DEEP LEARNING FOR LONG-TERM VALUE INVESTING

Jonathan MasciCo-Founder of NNAISENSE

General Manager at Quantenstein

COMPANY STRUCTUREJoint Venture between

‣ Asset manager since 1994

‣ Value philosophy

‣ Funds outperform on the long run

‣ AuM 3.7bn EUR (Feb. 2017)

‣ Large-scale NN solutions for 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: ‣ recipient of the NVIDIA AI

pioneers award

EVOLUTIONARY-RL DEMO

Learned behavior from driver perspectiveLearned parking behavior at NIPS conference

RL to the real world Without a teacher, no supervision

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

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

KEEP A LONG-TERM

VIEW ON THINGS

STOCK PICKER MODELS

SINGLE INSTRUMENT

DATABASE OF FUNDAMENTAL DATA

AI ALPHA GENERATOR

SUPERVISED SIGNAL

PREPROCESSING

‣ LSTM, CNN, etc.

‣ What supervised signal to use, 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

WALK-FORWARD TESTING

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TrainingTesting

BACKTESTINGExpected

Real

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WFT Step 1

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WFT Step 2TR

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WFT Step 5TR

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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 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

No supervision on what signal to extract

DATABASE OF FUNDAMENTAL DATA

AI ALPHA GENERATOR

FEATURES0

PREPROCESSING

AI ALPHA GENERATOR

FEATURESN

PREPROCESSING

AI PORTFOLIO BUILDER

FEATURESNFEATURES0

Universe of companies

RISK

CONSTRAINTS

LOSS

Optimized portfolio

Company 0 Company N

SYSTEM TRAINING

EXPERIMENT CONFIGURATION

MANAGER

EXPERIMENT INSTANCE

GPU#0

EXPERIMENT INSTANCE

GPU#1

EXPERIMENT INSTANCE

GPU#N

RESU

LTS D

ATAB

ASE

FRONTEND REPORTING AND

ANALYSIS

Each EXPERIMENT INSTANCE runs a full WFT training

Pool of experimentsscales linearly with numberof GPUs, but no speedupfor single experiment

EXPERIMENT

WFT STEP 0

GPU#0

WFT STEP 1

GPU#1

WFT STEP T

GPU#N

GATH

ER R

ESUL

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T OBJ

ECT

FRONTEND REPORTING AND

ANALYSIS

Each WFT step runs on a separate GPU in a MAP-REDUCE fashion

Experiment execution scales linearly with numberof GPUs.

RESULTS ANALYSIS AND VISUALIZATION

Outperformance Heat Map

Cumulative Performance

Rolling Performance

Performance Heat Map

BAYERNINVEST ACATIS KI AKTIEN GLOBAL MSCI WORLD INDEX

#positions 50 1654Performance 251.4% 104.7%

Performance p.a. 12.0% 6.7%Volatility p.a. 13.9% 13.0%

Return/Volatility 0.9 0.5Outperformance p.a. 5.3% —

Information Ratio 1.0 —Maximum Drawdown -49.1% -48.5%

Dividend yield 12M 2.5% 2.4%Calmar Ratio L36M 1.92 1.22

Investment Company BayernInvest, MünchenCustodian BayernLB, München

Manager ACATIS Investment GmbH, FrankfurtAI 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 GermanyCurrency EUR

Benchmark MSCI World NDR (EUR)Inception March 23rd, 2017

Fiscal Year-End Dec. 31stFront End Fee Max 5%

Ongoing Costs 1.03%

Performance FeeAt 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 DDistribution Distributed

MASTER FACTS

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.

THANK YOU