M.S. in Finance and M.S. in Financial Technology & Analytics Dual Degree Master's Program

The course provides a practical introduction to SQL databases and Hadoop cluster systems as available in the Hanlon Financial Systems Lab. Students will receive hands on instruction about setting up and working with databases. Most of the software will be introduced using case studies or demonstrations, followed by a lecture of related fundamental knowledge. The course covers SQL, NoSQL, and database management systems. The course will cover accessing databases using API.

This course provides an overview of issues and trends in data quality, data storage, data scrubbing and data flows. Topics include data abstractions and integration, enterprise-level data issues, data management issues, similarity and distances, clustering methods, classification methods, text mining, and time series. Furthermore, the Hadoop-based programming framework for big data issues will be introduced, along with any governance and policy issues. 
Corequisite: FE 513

Introduction to information theory: the thermodynamic approach of Shannon and Brillouin. Data conditioning, model dissection, extrapolation, and other issues in building industrial strength data-driven models. Pattern recognition-based modeling and data mining: theory and algorithmic structure of clustering, classification, feature extraction, Radial Basis Functions, and other data mining techniques. Non-linear data-driven model building through pattern identification and knowledge extraction. Adaptive learning systems and genetic algorithms. Case studies emphasizing financial applications: handling financial, economic, market, and demographic data; and time series analysis and leading indicator identification.

In this course the students will learn how to estimate financial data model and predict using time series models. The course will cover linear time series (ARIMA) models, conditional heteroskedastic models (ARCH type models), non-linear models (TAR, STAR, MSA), non-parametric models (kernel regression, local regression, neural networks), non-parametric methods of evaluating fit such as bootstrap, parametric bootstrap and cross-validation. The course will also introduce multivariate time series models such as VAR.

Effective visualization of complex data allows for useful insights, more effective communication, and making decisions. This course investigates methods for visualizing datasets from a variety of perspectives in order to best identify the best solution for a given task. Students will use a number of tools to refine their data and create visualizations, including: R and associated visualization libraries, Tableau 2020, Gephi, and web-based applications.

This course covers emerging topics in the area of financial technology and also allow students to develop practical programming, statistics, and mathematical skills that are valued in the Fintech industry. We will explore topics from both theoretical and practical perspectives in the areas of digital currency and blockchain technologies, automated wealth management, digital lending, peer-to-peer applications including payments and insurance, machine learning financial applications.

This course focuses on traditional machine learning algorithms (random forests, support vector machines and conditional random fields) as well as recent developments in deep neural networks focusing primarily on the TensorFlow library. The distinctions between various types of algorithms (supervised, unsupervised and reinforcement learning) are developed, as well as the relationship between the quality of the data and complexity of the model.

This course explores how to apply fundamental machine learning models to predict financial time series and solve financial problems. Some of the financial applications explored are algorithmic trading, model calibration, portfolio optimization and risk management.

Prerequisites: Any of BIA 656, FE 590, FE 690, MIS 637 or CS 559

A minimum of six credit hours is required for the thesis. Hours and credits to be arranged. You will need both an advisor and a reader to complete this course; interested students should contact their academic advisor for complete details.

509892 - Economic Models (9 ECTS)

The course aims at providing some basic knowledge on some fundamental economic models which can be considered as preliminary to further studies in Finance.

509897 - Applied Finance (6 ECTS)

509894 - Econometrics (6 ECTS)

The objective of this course is to provide the students with sound knowledge of the theoretical foundations of econometric methods. The course will cover topics as models specification, assumptions and formal derivation, testing and interpretation. The course will focus mainly on the linear regression model. At the end of the course, students will be able to understand the inferential and interpretative aspects of the econometric approach to data analysis. Students will be able to specify, estimate and interpret regression models under various scenarios and to carry out hypothesis testing for the empirical validation of economic theories.

509899 - Asset Pricing And Macroeconomics (9 ECTS)

Can be transferred from Stevens FA631 and FE542

508647 - Capital Markets And Eu Company Law (6 ECTS)

The main goal of the course is that of introducing the students to the topic of the corporate governance in Public companies as business firms whose shares are listed on Stock Exchange Markets and typically exposed to potential severe and multiple conflict of interests. Indeed, Public Companies are the organizational firm of medium/large enterprises, which raise their capital on (international) stock exchange markets; involve many and widely (supranational) spread interests (investors, employees, creditors, customers, ecc.); intrinsically bear a systemic risk. Therefor good governance of Public Companies is necessary for efficient allocation of resources and for general growth; it requires (inter alia) appropriate and complex rules and standards of law; appropriate legal statutes must be tailored combining a “supranational approach” (uniformity of rules/standards/practices across national jurisdictions) with a “domestic oriented” attention (no “one size fits all” principle).

509898 - Statistics For Finance (9 ECTS)

Can be transferred from Stevens FE590 and FA690

507903 - Firm Valuation And Capital Market Instruments (6ECTS)

The course has two main objectives. On one side to illustrate the topic of firm valuation. On the other to give an idea of financial instruments negotiated in financial markets, grouped in asset classes. At the end of the course, students will be able to operate the valuation of a firm using the DCF and market multiples. At the same time, students should be able, at the end of the course, to understand the main features of the most important asset classes.

504838 - Financial Econometrics (9 ECTS)

Can be transferred from Stevens FE542 and FA595

509900 - Laboratory on Financial Management (3 ECTS)

509893 - Topics In Portfolio Management (6 ECTS)

The course aims to illustrate some financial methods and models using Excel spreadsheets in order to formulate rational procedures yielding optimal and efficient solutions for portfolio management. The main field in which calculations and analyses are made is the Equity asset class, starting from basic data capable of providing a comprehensive overview of a single stock and of its relationship with the whole market. Integrating all results achieved, the goal is to provide students with the essential concepts needed to value a listed stock and to build and manage an equity portfolio, along with presenting and analyzing quantitative data with the software tool. They will also be capable of elaborating basic short-investment cases, to discuss both single stocks, on the basis of common analytical measures, and equity portfolio choices, illustrating the rationale for a quantitative allocation, following risk/return efficient concepts, in line with professional standards used in the financial community. All analyses are focused on practical examples and applications, linked to the stock market current situation, and supported by some useful techniques, utilizing common database and spreadsheet calculations. Students are strongly encouraged to participate actively to the lectures.

504844 - Probability And Stochastic Processes (9 ECTS)

This is a course on Probability and Stochastic Processes, having economic and financial applications in view. Accordingly, after introducing some basic notions of probability theory (including conditional expectation), lectures will focus on those processes which are popular in finance, including martingales, sub-martingales, Brownian motion and exponential martingale. As far as possible, technicalities are avoided. Various exercises and applications will be discussed as well. At the end of the course the Student will be able to deal with the basic tools of stochastic analysis necessary to attend the course of Quantitative Finance.

504845 - QUANTITATIVE FINANCE (6 ECTS)

The aim is to give some fundamental notions about a probabilistic approach to some financial problems, making use of the theory of stochastic processes.

509895 - NUMERICAL OPTIMIZATION AND DATA SCIENCE (6 ECTS)

This course will review the theory and applications of Data Analysis and Numerical Optiization, illustrating the main results and the applications of the theory to practical problems.

500000 - Thesis (24 ECTS)

Can be transferred from Stevens FA900