Oral Presentation Slides for The AAAI-20 Workshop on Knowledge Discovery from Unstructured Data in Financial Services.

1. The views expressed here are our own and do not necessarily reflect the views of Nomura Asset management.
Any errors and inadequacies are our own.
The AAAI-20 Workshop on Knowledge Discovery from Unstructured Data in Financial Services
February 7th, 2020
Masaya Abe1
and Junpei Komiyama2
A Robust Transferable Deep Learning Framework
for Cross-sectional Investment Strategy
1. Nomura Asset Management Co., Ltd.
2. New York University
Kei Nakagawa1,
https://arxiv.org/pdf/1910.01491.pdfArXiv version (Full paper):

2. 1. Introduction and Motivation
2. Data and Methodology
3. Experimental Results
4. Conclusion
Agenda
1

3. Agenda
1. Introduction and Motivation
2. Data and Methodology
3. Experimental Results
4. Conclusion
2

4. ROE
1 Month Return
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●
●
Score
Value
Growth
Quality
Momentum
・Linear Regression
Factor
Candidates
Factor Classification
by human
Calculate
Relative Goodness
Cross-sectional Investment Strategy
Relative Stock Returns
・Rank IC (Spearman correlation):
3
・ How average returns change with different stock attributes : Factor

5. RIC-NN: Our methodology
Score
Input Output
(3) Deep Transfer Learning
Loss
rank IC
Stop
Epoch
Time step t-1 Time step t
𝒗𝒊 𝒗 𝒇
(2) Weight Initialization and Stopping
Stop
𝒗 𝒇
Loss
(1) Multi-factor Deep Learning Approach
Score
Stock
Input Output
Score
Stock
Input Output
Source Domain
Target Domain
Transfer
Stock Factor
Stock
Factor
Stock
Stock
Factor
Initialization
𝒗𝒊
NorthAmericaAsiaPacific
Initialization
4

6. Score
Factor
Candidates Deep Learning
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・
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・
・
・
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・
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ROE
1 Month Return
●
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RIC-NN: Multi-factor Deep Learning Approach
Calculate
Relative Goodness
5
・ Deep learning for cross sectional investment strategy

7. ・ DL for stock return prediction easily overfits to training data.
✓ Use early stopping to control the fitness to the past data.
RIC-NN: Weight Initialization and Stopping
・Use RankIC (Spearman
correlation) in terms of the fitness.
-> intuitive and controllable.
Loss
rank IC
Epoch
Time step T-1 Time step T
Stop
Loss
Initialization
・ Epoch-based stopping,
Risk of overfitting or underfitting
because the training speed varies.0.20.16
Stopping: the rank IC reaches 0.20.
Initialization: Use the model of timestep t-1 when rank IC is 0.16.
c.f.: fitness of a good portfolio to
future return is around 0.10
Initialization
Our Proposed (RIC-NN)
✓ Training at time step t:
6

8. ScoreRIC-NN
Factor
Stock
Input Output
Score
Factor
Stock
Input Output
North America Stock Market Asia Pacific Stock Market
(Source Domain) (Target Domain)
Transfer
RIC-NN
・Augment the model using the knowledge of a larger market.
✓ Use transfer learning
RIC-NN: Deep Transfer Learning
We want to capture the asymmetric structure between the two markets.
7

9. Agenda
1. Introduction and Motivation
2. Data and Methodology
3. Experimental Results
4. Conclusion
8

10. ・ We use the 20 factors that are often used in practice.
✓ Calculated for each regional index constituents
- MSCI North America Index (NA)
- MSCI Pacific Index (PA)
Features (Various Factors)
No. Feature (Factor) No. Feature (Factor) No. Feature (Factor)
1 Book-to-market Ratio 8 Return on Invested Capital 15 EPS Revision(1 month)
2 Earnings-to-price Ratio 9 Accruals 16 EPS Revision(3 months)
3 Dividend Yield 10 Total Asset Growth Rate 17 Past Stock Return(1 month)
4 Sales-to-price Ratio 11 Current Ratio 18 Past Stock Return(12 months)
5 Cash flow-to-price Ratio 12 Equity Ratio 19 Volatility
6 Return on Equity 13 Total Asset Turnover Rate 20 Skewness
7 Return on Asset 14 CAPEX Growth Rate
※ Monthly data
Data sources: Namely, Compustat, WorldScope, Thomson Reuters, I/B/E/S and EXSHARE.
9

