Machine learning for factor investing - Tony Guida https://quspeakerseries5.splashthat.com/ Topic: Machine Learning for Factor Investing: case study on "Trees" In this presentation, Tony will first introduce the concept of supervised learning. Then he will cover the practitioner angle for constructing non linear multi factor signals using stock characteristics. He will show the added value of ML based signals over traditional linear stale factors blend in equity. This master class is derived from Guillaume Coqueret and Tony Guida's latest book "Machine Learning for Factor Investing"

1. Qu Speaker Series
Machine Learning for Factor Investing
An Afternoon with Tony Guida
2020 Copyright QuantUniversity LLC.
Hosted By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.qu.academy
08/05/2020
Online
https://quspeakerseries5.spla
shthat.com/

2. 2
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https://qufintech.splashthat.com/

6. Machine Learning for Factor Investing
An Afternoon with Tony Guida

7. 7

8. 8
• Tony Guida is a Quantitative Portfolio Manager and researcher. Tony’s work is focused primarily on
extracting market inefficiencies from different sources from traditional fundamentals, market
signals, alternative data, and machine learning. His expertise is in mid to low frequency in equities.
Tony started his career at Unigestion in 2006 where he joined the quantitative equity low volatility
team to work as a research analyst. He evolved into a member of the research and investment
committee for Minimum Variance Strategies, where he led the factor investing research group for
institutional clients.
• In 2015, he moved to Edhec Risk Scientific Beta as a Senior Consultant for Risk allocation and factor
strategies before going to a major UK pension fund in 2016 to build the in-house systematic equity,
co-managing 8 billion GBP as a senior quantitative portfolio manager. He joined RAM-Active
Investments in January 2019.
• Tony holds a Bachelor and Master degrees in Econometry and Finance from the University of Savoy
France, is the editor-in-chief and founder for the Journal of Machine Learning in Finance. Tony co-
wrote and edited book “Big Data and Machine Learning in Quantitative Investment” Wiley 2018, co-
wrote “Machine Learning for Factor Investing” Chapman Hall/ CRC.
Tony Guida

9. Machine Learning Ensemble for Factor Investing
For professional investors only - promotional material
Tony Guida

10. This material is addressed to professional clients for informative purposes only. It is neither an offer nor an invitation to buy or sell investment products and may not be interpreted as investment
advice. It is not intended to be distributed, published or used in a jurisdiction where such distribution, publication or use is forbidden, and is not intended for any person or entity to whom or
to which it would be illegal to address such a material. In particular, investment products are not offered for sale in the United States or its territories and possessions, nor to any US person
(citizens or residents of the United States of America). The opinions herein do not consider individual clients' circumstances, objectives, or needs. Before entering into any transaction, clients
are advised to form their own opinion and consult professional advisors to obtain an independent review of the specific risks incurred (tax, financial etc.). Upon request, RAM AI Group is
available to provide more information to clients on risks associated with investments. The information and analysis contained herein are based on sources deemed reliable. However, RAM AI
Group does not guarantee their accuracy, correctness or completeness, and it does not accept any liability for any loss or damage resulting from their use. All information and assessments
are subject to change without notice. Changes in exchange rates may cause the NAV per share in the investor's base currency to fluctuate. There is no guarantee to get back the full amount
invested. Past performances, whether actual or back-tested, are not necessarily indicative of future performance. Without prejudice of the due addressee’s own analysis, RAM understands
that this communication should be regarded as a minor non-monetary benefit according to MIFID regulations. Clients are invited to base their investment decisions on the most recent
prospectus, key investor information document (KIID) and financial reports which contain additional information relating to the investment product. These documents are available free of
charge from the SICAV’s and Management Company’s registered offices, its representative and distributor in Switzerland, RAM Active Investments S.A. and at Macard Stein & Co AG, Paying
and Information Agent in Germany; and at RAM Active Investments (Europe) SA – Succursale Milano in Italy. This marketing material has not been approved by any financial Authority, it is
confidential and addressed solely to its intended recipient; its total or partial reproduction and distribution are prohibited. Issued in Switzerland by RAM Active Investments S.A. which is
authorised and regulated in Switzerland by the Swiss Financial Market Supervisory Authority (FINMA). Issued in the European Union and the EEA by the Management Company RAM Active
Investments (Europe) S.A., 51 av. John F. Kennedy L-1855 Luxembourg, Grand Duchy of Luxembourg. The reference to RAM AI Group includes both entities, RAM Active Investments S.A.
and RAM Active Investments (Europe) S.A.
Important Information

11. Given the exponential increase in data availability, the obvious
temptation of any asset manager is to try to infer future returns from
the abundance of attributes available at the firm level.
Current computational power allows to “test” almost all types of new
characteristics/signals.
Sharing knowledge effect. Cross fertilization between Hard science and
finance is increasing (implicitly and explicitly).
A need to innovate. Legacy approach for constructing Style/Factor
equity portfolio has been delivering less return than 10 years ago.
Why this topic?

