Michael M. Beal presented this talk at QuantCon NYC 2017.

1. T H E F U T U R E O F I N V E S T I N G I N T H E D ATA E C O N O M Y
0

2. TABLE OF CONTENTS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 1
Saturday, April 29, 2017
Michael M. Beal
DCM@datacapitalmanagement.com
#DCMMilestones
THE FUTURE OF PUBLIC MARKET INVESTING
IN THE DATA ECONOMY

3. TABLE OF CONTENTS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 2
RECAP: QUANTCON 2016 – LESSONS FROM THE INDUSTRIAL REVOLUTION
QUANTCON 2017 – IN PURSUIT OF VIAARTIFICIAL GENERALIZED INTELLIGENCE
WHAT IS {‘MACHINE_LEARNING’, ‘ARTIFICIAL_INTELLIGENCE, ‘DEEP_LEARNING’}
MACHINE LEARNING CAN DO THAT!!! ...RIGHT?
NEW SOLUTIONS; BUT CORE LIMITATION… FOR ASSET MANAGEMENT
WHERE SHOULD SCIENTISTS FOCUS OUR RESEARCH?`

4. LESSONS FROM THE INDUSTRIAL REVOLUTION
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 3
Suggestions for Companies in the Data Economy
• Standardize industry taxonomies
• Be the best at depth of information for a given vertical
• Take responsibility for the veracity of information
• Focus on speed of delivery
• Focus on historical consistency
• Make adjustments visible to upstream users
• Focus on permissible data use rights
• Don’t be all things to all people
Got API?
dcm@datacapitalmanagement.com
Our goal is to help drive a standard for other players in our ecosystem to coalesce around
• This approach helps avoid the “tragedy of the commons” and maximize collective ROI
• We are set up to be “early customers” of new technologies / beta releases
• We drive a culture of focused and targeted feedback
For those with self-interests congruent to ours; let’s help each other

5. CHALLENGE 1: DATA INTEGRATION
Data are just summaries of thousands of stories – Chip & Dan Heath
OUTCOME: Broader, deeper, faster access to relevant data
Data Querying
Data Bi-temporality
Data linkage
Unified query layer on top of multiple data
storage engines
Treatment of bi-temporality as a
fundamental property of all data, with
support over distributed systems
DCM approach
Hybrid, explicit relationships as required,
and machine learning where appropriate
Relational, using SQL
queries over fact and
dimension tables of star
schemas
Data columns added and
treated as regular data, with
redundant indexing
Traditional approach
Implicit in encoded business
logic; all linkage done while
ingesting data
Task specialized: relational,
graph-like, key-based with
lightly integrated query
layers
Not really emphasized; geo-
location of data is similar
problem in essence
Silicon Valley approach
Machine learning based
clustering of identifiable
information
Source: http://www.slideshare.net/DavidColebatch/20121029-graph-tointro-to-pacer
For illustrative purposes only.
| DATA CAPITAL MANAGEMENT | 9
TRADEOFF: High Frequency Trading Speed

6. CHALLENGE 2: DATA ANALYSIS
Data by itself is useless. Data is only useful if you apply it – Todd Park
OUTCOME: Non-obvious data-driven investment opportunities
Entity interconnections
Strategy development
Regime determination
Data driven relationships, using
unsupervised machine learning on top of
available information
Adaptive model calibration as
information becomes available
DCM approach
Guided pattern recognition based on
financial specific feature engineering
built by traditional methods
Pre-defined relationships
through sector, region, client
relationships, etc
Static models with ad-hoc
re-calibration (usually batch
based)
Traditional approach
Generative models:
parametric models are fitted
to data
Data driven relationships, using
unsupervised machine learning on
top of connection information
Finance specific, no analog
Silicon Valley approach
Pattern recognition breakthroughs
in complete information games
(Go)
Source: http://cs231n.github.io/convolutional-networks/
http://www.businessinsider.com/magic-mushrooms-change-brain-connections-2014-10
For illustrative purposes only.
| DATA CAPITAL MANAGEMENT | 10

