## Product Description

## Overview

Where does machine learning show up in finance? Does it enhance portfolio analytics, risk analytics, or trading? By knowing where machine learning currently fits into these fields, you can be ready to incorporate enhancements as they are discovered. Machine learning techniques as subtle as out-of-sample testing can enhance portfolio optimization. Logistic regression combined with clustering algorithms can be used to not only predict risk of default, but more importantly, backtest and manage a loan portfolio. Machine learning competitions on Kaggle display a multitude of machine learning algorithms used for winning trading strategies or for superior credit risk estimation.

## Details

- Course Time: Mondays & Wednesdays | 7:00 PM – 9:30 PM EST
- Venue: 500 8th Ave., Suite 905, New York, NY 10018 (5 min from Penn Station)

## Goal

## Who Is This Course For?

## Prerequisites

It will be challenging to follow along through the code demos and exercises without some experience in:

- General coding
- Package installation
- Loading data

- General coding
- Loading data
- numpy
- pandas
- Matplotlib
- scikit-learn

- Algorithms
- Logistic Regression
- Decision Trees
- Concepts
- Cross Validation
- Regularization

- Finance
- Basic Assets: Stocks & bonds
- Basic Time Value of Money Calculations

## Outcomes

- Model the risk of stock and bond portfolios
- Manage a portfolio of loans
- Optimize a portfolio of assets
- Build your own high frequency trading strategies
- Model the risk of disparate investments and projects

## Syllabus

**Risk 1**

- Options, Swaps, & Futures
- Value at Risk
- Distributions, FreqNSeverity, Maximum Likelihood
- Distribution fitting
- Copula Simulation

**Risk 2**

- Fixed Income & Credit Risk
- Duration & Convexity
- Yield Curve Splines
- Cash Flow Mapping
- Principal Component Analysis
- Yield Curve Simulation
- Probability of Default Estimation
- Loan Portfolio Risk Estimation
- Logistic Regression
- Parameter and Process Risk
- Portfolio segmentation and Backtesting

**Loan Portfolio 1**

- Logistic Regression & Regularization
- Backtesting
- Segmentation

**Loan Portfolio 2**

- Kaggle Competition Review: Loan Default Prediction – Imperial College London
- Kaggle Competition Review: Give Me Some Credit
- Kaggle Data Set: Lending Club Loan Data

**Portfolio Optimization 1**

- Capital Asset Pricing Model
- Arbitrage Price Theory
- Fama-French & Its Extensions
- Rolling or Walk-Forward (Out-of-Sample) Testing
- Verifying & Deriving Factor Models Using Clustering, PCA, & Ridge Regression

**Portfolio Optimization 2**

- Markowitz Portfolio Theory
- Constrained Portfolio Optimization
- Robust Portfolio Optimization Methods
- Variance, VaR, & Optimal CVaR

**Portfolio Optimization 3**

- Black Litterman (BL)
- Combining Trading Strategies & ML Models w/ Market Equilibrium through BL

**High Frequency Trading 1**

- Limit Order Book
- Market Microstructure
- Empirical and Statistical Evidence

**High Frequency Trading 2**

- ML Competition 1: Kaggle Algorithmic Trading
- Exploratory Data Analysis
- Winner Solution Review

**High Frequency Trading 3**

- ML Competition 2: Kaggle Two-Sigma Financial Modeling
- Exploratory Data Analysis
- High Ranking Solution Review