AAII Phoenix Chapter by Scott Juds –PresentedSumGrowth by ScottStrategies Juds – Sept. 2019 December 12, 2019 President & CEO, SumGrowth Strategies 1 Disclaimers • DO NOT BASE ANY INVESTMENT DECISION SOLELY UPON MATERIALS IN THIS PRESENTATION • Neither SumGrowth Strategies nor I are a registered investment advisor or broker-dealer. • This presentation is for educational purposes only and is not an offer to buy or sell securities. • This information is only educational in nature and should not be construed as investment advice as it is not provided in view of the individual circumstances of any particular individual. • Investing in securities is speculative. You may lose some or all of the money that is invested. • Past results of any particular trading system are not guarantee indicative of future performance. • Always consult with a registered investment advisor or licensed stock broker before investing.

2 Merlyn.AI Prudent Investing Just Got Simpler and Safer

The Plan: • Brief Summary of our Base Technology • How Will Help • A Summary of How Merlyn.AI Works • The Merlyn.AI Strategies and Portfolios • Using Merlyn.AI within Sector Surfer • Let’s go Live Online and See How Things Work

3 Company History

2010 2017 2019 Merlyn.AI Corp. News Founded Jan 2019, Raised $2.5M, Exclusive License from SGS to Create & Market Merlyn ETFs

Solactive US Bank RBC Calculator Custodian Publisher Market Maker

Alpha Architect SGS Merlyn.AI ETF Advisor SectorSurfer SGS License Exemptive Relief NYSE AlphaDroid Investors Web Services MAI Indexes ETF Sponsor Compliance Quasar Marketing Distributor Cable CNBC Mktg. Approval Advisor Shares SEC FINRA G.Adwords Articles First Proved Momentum in Market Data

Narasiman Jegadeesh Sheridan Titman Emory University U. of Texas, Austin

Academic Paper: “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency” (1993) Signal-to-Noise Ratio

Controls the Probability of Claude Shannon Making the Right Decision National Medal of Science, 1966 Proved Signal-to-Noise Ratio Controls the Probability of Making the Right Decision Claude Shannon National Medal of Science, 1966 Matched Filter Theory

Design for Optimum Signal-to-Noise Ratio J. H. Van Vleck Noble Prize, 1977 Think Outside of the Box

Someplace to Start Designed for Performance Think Outside of the Box

J. H. Van Vleck Noble Prize, 1977 Someplace to Start Designed for Performance Differential Signal Processing Removes Common Mode Noise (Relative Strength) Samuel H. Christie Royal Society 1836

5 Years Full Span

Wheatstone Bridge Sectors Provide Power Strokes

Market Economic Cycle Cycle The Bear Market Problem

S&P 500

50 % 50 65 % 65

1991 2018 13 How Fast Can StormGuard Work?

14 The Death Cross Problem

50d Moving Average Crosses the 200d Moving Average

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Buy Buy High

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SellLow SellLow

15 Why is StormGuard-Armor Better?

It Analyses Three Different Kinds of Market Behavior.

It Incorporates Event Detection, not Simply Timing Adjustments.

16 StormGuard-Armor Charts

Better Performance: • Three Market Views • Twelve Separate Measures • Not Shifting the Problem

17 StormGuard - Armor Sneak Incorporating Price, Highs/Lows and Volume Data Preview Utilizing Matched Filter Theory, PID Algorithms & Fuzzy Logic Coming Spring 2016 10000

S&P-DA SG-Std SG-Armor

1000

S&P-DA SG-Std SG-Armor Ann. Return 9.9% 11.2% 13.3% Std. Deviation 16.9% 12.3% 10.4% Sharpe Ratio 0.58 0.91 1.28 Time In Market 100% 83% 73% Avg. Trades/Year 0.00 0.58 1.57

100

1991 1994 1998 2001 2005 2008 2012 2015 1989 1990 1992 1993 1995 1996 1997 1999 2000 2002 2003 2004 2006 2007 2009 2010 2011 2013 2014 2016

18 What is a Smoke Alarm For? Is it Perfect?

?

? ?