7. CHALLENGE 3 : DATA PROCESSING?
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 6

8. CHALLENGE 4 : NEAR-TERM DATA REGULATION?
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 7

9. TABLE OF CONTENTS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 8
RECAP: QUANTCON 2016 – LESSONS FROM THE INDUSTRIAL REVOLUTION
QUANTCON 2017 – IN PURSUIT OF VIAARTIFICIAL GENERALIZED INTELLIGENCE
WHAT IS {‘MACHINE_LEARNING’, ‘ARTIFICIAL_INTELLIGENCE, ‘DEEP_LEARNING’}
MACHINE LEARNING CAN DO THAT!!! ...RIGHT?
NEW SOLUTIONS; BUT CORE LIMITATION… FOR ASSET MANAGEMENT
WHERE SHOULD SCIENTISTS FOCUS OUR RESEARCH?`

10. LESSONS FROM PAST REVOLUTIONS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 9
dcm@datacapitalmanagement.com
It’s time for Asset Management’s Dance with Technological Disruption
• Traditional ”Fundamental Long/Short” managers face challenges and must adapt or fail
• The “Data Economy” enables machines to access information that was previously the exclusive domain of
fundamentally-trained human investors
• “Active Smart-Beta” will accelerate a shift to a world where alpha is a function of ability to rotate exposures to
factors most likely to outperform in the current regime
• Beta allocations over long durations are “good enough” to replicate many existing managers
• Increase in allocator tools to attribute returns post 2008 crisis and relative asset class underperformance since
• As new Betas are exposed and Alphas commoditized; only the strong will thrive:
• Large Diversified “Asset Managers”
• Highly-Differentiated Absolute Return Funds
• Access to new Distributed Technologies & advances in Artificial Intelligence have altered the barriers to entry:
2011 2014 2015Year Founded: 2015

11. ALPHA VIA ARTIFICIAL GENERALIZED INTELLIGENCE
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 10
Suggestions for Scientists in the Data Economy
• Consistent Absolute Returns Requires a Regime Aware Approach
• Machine Learning is better-suited for Regime-aware investing than Humans
• Machine Learning requires new Approaches
• Technological approaches
• Mathematical approaches
• Human approaches
• Scientists will make Artificial Generalized Intelligence a Reality
• Capturing the “Data Economy” opportunity requires:
• New frameworks for knowledge sharing
• Declining costs of compute power
• Transfer Learning to overcome catastrophic forgetting
• “Fair” Access to Data
dcm@datacapitalmanagement.com#GotSkills?

12. ALPHA VIA ARTIFICIAL GENERALIZED INTELLIGENCE
| DATA CAPITAL MANAGEMENT | 9
Fat FileTall File
“Smart-
Beta”
,
“Big
Data”

13. ALPHA VIA ARTIFICIAL GENERALIZED INTELLIGENCE
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 12
dcm@datacapitalmanagement.com#GotSkills?

14. ALPHA VIA ARTIFICIAL GENERALIZED INTELLIGENCE
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 13
https://www.ssga.com/investment-topics/risk-management/Optimizing-Asset-Allocations-to-Market-Regimes.pdf 13
Generalized Artificial Intelligence that outperforms the best human investors over any duration

15. ALPHA VIA ARTIFICIAL GENERALIZED INTELLIGENCE
| DATA CAPITAL MANAGEMENT | 9
Optimal Error (Bayesian Rate): 75%
• Human Level: 57%
• Training Set: 73%:
• Validation: 72%
• Test: 69%
Train Validation Test
Import Learning_algorithm as Awesome
Duration = {[x[0],,,x[k]]}
Win_periods = {}
Win_Loss = {}
Transaction_cost = h
df = LOTS_of_Data_Factors
Win_periods[x[0]…x[k]] = df[df[‘returns_Duration[k]’]=>transaction cost] for k in Duration]
Win_Loss[x[0]…x[k]] = [[len(Win_periods[0…k]) / len(df)] for k in Duration]
LifeofCodingOnTheBeach = Awesome.DeepLearning.CapitalMarketsTerminator[[df], [Win_Loss]]

16. ARTIFICIAL GENERAL INTELLIGENCE (AGI) AND THE FUTURE OF INVESTING
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 15
AGI + Data Economy enables investment across multiple company cycles
Positive
Momentum
Negative
Momentum
==+
ALPHA VIA ARTIFICIAL GENERALIZED INTELLIGENCE