11. Features (Various Factors)
・ Cumulative returns in NA(left-side) and PA(right-side) on 20 factors
・ Return of each factor varies largely over time.
10

12. Problem Formulation
MSE 𝑡 =
1
𝐾
෍
𝑡′=𝑡−𝑁
𝑡−1
෍
𝑖∈𝑈 𝑡′
𝑟𝑖,𝑡′+1 − 𝑓 𝒗𝑖,𝑇; 𝜽 𝑇+1
2
𝑁 = 120 (10 years)
𝐾 = ෍
𝑡′=𝑡−𝑁
𝑡−1
𝑈 𝑡′
・ We define the problem as a regression problem to minimize MSE.
✓ Approximate function 𝑓 ∙ with the parameter 𝜽 𝑇+1 that maps 𝒗𝑖,𝑇 to 𝑟𝑖,𝑇+1
𝑓 𝒗𝑖,𝑇; 𝜽 𝑇+1 → 𝑟𝑖,𝑇+1
✓ Train the models using the data of the latest 120 time steps from the
past 10 years.
11
𝒗𝑖,𝑇 ：Augmented factors
𝜽 𝑇+1：NN Weights
𝑟𝑖,𝑇+1：Relative Stock return

13. ✓ 20 factors:
Problem Formulation
・ Given a stock 𝑖 at month 𝑇 (𝑖 ∈ 𝑈 𝑇: a regional index constituents at 𝑇)
𝒙𝑖,𝑇 ∈ 𝑅20
✓ Features: 20 factors and preprocessed factors
𝑥/ 𝑅
𝑦 ≔ 2(𝑥 − 𝑦)/( 𝑥 + 𝑦 )
✓ Output variable: scaled one-month-ahead stock return
Scale to the range [0,1]
Pre-processing ＆ Feature augmentation
𝒗𝑖,𝑇 = (𝒙𝑖,𝑇, 𝒙𝑖,𝑇−3, … , 𝒙𝑖,𝑇−12, 𝒙𝑖,𝑇/ 𝑅
𝒙𝑖,𝑇−3, … , 𝒙𝑖,𝑇/ 𝑅
𝒙𝑖,𝑇−12) ∈ [0,1] 𝟏𝟖𝟎
𝑟𝑖,𝑇+1 ∈ [0,1]
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・Most of factors are updated quarterly ・Time difference between the present and each quarter ago

14. ・ Architecture of RIC-NN is quite standard
✓ Fully-connected feedforward neural networks
✓ 6 Hidden layers: { 150 – 150 – 100 – 100 – 50 – 50 }
Dropout rates: (50% – 50% – 30% – 30% – 10% – 10%)
✓ Activation function: ReLU function
✓ RIC-NN(Transfer Learning:TF)
Compared Models
・ Other off-the-shelf machine learning models
✓ Epoch-based Neural Network (NN(Epoch))
✓ Random Forest (RF)
✓ Ridge Regression (RR)
We use the weights of the first four layers that are trained
in the source region as the initial weight of the target region.
Our Proposed (RIC-NN)
13

15. Prediction Period
November 2004
:
December 1994
Scores
January 2005
Training
120 set
𝑓 𝒗𝑖,𝑡; 𝜽 𝒕+𝟏
∗
argmin
𝜽
MSE 𝑡
Features: 𝒗𝑖,𝑡 December 2004
𝒗𝑖,𝑡 𝜽 𝑡+1
∗
December 2004
:
January 1995
February 2005
January 2005
𝒗𝑖,𝑡 𝜽 𝑡+1
∗
・・・
October 2018
:
November 2008
December 2018
November 2018
𝜽 𝑡+1
∗𝒗𝑖,𝑡
14 years (168 months)
・ 14 years (from January 2005 to December 2018)
・ Updated by sliding one-month-ahead and carrying out a monthly forecast.
14

