MA Financial Economics (MAFE)

Foundations: Economics and Finance (Sample of Required Courses)

• Finance I: Foundations and Capital Markets

  Prof. Yaniv Grinstein

  The first part of the course deals with the concept of “time value of money”: We then apply this concept to valuation of stocks, bonds, projects and firms. By the end of this first part of the course, I expect students to master the techniques necessary to evaluate streams of cash flows over time and have a thorough understanding of the issues associated with the timing of different cash flows. Financial decision making often involves risk and uncertainty, the focus of the second part of the course. This part is devoted to developing the necessary insight and techniques for financial decision making under uncertainty. To this end, we will discuss relevant measures of risk and use these measures to develop a workable theory that relates different levels of risk to different levels of expected return on financial and physical assets. We will then integrate the knowledge of dealing with cash flow that come in at different points of time and with different degrees of risk to devise capital budgeting techniques in the presence of risk. We will also introduce the fundamentals of Options and Futures.
• Finance II: Corporate Finance

  Prof. Yaniv Grinstein

  Financial decisions can be divided into two groups: investing and financing. In Finance 1 we focus mainly on investing decisions. In Finance 2 we will study corporate financing decisions and the interaction between investing and financing. We will discuss the choice of firms among issuing different securities, the decision to pay dividend and buy back shares, and how financing choices affect investment decisions.
• Financial Accounting I

  Dr. Ron Lazer

  This first part of the course provides an introduction to the financial statements and to the financial reporting process from a user's perspective. Students will learn the mechanics of moving from business transactions to the principal financial statements: balance sheet, income statement, and stockholder’s equity. The second half of the course will focus on various accounting topics including revenue recognition, inventories, short and long term liabilities, shareholders’ equity, and investments in associates. The course is oriented towards those who are or will be users, rather than preparers, of accounting information. Overall, the goal of the course is to provide students with a set of skills that can be used to read and analyze financial statements, and to prepare students for financial statement analysis courses.
• Financial Accounting II

  This first part of the course provides an introduction to the financial statements and to the financial reporting process from a user's perspective. Students will learn the mechanics of moving from business transactions to the principal financial statements: balance sheet, income statement, and stockholder’s equity. The second half of the course will focus on various accounting topics including revenue recognition, inventories, short and long term liabilities, shareholders’ equity, and investments in associates. The course is oriented towards those who are or will be users, rather than preparers, of accounting information. Overall, the goal of the course is to provide students with a set of skills that can be used to read and analyze financial statements, and to prepare students for financial statement analysis courses.
• Investments

  Prof. Gill Segal

  Investment Theory is an introductory survey investments course. We structure the course to study a deceptively simple question – how should one invest his/her current excess liquidity (e.g., funds contributed to retirement funds) to meet specific future liquidity needs (e.g., pay for retirement)? We start with an overview of the investment process. After discussing the link between risk and expected returns, we build tools to estimate risk and return on traded asset. We then turn to look at the various asset classes available to global investors and their underlying trading platforms. We then turn to study the asset allocation decision -- its formation and objectives. Using the discussion of asset classes’ performance, we analyze the portfolio optimization problem and its implication for asset allocation. Finally, we aggregate the asset allocation choices of individuals and link them to expected returns and risk as they come together in the CAPM. We conclude this discussion with an analysis of factor models. Next, we turn to look at market efficiency and its implications. After understanding what it means for markets to be efficient we go beyond passive investment and ask whether there are dynamic equity portfolios that can deliver – stock portfolios that have better tradeoff between risk and expected return than the market index. In so doing, we discuss various quantitative strategies (size, book-to-market, and momentum), show how they are constructed, and study their properties.
• Options and Futures Markets

  Dr. Yael Eisenthal

  This course provides an overview of derivatives markets, specifically forward contracts, futures and options. The markets for these instruments have grown enormously, and derivatives have become one of the most important tools of modern finance, from both the academic and the practical standpoint. Students will learn about the different aspects of these instruments, including risk profile, pricing, and uses in active investing and hedging. We will also cover advanced applications and discuss derivatives in the context of historical cases and current events.
• Final Project/Thesis Workshop

  Prof. Doron Avramov, Dr. Oren Levintal

  The MA in Financial Economics requires students to submit a final project or a Thesis as part of graduation requirements. The goal of this seminar is to help and advise students in preparing their final project or thesis. The seminar will involve lectures on generating research ideas and conducting research and presentations by students on student ideas, proposals, and results.
• Microeconomics Foundations for Financial Economics

  Dr. Alon Cohen

  The aim of this short course is to introduce students who did not study Economics as an undergraduate discipline to the principles of microeconomics.
• Macroeconomics Foundations for Financial Economics

