Machine Learning in Finance

Machine Learning in Finance

Machine Learning in Finance

Intermediate

This course is a dense presentation of machine learning (ML) tools used in financial risk management, portfolio management, and trading. Ten classes are offered: two on risk management, two on loan portfolio management, three on portfolio optimization, and three on high-frequency trading. The risk classes cover the risk measurement of financial assets using distribution fitting, copulas, PCA, and splines. The loan portfolio management classes cover risk estimation and backtesting using logistic regression, regularization, clustering methods, and the applied statistics concepts such as parameter and process risk. Kaggle competitions for loan portfolios which used tree-based algorithms for predictions are also reviewed. The classes on portfolio optimization introduce classic theories for asset return estimation and their extensions (multi-factor models) while using unsupervised & supervised ML methods to verify & derive new factors; modern portfolio theory using constrained optimization & robust methods; and Black-Litterman model portfolios where asset-specific, ML-derived models are integrated. The classes on trading introduce the limit order book and market microstructure and then move on to tour the winning strategies of to Kaggle competitions on trading. The feature engineering and code of the winning solutions are reviewed in depth.

Course Overview
Intermediate

This course is a dense presentation of machine learning (ML) tools used in financial risk management, portfolio management, and trading. Ten classes are offered: two on risk management, two on loan portfolio management, three on portfolio optimization, and three on high-frequency trading. The risk classes cover the risk measurement of financial assets using distribution fitting, copulas, PCA, and splines. The loan portfolio management classes cover risk estimation and backtesting using logistic regression, regularization, clustering methods, and the applied statistics concepts such as parameter and process risk. Kaggle competitions for loan portfolios which used tree-based algorithms for predictions are also reviewed. The classes on portfolio optimization introduce classic theories for asset return estimation and their extensions (multi-factor models) while using unsupervised & supervised ML methods to verify & derive new factors; modern portfolio theory using constrained optimization & robust methods; and Black-Litterman model portfolios where asset-specific, ML-derived models are integrated. The classes on trading introduce the limit order book and market microstructure and then move on to tour the winning strategies of to Kaggle competitions on trading. The feature engineering and code of the winning solutions are reviewed in depth.

February Session
$3990.00
Early bird pricing
$3790.50
February Session
Feb 19 - Mar 21, 2018, 7:00-9:30pm

Date and Time

February Session Early-bird Pricing!

Feb 19 - Mar 21, 2018, 7:00-9:30pm
Day 1: February 19, 2018
Day 2: February 21, 2018
Day 3: February 26, 2018
Day 4: February 28, 2018
Day 5: March 5, 2018
Day 6: March 7, 2018
Day 7: March 12, 2018
Day 8: March 14, 2018
Day 9: March 19, 2018
Day 10: March 21, 2018
$3990.00$3790.50
Add to Cart

Instructors

Wes Aull
Wes Aull
Wes Aull, CPA/ABV works as portfolio manager & data scientist at JTW Capital while editing the blog Economic(a). His research interests include fundamental analysis, game theory, network science, data visualization, directional statistics, and algorithms relevant to nonlinear/periodic function approximation. He earned his M.B.A. from Columbia Business School, Master’s of Professional Accounting from University of Texas-Austin, and B.S. Mathematical Economics from University of Kentucky.
David Romoff
David Romoff
David Romoff is a risk management consultant with 10 years of experience modeling market and credit risk using the latest methods and technologies. David's recent work includes serving as Manager of Risk Management at On Deck Capital, a business lending company in the FinTech space that uses machine learning models to underwrite loans. David was responsible for estimating and reporting losses on the book of loans. Previously, David worked in Enterprise Risk Management at AIG for five years where he designed and supported models on insurance risk, credit risk, and capital allocation. Before AIG, he worked at Bear Stearns in counterparty credit risk. David has an MBA from the Zicklin School of Business in New York City and a Master of Science in Actuarial Science from Columbia University. His undergraduate degree is from the State University of New York at Albany, where he studied psychology and philosophy.

