This document summarizes Qraft's Deep Asset Allocation engine, which uses deep learning to perform tactical asset allocation. It trains on macroeconomic and market data to extract features and learn an optimal allocation strategy. It aims to improve on traditional approaches by continuously learning from new data. The document outlines problems with deep learning like small data and presents Qraft's solutions, such as pretraining and uncertainty quantification. It validates the strategy's performance against benchmarks on test data, demonstrating higher returns with lower drawdowns.

1. Qraft Deep Asset Allocation
QRAFT TECHNOLOGIES, INC.
August 2019
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Asset Allocation • Investment Strategy on Asset Class Level

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Old normal
Mean-
Variance
Portfolio
Optimization
Risk Parity
Conventional
60 : 40
Portfolio
• Old normal portfolio construction methods are assuming historical distribution continuity
-1
-0.5
0
0.5
1
1.5
5 Year Rolling Correlation Between US Treasury Note and S&P 500
• In fact, this assumption is not realistic
Mean-Variance Optimization
Under
Risk Parity Constraints

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Old normal
Time-series
Momentum
Long-term
Reversal
• The next move on was to use factors known as momentum factor, long-term reversal factor
on asset class, and it is known as tactical asset allocation using quant strategies
• However, factor formulation method matters on quant strategy.
➢ Time-series Momentum : 12M Return – 1M Return ➔ Is this really optimized?
Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of financial economics, 104(2), 228-250.
Tactical
Asset
Allocation

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New normal So we have developed Qraft Deep Asset Allocation Engine.
1. It continuously learns with recent data
2. It learns to extract features from cross-sectional macro data and time-series asset data
with deep neural network, eventually learning optimal factor formulation
Asset
Data
Macro
Economic
Data
Valuation
Data
QRAFT
Deep Asset Allocation
Engine
Model
Portfolio
End to End Learning
Quant
Strategy
Quant
Strategy
End to End Learning

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Problems Three major problems to consider for deep learning
Small
Data
Noisy
(Corrupted)
Data
Unknown
State

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Problems Three major problems to consider for deep learning
Small
Data
Noisy
(Corrupted)
Data
Unknown
State
1. Pretraining with self-supervised learning including validation data.

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Problems
uncertainty

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Problems Three major problems to consider for deep learning
Small
Data
Noisy
(Corrupted)
Data
Unknown
State
1. Pretraining with self-supervised learning including validation data.
2. Network Parameter as little as possible
➔ Structure optimized for extracting features needed

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Problems Three major problems to consider for deep learning
Small
Data
Noisy
(Corrupted)
Data
Unknown
State
1. Denoising with conventional method
2. Denoising through Deep AutoEncoder

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Problems Three major problems to consider for deep learning
Small
Data
Noisy
(Corrupted)
Data
Unknown
State
1. Uncertainty Quantification
➔ Homoscedastic + Heteroscedastic noise estimation
2. With higher uncertainty level ➔ Conservative Asset Allocation
Strategy

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Structure
Q-DNN
Feature Extraction Network
1. Reward Function
2. Target Volatility
Optimal Portfolio Label
Data From QRAFT API
Bayes Hyperparameter Optimization
Our deep neural network learns relation between inputs and optimal portfolio just by setting
1. target reward function and 2. target volatility
(e.g. sharpe ratio)

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CLOUD Network
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Structure
QRAFT Database API
S&P Compustat
FRED
Thomson Reuters Eikon
Thomson Reuters
Datastream
Portfolio
via
REST API
QRAFT
DEEP ASSET ALLOCATION
ENGINE
It is designed to be easily used without concerning about data pre-processing & inference server

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Structure Also capable to approach our engine with web GUI

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Speculative
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Structure
Conservative Neutral
Here are three sample strategies with three different target volatility through
Qraft Deep Asset Allocation Engine
Target Volatility
2.5~4.5%
Target Volatility
3.5~5.5%
Target Volatility
5.5~7.5%
Maximizing Sharpe Ratio

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Model validation Category Data Date
Asset Class
US Equity 1985.12.31~
Emerging Market Equity 1985.12.31~
Gold 1985.12.31~
US 3M T-bill 1985.12.31~
US 10Y Treasury Note 1985.12.31~
US 30Y Treasury Bond 1985.12.31~
Macro
&
Valuation
Moody’s Baa Corporate Bond Yield 1985.12.31~
US Effective Federal Funds Rate 1985.12.31~
US GDP Growth 1985.12.31~
US M1 Money Stock Growth 1985.12.31~
US PCE Inflation 1985.12.31~
Long-Short Yield Curve Difference 1985.12.31~
US Unrate 1985.12.31~
Trade Weighted US Dollar Index 1985.12.31~
S&P 500 P/E Ratio 1985.12.31~
And More … 1985.12.31~
Test : 2012.12.31 ~ 2019.06.30Training : 1985.12.31 ~ 2012.01.31
• Benchmark 1 : Equity 60% (S&P 500 40% & MSCI EM INDEX 20%) + US 10Y Treasury 40%
• Benchmark 2 : S&P Risk Parity Target Vol 10% TR Index

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0.9
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
Test data Simulation (DATA NOT USED FOR TRAINING)
QRAFT Deep Asset Allocation (Speculative) QRAFT Deep Asset Allocation (Neutral)
QRAFT Deep Asset Allocation (Conservative) Benchmark 1 : Conventional 60:40 Portfolio
Benchmark 2 : S&P Risk Parity Target Vol 10% TR Index
Model validation

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Evaluation Metric Period
QRAFT Deep Asset Allocation
(Speculative)
QRAFT Deep Asset Allocation
(Neutral)
QRAFT Deep Asset Allocation
(Conservative)
Benchmark 1
(Conventional 60:40)
Benchmark 2
(S&P Risk Parity Index)
Annualized
Sharpe Ratio
Total 1.23 1.34 1.39 0.71 0.70
Recent 1-Year 1.12 1.41 1.98 0.62 1.41
Recent 3-Year 1.21 1.54 1.64 0.86 1.00
Recent 5-Year 1.00 1.14 1.29 0.57 0.63
CAGR
(Annualized Return)
Total 8.94% 6.55% 4.03% 6.09% 4.13%
Recent 1-Year 7.21% 5.43% 4.77% 5.88% 9.39%
Recent 3-Year 7.56% 6.10% 4.22% 7.08% 5.67%
Recent 5-Year 6.29% 4.96% 3.33% 4.84% 3.68%
3-Month Interval
Maximum Drawdown
Total -7.63% -5.64% -2.83% -9.41% -8.07%
5-Day Interval
Maximum Drawdown
Total -4.25% -2.81% -1.84% -6.03% -4.09%
Model validation

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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Speculative Model
Historical Portfolio
US EQUITY GOLD
BOND 30Y NOTE 10Y
T-BILL EMERGING EQUITY
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Neutral Model
Historical Portfolio
US EQUITY GOLD
BOND 30Y NOTE 10Y
T-BILL EMERGING EQUITY
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Conservative Model
Historical Portofolio
US EQUITY GOLD
BOND 30Y NOTE 10Y
T-BILL EMERGING EQUITY
Model validation

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Model validation When it doesn’t learn anything

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Model validation Next, it learns to allocate statically to maximize sharpe ratio

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Model validation Finally, it learns dynamically and tactically to maximize sharpe ratio

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Application Hierarchical Alpha Formulation with Deep Asset Allocation + AI ETF Strategy
Deep
Asset
Allocation
AI ETF+

24. THANK YOU