or: the world of Finance as we know it is over

in a nutshell: why people choose to work in finance

What happened? Old days...

What happened?

=

+

The Growth of Finance as % of GDP

Source: Philippon (AER 2015) "Has the U.S. Finance Industry Become Less Efficient?"

Much of growth is fees...

Source: Greenwood & Scharfstein (JEP 2013) "The Growth of Finance"

... and unit costs are flat!

Source: Philippon (AER 2015) "Has the U.S. Finance Industry Become Less Efficient?"

Workers in the Finance Industry benefit greatly from growth of finance

• Philippon and Reshef (QJE 2012):
  • 1980: wage financial services=wage other industries​
  • 2006: financial services=170% x other
• ​​​Goldin & Katz (AER 2008)
  • ​​1969-74: 6% of Harvard grads went to Fin Industry
  • ​2006: 28%
• Oyer (JF 2008):
  • ​​wages Stanford GSB grads in finance industry
    • ​​300% of wages in non-finance

What drives high wages?

Deregulation or complementarity to IT?

Source: Philippon & Reshef (JEP 2013)

What explains high cost?

• High quality?
  • ​​​Bai, Philippon, Savov (JFE 2013): No, markets haven't become any better/more informative.
• ​​​​Better allocative efficiency?
  • ​​​Aguiar and Bils (AER 2015): no improvement in risk sharing.
  • ​​​Hsieh and Klemp (QJE 2009): no improvement in potential gains from TFP across time.
  • ​=> no evidence.​
• There is a dispute whether growth in finance has actually brought benefit to society ...

• Finance is very expensive.

• ​People earn relatively high wages.​​​

What do we know from the long history of capitalism?​

Disruptive entry is bound to take place!

My View

• Financial Industry is self-contained
  • for many years, hired a "certain" type of person
  • "fixed" set of tech vendors
  • access to financial infrastructure essential
    • outside disruption difficult
• Technological advances have the capacity to break into the industry

Example: One of the first segments that were disrupted from the outside: equity trading

Old Days of Trading: highly specialized humans

Old Days of Trading: highly specialized humans

Modern Day Trading

Autonomous Algorithms run the show

Modern Day Trading

And the former heart of Wall Street is now in a faceless & glamourless suburb

Why?

• Many trading strategies are more-or-less mechanical/probabilistic
• computers are
  • faster,
  • cheaper, and
  • more reliable 
• Computer algos allow much wider scope

Source: Bloomberg News, Feb 20, 2015

Note: The biggest disruptors (the HFTs) came from the outside of the traditional system (kinda).

And what's the result?

entire career streams disappear

The HR Component

• Old skill-set of an equity trader
  • good and fast with numbers
  • brash
  • strong vocab in profanities
  • whatever this koala has...
• New skill set
  • programming
  • stats/data
  • econometrics
  • => increase scope

And what's the result?

Markets get better

Source: Martineau (future UofT faculty) (WP2017)

And what's the result?

Markets get better: the post earnings announcement drift had been a long-standing puzzle in finance

Source: Martineau (future UofT faculty) (WP2017)

Much of the financial industry will see similar changes as the world of trading!

What is it that a bank does?

obtain & store information/data

process information

make decision

allocate capital

monitor and revise

automation

!

!

?

AI?

AI?
ECO?

Obstacles

Source: Philippon (2017)

• non-integrated IT systems

My view of the dinosaur

• cumbersome legacy system
• unintegrated databases
• silo-ed data
• back-office infrastructure that requires many parties
• rigid regulatory framework

And this is where FinTech comes in...

What is FinTech?

my take:

=> this is really squishy ...

The Sell Side

• sell services to investors and customers
• mainly banks & brokers
• services include
  • accounts
  • research
  • execution services

The Buy Side

• buyers of services
• mainly institutional investors
  • mutual funds
  • hedge funds
  • endowments, pension funds etc

A Taxonomy of the Financial Industry Ecosystem

Vendors and Services

• develop service products that help run FI businesses
• examples are tech suppliers (including big firms like IBM), exchanges

Regulators

• securities and banking regulators
• Bank of Canada, OSC, SEC, IIROC, FINRA

Let's look at a Big Bank: What does it do?

loans/credit

deposits

consumer

business

consumer

business

securitization

trading

clients

market making/prop

investment banking

M&A

securities issuance (debt/equity)

credit cards

insurance

investment advice

payments

Main role: Intermediation

• time intermediation
  • people have and need money at different times
• size intermediation
  • many small deposits but firms need big loans
• risk intermediation
  • aggregate and then spread ​idiosyncratic risk  

Important Feature banks were necessary ​for the financial system

• gigantic infrastructure
• people need accounts to store money etc. 

or are they?

Main Structural Change

Main Structural Change

Main Structural Change

Moore's Law

"the number of transistors per square inch on integrated circuits had doubled every year since their invention."

Moore's Law: the future

Main Changes

• Bottom line: Moore's Law caught up with Banks
• Key contributing factors
  • cheap cloud computing:
    • =>instead of building expensive hardware, rent AWS
    • => flexibility (computing power and storage)
  • API
    • Application Programming Interface
    • developers can come up with ideas outside of banks
  • Big Data
    • willingness to analyze large and often unstructured data
  • Advances in analysis
    • predictive tool revolution: machine learning & AI
  • Infrastructure revolution
    • Blockchain technology

My own taxonomy of the FinTech Ecosystem

total in Canada: ~420 firms (changing daily)

RegTech
Compliance
Security

Crowdfunding

Payments

Lending and Borrowing

Digital Wealth
Personal Finance

Platforms,
Accounting

Data and Analytics

InsurTech

Blockchain, DLT
Cryptocurrencies

 FinTech

24

36

39

50

12

93

83

36

57

In this course: we will discuss tools relating to

Data and Analytics

Blockchain, DLT
Cryptocurrencies

 FinTech

Keywords:

• AI and Machine learning
• big data

In this course, there will be speakers on

Payments

Platforms,
Accounting

Data and Analytics

InsurTech

Blockchain, DLT
Cryptocurrencies

 FinTech

Baiju Divani

Ethan Wilding

Adam Kadar

Baiju Divani

Elizabeth Duke

Pavel Rahman

Venture Capital

Ted Liu

Incubation

Sam Dumcum

What I hope you'll get out of the course

1. solid understanding of FinTech ecosystem & FinTech Startup world
2. basic understanding of critical tools
   • ML/AI + big data analytics
   • blockchain tech
3. idea generation: find your business creativity!