Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
QUANTAMENTAL
INVESTING
Integrating Artificial Intelligence (Machine Learning)
With A Traditional Fundamental Investment Pr...
Table Of Contents
1. What Is This Number ?
2. Ancient Game
3. Advances in Artificial Intelligence
4. Risks With AI
5. AI –...
What Is This Number ?
Ancient Chinese Game - Go
• The number of potentially legal board
positions in the game, Go
• This number is greater than ...
Advances in Artificial Intelligence
• AlphaGo’s victory highlighted important advances
in AI’s ability recognize and learn...
Risks with AI
• Risks generally revolve around
malfunctioning algorithms, security, privacy,
data quality, and regulation
...
AI – About Depth and Insight
• Machine learning allows traders to find
obscure relationships across asset classes
from an ...
Quantum Moves-Human Intuition
Trumps Algorithms ?
• In Quantum Moves, a complex physics game to aid in the
development of ...
Quantamental Approach – Merging Quantitative
Analysis, AI, and Fundamental Research
• Integrates emerging quantitative-bas...
Quantamental – Extracting Investing
Information From Everything
Traditional Quantamental Approach
Evolving Quantamental Approach
Quantitative – Developing Algorithms
Machine Learning / AI – Dynamically
Applying/Refining Algorithmic Approaches
Fundamental – Off Balance Sheet Risks ,
Intuitive Reasoning, Imagination
Quantamental – Merging Modalities At
Intersection of Quant & Fundamental
Trading Moving From HFT to HIT
High Frequency
Trading (Speed)
Big Data (4
Vs)
High
Intelligence
Trading (HIT –
Best
Algori...
Contact Information
Gurraj Singh Sangha
• LinkedIn: https://www.linkedin.com/in/gurrajsangha
• Email: gurrajsangha@gmail.c...
You’ve finished this document.
Download and read it offline.
Upcoming SlideShare
Investment Management For Family Wealth
Next
Upcoming SlideShare
Investment Management For Family Wealth
Next
Download to read offline and view in fullscreen.

Share

Quantamental Investing - Merging Machine Learning, Fundamentals, & Insight

Download to read offline

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

Related Books

Free with a 30 day trial from Scribd

See all

Related Audiobooks

Free with a 30 day trial from Scribd

See all

Quantamental Investing - Merging Machine Learning, Fundamentals, & Insight

  1. 1. QUANTAMENTAL INVESTING Integrating Artificial Intelligence (Machine Learning) With A Traditional Fundamental Investment Process Gurraj S. Sangha December 2016
  2. 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. 3. What Is This Number ?
  4. 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. 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. 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. 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. 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. 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. 10. Quantamental – Extracting Investing Information From Everything
  11. 11. Traditional Quantamental Approach
  12. 12. Evolving Quantamental Approach Quantitative – Developing Algorithms
  13. 13. Machine Learning / AI – Dynamically Applying/Refining Algorithmic Approaches
  14. 14. Fundamental – Off Balance Sheet Risks , Intuitive Reasoning, Imagination
  15. 15. Quantamental – Merging Modalities At Intersection of Quant & Fundamental
  16. 16. Trading Moving From HFT to HIT High Frequency Trading (Speed) Big Data (4 Vs) High Intelligence Trading (HIT – Best Algorithms)
  17. 17. Contact Information Gurraj Singh Sangha • LinkedIn: https://www.linkedin.com/in/gurrajsangha • Email: gurrajsangha@gmail.com • Telephone: (917) 378-5478
  • BalsubramaniGoudar

    May. 17, 2017
  • iktinosparthenon

    May. 15, 2017

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

Views

Total views

1,574

On Slideshare

0

From embeds

0

Number of embeds

651

Actions

Downloads

43

Shares

0

Comments

0

Likes

2

×