  Dr. Avihai Lifschitz

  The aim of this short course is to introduce students who did not study Economics as an undergraduate discipline to the principles of macroeconomics. The course centers on the neoclassical framework, which is the basic model, but also the Keynesian framework is presented.
• Policy Decisions by Central Banks: The Fed, the ECB and the BOI (Macro II)

  Prof. Zvi Eckstein

  This class focuses on understanding and predicting Central Banking (CB) decision making and how monetary policy is conducted today. We provide the methods and knowledge on how central banks think and implement policies to reach the goals of price and financial stability as well as support of growth and employment. The core of this section connects economic knowledge, legal frameworks and the goals and methods followed by central banks. We explain the rationale for the policy prescriptions implemented today mainly by the Federal Reserve Bank (Fed) in the US as well as by the European Central Bank (ECB), Bank of Israel (BOI) and some other major countries. We discuss the Covid-19 crisis and the events before and after the 2008 financial crisis. Students will simulate and forecast upcoming policy decisions based on current data and the latest theory. We shall simulate in class current decisions based on assignments related to past policies and the theory presented in class.  

Foundations: Analytical Tools (Required)

• Econometrics I

  Dr. Doron Israeli

  The goal of this course is to equip students with knowledge, tools, and experience necessary to conduct rigorous empirical investigations in financial economics and perform critical evaluation of econometric analyses reported in academic articles and in the media. To achieve this goal, the course will (1) familiarize students with key principles of multiple regression analysis, (2) provide experience with professional implementation of classical and modern econometric methods, using computation and visualization software R, and (3) offer a solid foundation for more advanced courses in econometrics, machine learning, and Big Data analytics. Topics covered in these courses are essential for financial economists and students with career aspirations in consulting, active investment management, and Big Data analytics. Students who already took undergraduate level courses in statistics and econometrics will be familiar with some of the topics. However, the focus of the two-course sequence will be on deep understanding and professional implementation of various methods in R. Also, the level required in these courses will be higher than that in undergraduate courses.
• Econometrics II

  This course completes the two-course sequence in graduate econometrics. The ultimate goal of the two-course sequence is to provide students with knowledge and tools necessary to conduct rigorous empirical investigations in financial economics and to critically evaluate econometric analyses reported in academic studies and in the media. As the second course in the sequence, Econometrics II aims to (1) cover the theory and financial-economics-related applications of rigorous linear regression analysis using cross-sectional data, and (2) expose students to tools necessary for professional implementation of linear-regression-related methods in R. The topics in this course are essential for financial managers and financial economists with career aspirations in financial consulting and active trading in capital markets.
• Mathematics for Financial Economists

  Dr. Yossi Shamai

  The course consists of four parts, given in a 4 week course: Part 1: One variable calculus: We go through the basic concepts in calculus of one variable that are relevant to economics: limits, continuity, the mean value theorem, weirstrass's theorem, derivatives, rules of differentiation, Fermat, Rolle and Lagrange's theorems, L'hopital's rule, Convex and Concave functions, graphing functions. Integrations, Lorentz curve and the Gini index. Taylor approximation. Taylor series. Part 2: Linear algebra: Gauss Jordan elimination process. Matrix multiplication and inversion. The transpose matrix. Rank, determinants. Eigenvalues and eigenvectors, and diagonalisation. Quadratic forms Part 3: Calculus of several variables. Norms and inner products. Limits and continuity. Partial derivatives. The Jacobian and Hessian matrices. Young's theorem, the chain rule. The implicit function theorem. Multi-dimensional integration. Critical points, optimization, constrained optimization. Part 4: Probability and statistics: the exclusion-inclusion principle. Conditional probability. Law of total probability. Baye's law. Independent events. Probability distribution function, probability density function. Cumulative distribution function. Expectation, variance and moments . Joint distribution. Correlation.
• Introduction to R Programming

  Dr. Doron Israeli

  The goal of this course is to familiarize students with fundamental programming concepts of the computation and visualization software R and equip students with necessary tools for professional analysis and visualization of large-scale data sets (Big Data). Among other things, the course will teach students how to (1) generate, manipulate, and combine data structures commonly encountered in financial economics; (2) read data from various sources and write data files in various formats; (3) deal with date and date-time variables, (4) create and write graph files in various formats, and (5) conduct professional univariate and multivariate data analyses. The topics covered in this course are essential for students with career aspirations in financial consulting, Big Data analytics, and for MA in Financial Economics (MAFE) students who aim to enroll in graduate Econometrics I and Econometrics II courses.