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


Goal

 

The goal is to provide a bridge from knowledge of machine learning, programming, and statistics to a foundational understanding of how those resources are applied to finance. And ambitiously, the course also strives to summarize some current practices and empower students to take the next steps in their development of any of the three main topics: risk, portfolio management, and trading.

Who Is This Course For?

 

The course can be a great learning experience for traders, risk managers, portfolio managers, investors, and those looking to build skills to work in the finance industry. It is designed for those who already have a foundation in Machine Learning and want to see the tools and concepts applied to finance. The class will alternate between Excel, R, and Python. Basic knowledge of coding, machine learning, and finance are assumed.

Prerequisites

 

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

R Programming
  • General coding
  • Package installation
  • Loading data
Python Programming
  • General coding
  • Loading data
  • numpy
  • pandas
  • Matplotlib
  • scikit-learn
Machine Learning
  • Algorithms
    • Logistic Regression
    • Decision Trees
    • Concepts
    • Cross Validation
    • Regularization
  • Finance
    • Basic Assets: Stocks & bonds
    • Basic Time Value of Money Calculations

Outcomes

 

By the end of the course, you will be able to:
  • 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

Instructors

Wes Aull
Wes Aull
Wes Aull, CPA/ABV works as portfolio manager & data scientist at JTW Capital while editing the blog Economic(a). His research interests include fundamental analysis, game theory, network science, data visualization, directional statistics, and algorithms relevant to nonlinear/periodic function approximation. He earned his M.B.A. from Columbia Business School, Master’s of Professional Accounting from University of Texas-Austin, and B.S. Mathematical Economics from University of Kentucky.
David Romoff
David Romoff
David Romoff is a risk management consultant with 10 years of experience modeling market and credit risk using the latest methods and technologies. David's recent work includes serving as Manager of Risk Management at On Deck Capital, a business lending company in the FinTech space that uses machine learning models to underwrite loans. David was responsible for estimating and reporting losses on the book of loans. Previously, David worked in Enterprise Risk Management at AIG for five years where he designed and supported models on insurance risk, credit risk, and capital allocation. Before AIG, he worked at Bear Stearns in counterparty credit risk. David has an MBA from the Zicklin School of Business in New York City and a Master of Science in Actuarial Science from Columbia University. His undergraduate degree is from the State University of New York at Albany, where he studied psychology and philosophy.

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


Goal

 

The goal is to provide a bridge from knowledge of machine learning, programming, and statistics to a foundational understanding of how those resources are applied to finance. And ambitiously, the course also strives to summarize some current practices and empower students to take the next steps in their development of any of the three main topics: risk, portfolio management, and trading.

Who Is This Course For?

 

The course can be a great learning experience for traders, risk managers, portfolio managers, investors, and those looking to build skills to work in the finance industry. It is designed for those who already have a foundation in Machine Learning and want to see the tools and concepts applied to finance. The class will alternate between Excel, R, and Python. Basic knowledge of coding, machine learning, and finance are assumed.

Prerequisites

 

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

R Programming
  • General coding
  • Package installation
  • Loading data
Python Programming
  • General coding
  • Loading data
  • numpy
  • pandas
  • Matplotlib
  • scikit-learn
Machine Learning
  • Algorithms
    • Logistic Regression
    • Decision Trees
    • Concepts
    • Cross Validation
    • Regularization
  • Finance
    • Basic Assets: Stocks & bonds
    • Basic Time Value of Money Calculations

Outcomes

 

By the end of the course, you will be able to:
  • 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

Date and Time

February Session Early-bird Pricing!

Feb 19 - Mar 21, 2018, 7:00-9:30pm
Day 1: February 19, 2018
Day 2: February 21, 2018
Day 3: February 26, 2018
Day 4: February 28, 2018
Day 5: March 5, 2018
Day 6: March 7, 2018
Day 7: March 12, 2018
Day 8: March 14, 2018
Day 9: March 19, 2018
Day 10: March 21, 2018
$3990.00$3790.50
Add to Cart