12. To test more characteristics/signals
To leverage on non-linear complex patterns, rule based
To adapt and identify to trends by re-running models
To ensemble more models, wisdom of the crowd
To be less biased than trad. dogmatic quant. approach
What can we expect from ML in Factor Investing?

13. 6
CASE STUDY :
Data “the” key element

14. A bit of Epistemology
THEORY
PREDICTIONS
EXPERIMENT
OBSERVATIONS

15. DATA
TRAINING
PREDICTIONS
OBSERVATIONS
A New Way for Research

16. 9
CASE STUDY :
Data “the” key element

17. Features correlation example
10
Valuation
Volume
Risk Price
based
Source: Guida, Coqueret. Chapter 7, Ensemble Learning Applied to Quant Equity – Big Data and Machine Learning in Quantitative Investment

18. Creating the dataset
11
Instances>>>>~600000
Some features examples
• Fundamental trailing
• Price based
• Volume based
• Risk based
• Composites

19. Rolling Windows for training ( case for 12M forward)
In this example we use a rolling window of 60 months to predict the 12M forward performance of a
stock.
Training set (80%)
Test
set(20%)
12M Holdout
to avoid look
ahead bias
Dec 99 Dec 04 1st Prediction using trained model, Dec 05
TRAINING (60 Months)
Training set (80%)
Test
set(20%)
12M Holdout
to avoid look
ahead bias
Jan 05 2nd Prediction using trained model, Jan 06Jan 00
Feb 00 […………………] [……]

20. Hyperparameters:
13
• The learning rate, 𝜂: it is the step size shrinkage used in update to prevents overfitting.
After each boosting step, we can directly get the weights of new features and 𝜂 actually
shrinks the feature weights to make the boosting process more conservative.
• The maximum depth: it is the longest path (in terms of node) from the root to a leaf of the
tree. Increasing this value will make the model more complex and more likely to be
overfitting.
• Regression 𝝀: it is the 𝐿2 regularization term on weights (mentioned in the technical
section) and increasing this value will make model more conservative.
• gamma: minimum loss reduction required to make a further partition on a leaf node of the
tree. The larger, the more conservative the algorithm will be.
model max_depth eta round eval_metric subsample
XGB 5 1% 150 error 0.8
col_by_sample
0.8

21. 14
CASE STUDY #1:
Data “the” key element

22. We will only predict 12M future performance
We will use a boosted tree classification ML model
Our Investment universe is composed of global stocks (~2000)
Full dataset from Dec-1999 until April-2020.
Dataset contains alt and traditional data.
Data engineering from Exploratory Data Analysis (EDA) is based on:
o Training on tails (extreme quantile from Label/fit cross section) training
o Outliers removals
o Low-coverage instance (row) removal
o Low-coverage feature (column) removal
• (~ 400) features, monthly normalised in percentile
• We use a rolling window of 5 years- 80% Training 20% Testing
• Compare the results according to 3 angles : accuracy, interpretability and out of
sample performance analytics where we will dig into the data engineering.
Objective, Data and Protocol
Paper name

23. 16
Check #1:
Models’ accuracy

24. First things first: L/S performance from ML models
Performance is Gross of TC and gross of management fees. Backtested performance in USD using London fixing. It is neither an offer nor an invitation to buy or sell investment products and may
not be interpreted as investment advice.
Source: RAM, Bloomberg, Factset, RAM’s alternative data providers.

