Financial Technology & Analytics and Finance Dual Master's Degrees

Financial Statistics - 19325 (3 ECTS)

This course is classified in the area of quantitative methods.The student learn about the basic concepts on the analysis of financial time series. Also, basic models to represent and forecast the evolution of these series are described. Instruments useful for theoretical financial models are also considered.

The first part of the course deals with basic concepts in the analysis of time series which are basic for the analysis of financial data. In particular, the students learn about the difference between independence and uncorrelatedness, white noise and martingale difference. In the second part of the course, the basic models to represent the evolution of the conditional mean of time series are described. Finally, the last part of the course deals with models to represent the evolution of volatilities which are central to many financial models.

Financial Markets - 19326 (3 ECTS)

The content of the program is divided into 8 sessions that cover the main aspects of the financial system. After an introductory track to financial markets the section deals with liquidity aspects. Also we will focus our attention to other financial markets such as Stock Exchanges, Derivatives, Fixed Income and interbank market. When it becomes necessary for each of the markets discussed above products traded and the form and procedure in which these products are created and then traded. Finally, we examine the role of European Central Bank, its monetary policy and regulatory bodies.

Fixed Income 19327 (3 ECTS)

This course consists of three sections:

1. The first section focuses on the essentials of the fixed-income markets. This section starts with defining elements, which surveys the diversity of fixed-income securities and provides details on the different features of all types of bonds. Next, we focus on issuance, trading, and funding in fixed-income markets and describe the markets, venues, and conventions for bond trading.

2. The second section of the course deals with several quantitative issues that are especially relevant in fixed-income markets. In more detail, this section focuses on valuation and interest rate risk in these markets jointly with an introduction to the term structure of interest rates. First, the introduction to fixed-income valuation provides a basic understanding of the methods used to value fixed-income securities and to determine relative values between them. Owning fixed-income securities entails various risks. After this valuation, the course deals with identifying and quantifying interest rate risks with special emphasis on risk measures as duration and convexity. Finally, the term structure of interest rates is presented and discussed

3. The third section of the course will be presented by a practitioner and focuses on a practical perspective of fixed-income products.

Select 2 out of 3.

Financial Accounting - 19332 (3 ECTS)

This course is designed to provide students with the elementary foundations of basic accounting theory. The focus will apply the basic objectives, principles and procedures of accountancy in the preparation, analysis, interpretation, communication and reporting of general-purpose financial statements. The first part of the course is a general introduction of financial accounting information, financial accounting rules, and the four financial statements. The second part of the course focuses on financial accounting cycle (from journalizing business transactions to financial statements preparation and closing accounts). The third part of the course introduces the accounting of each part of the balance sheet (current assets, long-term assets, liabilities, and equities) in detail.

Macroeconomics - 19333 (3 ECTS)

This course will study economic models to prepare students to analyze the macroeconomic environment under which policies are designed. Students will learn to think critically about the limits of any economic analysis and will be able to question economist´s assessments with solid arguments. They will develop the ability to communicate and discuss economic issues and will improve their confidence and leadership skills through a better understanding of the economic environment where they live.

Computer Science For Finance - 19334 (3 ECTS)

Students will receive an introduction to programming using MATLAB technical computing environment as the main platform. They will be able to use the desktop environment and the basic command-line instructions. They will then develop the competency of developing their own programs using MATLAB. These will potentially include flow control elements, input and output control, charts and functions, among other components.

Financial Statement Analysis - 19328 (3 ECTS)

Learn how to analyze financial statements and to rate and value companies. From the user perspective, the course develops a framework for business analysis and valuation that can be applied to multiple decision contexts. Two main parts are identified: (1) Fundamental Analysis (analysis of corporate strategy and industry analysis, analysis of earnings quality, profitability and ratio analysis, bankruptcy detection, etc.), and (2) Prospective Analysis (forecasting and valuation).

Investment - 19329 (6 ECTS)

This course provides a rigorous panoramic analysis of the interplay between portfolio selection, asset pricing theory, and the empirical evidence. After a traditional discussion on risk aversion and mean-variance portfolio choice, we present the main asset pricing models (including the CAPM, multi-factor asset pricing models and Conditional Asset Pricing Models). A complete description of Investment Companies (mutual funds, hedge funds, among others) are presented, and the most relevant performance measures. Students will learn how to evaluate a portfolio or a portfolio manager using both the traditional and newest performance measures. Finally, the most relevant problems of the mean-variance model are presented and potential improvements of this traditional way to manage a portfolio. This course combine both theoretical foundations and practical exercises using real data from financial markets or mutual funds industry.

Derivatives - 19330 (3 ECTS)

The student will learn how to price and use vanilla derivative products written on equity and commodities. This includes designing a hedging strategy against financial and commodity price risk using positions in forwards, futures options and volatility. This requires the skills to compute precisely the value of any position in derivatives at any given point in time as well as to foresee its dynamics over time as a response to changes in the underlying variables.

Econometrics for Finance - 19331 (3 ECTS)

This course aims at providing the student with basic econometric skills used in empirical economic research. This goal will be accomplished through classroom lectures, practical sessions, and problem sets. Specifically, by the end of the course the student should be able to apply basic linear regression techniques in economic problems and use appropriate software (Eviews) to implement quantitative research.