1991 2018 Integrated Bear Market Strategy

This is Why its Needed StormGuard-Armor + BMS-A

No SG-Armor The Value is Obvious. No BMS-A

21 StormGuard Evolution StormGuard-Armor(+) Developed in 2016, (2018)

From SG-Armor Web Page Three Market Views Signal Danger Better

• Improved Exit Decisions StormGuard-Armor More • Improved Re-Entry Decisions Is Smarter – Not Faster Information • Exit to a Bear Market Strategy 22 Black Swan Events January 1950 to April 2020 Black Swan Mitigation (Required if Not Vaccinated)

Suddenly… Before You Know It… • A Back Swan Is Coming at You • The Back Swan Bites Your Butt

Ouch! Black Swan Mitigation (Required if Not Vaccinated)

Suddenly… Before You Know It… • A Back Swan Is Coming at You • The Back Swan Bites Your Butt • And It’s Got You by Your Wallet Black Swan Mitigation (Required if Not Vaccinated)

Suddenly… Before You Know It… • A Back Swan Is Coming at You • The Back Swan Bites Your Butt • And It’s Got You by Your Wallet

Mitigation Means Taking Action to Keep Your Wallet and Letting the Black Swan Just Fly Away. Black Swan Mitigation (Required if Not Vaccinated)

Suddenly… Don’t Sell to Before You Know It… the Black Swan • A Back Swan Is Coming at You • The Black Swan The Back Swan Bites Your Butt Always Flies Away • And It’s Got You by Your Wallet

AVOID Mitigation Means Dec 2018 Whipsaw Losses Taking Action to Keep Your Wallet and FED Shock Letting the Black Swan Just Fly Away. Mitigation Response #1 Don’t Lock In Whipsaw Losses

Stocks notched their fastest bear market on record Morgan Stanley

SG-Delta MSI indicated oversold before SG-Armor triggered.

Translation Don’t Trigger SG-Armor if Delta MSI indicates Oversold Mitigation Response #2 Don’t Lock In Technical Whipsaw Losses

Translation Oversold … Mean Reversion … Buy the Dips Inverse Trend Order Best Predicts Next Month’s Return Which Kind of Recovery? L-U-V? ..... It Depends

Not All Boats Rise Together

Bad Breadth is Good... for Momentum Strategies Mitigation Response Deployment Status

March 20, 2020 Deployment of Both Complete Throughout SectorSurfer, AlphaDroid and Merlyn.AI

Mitigation #1 Mitigation #2 Oversold SG-Armor Trigger Block Select via Inverse Trend Following Oversold Vaccination Response Real-Time BS Detection

Black Swans are Like an Earth Quake. 1. When VIX >25, Move to Treasury 2. Otherwise do Momentum Stuff.

SWAN owns market and treasury futures SSS6 owns 50% SPY and 50% TLH (7-10yVIX treasury)Volatility Vaccination Response Real-Time BS Detection Vaccination Deployment Status Complete for SectorSurfer & AlphaDroid Vaccination Deployment Status Complete for SectorSurfer & AlphaDroid Enabling SwanGuard Let’s Go Online... For a Bit... 12-Month SMA Sector Strategy FWPT DEMA Strategy in painted path. FWPT DEMA Strategy w/ Death Cross to Cash FWPT DEMA Strategy w/ AG-Armor to Cash FWPT DEMA Strategy w/ AG-Armor to BND FWPT DEMA Strategy w/ AG-Armor to TLH FWPT DEMA Strategy w/ AG-Armor to BMS-W FWPT DEMA Strategy w/ AG-Armor to BMS-G These Guys Got us Here

Is There More? What About Selection Bias? Who Needs XLV-Healthcare and XLE-Energy?

.