25. Data engineering: What’s the impact of NOT doing it

26. Data engineering: What’s the impact of NOT doing it

27. Data engineering: What’s the impact of NOT doing it

28. 21
Check #2:
Models’ interpretability

29. Normalised Feature’s importance breakdown
Source: RAM, Bloomberg, Factset, RAM’s alternative data providers.

30. 23
Check #3.1:
Performance analytics

31. Turnover analysis for both models
BASE_EDA Average of D1 Average of D2 Average of D3 Average of D5 Average of D4 Average of D6 Average of D7 Average of D8 Average of D9 Average of D10
2006 234% 476% 584% 611% 603% 603% 613% 546% 464% 279%
2007 293% 618% 712% 791% 772% 782% 752% 718% 635% 232%
2008 266% 581% 714% 760% 746% 750% 765% 750% 673% 364%
2009 383% 669% 800% 843% 845% 847% 835% 792% 713% 398%
2010 353% 650% 744% 741% 782% 760% 746% 684% 650% 290%
2011 255% 637% 781% 883% 812% 845% 793% 710% 633% 360%
2012 168% 535% 745% 856% 823% 852% 785% 679% 563% 380%
2013 229% 586% 763% 857% 856% 852% 814% 721% 636% 380%
2014 392% 653% 700% 752% 721% 747% 747% 688% 530% 364%
2015 314% 554% 662% 674% 694% 644% 565% 577% 445% 341%
2016 309% 583% 700% 731% 697% 677% 678% 655% 549% 322%
2017 234% 465% 620% 556% 619% 564% 629% 656% 568% 355%
2018 353% 640% 756% 745% 783% 723% 726% 705% 640% 320%
2019 429% 734% 772% 821% 807% 794% 779% 731% 685% 330%
2020 (as of end of April) 379% 704% 779% 806% 811% 819% 804% 782% 682% 335%
BASE_NoEDA Average of D1 Average of D2 Average of D3 Average of D5 Average of D4 Average of D6 Average of D7 Average of D8 Average of D9 Average of D10
2006 688% 513% 622% 690% 654% 729% 715% 648% 586% 356%
2007 344% 653% 775% 791% 746% 804% 772% 708% 607% 282%
2008 238% 527% 790% 754% 748% 843% 804% 723% 614% 339%
2009 270% 591% 889% 846% 905% 866% 826% 776% 716% 407%
2010 586% 757% 783% 897% 870% 939% 903% 854% 731% 366%
2011 755% 739% 859% 877% 906% 898% 838% 795% 681% 394%
2012 425% 728% 618% 803% 743% 787% 821% 778% 631% 471%
2013 535% 791% 739% 789% 816% 794% 791% 661% 577% 319%
2014 730% 597% 663% 719% 698% 739% 753% 674% 600% 307%
2015 760% 677% 685% 662% 670% 621% 608% 544% 503% 279%
2016 659% 559% 609% 516% 606% 476% 530% 524% 464% 229%
2017 628% 610% 661% 605% 650% 580% 531% 569% 552% 324%
2018 588% 554% 592% 670% 626% 712% 655% 683% 650% 368%
2019 779% 781% 776% 793% 766% 844% 804% 740% 623% 329%
2020 (as of end of April) 863% 689% 751% 887% 818% 828% 821% 783% 605% 287%

32. Rolling 12 months performance
Performance is Gross of TC and gross of management fees. Backtested performance in USD using London fixing. It is neither an offer nor an invitation to buy or sell investment products and may
not be interpreted as investment advice.
Source: RAM, Bloomberg, Factset, RAM’s alternative data providers.

33. Check #3.2:
Breaking down the data
engineering part.

34. Data engineering: What’s the impact of NOT doing it
Portfolio Long
D10/Short D1. Legs
equal weighted.
Cash Neutral.
Performance
p.a. (USD
gross of TC
and mgmt.
fees)
Data
engineering
Volatility Sharpe (
risk free
assumed
zero )
Run time
(seconds)
Turnover
(Yearly
average 2-
ways)
BASE_EDA 10.4% ALL 10.6% 0.99 720 362%
BASE_NoEDA 4.2% NONE 10.8% 0.38 4200 450%
BASE_NoEDA1 6.7% ONLY TAILS 12.1% 0.55 1120 469%
BASE_NoEDA2 4.8% ONLY
COVERAGE
10.7% 0.45 3800 497%
BASE_NoEDA3 5.0% ONLY
OUTLIERS
9.72% 0.51 3750 518%

35. What tails training does on accuracy
Source: Hypothetical exercice based on a different yet similar datasets (World including EM)

36. Peer reviewed published paper on tails training

37. 30
CASE STUDY # :
Concept and protocol

38. We will predict 1M future performance
We will use a boosted tree classification ML model
Our Investment universe is composed of global stocks including EM (~1700)
Full dataset from Dec-1999 until May-2020. Style datasets are subpart of the global.
Stocks are filtered according absolute and relative metrics for MCAP and ADV.
Data engineering for training is based on:
o Training on tails (extreme quantile from Label/fit cross section) training
o Outliers removals (from label and features)
o Low-coverage instance (row) removal
o Low-coverage feature (column) removal
• (~ 200) features, monthly normalised in percentile
• We use a rolling window of 5 years- 80% Training 20% Testing
From the ML output (probability of outperforming) we create a signal and we
construct portfolio from top/bottom decile (around 150 stocks in each portfolio).
Protocol for ML

39. Implementation ML vs trad. Signal blending
EW portfolios based on ML alpha. Selecting top decile.
Comparing against simple linear average of composite factor MF made of
Profitability, Volatility, Value, Momentum and Size. TRAD VS MODERN
Comparing against linear average of the top 15 most important feature.
PROXY FOR NON LINEARITY ADDED VALUE.