17. TABLE OF CONTENTS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 16
RECAP: QUANTCON 2016 – LESSONS FROM THE INDUSTRIAL REVOLUTION
QUANTCON 2017 – IN PURSUIT OF VIAARTIFICIAL GENERALIZED INTELLIGENCE
WHAT IS {‘MACHINE_LEARNING’, ‘ARTIFICIAL_INTELLIGENCE, ‘DEEP_LEARNING’}
MACHINE LEARNING CAN DO THAT!!! ...RIGHT?
NEW SOLUTIONS; BUT CORE LIMITATION… FOR ASSET MANAGEMENT
WHERE SHOULD SCIENTISTS FOCUS OUR RESEARCH?`

18. WHAT IS MACHINE LEARNING
| DATA CAPITAL MANAGEMENT | 9

19. WHAT IS MACHINE LEARNING
| DATA CAPITAL MANAGEMENT | 9

20. TABLE OF CONTENTS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 19
RECAP: QUANTCON 2016 – LESSONS FROM THE INDUSTRIAL REVOLUTION
QUANTCON 2017 – IN PURSUIT OF VIAARTIFICIAL GENERALIZED INTELLIGENCE
WHAT IS {‘MACHINE_LEARNING’, ‘ARTIFICIAL_INTELLIGENCE, ‘DEEP_LEARNING’}
MACHINE LEARNING CAN DO THAT!!! ...RIGHT?
NEW SOLUTIONS; BUT CORE LIMITATION… FOR ASSET MANAGEMENT
WHERE SHOULD SCIENTISTS FOCUS OUR RESEARCH?`

21. EXTREMELY CONFIDENTIAL
20
MACHINE LEARNING CAN DO THAT!
. . . RIGHT?

22. EXTREMELY CONFIDENTIAL
21
MACHINE LEARNING CAN DO THAT!
. . . RIGHT?

23. MACHINE LEARNING CAN DO THAT!
. . . R I GHT ?
| DATA CAPITAL MANAGEMENT | 9

24. MACHINE LEARNING CAN DO THAT!
. . . R I GHT ?
| DATA CAPITAL MANAGEMENT | 9

25. TABLE OF CONTENTS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 24
RECAP: QUANTCON 2016 – LESSONS FROM THE INDUSTRIAL REVOLUTION
QUANTCON 2017 – IN PURSUIT OF VIAARTIFICIAL GENERALIZED INTELLIGENCE
WHAT IS {‘MACHINE_LEARNING’, ‘ARTIFICIAL_INTELLIGENCE, ‘DEEP_LEARNING’}
MACHINE LEARNING WORKS WE’VE ALL SEEN IT!!
NEW SOLUTIONS; BUT CORE LIMITATION… FOR ASSET MANAGEMENT
WHERE SHOULD SCIENTISTS FOCUS OUR RESEARCH?`

26. | DATA CAPITAL MANAGEMENT | 9
Training Error High?
Bigger Model / More
Data / Train Longer /
New Model
Architecture
Train-Validation Error
High?
More Data
Regularization New
Model Tuning
Validation error high?
More Data similar to
test
Data synthesis
New architecture
Test Set Error High Overfit Dev-Set:
More Dev Data
Better Labels
Error Analysis
Train
Training-
Dev
Dev-
Validation
Test
BUT MACHINE LEARNING WORKS; WE HAVE ALL SEEN IT!
Optimal Error (Bayesian Rate): ??
• Human Level: 57%
• Training Set: 73%:
• Validation: 67%
• Test: 52%

27. BUT MACHINE LEARNING WORKS; WE HAVE ALL SEEN IT!
| DATA CAPITAL MANAGEMENT | 12
Source: Y LeCun; MA Ranzato:
Shallow Depth Approaches Deep Depth Approaches
Asynchronous Learning
Hyper Paramaters
Simulations
Synthetic Data
Optimal Error (Bayesian Rate): ??
• Human Level: 57%
• Training Set: 73%:
• Validation: 72%
• Test: 69%

28. BUT MACHINE LEARNING WORKS; WE HAVE ALL SEEN IT!
| DATA CAPITAL MANAGEMENT | 12

29. BUT MACHINE LEARNING WORKS; WE HAVE ALL SEEN IT!
| DATA CAPITAL MANAGEMENT | 9
Answer = [[0,1],[[0,,,k]]
https://www.slideshare.net/perone/deep-learning-convolutional-neural-networks

30. | DATA CAPITAL MANAGEMENT | 9
BUT MACHINE LEARNING WORKS; WE HAVE ALL SEEN IT!
Asynchronous Learning
Hyper Paramaters
Simulations
Synthetic Data
=
Lots of Compute and
Data Transfer!

