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Course Description For Ph.D. in Fintech, SKKU Graduate School


  • 1)Courses included in preliminary exam
    • all courses are 3 credit hours
Courses included in preliminary exam
Course name (code) Description
Financial Statistics
This course covers an introductory level of probability and statistical analysis for graduate students who major in finance. We emphasize topics of probability and statistical theories that students will encounter in graduate finance and econometric courses. Topics include probability theory, sampling, statistical estimation, and hypothesis testing.
Advanced financial theory
This course brings the student to the point where she or he can accomplish theoretical research in finance. To accomplish this goal, we discuss the skills commonly used by the theoreticians in finance, major findings in finance last 20 years, and developing an appreciation of literature progress in finance.
Machine Learning in Finance
The amount of data used in financial practice is increasing, and the trend toward using unstructured data goes beyond traditional structured data. Therefore, it is very important to develop the ability to apply Machine Learning methods to deal with financial tasks in order for students to be competitive and to become leaders in the future of finance. To this end, this course aims to create the ability to use financial data in practice by using R and Python to lay the groundwork for overall data science and machine learning, from data collection, organization, analysis, visualization, machine learning, and report writing.
Financial Data Analysis
In finance area, basic understanding and analyses of financial data is becoming more crucial. In order to implement meaningful analyses of financial data, students need to know how to get access to the key data sources as well as understand the key structure of the financial data.
In this course students will learn how to get access to the key financial data (Compustat, CRSP, Fn DataGuide, etc.), how to handle the large data set, and how to analyze the data. The purpose of this course is to provide exposure to the key financial data, and to provide meaningful experience in handling and analyzing the large data set so that students can have a foundation in advanced data analyses.
This course will require students to use the data handling and analyses skills to replicate the real research papers from the quality international journals. Furthermore, students are going to be required to extend the research paper that they have replicated. This will provide students to get exposure to the whole process of accessing, handling, and analyzing the financial data.    
Behavioral finance and fintech
Finance is the field that is affected the most by technological revolution such as FinTech, Blockchain, and Big Data. Interestingly, the development of FinTech goes hand in hand with the development of "behavioral finance". Until recently, traditional neoclassical economists largely ignored the effect of psychological factors of the CEOs and investors in financial markets because there was not enough data. However, with the explosive growth in computing power and Big Data, researchers keep finding that psychological factors are very important elements in financial market, which established the field of behavioral finance. In this course, we will start by studying the Efficient Market Hypothesis, which is the backbone of neoclassical economics. Then we move on to study behavioral concepts such as momentum and bubble. We also study how narcissism, overconfidence, and biological traits of the CEO affect corporate financial decision making such as financial leverage and M&A. We study how the financial technologies help us capture the psychological biases. Lastly, we study how blockchain technology is changing the game of finance.
Seminar in Investment
This course is designed to provide students with a framework for the analysis of the capital markets. Each meeting will cover leading theoretical and empirical papers on each topic in a seminar format.  The students have to prepare a term paper which is publishable in a conference.
Derivatives seminar
The course will be delivered in a seminar format with the main objective of establishing the research foundations of financial derivatives and markets. Specifically, the course will cover the cost-of-carry model of futures and the various option pricing models. The course will also deal with research articles on stock index futures, interest rate futures, currency futures, options and options on futures by focusing on the issues of price discovery and hedge.


  • 2) Electives
Course name (code) Description
Python programming
This is a basic course in python programming for FinTech Master or Ph.D. program. Today business professionals require basic knowledge of algorithms concepts and computer programming. These two concepts differ in their scope. An algorithm refers to a set of instructions to be followed to solve a problem. A computer program is as a set of instructions that a computer must follow. The aim of this course is to learn how to define algorithms and to use computer programs to implement them. This course will allow business students not only to communicate with computer science professionals and engineers but also to play an active role in the development of new digital products. The basic knowledge of Python programming is required. We discuss using algorithms and computer programming to solve problems in a business environment. The course consists of three modules. First, we discuss how to define a business problem and the requirements for its solution. Second, we review the principles and fundamental concepts of algorithms. Third, we use Python to translate a generic algorithm to a computer program. This will cover the software development basic cycle.
Data analytics in action with Python
This course uses Python programming language for practicing examples of descriptive statistics, inferential statistics, regression, clustering analysis, as well as machine learning and deep learning. Its focus is more on applications than theory building. Students are encouraged to present the examples they found, and instructor and other students are doing questions and answers. This study is a social science-based trans-disciplinary course, rather than just a methodology or programming course.
Theories of artificial intelligence
In this course, students will learn the fundamental algorithms of Artificial Intelligence including the problem-solving techniques, search algorithms, logical agents, knowledge representation, inference, and planning. After taking the course, students are expected to implement the algorithms using computer programming languages.