40. 33
Check #:
ML vs MF

41. Comparing Monotonicity

42. Comparing Performance

43. Comparing Factor Exposure

44. 37
Check #:
ML vs Linear MIF ML

45. Comparing Monotonicity

46. Comparing Performance

47. Comparing Factor Exposure

48. 41
Check #:
ML vs Style ML

49. Comparing Monotonicity

50. Comparing Performance

51. Comparing Factor Exposure

52. 45
Check #:
ML vs NLP Sentiment
Dataset

53. Comparing Monotonicity

54. Comparing Performance

55. Comparing Factor Exposure

56. 49
Knowledge is Power:
Some ML/alt. data books

57. For Professional Investors Only
ML books from the “Community”
50

58. For Professional Investors Only
My contributions to the “community”
51

59. Machine Learning model debate: Deep Learning vs RestOfML,
Python vs R, Classification vs Regression etc..
What this presentation was NOT about
What type of dataset to use or not.
A lecture on “ML how to” for hyperparameters.
A full portfolio construction/optimisation with investment’s
constraints + risk management, trading implementation
constraints..

60. 53
Conclusion
• Machine learning is not new but a “new” way for doing research today.
• ML used with traditional data proved to add a non-linear adaptative
component to alpha prediction
• Matter of survival to be capable on onboarding, analysing and implementing
• Big/alt data in the investment toolbox.

62. This material is addressed to professional clients for informative purposes only. It is neither an offer nor an invitation to buy or sell investment products and may not be interpreted as investment
advice. It is not intended to be distributed, published or used in a jurisdiction where such distribution, publication or use is forbidden, and is not intended for any person or entity to whom or
to which it would be illegal to address such a material. In particular, investment products are not offered for sale in the United States or its territories and possessions, nor to any US person
(citizens or residents of the United States of America). The opinions herein do not consider individual clients' circumstances, objectives, or needs. Before entering into any transaction, clients
are advised to form their own opinion and consult professional advisors to obtain an independent review of the specific risks incurred (tax, financial etc.). Upon request, RAM AI Group is
available to provide more information to clients on risks associated with investments. The information and analysis contained herein are based on sources deemed reliable. However, RAM AI
Group does not guarantee their accuracy, correctness or completeness, and it does not accept any liability for any loss or damage resulting from their use. All information and assessments
are subject to change without notice. Changes in exchange rates may cause the NAV per share in the investor's base currency to fluctuate. There is no guarantee to get back the full amount
invested. Past performances, whether actual or back-tested, are not necessarily indicative of future performance. Without prejudice of the due addressee’s own analysis, RAM understands
that this communication should be regarded as a minor non-monetary benefit according to MIFID regulations. Clients are invited to base their investment decisions on the most recent
prospectus, key investor information document (KIID) and financial reports which contain additional information relating to the investment product. These documents are available free of
charge from the SICAV’s and Management Company’s registered offices, its representative and distributor in Switzerland, RAM Active Investments S.A. and at Macard Stein & Co AG, Paying
and Information Agent in Germany; and at RAM Active Investments (Europe) SA – Succursale Milano in Italy. This marketing material has not been approved by any financial Authority, it is
confidential and addressed solely to its intended recipient; its total or partial reproduction and distribution are prohibited. Issued in Switzerland by RAM Active Investments S.A. which is
authorised and regulated in Switzerland by the Swiss Financial Market Supervisory Authority (FINMA). Issued in the European Union and the EEA by the Management Company RAM Active
Investments (Europe) S.A., 51 av. John F. Kennedy L-1855 Luxembourg, Grand Duchy of Luxembourg. The reference to RAM AI Group includes both entities, RAM Active Investments S.A.
and RAM Active Investments (Europe) S.A.
Important Information

63. 9
Demos, slides and video available on QuAcademy
Go to www.qu.academy
9

64. 10
Register at:
https://quspeakerseries6.splashthat.com/

65. Thank you!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
Contact
Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be
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11