Fintech at D'Amore-McKim

FINA 2730 Fintech and Financial Innovation
Offers a broad overview of the world of fintech, from the perspectives of both large financial institutions and small startups. Evaluates the financial services industry, forces at play that may lead to disruption in the industry, startups that have already succeeded in bringing about change, the technological tools that may be used to make changes, and how both startups and established firms might respond to the continued pace of change.  
Featured instructors: Alper Koparan, Xiaochuan Tong, and Lingfei Kong

FINA 4335 Computational Methods and Their Applications in Finance
Introduces Python and its data-oriented library ecosystem (NumPy, Pandas, Matplotlib, Statsmodels, SciPy, etc.). Focuses on developing a strong foundation for working with financial data in Python and implementing various financial models.
Featured instructors: Marius Popescu, Richard Herron, Ali Sharifkhani, and Weiling Liu

FINA 4340 Blockchain Applications in Finance
Introduces the fundamental concepts and an overview of the blockchain and cryptocurrency space. Offers a background in fundamental concepts in blockchain technology and functionality. Explores the basics of how blockchains record and verify information, including the related definitions and terminology. Provides an in-depth overview of blockchain applications in finance. Concludes by examining the legal and regulatory framework, along with potential risks and hurdles faced by blockchain technologies.  
Featured instructor: Felipe Cortes

FINA 4350 Applied Financial Econometrics and Data Modeling
Examines how to understand and analyze data using a set of analytical tools in financial econometrics. Emphasizes time-series financial data and financial modeling. The course uses programming languages, such as Python, that are standard in fintech applications.
Featured instructors: Sunayan Acharya, Weiling Liu

FINA 4390 Machine Learning in Finance
Offers students an opportunity to prepare for rapid changes in the financial services world due to technological innovations and to understand how ML and AI tools are relevant in the financial services industry; to learn the basics of these tools, including machine learning; and to analyze the ethical considerations in the use of these tools in financial services. Seeks to train students to develop data analytics solutions using machine learning and deep learning models, allowing them to answer analytical questions that are encountered in the finance space.
Featured instructor: Karthik Krishnan

FINA 4460 Algorithmic and Robo-Trading
Covers the basis and implementation of trading strategies commonly used by investment professionals, such as fundamental analysis, factor investing, covariance models, and high-frequency trading. Today's asset management industry increasingly incorporates the delivery and execution of investment strategies through automated algorithms. Offers students an opportunity to understand the advantages and limitations of systematic trading and execution strategies. Students apply and modify such strategies and evaluate their performance using real-time data and incorporate aspects of Big Data in their quantitative strategies.
Featured instructor: Stephen Maloney

FINA 6237 Fintech, Financial Innovation, and Blockchain
Offers a broad overview of the world of fintech from the perspectives of both large financial institutions and small startups. Explores the dynamic intersections of finance and technology with a deep understanding of financial innovation, fintech trends, and blockchain technology. Presents a comprehensive understanding of regulatory challenges, risks, and opportunities within the fintech landscape. Focuses on cutting-edge concepts that are shaping the financial industry. Analyzes the impact of blockchain technology, understanding its role in creating secure, transparent, and immutable financial ecosystems.
Featured instructors: Alper Koparan and Xiaochuan Tong

FINA 6333 Data Analytics in Finance
Introduces Python and its use as a financial data analytics tool. Python has become one of the most widely used open-source, cross-platform programming languages. Covers the basics of programming in Python and key libraries (NumPy, Pandas, Matplotlib, etc.) used in data analytics, then focuses on implementing various financial models in Python. Topics include single and multifactor portfolio models, portfolio theory and the efficient frontier, algorithmic trading, options and futures, and value at risk.
Featured instructors: Richard Herron and Jim Campasano

FINA 6337 Computational Methods in Finance
Studies various computational methods in finance. Analyzes market data and build trading strategies. Uses interpolation, solver, and optimization methods to calibrate discount curve and volatility surfaces to market prices. Analyzes market data and applies dimension-reduction techniques such as principal component analysis (PCA). Applies time-series analysis and PCA to implement and back test trading strategies.
Featured instructors: Sunayan Acharya and Milivoje Davidovic

FINA 6339 Quantitative Portfolio Management
Offers an introduction to portfolio management with a focus on quantitative methods. Major topics include portfolio construction, revision, and performance measurement. Examines portfolio construction using constrained mean-variance optimization, as well as performance evaluation using factor models such as the Fama-French three-factor model. Additional topics include the effects of diversification on risk reduction and the costs of inflation, taxes, and transaction costs on management of fixed-income and equity security portfolios. Also covers quantitative approaches to manage specific sources of risk. Students employ historical data to construct backtests to assess the performance of various portfolio strategies.
Featured instructors: Joseph Marks

FINA 6342 Financial Data and Fintech
Covers methods of managing data and extracting insights from real-world financial sources. Topics include extracting and organizing data from financial, geospatial and supply chain sources; financial aggregation; and reporting. Applications include current and emergent data sources employed in finance, risk-weighted assets, market and credit risk modeling, stress testing, and climate risk. Studies major sources of financial data, data visualization, and architecture. Offers hands-on instruction in tools used in the financial industry to process datasets.
Featured instructor: Stephen Maloney