Blockchain and financial application
Blockchain, which was first introduced in the paper titled “Bitcoin: A Peer-to-Peer Electronic Cash System” in October 2008 by Satoshi Nakamoto, provides a new way of ledger management technology. In recent, blockchain technology, as a cutting-edge technology, is seeking its applications in almost all major industries, including insurance, transportation, healthcare, energy, logistics and delivery, music, manufacturing, IoT, social media and public sectors. Especially, in the field of finance, because of the emergence of cryptocurrency, blockchain technology is widely used than any other field. This lecture introduces overview on blockchain technology and studies the characteristics of cryptocurrency market.  
Deep learning
This course covers deep learning based on artificial neural network which is advanced on various industries. This course, especially, gives students basic understanding of modern neural networks and their applications in computer vision and natural language understanding. Students learn about Convolutional networks, RNNs, LSTM, Dropout and more. This course introduces the major technology trends driving Deep Learning.
Data-driven credit modeling
The purpose of this course is to help FinTech master program students understand basic credit risk analysis and AI application to credit risk management. It begins with an introduction to commonly used models of credit risk. It then focuses on techniques to measure and manage credit risks in financial industries. The course also discusses with modeling and computing AI skills to solve business problems in credit risk management. At the end of this course, students are expected to obtain a deeper understanding of credit risk management with AI techniques.
AI and wealth management
The course will primarily focus on two quantitative approaches in investment: optimal asset allocation and machine-learning and AI based asset pricing. Although asset allocation is known to be the most important performance determinant, quantitative approaches (portfolio optimization) are rarely used in practice. There are several reasons behind this, such as being a black box and difficult to interpret the outcomes, but the most crucial reason is the high sensitivity of optimal portfolios to the input parameters (expected returns and covariances of returns). With the inevitable estimation errors in these parameters, it is difficult to obtain a robust yet reliable portfolio. Much of the research in portfolio management over the last decades has been devoted to addressing this limitation of the classic portfolio optimization models, and some notable progress has been made, especially using Bayesian methods and shrinkage estimators. The course will assume students are equipped with fundamental portfolio theories and spend most time on introducing recent developments in portfolio optimization.
InsurTech theory and practice
When compared to other sectors of “big business”, the insurance industry has for long been left to operate uninterrupted, out of reach from the aggressive startup movement that has radically transformed and reshaped so many other industries. Now is the time of change. Over the last couple of years, startup funding has increased dramatically in the insurance sector fueling what is known as insurance technology companies or InsurTech. In this course, we'll navigate through the new hot area of InsurTech. Firstly, we'll have a quick introduction to InsurTech. Then, we'll move on to have an overview on the insurance industry and its digitization efforts, following that we'll learn the categories of InsurTech companies as well as InsurTech Technology Enablers. From that point, we'll get to learn InsurTech business model, key commercial drivers, and finally we'll explore the future of InsurTech.
Emerging market finance seminar
This seminar course is designed to study an array of corporate finance, investment finance and corporate governance issues regarding emerging countries or their financial markets. For example, when it comes to corporate finance issues, the course can study emerging-market firms’ financing, investing and payout policies — also in comparison to developed-market firms’ policies. When it comes to investment finance issues, the course can study the presence or patterns of value and growth effects. When it comes to corporate governance issues, the course can study investor protection in emerging market countries and its relevance to firm value and operating performance.
Empirical analysis in accounting and financial studies
This course introduces various methodologies that are necessary for Accounting and Finance studies. It includes lectures of Statistics and workshops for SAS programing and financial databases, which are essential for empirical studies in Accounting and Finance.
Corporate finance seminar
The course starts with foundations of micro- and information economics, to be followed by an analysis of corporate finance decisions in the presence of uncertainty and information asymmetry. Emphasis will be on the study of seminal journal articles, aiming to bring the student up to the level necessary to do research in this area.
Advanced statistical computing
This course covers selected topics from general statistical computing area. It reviews current issues and literatures of selected area and tries to predict future directions and develop new theories.
AI Finance Seminar I
This course focuses on examining various issues in finance using various ML techniques. One of the advantages of ML techniques is that the high-dimensional nature of the methods enhance their flexibility relative to more traditional econometric prediction techniques. ML may better approximate the unknown and likely complex data processes in asset returns or corporate financial decisions. In particular, we will use machine learning techniques to estimate asset premiums, to classify various classes of companies and stocks, to check extra alphas from ML-based portfolio formation, to detect new types of risk factors, to identify stock market anomalies and suspicious trading patterns, and to extract new variables from various texts, voices and images data. We will first review the existing related finance literature for your solid theoretical background, and then apply ML techniques to those problems to see whether ML techniques can expand our understandings of traditional finance.
Advanced time series analysis
Recent advances in time series analysis such as high dimensional time series models and nonlinear time series models are discussed with latest research papers. This course requires paper readings, presentations and real data analysis projects.
Data-driven security and privacy
This course is about the role of data and data analytics in security and privacy. This course focuses on applications of machine learning and big data analytics to various security and privacy problems, using various AI techniques to solve challenging security and privacy issues.