31. TABLE OF CONTENTS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 30
RECAP: QUANTCON 2016 – LESSONS FROM THE INDUSTRIAL REVOLUTION
QUANTCON 2017 – IN PURSUIT OF VIAARTIFICIAL GENERALIZED INTELLIGENCE
WHAT IS {‘MACHINE_LEARNING’, ‘ARTIFICIAL_INTELLIGENCE, ‘DEEP_LEARNING’}
MACHINE LEARNING CAN DO THAT!!! ...RIGHT?
NEW SOLUTIONS; BUT CORE LIMITATION… FOR ASSET MANAGEMENT
WHERE SHOULD SCIENTISTS FOCUS OUR RESEARCH?`

32. | DATA CAPITAL MANAGEMENT | 31
• Long History of Objective Function (price returns)
• Short History of Clean Explanatory “Big Data” Variables
• Sequential Learning on BiTemporal Data Problem
• Never see the exact same combination of paramaters more than once
WHAT MAKES FINANCE SO DIFFERENT FOR MACHINE LEARNING?
Answer = [[0,1],[[0,,,k]]

33. EXTREMELY CONFIDENTIAL
32
NEW SOLUTIONS; BUT CORE LIMITATION… F OR ASSET MANAGEMENT

34. NEW SOLUTIONS…. BUT CORE LIMITATION… F OR ASSET MANAGEMENT
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 33
“The ability to learn tasks in a sequential fashion is crucial to the development of artificial
[Generalized] intelligence. Neural networks are not, in general, capable of this and it has
been widely thought that catastrophic forgetting is an inevitable feature of connectionist
models” – Google Deepmind
PROGRESSIVE NEURAL NETWORKS AND CATASTROPHIC FORGETTING

35. TABLE OF CONTENTS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 34
RECAP: QUANTCON 2016 – LESSONS FROM THE INDUSTRIAL REVOLUTION
QUANTCON 2017 – IN PURSUIT OF VIAARTIFICIAL GENERALIZED INTELLIGENCE
WHAT IS {‘MACHINE_LEARNING’, ‘ARTIFICIAL_INTELLIGENCE, ‘DEEP_LEARNING’}
MACHINE LEARNING CAN DO THAT!!! ...RIGHT?
NEW SOLUTIONS; BUT CORE LIMITATION… FOR ASSET MANAGEMENT
WHERE SHOULD SCIENTISTS FOCUS OUR RESEARCH?`

36. Predict Return based upon
Shallow Network Learning
Predict Epsilon based upon
Deep Network Learning
| DATA CAPITAL MANAGEMENT | 35
SCALABLE AP P ROACHES F OR REGIME AWARE INVESTING
Focus on Specific Subsets of the problem:

37. TABLE OF CONTENTS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 36
RECAP: QUANTCON 2016 – LESSONS FROM THE INDUSTRIAL REVOLUTION
QUANTCON 2017 – IN PURSUIT OF VIAARTIFICIAL GENERALIZED INTELLIGENCE
WHAT IS {‘MACHINE_LEARNING’, ‘ARTIFICIAL_INTELLIGENCE, ‘DEEP_LEARNING’}
MACHINE LEARNING CAN DO THAT!!! ...RIGHT?
NEW SOLUTIONS; BUT CORE LIMITATION… FOR ASSET MANAGEMENT
WHERE SHOULD SCIENTISTS FOCUS OUR RESEARCH?`

38. SEEK TO UNDERSTAND THE INTERMEDIATE REPRESENTATIONS
| DATA CAPITAL MANAGEMENT | 12
SCALABLE APPROACHES FOR REGIME AWARE INVESTING