. Merlyn.AI Is a Genetic Algorithm Layered on Top of a Strategy

Genetic Algorithm on Top

Why? To Evolve its Set of Funds Each Month

Why? To Remove Hindsight Selection Bias Artificial Intelligence Algorithms

Types of algorithms[edit] • Bayesian[edit] Semi-[edit] •Almeida–Pineda recurrent •Repeated incremental pruning to produce error reduction (RIPPER) Bayesian statistics Semi-supervised learning •ALOPEX •Rprop •Bayesian knowledge base •Active learning – special case of semi-supervised learning •Backpropagation •Rule-based machine learning •Naive Bayes Generative models • •Skill chaining •Gaussian Naive Bayes •Low-density separation •CN2 algorithm •Sparse PCA •Multinomial Naive Bayes •Graph-based methods •Constructing skill trees •State–action–reward–state–action •Averaged One-Dependence Estimators (AODE) •Co-training •Dehaene–Changeux model •Stochastic gradient descent •Bayesian Belief Network (BBN) •Transduction •Diffusion map •Structured kNN • (BN) [edit] •Dominance-based rough set approach •T-distributed stochastic neighbor embedding Decision tree algorithms[edit] Deep learning •Dynamic time warping •Temporal difference learning Decision tree algorithm •Deep belief networks •Error-driven learning •Wake-sleep algorithm •Decision tree •Deep Boltzmann machines •Evolutionary multimodal optimization •Weighted majority algorithm (machine l •Classification and regression tree (CART) •Deep Convolutional neural networks •Expectation–maximization algorithm •Iterative Dichotomiser 3 (ID3) •Deep Recurrent neural networks •FastICA •C4.5 algorithm •Hierarchical temporal memory •Forward–backward algorithm Supervised learning •C5.0 algorithm •Generative Adversarial Networks •GeneRec •AODE •Chi-squared Automatic Interaction Detection (CHAID) •Deep Boltzmann Machine (DBM) •Genetic Algorithm for Rule Set Production •Artificial neural network •Decision stump •Stacked Auto-Encoders •Growing self-organizing map •Association rule learning algorithms •Conditional decision tree Other machine learning methods and problems[edit] •HEXQ • Apriori algorithm •ID3 algorithm • •Hyper basis function network • Eclat algorithm • •Association rules •IDistance •Case-based reasoning •SLIQ •Bias-variance dilemma •K-nearest neighbors algorithm •Gaussian process regression Linear classifier[edit] •Classification •Kernel methods for vector output •Gene expression programming Linear classifier • Multi-label classification •Kernel principal component analysis •Group method of data handling (GMDH) •Fisher's linear discriminant •Clustering •Leabra •Inductive logic programming • •Data Pre-processing •Linde–Buzo–Gray algorithm •Instance-based learning • •Empirical risk minimization • •Lazy learning •Multinomial logistic regression • • •Learning Automata • • •LogitBoost •Learning Vector Quantization • • •Manifold alignment • •Support vector machine • •Minimum redundancy •Minimum message length (decision trees, decision graphs, etc.) [edit] • •Mixture of experts • Nearest Neighbor Algorithm Unsupervised learning •PAC learning • • Analogical modeling •Expectation-maximization algorithm •Regression •Non-negative matrix factorization •Probably approximately correct learning (PAC) learning •Vector Quantization •Reinforcement Learning •Online machine learning •Ripple down rules, a knowledge acquisition methodology •Generative topographic map •Semi-supervised learning •Out-of-bag error •Symbolic machine learning algorithms •Information bottleneck method •Statistical learning •Prefrontal cortex basal ganglia working memory •Support vector machines Artificial neural networks[edit] • •PVLV •Random Forests Artificial neural network • Graphical models •Q-learning •Ensembles of classifiers •Feedforward neural network Logic learning machine • Bayesian network •Quadratic unconstrained binary optimization • Bootstrap aggregating (bagging) •Self-organizing map • (CRF) •Query-level feature • Boosting (meta-algorithm) Association rule learning[edit] • (HMM) •Quickprop •Ordinal classification Association rule learning •Unsupervised learning •Radial basis function network •Information fuzzy networks (IFN) •Apriori algorithm •VC theory •Randomized weighted majority algorithm •Eclat algorithm How SumGrowth Will Use AI To Perceive the environment and take action to maximize success.

Adaptively changing the algorithm FWPT: Forward Walk based on the past character of the Progressive Tuning Old data. Walks through out-of-sample data for its buy/sell decisions.

Employs Fuzzy Logic to evaluate a composite of 12 measures of the StormGuard - Armor Old market’s character to determine current investment safety.

Uses a Genetic Algorithm to evolve Merlyn.AI the candidate funds in a population FWPP: Forward Walk New Progressive Picking of momentum strategies to eradicate remnants of hindsight selection bias. How Our Genetic Algorithm Works Consider This Analogy to Humans

Genetic Evolution: Mutation Crossover

Human Strategy What are the Gene Mutations

All of our Published Indexes Employ Merlyn.AI Genetic Algorithms Comparison of MAI Indexes

Selected Funds Each Must Have >$10B AUM

SSM SectorSurfer Momentum Index BOBCM Best-of-Breed Core Momentum Index BRBF Bull-Rider Bear-Fighter Index TGI Tactical Growth and Income Index Comparison of our Indexes

ETF Tax Efficiency  L.T. Cap Gains www.MAIindexes.com www.MerlynAI.com www.Merlyn.AI Let’s Go Online... For a Bit... Presented by Scott Juds President & CEO, SumGrowth Strategies

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