This document discusses the use of artificial intelligence in financial investing and identifies some potential pitfalls. It notes that while AI can help analyze large datasets and identify new opportunities, financial time series data can be non-stationary, and AI models may overfit data and lack proper out-of-sample testing. Subject matter expertise is still key to integrating new data sources like news articles. The document provides examples of AI applications at J.P. Morgan Asset Management in areas like trading, earnings estimates, and thematic portfolios.

1. FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE
The Pitfalls of Using AI in Financial Investing
November 2018
Yazann Romahi, PhD

2. 1 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE
Areas of AI Applications in Finance
Trading
Fraud Detection
Credit LendingImage Recognition
Robo-Advisors
For illustrative purposes only
$
Asset Management

3. 2 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE
The March of Quantitative Methods in Financial Investing
Markowitz
Modern
Portfolio
Theory
Louis
Bachelier’s
Thesis
published
Rapid
Advances
in Portfolio
Theory
1980s
First CTA
funds
1990s
Growth of
Quant Equity
Long Short
Hedge Funds
2000s
Increasing Use
of AI in Trading
Strategies
2009-
Growth of
Alternative
Beta
Growth of Quantitative Investing Strategies
New sources
Of Data
Rapidly Being
Created
1900 1952 1960s 1980 2010 2014
𝒌=𝟎
𝒏
𝒏
𝒌
𝒙 𝒌
For illustrative purposes only

4. 3 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE
Understanding Approaches to Building Investment Strategies
$370 Bn
$973 Bn
Hedge Fund and CTA AUM
Trend Following
 Trend-following hedge funds (also known as
CTAs) are exclusively quantitatively based
 Typically employ momentum and reversion
signals at different time frequencies
 AI is used, but traditional methods are more
prevalent
Fundamental
Equity
 Quantitative, equity long/short approaches
began to gain prominence in the early 90s.
 Fundamental signals (e.g. valuation, quality) are
an important component of these processes
 New sources of data are creating new
opportunities for alpha generation
Source: BarclayHedge as of 2Q 2018. For illustrative purposes only

5. 4 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE
An Expansion of the Quant’s Toolkit
NEW TECHNIQUES
TRADITIONAL
DATA
NEWDATA
TRADITIONAL TECHNIQUES
Building a neural
network (machine
learning) on textual data
Building random
forests for fraud
detection
Time series extracted
from satellite data of
industrial sites in China
Regression on
Earnings Yield;
Time series analysis
using econometrics
For illustrative purposes only

6. 5 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE
New Data Sources Abound
of the data available today has been
collected in the last two years90%
Alternative Data
Individuals Business Processes Sensors
Social Media Transaction Data Satellites
News and Reviews Corporate Data Geo-location
Web Searches,
Personal Data
Government Agencies Data Other Sensors
For illustrative purposes only

7. 6 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE
Pitfalls of Time Series Analysis in Trading Strategies
New sources of data have cross-sectional depth, but lack time-series depth1
Probability of random noise ~30% Not statistically significant (p=0.12)
For illustrative purposes only
Monthly Trading Model (55% Success Rate) Weekly Trading Model (55% Success Rate)

8. 7 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE
Pitfalls of Time Series Analysis in Trading Strategies
Financial time-series are non-stationary2
Stationary Data
-15%
-10%
-5%
0%
5%
10%
15%
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
S&P 500
(Daily Returns, 2008)
-1,000
-800
-600
-400
-200
0
200
400
600
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
Total Nonfarm Payrolls
(Change, Thousands of Persons)
Source: Bloomberg, as of November 2018. For illustrative purposes only

9. 8 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE
Jan-05 Jan-06 Jan-07 Jan-08 Jan-09
S&P500Level
Example Financial Time Series
Pitfalls of Time Series Analysis in Trading Strategies
Everything should be made as simple as possible, but not simpler3
Traditional Methods
• Require modeler to determine functional form
• Econometrics methods are often perfectly adequate
Artificial Intelligence
• Significantly higher degrees of freedom allows
for flexibility, but is often less statistically robust
due to inability to properly test out of sample
Source: Bloomberg, as of November 2018. For illustrative purposes only
0
20
40
60
80
100
120
ModelError
Model Complexity
Model Complexity vs. Error
Total Model Error
In-sample Error
Best Model

10. 9 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE
100
120
140
160
180
200
220
240
Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Sep-17 Oct-17
Subject Matter Expertise is Key: An Example
Corporate activity is a source of binary idiosyncratic risk
Stock price movements following confirmation
or denial of a merger agreement
News articles
are a source of
useful
information, if
appropriately
handled
For illustrative purposes only

11. 10 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE
2% RELEVANT
NEWSMILLIONS OF
ARTICLES
PER YEAR1
Investment Process Application of AI: NewsFilter
1 For illustrative purposes only
2 Awarded based on use of machine-learning based News-Filter
Most Cutting-Edge IT Initiative
Best use of Emerging or Innovative Technology
AWARDS2
AI can provide a solution to managing large data sets more effectively allowing it to be systematically incorporated
in factor portfolios

12. 11 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE
Investment Process Application of AI: NewsFilter
Machine learning algorithm can improve performance by reducing idiosyncratic risk
Source: J.P. Morgan Asset Management, Bloomberg
15
20
25
30
35
1-Sep 15-Sep 29-Sep 13-Oct 27-Oct 10-Nov 24-Nov 8-Dec 22-Dec
General Cable Corporation
~75% increase in
price post rumor
Identification of M&A rumor led to short constraint on General Cable in
equity factor models, preventing loss of 25bps at portfolio level

13. 12 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE
Areas of AI Applications at J.P. Morgan Asset Management
Trading
Using reinforcement learning in
enhanced trading
Earnings Revisions
Analysis of company earnings reports
and Q&A yielding better estimates of
earnings
Image Recognition
Analysis of industrial site satellite
imagery in China as a leading
indicator of industrial production
Thematic Portfolios
Using NLP of news, social media and
financial reports, to create on demand
thematic portfolio ideas
$

14. 13 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE
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