16. Agenda
1. Introduction and Motivation
2. Data and Methodology
3. Experimental Results
4. Conclusion
15

17. ・ Simple portfolio strategies
Performance Measure
✓ Long Portfolio Strategy
✓ We make quintile portfolios.
- Buy (Long) the top 1/5 score stocks with equal weighting
- Benchmark: the average return of all stocks
→ Relative performance evaluation
１ ２ ３ ４ ５
Relative goodness
Investment Universe
16

18. Performance Measure
・ We use the following (standard) measures.
✓ Alpha Return ≔ ς 𝑡=1
𝑇
1 + 𝛼 𝑡
12/𝑇 − 1
✓ 𝑇𝐸 Risk ≔
12
𝑇−1
𝛼 𝑡 − 𝜇 𝛼
2
✓ 𝐼𝑅 Return/Risk ≔ Alpha/𝑇𝐸
portfolio return – benchmark return
𝜇 𝛼: Average of Alpha
𝛼 𝑡:
✓ MaxDD Worst − case Loss ≔ min
𝑘∈[1,𝑇]
(0,
𝑊𝑘
𝑃𝑜𝑟𝑡
max
𝑗∈ 1,𝑘
𝑊𝑗
𝑃𝑜𝑟𝑡 − 1)
𝑊𝑘
𝑃𝑜𝑟𝑡
: Cumulative return of the portfolio
17

19. Experimental Results (1/2)
・ NA: RIC-NN without transfer learning performed best.
・ PA: RIC-NN with transfer learning performed best.
・ NA as a source domain enhances the performance of PA,
not vice versa.
MSCI North America
Linear
LR RF DL(Epoch) RIC-NN RIC-NN(TF from PF)
Alpha 0.62% 0.79% 0.82% 1.23% 1.20%
TE 5.40% 5.14% 4.48% 4.14% 4.43%
IR 0.11 0.15 0.18 0.30 0.27
MaxDD -21.84% -24.57% -17.41% -14.37% -20.57%
Long
Nonlinear
MSCI Pacific
Linear
LR RF DL(Epoch) RIC-NN RIC-NN(TF from NA)
Alpha 5.35% 3.79% 4.34% 5.25% 5.78%
TE 5.17% 5.75% 4.18% 4.20% 3.95%
IR 1.04 0.66 1.04 1.25 1.46
MaxDD -11.53% -11.43% -9.37% -7.51% -3.37%
Long
Nonlinear
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20. Experimental Results (2/2)
・ While NN at epoch 50 performs better in NA, NN at epoch 60 performs better in PA.
・ NN(Epoch) is very sensitive to the choice of the epoch.
・ RIC-NN outperforms epoch-based stopping: rank IC controls the fitness of
the stock prediction models consistently.
MSCI North America
40 50 56 60 80
Alpha 1.23% 0.18% 1.48% 0.82% 1.25% 0.70%
TE 4.14% 4.52% 4.35% 4.48% 4.49% 4.14%
IR 0.30 0.04 0.34 0.18 0.28 0.17
MaxDD -14.37% -22.67% -13.48% -17.41% -20.98% -15.94%
Long RIC-NN
NN(Epoch)
MSCI Pacific
40 46 50 60 80
Alpha 5.25% 4.13% 4.34% 4.28% 4.52% 2.99%
TE 4.20% 4.36% 4.18% 4.73% 4.34% 4.06%
IR 1.25 0.95 1.04 0.90 1.04 0.74
MaxDD -7.51% -8.08% -9.37% -7.16% -7.45% -7.52%
NN(Epoch)
Long RIC-NN
*These epochs are chosen so that the rank IC reaches 0.20 during the training of the first time step.
*
*
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21. ・ We have proposed a new stock price prediction framework called RIC-NN
by introducing three practical ideas:
(1) A nonlinear multi-factor approach is better than a linear approach.
(2) Rank IC-based stopping outperforms epoch-based stopping.
(3) Multi-region transfer learning works well.
・ Better return of the portfolio, better control of the fitness of the model to
the past dataset.
Conclusion
20