DEEP LEARNING FOR LONG-TERM VALUE INVESTING Jonathan Masci Co-Founder of NNAISENSE General Manager at Quantenstein
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
EVOLUTIONARY-RL DEMO Learned parking behavior at NIPS conference RL to the real world Without a teacher, no supervision Learned behavior from driver perspective
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
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
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
300 250 BACKTESTING Expected 200 Real 150 100 Training Testing 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 WALK-FORWARD TESTING
300 250 WFT Step 1 TRAIN 200 150 100 300 250 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 TEST 200 150 100 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
300 250 WFT Step 2 TRAIN 200 150 100 300 250 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 TEST 200 150 100 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
300 250 WFT Step 5 TRAIN 200 150 100 300 250 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 TEST 200 150 100 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
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
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
SYSTEM TRAINING
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
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.
RESULTS ANALYSIS AND VISUALIZATION
Cumulative Performance Outperformance Heat Map
Performance Heat Map Rolling Performance
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 -49.1% -48.5% Dividend yield 12M 2.5% 2.4% Calmar Ratio L36M 1.92 1.22
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).
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