Corporate Finance - 19335 (3 ECTS)

Students will develop the basic skills to value companies, focusing on the main concepts and methodologies: weighted average cost of capital, capital structure, comparables methodology and DCF methodology. Additionally they will get familiar with the basic concepts impacting valuations and transaction structuring from a corporate finance viewpoint: synergies, control premium, hybrid securities, etc.

Risk Management - 19336 (3 ECTS)

This course introduces the different types of risk financial entities face, with a special focus on market risk, and the guidelines that they follow to properly manage them.
Students will learn different hedging techniques and assess the market risk of a portfolio with the traditional measures used in the industry (Value at Risk and Expected Shortfall), understanding their differences and limitations and testing their accuracy through backtesting analysis.

In addition to managing the risk of a portfolio, the course provides an overview of how the structural risk is managed in financial entities

Finally and given the increasingly important role of regulators in the banking industry, the course summarizes the current European regulatory framework.

Select 3 out of 4.

Advanced Derivatives - 19337 (3 ECTS)

This course consists of three parts. The first part refers to fixed income derivatives: we will study interest rate swaps, plain vanilla options (caps and floors) and swaptions. Also we will study complex payoffs as Constant Maturity Swaps, quanto options and we will review the Monte Carlo engine: Hull-White or Libor Market Model. The second part studies equity derivatives using the Black-Scholes-Merton option pricing framework for pricing non-vanilla options and Monte Carlo methods for pricing exotic options (path-dependent) and their use to design structured products. The third part covers the impact in the valuation techniques due to the changes in the market: OIS discounting, counterparty issues, funding cost, negative rates and the topics where the regulators put the focus: observability of inputs, levelling, etc.

Risk Management, Central Banks - 19338 (3 ECTS)

Students will understand the role of banks in the economy and the challenges faced by regulators. They will be able to asses the positive and negative consequences of bank regulation and their impact on financial stability.

International Finance - 19339 (3 ECTS)

Nowadays it is essential not only the knowledge about the theory but also the understanding of how the financial markets work from the inside. Either to start your career within the financial industry or to work in a private or public entity outside this financial industry, there are some key markets you will be in contact with if you want to have a global perspective for your company.

This subject will be focus on one of the most relevant financial markets in the world; the Foreign Exchange Market (FX). Taking advantage of the teacher's privileged position within the Corporate & Investment Banking - Global Markets unit of a universal bank, we will provide the students with a clear understanding about the following concepts; market fundamentals, products traded in FX markets, who are the main market participants, what regulation is changing and why it will be determinant in the coming future, skills needed in the most important roles of the FX industry, and why Strategists are so relevant within the FX value chain.

Advanced Portfolio Management - 19340 (3 ECTS)

The first part of the course focuses in introducing alternative investments. The second part focuses on investment process in Hedge Funds and their investment strategies. The third part is devoted to Real Estate, and advanced portfolio strategies.

Select 3 out of 5.

Corporate Governance And Corporate Social Responsibility - 19341 (3 ECTS)

The aim of the course is to discuss how decision power is assigned inside the firm and how to handle the efficiency problems generated by the separation of ownership and control. Topics covered include optimal allocation of control rights, conflicts of interest between shareholders and debt-holders, conflicts of interest between shareholders and manager, concentrated versus dispersed ownership structures as a control mechanism, the design of managerial incentives, the use of M&A, leveraged buyouts and dividend policy as control mechanisms and the discussion of the stakeholders' society and Corporate Social Responsibility as alternatives to the prevailing shareholders' value maximization paradigm. These topics will be covered both at a theoretical and empirical level. The theory and empirical evidence will be covered in the lectures. But the exercises and the presentations of the cases and empirical papers are fundamental in order to grasp the practical applications of the theory.

Energy Markets And Commodities - 19342 (3 ECTS)

The course provides an overview of commodities markets with a focus on energy markets. Students will learn not only the main characteristics and determinants of commodities markets and commodities futures prices but also how to employ financial techniques (risk management, valuation and investment analysis) into the context of commodities markets. Students will also see with practical examples how these techniques are applied in real life into energy companies, achieving skills to manage large database and work with real empirical problems.

Advanced Financial Statistics - 19343 (3 ECTS)

Financial econometrics is the intersection of statistical techniques and finance to ascertain how financial prices are determined and to test models that try to replicate how financial markets work. The course will cover the tools of financial econometrics and empirical finance with a moderate degree of sophistication, starting by introducing the extensions to the basic generalized autoregressive conditional heteroskedasticity model (GARCH) in terms of statistical properties, estimated parameters, and volatilities. Then, we will learn to differentiate between volatility, uncertainty, and risk, learning to apply these concepts to different financial assets. Finally, we will learn to estimate the implied volatility of equity indices and discuss the information contained in the implied volatility term structure and smile. Over the whole course, there will be a heavy emphasis on applications.

