital structure.

The primary objective of the course is to introduce students to the basic concepts of financial accounting based on international financial reporting standards. Upon successful completion of the course, the students will be able to understand the structure and key line items of the main financial statements (balance sheet, income statement, and statement of changes in equity) of the firms. They will also familiarize themselves with the accounting treatment for cash and cash equivalents, accounts receivable, inventories, and non-current assets.

The aim of this course is to introduce to the main methods of Machine Learning used in Financial Management applications. Specifically, it covers popular classification techniques (K-means, Support Vector Machine, naive Bayes classifier, random forests), and methods of generalized regression (Ridge, LASSO, LARS). It presents the main types of neural networks (Multilayer Perceptrons, Convolutional NNs, Recurrent NNs, Self-Organized Maps, Kernel Networks), while highlighting characteristic applications of these methods in the field of financial management (credit-scoring, algorithmic trading, portfolio management, fraud detection). The distinction between supervised, unsupervised and reinforced learning is made, and the student learns how to implement such application using the relevant Python libraries (Tensorflow, Keras).

The aim of this course is to explain the algorithms underlying machine learning (ML) so that the results from using the algorithms can be assessed knowledgeably. The course explores challenges that arise when applying ML methods in corporate finance. In addition, the course enables students to turn structured or unstructured data into insights that promote better decision making.

This course examines the most important issues in the theory and practice of modern portfolio management. Topics include efficient capital markets, risk and return, asset pricing models, valuation, equity portfolio management strategies, bond portfolio management strategies, the professional asset management industry, evaluation of portfolio performance, main investment decision biases, investor contrarian and momentum strategies, and herd behavior.      

The aim of the course is to present complex issues arising from the implementation of International Financial Reporting Standards (IFRS) in the presentation of financial statements. Applications of data management practices in basic elements (accounts) of financial statements is given. In addition, techniques for forecasting, analysis, planning and decision making are presented.

The aim of the course is to present basic concepts in terms of sustainability principles. In addition, the application of sustainability reporting requirements to corporate financial statements is discussed. Finally, financial reporting management practices related to sustainability issues are presented.

The course aims to examine corporate financial needs and ways to cover them. Financial planning is an issue of primary importance when it comes to short-term and long-term business development and sustainability

The aim of the course is to introduce students to the current developments in the field of Financial technology (Fin tech) and its important implications on the financial services industry, markets and global economy.  The course analyses the context within which the application of blockchain, artificial intelligence, robo-advisory, and many other technological innovations create new business opportunities in the financial services industry. In addition, it provides a detailed insight into the challenges associated with the regulation and adoption of new technologies in the financial services.

The aim of the course is to present basic audit principles. Additionally, the use of new technologies and analytic methods in the documentation and control of fraud detection is presented.

The past few decades have been characterized by an extraordinary growth in the use of quantitative methods in the analysis of various asset classes; be it equities, fixed income securities, commodities, and derivatives.In addition, both financial economists and practitioners have routinely been using advanced mathematical, statistical, and econometric techniques in a host of applications including, asset pricing, portfolio management, investment decisions, and risk management, among other.This course attempts to provide an introductory-level basis for the learning of such techniques.The purpose is twofold, to provide research tools in financial economics and comprehend investment designs employed by practitioners.

The course aims at providing a solid understanding of the global financial environment. Both theoretical and practical aspects are analysed, which are important for comprehending global financial markets, and how these operate under different currency regimes. The goal is to highlight the importance of working an internationalized market for the purposes of financial management and provide specific examples related to the industry.

The aim of the course is to develop a framework for business analysis and valuation using financial statement data. Financial ratios are presented which result from the analysis of large chronological series. Profitability and cash flow forecasting scenarios are performed. Business valuation models (such as Discount cash flows) are assessed by using stress and sensitivity scenarios.

The course studies the pricing and use of derivative securities (forward/futures contracts, swaps and options), i.e., financial instruments whose value depends on the price of other basic underlying variables (such as stock prices, indices, foreign currencies, interest rates or commodities). The no-arbitrage pricing principle and its use in pricing forward, futures and swap contracts and in deriving option pricing restrictions is first developed together with the Binomial-tree valuation approach and the Black-Scholes option-pricing model. Then, various extensions of the theoretical option models (adjusted for dividends and early exercise) are presented and various applications are provided, in the pricing of options on stock indices, currencies, or futures and in the risk management (e.g., hedging stock market, foreign currency and interest-rate risk exposure).

The aim of this course is to combine financial applications with computer programming. The course aims to provide students with the necessary tools and expertise to use Python programming in solving complex financial problems. It covers a variety of financial applications including financial data analytics, portfolio optimization, derivatives and risk management.

The purpose of this course is to introduce basic data protection principles and data security management and policies for a business entity. Cyber security issues and personal data security issues both for the business entity itself along with third party data maintained by the entity are discussed.

Data Collection, Analysis, Writing, and Presentation of Dissertation. Students may undertake their dissertation in pairs or on their own, upon approval of the Steering Committee. Full time (or even part time) students may alternatively substitute the dissertation with an internship, upon approval of the Steering Committee.

It is stated that as the thesis is assigned, the student is required to attend a Research Methods Seminar for the Preparation of the Thesis, which is offered during the second semester and the summer period. For part-time students, the seminar takes place during the third semester. Successful completion of the seminar is a prerequisite for the initiation of the dissertation