For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| DBA5081 | Empirical Analysis in Accounting and Finance Studies | 3 | 6 | Major | Master/Doctor | 1-4 | English | Yes | |
| This course introduces various methodologies that are necessary for Accounting and Finance studies. It includes lectures of Statistics and workshops for SAS programimg and financial databases, which are essential for empirical studies in Accounting and Finance. | |||||||||
| DBA5083 | Seminar on Contemporary Issues in Finance | 3 | 6 | Major | Master/Doctor | 1-4 | English | Yes | |
| This seminar course focuses on recent developments in financial research. Based on critical analyses of academic papers, the course will help students develop abilities to formulate research ideas and carry out empirical analyses. | |||||||||
| DBA5085 | Derivatives Seminar | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| 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. | |||||||||
| DBA5087 | Coporate Finance Seminar | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| We study first a foundations of micro-economic and information topics, to be followed by an analysis of corporate finance decisions in the presence of uncertainty and information asymmetry. Emphasis throughout 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. | |||||||||
| DBA5088 | Bigdata Marketing Seminar | 3 | 6 | Major | Master/Doctor | - | No | ||
| The Bigdata Marketing Seminar is discussed in depth discussions on academic research papers in related fields. This seminar aims at incorporating the comtemporary issues related to innovation in a marketing aspect and applying the research findings creatively to the corporate management. We intend to focus on the impact of innovative uses of big data on corporate governance. The use of big data enables new innovations in the fields of value creation, innovative new product development, access to customer information exploration, customer relationship management, and evaluation of marketing performance. The seminar aims to achieve objectives by combining three classes of teaching methods. The first is to establish a theoretical framework for utilizing the most up-to-date text books with the most up-to-date text book. The second lesson is to enable greater insight into the use of Big Data by studying materials and materials related to each Chapter. The third is to develop skills that actually analyze big data through a Hands-on Lab on Big Data. In order to do so, we plan to provide 5 to 6 extra lectures on Big Data Lab analysis. Finally, the seminar aims to foster innovative marketing capabilities that are needed in the timing of the new digital revolution called the 4th Industrial Revolution, and will focus on the perception of innovation in the whole process of transforming the company's value. | |||||||||
| DBA5089 | Emerging Market Finance Seminar | 3 | 6 | Major | Master/Doctor | - | No | ||
| 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 issuses, the course can study investor protection in emerging market countries and its relevance to firm value and operating performance. | |||||||||
| DBA5091 | CrossManagementandCurrentIssuesinMarketing | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course is designed to explore new research topics in the academic field of marketing by exposing students to new research. Specifically, as the business environment is radically changing, with infusion of new technology, it is required to study how to reinterpret the exiting theories and literature, and how to create new contextual knowledge. This will enable students to develop new research topics and make their research stream more fruitful. | |||||||||
| DBA5095 | BehavioralFinance&FinTech | 3 | 6 | Major | Master/Doctor | English | Yes | ||
| Finance is the field that is affected the most by technological revolution such as FinTech and Big Data. Interestingly, the development of FinTech goes together with the development of methodology in "behavioral finance." With the explosive growth in computing power and Big Data, researchers keep finding that psychological factors are very important elements in financial market, and such technique can be easily applied to better understand retail clients. In this course, we study such concepts as momentum and quant trading. We also study how psychological traits of the CEO affect corporate financial decisions, such as financial leverage and M&A. We study how the FinTech help us capture the psychological biases better and help market efficiency. | |||||||||
| DBA5096 | SocialScienceResearchMethodology | 3 | 6 | Major | Master/Doctor | - | No | ||
| Research methodology is more than research methods. Methodology focuses more on the core principles for conducting quality research and learning to make sound judgments about method. The purpose of this course is to move beyond preparing the student to become research technician and to help him or her develop into a reflective social scientist. | |||||||||
| DBA5097 | Special Topics in Intelligence and Customer | 3 | 6 | Major | Master/Doctor | - | No | ||
| With the advancement of Big Data technologies and artificial intelligence, business organizations are pursuing ‘Hyper-Personalization' that identifies exact customer needs and requirements and providing personalized services based on their individual preference. The prevalence of wearable devices and the Internet of Things (IoT) technology is accelerating ‘hyper-personalization’ by enabling a large number of unstructured data such as text, voice, video, and images to be collected and analyzed as big data. This course is designed for students who want to learn customer management strategies and application cases in the era of hyper-personalization by combining artificial intelligence, big data, and consumer behavior. Specifically, students will explore how the convergence of artificial intelligence and big data drives the hyper-personalization in the finance, entertainment, retail, and healthcare industries. Students also develop practical and effective digital transformation strategies by understanding legal and ethical issues that might arise from hyper-personalization. This course covers the following topics (not limited to): -Big data, artificial intelligence, and personalization -Transition to the era of hyper-personalization -Analytical approaches for hyper-personalization (Data collection and analysis) -Hyper-personalization frameworks for customer engagement -Convergence of artificial intelligence and big data its applications (Finance) -Convergence of a | |||||||||
| DBA5098 | AI and Business Informatics | 3 | 6 | Major | Master/Doctor | - | No | ||