Advanced Corporate Finance - 19344 (3 ECTS)

Students will gain a critical understanding of advanced topics in corporate finance, such as mergers and acquisitions, initial public offerings and venture capital investment. They will become familiar with the techniques used by practitioners and the institutional framework involved in these transactions; while gaining a sound theoretical background that will allow them to evaluate the appropriateness of "commonly used" practices for their own purposes.

Advanced Risk Management - 19345 (3 ECTS)

This course is designed to train students in managing financial risks with special focus on credit risk and operational risk. The course starts by introducing the main concepts of credit risk and its background, then the most important credit risk measurement procedures and models are presented. Later, the course addresses how credit risk is transferred and hedged, and the financial instruments and techniques required to do so. Finally, the course deals with counterparty risk. In addition, the course introduces operational risk from a conceptual perspective and provides examples of its materialization.

Students will work with technical and complex problems in Finance and look for a solution in the Academic references. They will apply scientific methods for different business and financial problems, organize and write a technical or academic report, and present and defend it from a group of professionals or experts.

Students will find the answers for new and specific problems from academic publications. In addition, the student will improve their capacity to criticize and propose improvements in methodology or in the hypothesis. Students will also develop their summarizing, analyzing and planning skills by creating a summary about several academic papers on a specific topic.

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

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.

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.

This course is designed to teach students the nature and availability of the financial data available at Stevens. The focus of the course will be on equity, futures, FX, options, swaps, CDS’s, interest rate swaps etc. They will learn to how use a Bloomberg terminal. As part of the course the students will be certified in the 4 areas that Bloomberg offers certification. We will cover the Thomson–Reuters Tick history data and basics of using this data. The course also introduces basics of applied statistics. Bloomberg terminal access will be required for any student taking the course on the web.

In this course the students will learn the basics of the open source programming language R. The language will be introduced using financial data and applications. Basic statistical knowledge is required to complete the course. The course is designed so that upon completion the students will be able to use R for assignments and research using data particularly in finance.

This course is a primer on Python (language syntax, data structures, basic data processing, Python functions, modules and classes). The remainder of the course covers open source Python tools relevant to solving financial programming problems. The lecture, supporting examples, and practical applications are intertwined. The content will be delivered in a fully equipped financial computing laboratory where the students are immersed in case studies of real life applications. There will be reading assignments of the corresponding chapters in the textbook and additional materials will be provided.

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.

Prerequisite: BIA 652 or MGT 700 or FA 541

Three credits for the degree of Master of Science (Financial Analytics). This course is typically conducted as a one-on-one course between a faculty member and a student. A student may take up to two special problems courses in a master's degree program. A department technical report is required as the final product for this course.

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

The course introduces required techniques and fundamental knowledge in data science techniques. It familiarizes students with database and data analysis tools, teaching them to manage databases and solve financial problems using R. 

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.

This course is designed to teach students the nature and availability of the financial data available at Stevens. The focus of the course will be on equity, futures, FX, options, swaps, CDS’s, interest rate swaps etc. They will learn to how use a Bloomberg terminal. As part of the course the students will be certified in the 4 areas that Bloomberg offers certification. We will cover the Thomson–Reuters Tick history data and basics of using this data. The course also introduces basics of applied statistics. Bloomberg terminal access will be required for any student taking the course on the web.

In this course the students will learn the basics of the open source programming language R. The language will be introduced using financial data and applications. Basic statistical knowledge is required to complete the course. The course is designed so that upon completion the students will be able to use R for assignments and research using data particularly in finance.

This course is a primer on Python (language syntax, data structures, basic data processing, Python functions, modules and classes). The remainder of the course covers open source Python tools relevant to solving financial programming problems. The lecture, supporting examples, and practical applications are intertwined. The content will be delivered in a fully equipped financial computing laboratory where the students are immersed in case studies of real life applications. There will be reading assignments of the corresponding chapters in the textbook and additional materials will be provided.

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.

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.

Prerequisite: BIA 652 or MGT 700 or FA 541

The course will introduce concepts of Blockchain technologies as they apply to decentralized finance. The course starts with cryptocurrency and advances the concept of smart contracts as they apply to financial instruments. The course is technical and requires knowledge of programming in Python as well as financial instruments and concepts. Programming in solidity is learned throughout the class. The course discusses risk management concepts, stable coins as well as how regulations may impact the area.  

Prerequisite: Technical background from either FE, FA, BI&A, or CS. Basic finance principles acquired through FE620, FA 535 or equivalent. Basic programming skills in Python, FE520 or equivalent. 

This course introduces students to the up-to-date payment systems and innovative financial technologies (FinTech) in the payment systems. Common payment systems such as checking, ACH, cards, cash, wire transfer are discussed. Students learn the mechanisms of FinTech innovations, including the Non-Fungible Tokens (NFTs). Students also learn how to set up digital wallets and interact with the blockchain.  

Prerequisite: FA 591 and [FE520 Python, or FE515 R, or FE516 Matlab], or instructor permission 

Three credits for the degree of Master of Science (Financial Analytics). This course is typically conducted as a one-on-one course between a faculty member and a student. A student may take up to two special problems courses in a master's degree program. A department technical report is required as the final product for this course.