DEEP LEARNING FOR LONG-TERM VALUE INVESTING

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1 DEEP LEARNING FOR LONG-TERM VALUE INVESTING Jonathan Masci Co-Founder of NNAISENSE General Manager at Quantenstein

2 COMPANY STRUCTURE Joint Venture between Large-scale NN solutions for Asset manager since 1994 superhuman perception and motor control ultimate goal of marketing AGI leverages 25-year track record of IDSIA, one of the leading research teams in AI: Value philosophy Funds outperform on the long run AuM 3.7bn EUR (Feb. 2017) recipient of the NVIDIA AI pioneers award

3 EVOLUTIONARY-RL DEMO Learned parking behavior at NIPS conference RL to the real world Without a teacher, no supervision Learned behavior from driver perspective

4 WHAT WE DO Fully automated portfolio manager Long-Term Vision Build custom portfolios directly from fundamental data No human in the loop: Deep Learning and Reinforcement Learning Less biased

5 MAJOR DIFFERENCES BETWEEN FINANCE AND OTHER DOMAINS Rules of the game change over time: how to avoid forgetting what worked and not mixing things up? Lot of state aliasing : similar market configurations lead to opposite developments, state is only partially observable Limited history, and only one history No clear single objective, not as simple as classifying cats and dogs Rules for neural network design don t transfer to finance as straightforwardly as it may seem

6 KEEP A LONG-TERM VIEW ON THINGS

7 DATABASE OF FUNDAMENTAL DATA LSTM, CNN, etc. What supervised signal to use, and how to optimize for it? PREPROCESSING AI ALPHA GENERATOR SINGLE INSTRUMENT STOCK PICKER SUPERVISED SIGNAL MODELS

8 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

9 BACKTESTING Expected 200 Real Training Testing WALK-FORWARD TESTING

10 WFT Step 1 TRAIN TEST

11 WFT Step 2 TRAIN TEST

12 WFT Step 5 TRAIN TEST

13 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

14 PLENTY OF DATA WHEN GOING END-TO-END Given a set of companies and their corresponding series of fundamental data produce a set of portfolios, optimized over a given time horizon, that maximize criteria such as 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

15 DATABASE OF FUNDAMENTAL DATA Universe of companies FEATURES 0 FEATURES N Company 0 Company N PREPROCESSING PREPROCESSING AI RISK AI ALPHA GENERATOR AI ALPHA GENERATOR PORTFOLIO BUILDER CONSTRAINTS LOSS FEATURES 0 FEATURES N Optimized portfolio No supervision on what signal to extract

16 SYSTEM TRAINING

17 Each EXPERIMENT INSTANCE runs a full WFT training EXPERIMENT INSTANCE GPU#0 EXPERIMENT CONFIGURATION MANAGER EXPERIMENT INSTANCE GPU#1 RESULTS DATABASE FRONTEND REPORTING AND ANALYSIS Pool of experiments EXPERIMENT INSTANCE scales linearly with number of GPUs, but no speedup GPU#N for single experiment

18 Each WFT step runs on a separate GPU in a MAP-REDUCE fashion WFT STEP 0 EXPERIMENT GPU#0 WFT STEP 1 GPU#1 WFT STEP T GATHER RESULTS AND PACK THEM INTO RESULT OBJECT FRONTEND REPORTING AND ANALYSIS Experiment execution scales linearly with number GPU#N of GPUs.

19 RESULTS ANALYSIS AND VISUALIZATION

20 Cumulative Performance Outperformance Heat Map

21 Performance Heat Map Rolling Performance

22 BAYERNINVEST ACATIS KI AKTIEN GLOBAL MSCI WORLD INDEX #positions Performance 251.4% 104.7% Performance p.a. 12.0% 6.7% Volatility p.a. 13.9% 13.0% Return/Volatility Outperformance p.a. 5.3% Information Ratio 1.0 Maximum Drawdown -49.1% -48.5% Dividend yield 12M 2.5% 2.4% Calmar Ratio L36M

23 MASTER FACTS 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 Permission for Public Distribution D Distribution Distributed 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).

24 DISCLAIMER This document is only intended for information purposes. It is solely directed at professional clients or suitable counterparties in terms of the Securities Trading Act, and is not intended for distribution to retail customers. Past performance does not guarantee future results. Quantenstein accepts no liability that the market forecasts will be achieved. The information is based on carefully selected sources which Quantenstein deems to be reliable, but Quantenstein makes no guarantee as to its correctness, completeness or accuracy. Holdings and allocations may change. The opinions promote understanding of the investment process and are not intended as a recommendation to invest. The investment opportunity discussed in this document may be unsuitable for certain investors depending on their specific investment objectives and depending on their financial situation. Furthermore, this document does not constitute an offer to persons to whom it may not be distributed under the respectively prevailing laws. The information does not represent an offer nor an invitation to subscription for shares and is intended solely for informational purposes. Private individuals and non-institutional investors should not buy the funds directly. Please contact your financial adviser for additional information. The information may not be reproduced or distributed to other persons.

25 THANK YOU

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