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COVID-19 Updates

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The Digital Track will prepare future business leaders with skills in data science and analytics to cope with challenges in the emerging digital economy.

Effective Fall, 2020, the Department of Finance will offer a specialized Digital Track for students interested in experiencing in-depth exposure to modern computing applications in finance. In addition to the traditional requirements for finance majors, students in the Digital Track will take Finance course sections identified as “Digital” in MSU’s Schedule of Courses (student.msu.edu). For classes taken in the Fall of 2021 and later, the classes have to be completed with the grade of 3.0 or higher.

The Department of Finance will honor students who complete four Digital Track Finance classes with a non-transcriptable Finance Digital Track Credentials. Please complete the Declaration Form by April 15th (Spring graduation) or December 1st (Fall graduation).

Background

Recent advances in artificial intelligence using big data are rapidly transforming many parts of our society, including the financial sector. These changes reach beyond simple automation of manual labor and have begun replacing mental tasks associated with white-collar jobs. To cope with these rapid changes in the financial sector, there is an increasing demand for students with the ability to harness these new data science and analytics tools for traditional corporate decisions. The Finance Digital Track fills this void and exposes students to various aspects of the digital economy through an integrated curriculum that combines regular business training and modern computing technology.

Courses

  • FI 312 Introduction to Investments

    Course description: Theoretical and empirical analyses of securities. Risk and return formation. Security analysis and concepts of market efficiency. Common stocks, bonds, options, futures, and mutual funds. Digital content includes an overview of commonly used data and methodology in investments. Assignments include the application of computer programming to the fundamentals of finance. Please check for an appropriate section at MSU’s Schedule of Courses (student.msu.edu).

  • FI 355 Financial Modeling

    Course description: Development of computer spreadsheet-based models to analyze corporate financial strategies and valuation issues. (All sections of FI 355 qualify for the Digital Track).

  • FI 414 Advanced Business Finance

    Course description: Advanced financial management of business firms. Theoretical analysis and case applications, including topics from capital budgeting, capital structure, options, and corporate restructuring. Digital contents include an introduction to sources for corporate financial data, assignments, and projects that implement concepts from corporate finance using computer programs. Please check for an appropriate section at MSU’s Schedule of Courses (student.msu.edu).

  • FI 422 Financial Data Analytics

    Course description: Introduction to the analysis of real-world financial data in a variety of settings. Applying textual analysis to large documents, identifying “sentiment” in Google search data, and back-testing trading strategies. Developing the programming skills necessary to both collect and prepare data for analysis. Identifying, downloading, cleaning, and shaping data. Please view course description at MSU’s Schedule of Courses (student.msu.edu).

  • FI 424 Deep Learning and Neural Networks in Finance

    Course description: Basic concepts of deep learning and neural networks in finance and economics. Practical experience implementing deep learning methods with state-of-the-art algorithms in a variety of machine learning packages with applications such as forecasting, algorithmic trading, and fraud detection. Please view course description at MSU’s Schedule of Courses (student.msu.edu).

  • FI 491 Advanced Investments

    Course description: This course aims to equip students with crucial investment concepts, core theories, and leading investment techniques. The goal is to develop the ability to integrate a wide range of skills and interdisciplinary knowledge to achieve investment excellence. We will combine standard lectures, research projects, and trading labs to catalyze the learning path. Part 1 of the course deals with portfolio management theory and technique. Part 2 introduces advanced investment strategies pursued by quantitative investment funds. Part 3 is an in-depth investigation into various industries and thematic investment topics. Students will be asked to conduct team-level investment research, which informs superior investment decisions. The programming language for the investment research is Python. Part 4 translates the investment decisions into trading, based on student trading labs offered by a leading brokerage house. Please view course description at MSU’s Schedule of Courses (student.msu.edu).