Financial Markets (Electives)

• Active Portfolio Management

  Prof. Doron Avramov

  The investment world has been going through a radical revolution over the past decade and is expected to keep the ongoing trends for the years to come. Until recently, traditional asset management has been the dominating approach. The approach focuses on old school fundamental analysis intended to understand the firm's economic environment and the quality of its management. However, based on decades of investment performance, we can safely say that traditional approaches have altogether failed to accomplish the goal of creating value for investors. Hence, the profession tilts into automated trading strategies that involve little emotional trading and focus, instead, on newer and older signals known to predict asset returns, such as momentum and value. Automated strategies have become feasible with the evolution of Artificial Intelligence and the increasing number of predictive signals discovered by academics and practitioners. An essential question follows: how can one formulate component strategies when the number of investable assets is ever increasing and the number of predictive signals is in the hundreds? This course aims to provide a comprehensive analysis of machine learning methods that process a huge number of signals and ultimately deliver possibly outperforming strategies. We will cover both shallow learners as well as deep learning approaches involving neural networks and its extensions. The workshop is advanced and targets students with some quantitative background in Econometrics. In the end of the workshop, students will get background sufficient to formulate automated trading strategies.
• Advanced Bond Markets

  Prof. Jacob Boudoukh

  This course describes the important fixed income securities and markets, and in turn develops tools for valuing these securities and managing their interest rate and credit risk. Historically, fixed-income refers to securities which promise fixed cash flows over their lives. Now, we generally view any fixed-income instrument as one in which its value depends on the level of interest rates and/or the health of the underlying assets. Thus, along with an analysis of fixed-rate bonds, we will also look at other securities, such as floaters, inverse floaters, bond options, caps/floors, callable bonds, interest rate swaps and mortgage-backed securities.

  The study of fixed income securities is highly quantitative in nature. Students should be comfortable with mathematics such as algebra, linear algebra and basic calculus, as well as statistical concepts such as probability distributions, mean, variance, covariance, and regression. Students are expected to be very familiar with a spreadsheet package like Excel (including, for example, its solver function).
• Practicum in Financial Economics

  Prof. Ronen Israel

  This course introduces students to different career opportunities post-graduation. It is designed to give students supervised practical application of material covered in classes. The course includes a study trip to London, which includes sessions with prominent figures in global financial firms. The course content and schedule may vary based on the hosting firms and lecturers.

Corporate Finance (Electives)

• Valuation

  Dr. Tal Mofkadi

  The objective of the course is to discuss corporate valuation tools and to illustrate their use. The course is structured according to the main components of corporate valuation: revenue projection, pro-forma financial models, cost of capital estimation, valuation with multiples, and division of firm value among different security holders.
• Topics in Corporate Finance

  Prof. Yaniv Grinstein

  The Purpose of this course is to introduce the students to advanced topics in Corporate Finance. The topics that will be covered are as follows: a. Valuation, with an emphasis on evaluating real options and the use of decision trees. b. Bankruptcy and financial restructuring. c. Financing sources and choices among financing sources d. Takeovers/hostile takeovers and shareholder activism. The course is composed of lectures and cases that students will need to solve. The cases illustrate using real-life examples how to implement the tools and knowledge studied in class.
• Financial Statement Analysis and Valuation

  Dr. Ron Lazer

  The objective of the course is to provide students with hands-on experience in financial
  statement analysis. Students will be exposed to the theoretical concepts of fundamental
  analysis, general tools of financial analysis, and practical valuation issues. By the end of the
  course, students should be comfortable with using firms' financial statements (in conjunction
  with other information) to develop an understanding of their performance and to establish a
  basis for making reasonable valuation estimates. The course does not assume market
  efficiency - it develops techniques for challenging traded prices, to assess whether
  investments are appropriately priced.

Fintech and Data Analytics (Electives)

• Fintech - Finance, Business, and Technology

  Prof. Kogan Shimon

  Technology is playing an increasingly dominant role in the financial service industry. It is changing how existing players operate and it is creating new ways to deliver core services like saving, investing, borrowing, and transacting. The course provides an overview of the most significant technological advances that are radically changing the industry, focusing on AI and Blockchain. We will analyze how these technologies create value in the financial industry by lowering frictions — from unit processing cost, through asymmetric information and network effects. The course will integrate a high-level discussion of the competitive landscape and the market opportunities for new entrants, with an in-depth understanding of the technologies and their applications. We will do so by focusing on three areas in which these technologies are driving change: (I) Lending, (II) Clearing (III) Trading. In each of these areas, we will cover examples and developments from (1) marketplace lending, (2) blockchain and distributed ledgers, (3) quantitative trading and its use of non-standard data and analytics. In each of these areas, we start by analyzing the marketplace, the incumbents, and the strategies of the incoming technology-based new entrants. We then proceed to understand the relevant technological applications in each area using real-world data.
• Data Science for Finance

  Prof. Shimon Kogan

  Information is the bloodline of capital markets. The proliferation of data sources (“alternative data”) along with the rise of new tools that help extract meaning out of such data (“machine learning”) are therefore shifting industries that interface with capital markets. The course aims to prepare students for a wide range of careers in the financial industry and consulting, including asset management, and it places a strong emphasis on financial economics and data analysis.

