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A. Introduction

Lectures
1
Difficulty
Easy
Homework
Yes
Small Description
Machine learning in finance.
journals
This module is your first introduction to machine learning and finance.
You won’t become an expert in machine learning and finance overnight, this course would be highly exploratory, and my hope is to expose you to a board range of pertinent topics, as a result you are expected to do some self-study to perform well on the course assigned projects.
Gallery
Introduction Topics
This class preparation focuses on Pandas and NumPy exercises, these are crucial tools in finance.
1st Class Preparation Exercise

Many Forms of Machine Learning

Focus on two main areas:
1.
Supervised learning
There are at least one firm that doesn’t spend much money on data but has good performance. There are also firms that spend a lot on data, but they don’t perform well.
Summary
The above example looks at the relationship between how much funds spend on data and their performance.
•
In the first graph there is a weak, but visible relationship between money spent and performance. For every $1 spent, one more $1 is made in profit.
•
It generally seems to be true that firms that did not spend enough on data shut down.
•
There is no clear relationship, but the firms that spent a lot on data without additional performance closed down.
That is it, supervised learning is just predictions that we obtain by training a model (the lines we drew is the model).
2.
Unsupervised learning
VI = Value Investment, HF= High Frequency, QF= Quantamental Fund, PE = Private Equity Fund.
Clustering (left): you can see a clustering model successfully creating a statistical cluster that almost perfectly recovers the four types of funds (we eventually verify it with labels).
A. Introduction to Machine Learning
Machine Learning in Asset Management.pdf
Easy

Introduction

Finance is any activity that assesses and exchanges items of economic value.
Because machine learning is broadly used to (1) make predictions, (2) estimate values, and (3) uncover patterns, it can use be used for many tasks within finance.
Finance is when you exchange cash for a house; when you pay a premium for an equity stock option; when you extend a loan to a borrower; when you merge your companies by exchanging shares; when hedge your risk by buying a currency futures contract.
Every time you make a transaction that data is recorded, stored → this data can later be processed and used in a machine learning model.

Tasks

Machine learning allows you to use the data you collected to:
•
Predict what will happen next for an item of interest (predicting the stock market)
•
Estimate the value or other characteristic for an item of interest (valuing a firm)
•
Map and uncover relationships and patterns between items (optimizing a portfolio)
In a traditional finance class one come across various models and types of analysis.
•
We can use machine learning to replace and support some of the above abstractions.
•
We will be looking at a valuation model below, to see how machine learning can support our use of the Discounted cash flow model.
B. Machine Learning Applied to Finance
AI_FinTech_Poster_2019.pdf
britten-jones1999.pdf
Emperical Asset Pricing via Machine Learning - Kelly, Gu.pdf
Easy

History of Machine Learning in Finance

How did we, for example, get to the point where we can use machine learning in asset management?
You might be surprised to find that machine learning as we know it today have been part of a collective effort for a couple of centuries.
The first two use cases in Finance and Economics was indeed supervised machine learning, but not long after classification did reinforcement learning and unsupervised learning appear in applied finance research.
’’A computer will never tell you to buy one stock and sell another… (there is) no substitute …for flair in judgement, and a sense of timing.’’ - Wall Street Journal 1962.
Wall Street Journal, April 23, 1962, p. 4
Although economics and to extent finance has been slow to acknowledge modern machine learning, the development of machine learning and these domains are fundamentally intertwined as this sub-module will reveal.

Supervised Regression

Supervised Classification

Unsupervised Learning

Future Supervised and Unsupervised Innovations

Reinforcement Learning

C. History of Machine Learning in Finance
A Historical Note of Machine Learning in Finance and Economics - Snow.pdf
Easy

The Linear Regression

Before investigating advanced machine learning methods let's start with standard linear regression.
•
Machine learning researchers typically tries to avoid making strong assumptions about ff such as assuming that ff is linear and that xx is low-dimensional.
•
Traditional linear regression models, such as OLS regressions unfortunately makes these assumption and is therefore a highly constrained machine learning model.
However linear regression methods can provide us with a strong intuition of how more advanced methods work — moreover they have certain benefits that become useful when applied to finance.
We see many examples of linear models in finance and econometrics:
1.
The Capital Asset Pricing Model (CAPM) as well as
2.
Arbitrage Pricing Theory (APT) come to mind.
In fact, the Chartered Financial Analyst (CFA) Institute did a study that confirmed that linear regression remains the primary workhorse for financial modeling.
We will start with the most basic univariate linear regression (one feature) and then work our way to more advanced methods.
Sklearn Python Example
D. Finance and Linear Regressions
Medium

Presentation Deck

•
Submit on Brightspace as PDF
A ten-slide presentation deck that describes:
(a) How machine learning can be used as part of the traditional discretionary investment process, i.e., quantamental investing;
(b) The advantages and disadvantages of using machine learning in the investment decision-making process;
(c) Describe one unique idea using machine learning in finance that can not yet be found in literature, on GitHub, or in a blog-post.
1.
This counts 20% towards your final grade so give the appropriate effort.
2.
You can use any type of software e.g., google slides, power-point, etc as long as you can export the final result as a PDF.
3.
This project does not yet require any coding or programming, the purpose it familiarize yourself with the language
4.
You can have ten slides or less, but no more;
5.
You will not be presenting the deck, it would only be read by the assessor.
6.
You can decide how many slides you allocate to each question e.g: for example, one approach might be 8 slides to answer question (a) , 1 slide to answer (b), and 2 slides to answer (c). This distribution won’t affect your score.
E. Presentation Deck
Medium
The introduction starts with (1) an overview of some basic machine learning concepts, (2) the history of machine learning in finance, (3) how machine learning is currently applied to finance, (4) and an introduction to one of the first machine learning methods, known as linear regression, and (5) your first project.
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