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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 (schedule.msu.edu). The Department of Finance will honor students who complete four Digital Track Finance classes with a non-transcriptable Finance Digital Track Credentials.

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 (schedule.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 (schedule.msu.edu).

  • FI 491 Topics in Finance: Deep Learning and Neural Networks in Finance

    This section of FI 491 Topics in Finance prepares students for an AI-driven world by introducing fundamental concepts of deep learning and applications in economics and finance. The main focus will be on machine learning methods using neural networks such as supervised learning, unsupervised learning, and reinforcement learning. After covering basic concepts and underlying principles of these methods, students will reinforce these ideas through various examples from economics and finance such as economic forecasting, fraud detection, and dynamic optimization for financial decisions. Please check for an appropriate section at MSU’s Schedule of Courses (schedule.msu.edu).

  • FI 491 Topics in Finance: Financial Data Analytics

    This section of FI 491 Topics in Finance introduces students to the analysis of real-world financial data in a variety of settings. Students will learn to apply textual analysis to large documents, identify “sentiment” in Google search data, conduct a financial analysis of corporate filings and disclosures, and back-test trading strategies, to name just a few applications. To support the analysis necessary for these applications, students will develop the programming skills sufficient to collect and prepare data for analysis. The course emphasizes working with data from out in the wild, where data is messy and must be identified, downloaded, cleaned, and shaped before it can be analyzed. Many applications are drawn from recent academic research, giving students additional exposure to current issues in finance. Particular focus is placed on predictive and prescriptive analytics. Please check for an appropriate section at MSU’s Schedule of Courses (schedule.msu.edu).