  The first part of the course (Mini 4) is designed around a set of modules, each centered around a practical application relevant for capital markets. Students investigate these questions by thinking about the research design, implement it using Python and its ecosystem of packages (e.g., Numpy, Pandas, Scikit-Learn), and learn to map the analysis into measurable results. Each module goes through the data acquisition, data cleaning, visualization, and analysis process. Within each application, we develop a different machine learning approach and apply it. These include both supervised regression methods (e.g., Lasso, Ridge, Elastic Net), supervised classification methods (e.g., Decision Trees and Random Forest), and unsupervised methods (e.g., PCA).
• Data Science for Finance Workshop

  Prof. Shimon Kogan

  Information is the bloodline of capital markets. The proliferation of data sources (“alternative data”) along with the rise of new tools that help extract meaning out of such data (“machine learning”) are therefore shifting industries that interface with capital markets. The course aims to prepare students for a wide range of careers in the financial industry and consulting, including asset management, and it places a strong emphasis on financial economics and data analysis.

  The first part of the course (Mini 4) is designed around a set of modules, each centered around a practical application relevant for capital markets. Students investigate these questions by thinking about the research design, implement it using Python and its ecosystem of packages (e.g., Numpy, Pandas, Scikit-Learn), and learn to map the analysis into measurable results. Each module goes through the data acquisition, data cleaning, visualization, and analysis process. Within each application, we develop a different machine learning approach and apply it. These include both supervised regression methods (e.g., Lasso, Ridge, Elastic Net), supervised classification methods (e.g., Decision Trees and Random Forest), and unsupervised methods (e.g., PCA).
• Crypto Currencies and Decentralized Finance

  Prof. Shimon Kogan

  This course will explore the emerging field of cryptocurrencies and decentralized finance (DeFi). Students will gain a deep understanding of the technological underpinnings of these systems, including blockchain, consensus algorithms, and smart contracts. The course will also cover the economic, legal, and regulatory aspects of these new financial instruments, including their potential impact on traditional financial markets. Topics covered will include the development of cryptocurrencies and their emergence as an investment asset class, the functions of decentralized exchanges and lending platforms, the creation of digital assets and tokenization, and various forms of stable coins. In addition, students will have the opportunity to engage with current research and projects in the field through readings, discussions, and hands-on projects. Upon completion of the course, students will have a comprehensive understanding of the cryptocurrency and DeFi landscape and be well-positioned to critically evaluate and contribute to the ongoing development of these innovative financial systems.

Banking Macroeconomics and Monetary Policy (Electives)

• Risk Management in Financial Institutions

  Prof. Jacob Boudukh

  The course focuses on risk measurement in financial institutions. The Value at Risk approach will be explained and discussed critically. The properties of asset prices and methods to accommodate for them within the common framework will be presented. More advanced topics such as the risk of derivatives and methods for stress testing and scenario analysis will be presented.
• Topics in Global Economics

  Dr. David Woo

  The efficiency market hypothesis says that it would be impossible to beat the market over time. However, this has not stopped millions of professional and amateur investors from trying to do exactly that every day. This course is designed to help prepare students who want to work in financial markets with a basic tool kit that hopefully will provide them with an edge in macro investing (currencies, rates, commodities and equity indices). At the end of the course, students are expected to have a good understanding of the interaction between economic fundamentals and asset prices and how different markets are linked. Students will also develop a rudimentary investment framework with macro, quant, flow and technical drivers. Although we will leverage the latest academic literature, this is a very hands-on course intended to help students develop practical skills that will help them transition to financial market related positions on both the sell-side and buy-side smoothly.
• International Finance

  Prof. Leonardo Leiderman

  This course will deal with the joint determination of exchange rates and interest rates within the theoretical and practical area of international finance. Special emphasis will be given to the links between domestic and foreign monetary policy, including foreign exchange market intervention, and the behavior of exchange rates, capital flows and output. We will discuss international financial aspects of various types of crises and will derive conclusions from the experiences of, for example, the 2008 Great Recession (or Subprime Crisis), the Greek and Eurozone Crisis of 2011, the COVID-19 episode, and ongoing developments under the war in Ukraine. The analysis will also cover international financial themes, including crises, in Emerging Market Economies. 

Research Focus Students interested in a research focus can choose to submit a thesis instead of a final project. Students who do so are required to take only five elective courses (instead of seven), and are encouraged to take research workshops as electives.