Developing strategies at the intersection of structural, statistical, and fundamental trading - integrating quantitative and discretionary approaches across multiple asset classes and time frames.

1. QUANTAMENTAL
INVESTING
Integrating Artificial Intelligence (Machine Learning)
With A Traditional Fundamental Investment Process
Gurraj S. Sangha
December 2016

2. Table Of Contents
1. What Is This Number ?
2. Ancient Game
3. Advances in Artificial Intelligence
4. Risks With AI
5. AI – About Depth and Insight
6. Quantum Moves – Human Intuition Trumps Algorithms
7. Quantamental Approach – Merging Quantitative Analysis, AI, and
Fundamental Research
8. Quantamental – Extracting Investing Information From Everything
9. Traditional Quantamental Approach
10. Evolving Quantamental Approach -- Quantitative – Developing Algorithms
11. Machine Learning/AI–Dynamically Applying/Refining Algorithmic
Approaches
12. Fundamental – Off Balance Sheet Risks , Intuitive Reasoning, Imagination
13. Quantamental – Merging Modalities At Intersection of Quant & Fundamental
14. Trading Moving From HFT to HIT

3. What Is This Number ?

4. Ancient Chinese Game - Go
• The number of potentially legal board
positions in the game, Go
• This number is greater than the number of
atoms in the known universe
• On the 15th March 2016, an artificially
intelligent (AI) software program called
AlphaGo (Google’s Deepmind Artificial
Intelligence), defeated the world champion of
an ancient board game called Go.

5. Advances in Artificial Intelligence
• AlphaGo’s victory highlighted important advances
in AI’s ability recognize and learn obscure
patterns, adapt and develop new strategies to
ever-changing circumstances
• Euromoney (“Ghosts In The Machine”): Many see
AI as a tool that will
– help improve financial institutions’ risk management
– more in-depth assessment of risk in portfolios
– more incisive, comprehensive and informed credit-
risk assessment
– unprecedented depth and breadth of insight, and the
ability to act on information and learn from its actions

6. Risks with AI
• Risks generally revolve around
malfunctioning algorithms, security, privacy,
data quality, and regulation
• Regulators, although working very diligently,
are likely only beginning to understand the
ramifications for AI for markets and
companies, in context of their supervisory
roles
• Overreliance on AI can magnify systemic
risks

7. AI – About Depth and Insight
• Machine learning allows traders to find
obscure relationships across asset classes
from an ever-increasing amount of
structured and unstructured data
• As such, investment decision making is
moving away from speed alone to
sophisticated intelligence across multiple
time horizons

8. Quantum Moves-Human Intuition
Trumps Algorithms ?
• In Quantum Moves, a complex physics game to aid in the
development of quantum computing, Professor Jacob Sherson
(Aarhus University) “found that computerized numerical
optimization failed to find solutions for the tough problems
associated with quantum computing tasks, whereas the human
players were successful at it.”
• "The big surprise we had was that some of the players actually
had solutions that were of higher quality and of shorter
duration than any computer algorithms could find,"
• "One of the most distinctly human abilities is our ability to forget
and to filter out information and that's very important here
because we have a problem that's just so complicated you will
never be finished if you attack it systematically."
• http://www.scienceworldreport.com/articles/38351/20160416/hu
man-intuition-defeats-artificial-intelligence-quantum-
computing-game.htm

9. Quantamental Approach – Merging Quantitative
Analysis, AI, and Fundamental Research
• Integrates emerging quantitative-based AI approaches
with a fundamental understanding/appreciation of
shifts in data metrics and intangibles
• Developing strategies at the intersection of structural,
statistical, and fundamental trading - integrating
quantitative and discretionary approaches across
multiple asset classes and time frames.
• Automating the process of discretionary investors by
integrating fundamental, microeconomic,
macroeconomic, and microstructure reasoning in
model development.

10. Quantamental – Extracting Investing
Information From Everything

11. Traditional Quantamental Approach

12. Evolving Quantamental Approach
Quantitative – Developing Algorithms

13. Machine Learning / AI – Dynamically
Applying/Refining Algorithmic Approaches

14. Fundamental – Off Balance Sheet Risks ,
Intuitive Reasoning, Imagination

15. Quantamental – Merging Modalities At
Intersection of Quant & Fundamental

16. Trading Moving From HFT to HIT
High Frequency
Trading (Speed)
Big Data (4
Vs)
High
Intelligence
Trading (HIT –
Best
Algorithms)

17. Contact Information
Gurraj Singh Sangha
• LinkedIn: https://www.linkedin.com/in/gurrajsangha
• Email: gurrajsangha@gmail.com
• Telephone: (917) 378-5478