39. SCALABLE APPROACHES FOR REGIME AWARE INVESTING
| DATA CAPITAL MANAGEMENT | 12

40. TABLE OF CONTENTS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 39
RECAP: QUANTCON 2016 – LESSONS FROM THE INDUSTRIAL REVOLUTION
QUANTCON 2017 – IN PURSUIT OF VIAARTIFICIAL GENERALIZED INTELLIGENCE
WHAT IS {‘MACHINE_LEARNING’, ‘ARTIFICIAL_INTELLIGENCE, ‘DEEP_LEARNING’}
MACHINE LEARNING CAN DO THAT!!! ...RIGHT?
NEW SOLUTIONS; BUT CORE LIMITATION… FOR ASSET MANAGEMENT
WHERE SHOULD SCIENTISTS FOCUS OUR RESEARCH?`

41. | DATA CAPITAL MANAGEMENT | 9
SCALABLE APPROACHES FOR REGIME AWARE INVESTING

42. | DATA CAPITAL MANAGEMENT | 9
SCALABLE APPROACHES FOR REGIME AWARE INVESTING

43. | DATA CAPITAL MANAGEMENT | 9
SCALABLE APPROACHES FOR REGIME AWARE INVESTING

44. | DATA CAPITAL MANAGEMENT | 9
Source: Deutsche Bank
SCALABLE APPROACHES FOR REGIME AWARE INVESTING

45. | DATA CAPITAL MANAGEMENT | 9
SCALABLE APPROACHES FOR REGIME AWARE INVESTING

46. | DATA CAPITAL MANAGEMENT | 9
SCALABLE APPROACHES FOR REGIME AWARE INVESTING

47. TABLE OF CONTENTS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 46
RECAP: QUANTCON 2016 – LESSONS FROM THE INDUSTRIAL REVOLUTION
QUANTCON 2017 – IN PURSUIT OF VIAARTIFICIAL GENERALIZED INTELLIGENCE
WHAT IS {‘MACHINE_LEARNING’, ‘ARTIFICIAL_INTELLIGENCE, ‘DEEP_LEARNING’}
MACHINE LEARNING CAN DO THAT!!! ...RIGHT?
NEW SOLUTIONS; BUT CORE LIMITATION… FOR ASSET MANAGEMENT
WHERE SHOULD SCIENTISTS FOCUS OUR RESEARCH?`

48. SCALABLE AP P ROACHES F OR REGIME AWARE INVESTING
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 47
Source: Google Images
For illustrative purposes only. Back test results are not indicative of future returns. 47

49. Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 48
Source: Google Images
For illustrative purposes only. Back test results are not indicative of future returns. 48
Focus on Progressive Neural Networks via Transfer Learning:
SCALABLE APPROACHES FOR REGIME AWARE INVESTING

50. SCALABLE AP P ROACHES F OR REGIME AWARE INVESTING
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 49
Source: Google Images
For illustrative purposes only. Back test results are not indicative of future returns. 49

51. SCALABLE AP P ROACHES F OR REGIME AWARE INVESTING
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 50
Source: Google Images
For illustrative purposes only. Back test results are not indicative of future returns. 50

52. LESSONS FROM PAST REVOLUTIONS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 51
dcm@datacapitalmanagement.com
It’s time for Asset Management’s Dance with Technological Disruption
• Traditional ”Fundamental Long/Short” managers face challenges and must adapt or fail
• The “Data Economy” enables machines to access information that was previously the exclusive domain of
fundamentally-trained human investors
• “Active Smart-Beta” will accelerate a shift to a world where alpha is a function of ability to rotate exposures to
factors most likely to outperform in the current regime
• Beta allocations over long durations are “good enough” to replicate many existing managers
• Increase in allocator tools to attribute returns post 2008 crisis and relative asset class underperformance since
• As new Betas are exposed and Alphas commoditized; only the strong will thrive:
• Large Diversified “Asset Managers”
• Highly-Differentiated Absolute Return Funds
• Access to new Distributed Technologies & advances in Artificial Intelligence have altered the barriers to entry:
2011 2014 2015Year Founded: 2015

53. SKY NET HAS NOT BEEN BORNE… YET… BUT WE KNOW WHAT
THE DAY WILL LOOK LIKE WHEN IT ARRIVES
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 52
Monday, November 14, 2016
HFM US Hedge Fund Technology Leaders Summit 2016
# Gotskill
dcm@datacapitalmanagement.com

54. LESSONS FROM THE INDUSTRIAL REVOLUTION
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 53
Got API?
dcm@datacapitalmanagement.com
For those with the Skill and Will
DCM@datacapitalmanagement.com