DEEP LEARNING FOR LONG-TERM Jonathan Masci VALUE INVESTING - - PowerPoint PPT Presentation
DEEP LEARNING FOR LONG-TERM Jonathan Masci VALUE INVESTING Co-Founder of NNAISENSE General Manager at Quantenstein COMPANY STRUCTURE Joint Venture between Large-scale NN solutions for Asset manager since 1994 superhuman
Jonathan Masci
Co-Founder of NNAISENSE General Manager at Quantenstein
COMPANY STRUCTURE
Joint Venture between
run
superhuman perception and motor control
research teams in AI:
pioneers award
EVOLUTIONARY-RL DEMO
Learned behavior from driver perspective Learned parking behavior at NIPS conference
RL to the real world Without a teacher, no supervision
WHAT WE DO
MAJOR DIFFERENCES BETWEEN FINANCE AND OTHER DOMAINS
and not mixing things up?
developments, state is only partially observable
as it may seem
SINGLE INSTRUMENT
DATABASE OF FUNDAMENTAL DATA AI ALPHA GENERATOR SUPERVISED SIGNAL PREPROCESSING
and how to optimize for it?
ARE WE REALLY IN THE BIG DATA REGIME?
▸ Data: 10K companies, 20 years, new signal every month ▸ 240 data points per company, 2.4M data points in total ▸ Using sequences reduces the number of samples, what’s the sweet spot? ▸ Only one history and the rules of the game change over time ▸ Data augmentation: ▸ If good prior, one can try to augment the training data ▸ In finance if you have a good prior you don’t need AI
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Training Testing
BACKTESTING
Expected Real
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WFT Step 1
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TRAIN TEST
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WFT Step 2
TRAIN TEST
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WFT Step 5
TRAIN TEST
WALK FORWARD TESTING
▸ Tries to minimize “double dipping” as much as possible ▸ Can involve training a very large number of models ▸ e.g. monthly retraining for 10 years produces 120 training stages ▸ Tradeoff between retraining periods and target horizon not easy to
determine, many models will have to tick at different time-scales
PLENTY OF DATA WHEN GOING END-TO-END
▸ Given a set of companies and their corresponding series of fundamental data produce a set
SharpeRatio and InformationRatio
▸ Select a random start date ▸ Select a sub-universe of K companies out of the N ▸ this gets us a choose(K, N)-fold increase in the amount of data ▸ Issue with current systems is that they try to get alpha from fundamental data, what we want
is conditional alpha. No prior on what is a good signal to be extracted, the system implicitly learns features that work for portfolio construction. This is the foundation of Deep Learning
No supervision on what signal to extract DATABASE OF FUNDAMENTAL DATA AI ALPHA GENERATOR FEATURES0 PREPROCESSING AI ALPHA GENERATOR FEATURESN PREPROCESSING
AI PORTFOLIO BUILDER
FEATURESN FEATURES0 Universe of companies RISK CONSTRAINTS LOSS Optimized portfolio
Company 0 Company N
EXPERIMENT CONFIGURATION MANAGER EXPERIMENT INSTANCE GPU#0 EXPERIMENT INSTANCE GPU#1 EXPERIMENT INSTANCE GPU#N RESULTS DATABASE FRONTEND REPORTING AND ANALYSIS Each EXPERIMENT INSTANCE runs a full WFT training Pool of experiments scales linearly with number
for single experiment
EXPERIMENT WFT STEP 0 GPU#0 WFT STEP 1 GPU#1 WFT STEP T GPU#N GATHER RESULTS AND PACK THEM INTO RESULT OBJECT FRONTEND REPORTING AND ANALYSIS Each WFT step runs on a separate GPU in a MAP-REDUCE fashion Experiment execution scales linearly with number
Outperformance Heat Map Cumulative Performance
Rolling Performance Performance Heat Map
BAYERNINVEST ACATIS KI AKTIEN GLOBAL MSCI WORLD INDEX
#positions
50 1654
Performance
251.4% 104.7%
Performance p.a.
12.0% 6.7%
Volatility p.a.
13.9% 13.0%
Return/Volatility
0.9 0.5
Outperformance p.a.
5.3% —
Information Ratio
1.0 —
Maximum Drawdown
Dividend yield 12M
2.5% 2.4%
Calmar Ratio L36M
1.92 1.22
Investment Company BayernInvest, München Custodian BayernLB, München Manager ACATIS Investment GmbH, Frankfurt AI Model Developer Quantenstein GmbH, Frankfurt ISIN DE000A2AMP25 (Institutional class) Bloomberg Ticker BIAKIAK GR Equity Minimum Investment 50,000 Euro (institutional class) Investment Focus Equity Global Domicile Germany Currency EUR Benchmark MSCI World NDR (EUR) Inception March 23rd, 2017 Fiscal Year-End Dec. 31st Front End Fee Max 5% Ongoing Costs 1.03% Performance Fee At present, starting at 3% outperformance 25% of yield generated by the fund during the settlement period is above the reference value MSCI World NDR (EUR). Permission for Public Distribution D Distribution Distributed
MASTER FACTS
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▸ Past performance does not guarantee future results. Quantenstein accepts no liability that the market forecasts will be
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