| AI (artificial intelligence) has been widely accepted as critically crucial competitive weapon for the sake of enhancing firms’ QoD (quality of decision). Besides, AI also becomes critical academic knowledge for the sake of business scholars and researchers. In this sense, those essential themes to be covered in this course encompass the following themes. - Recent trends about AI-based business big data analytics (i.e., machine learning, deep learning classifiers (i.e., RNN, LSTM, CNN, GAN, etc) , multi-label classification, etc) - AI-based business mining techniques for HRM (human resource management) issues - AI-based business mining techniques for manufacturing issues (i.e., IoT, Digitial Twin, Smart Factory, etc) - AI-based business analytics for financial engineering issues (i.e., Fintech, relationship between firm’s profits (or stock price return) and CEO’s fWHR, digit ratio, voice pitch, etc) - Deep learning-driven business model development The course format combines lectures, article presentations, replication of articles, and class discussions. Class software for data mining is either WEKA, R or Python. Instructor suggests list of articles related to the class topics above, and those articles will be carefully selected from prestigious journals in the fields of business and AI. | |||||||||
| DBA5099 | Special Topics in Intelligence and Industry | 3 | 6 | Major | Master/Doctor | - | No | ||
| In “Special Topics in Intelligence and Industry,” we shed light to industry-specific research topics and themes that are of common interest to a pair of business disciplines (among MIS, Marketing, Finance, Accounting, and Management). Given that the business disciplines are intertwined in practice, we expect to identify several important and interesting interdisciplinary industry-specific research domains. For instance, in one year, we may design the course to cover digitization-driven pricing models and new ways of music production and consumption undertaking in the music industry and survey related research drawn from MIS and Marketing. In another year, we may cover the various forms of digital collaboration found in tech companies after learning about the theoretical and methodological foundations from the traditional team research. In doing so, the course spans the boundaries of MIS, Organizational Behavior, and Tech industry. As such, the course aims (1) to identify new research opportunities by studying a particular industry-specific research domain from different disciplinary positions, and (2) to train students to acquire more comprehensive and balanced interdisciplinary perspectives and approaches. | |||||||||
| DBA5101 | Special Topics in Culture and Customer | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course has two objectives as follows. Firstly, being aware of the necessity to produce a new management concept called cross management (CM), this course provides new thoughts about how to reorganize conventional management paradigm through the lens of the CM and its two main forces like “new culture” and “new customer”. Secondly, this course investigates those problem-solving steps and strategies required to address a number of ill-structured management issues that have been arising amid joint forces of intelligence and culture in the 4th Industrial Revolution (4IR) era. Main contents which student may learn from this course are as follows: - Cultural aspects of CM paradigm that would be effective in the 4IR era - AI-driven problem-solving techniques that are deemed effective for a wide variety of new issues belonging to joint forces of new culture and new customer Students can expect to get the capabilities of understanding new CM paradigms influenced by new culture and new customers, and possessing AI-driven techniques necessary for top-notch managers facing the 4IR. | |||||||||
| DBA5102 | Special Topics in Culture and Intelligence | 3 | 6 | Major | Master/Doctor | 1-4 | English | Yes | |
| This course has two objectives as follows. Firstly, conventional management paradigm is reorganized through the lens of the cross management (CM), and the two crucial factors of the CM paradigm like intelligence and culture are specifically investigated with relevant management theories and cases. Secondly, this course explore those problem-solving steps and strategies required to address a number of ill-structured CM issues governed by joint forces of intelligence and culture. Main contents which student may learn from this course are as follows: - Intelligence aspects of new CM paradigm that would be effective in the 4IR (4th Industrial Revolution) era - How to seek 'Human-AI Symbiosis ' in working places - Cultural aspects of CM paradigm, and its effects on manager's decision-making strategies - Analysis of a wide variety of CM issues belonging to joint forces of intelligence and culture, and how to solve them with focus on AI-based methods Students can expect to get the following benefits from this course- (1) understanding new CM paradigm that can help firms survive in the fiercely competitive environments intensified by the 4IR, (2) possessing AI-driven strategic techniques and problem-solving capabilities which are necessary for top-notch managers facing the 4IR. | |||||||||
| DBA5104 | Business Analytics Seminar | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course covers big data mining from the perspective of management. Therefore, the course title is business mining to represent that our main task is focused on the business analytics. As you know, business data mining recently receives great popularity and importance from both researchers and practitioners. It is because both the web-based online environment and smartphone-driven applications permeate into every corner of our personal lives and corporate activities. In this sense, realms of the topics which this course deals with include, for example, ● Business mining with big data analytics ● Deep learning classifiers (i.e., RNN, LSTM, CNN, etc) vs machine learning classifiers ● Emotion Prediction from business big data ● Sentiment analysis from online review comments for sharing economy (i.e., Airbnb, Uber, Youtube, etc) and crowdfunding companies (i.e., Kickstarter) ● Online review based affective computing by using deep neural networks ● CNN-driven opinion mining ● Deep learning based business model development The course format combines lectures, article presentations, replication of articles, and class discussions. Class software for data mining is either WEKA, R or Python. Instructor suggests list of articles related to class topics above, and those articles will be carefully selected from prestigious journals. | |||||||||