Basics on Python: NumPy, pandas, SciPy, Plotly, seaborn, statsmodels, scikit-learn, arch and Object Oriented Programming (library free coding).
Basic notions on Data Management on pandas (data pre-processing, joins, vectorizing).
Implementation of:
-MonteCarlo simulations applied to Asset Pricing forecast (assuming the log-returns follow a GBM vs ARMA vs GARCH) and Intrinsic Valuation with DCF Analysis (Discounted Cash Flow) for M&A,
-Portfolio Management models (mean-variance optimization and efficient frontier).
-Asset Pricing models (Bond valuation, CAPM, Fama-French, Black-Scholes-Merton).
-Time Series econometric models for finance (linear regression and statistical tests, ARMA vs GARCH for asset returns).
-Technical Analysis (Momentum & reversion trading signals, Sentiment Analysis on Trump's tweets with VADER and with ML).
-Trading strategies and backtesting procedures
The main goal of this lecture is to automate reportings in corporate finance (M&A, PE, TS) and in asset management.
Notions of data management, database operations and data science (DBMS, relational, hierarchical, object oriented, NoSQL, integrity constraints, DDL, DML, DCL, database conception, database normalization, transaction, ACID properties, permissions, cryptography, General Data Protection Regulation)
Link between Excel and Access with VBA and SQL
Link between Excel and Outlook (mail) with VBA and HTML
Link between Excel and Python with VBA and Python scripts
Different types of reporting (overnight, portfolio management, reconciliation, client, manager, fund...)
SQL:
Chapter 1. Introduction (relational database, ERD, simple queries, projection)
Chapter 2. Arithmetic computation, string operations and dates
Chapter 3. Count, statistical application, aggregates and restrictions
Chapter 4. Merge (natural, inner, outter, multiple joins)
Chapter 5. Subqueries, Exists
Chapter 6. Misc (union, order and limit, intersection, difference)
Python: Python scripts on MacOS (Applescript) and Windows called by VBA macros. Use python course knowledge for better reporting and alternative to SQL for small datasets (pandas).
Computer Science Applied to Finance (lecture)
Bachelor 3rd year, Economics and Financial Engineering
2021 - Present
Basic notions of coding (typing, loop, function, user interface) and implementation in VBA of various technical analysis and finance models:
Monte Carlo simulations
Runs
High water mark and max DrawDown
Life cycle and retirement
Portfolio screening
Expected recovery
Replicating portfolio
Entry and exit signal indicator
Chapter 1. Some simple criteria
Chapter 2. Expected utility theory
Chapter 3. Notions derived from the utility expectation criterion
Chapter 4. Risk aversion measures
Chapter 5. Risk measurement
Chapter 6. Investment decisions in a risky universe
Microeconomics (TA)
Bachelor 1st year, Organisational Science
2020-2021
Microeconomics of the consumer and the producer. Introduction of mathematical tools such as constrained optimisation and Lagrangian. Introduction of economic concepts such as the utility function, the marginal utility, the budget constraint, the Pareto rule, or the welfare theorems.
Chapter 1. Introduction microeconomics of the producer
Chapter 2. Production cost
Chapter 3. Returns to scale
Chapter 4. Short term firm equilibrium
Chapter 5. Producer's equilibrium
Chapter 6. Equilibria and optimality