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by Scott Juds – SumGrowth Strategies – Sept. 2019

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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.

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The Plan:

• Brief Summary of our Base Technology • How Artificial Intelligence Will Help• A Summary of How Merlyn.AI Works• The Merlyn.AI Strategies and Portfolios• Using Merlyn.AI within Sector Surfer• Why AI is “Missing in Action” on Wall Street?

• Let’s go Live Online and See How Things Work

Merlyn.AI Prudent Investing Just Got Simpler and Safer

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Simply Type In

Merlyn.AILogistics:

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Merlyn.AI Builds On(and does not have to re-discover)

Other Existing Knowledge

It Doesn’t Have to ReinventAll of this From Scratch

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Merlyn.AI

Builds On Topof SectorSurfer

Academic Paper:

“Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency”

(1993)

Narasiman JegadeeshEmory University

Sheridan TitmanU. of Texas, Austin

Momentum in Market DataFirst Proved

Merlyn.AIAccepts This

Re-Discovery Not Necessary

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“the premier market anomaly” that’s “above suspicion.”

Momentum in Market Data

Academic Paper - 2008:

“Dissecting Anomalies”

Eugene FamaNobel Prize, 2013

Kenneth FrenchDartmouth College

Formally Confirmed

Merlyn.AIAccepts This

Re-Discovery Not Necessary

Signal-to-Noise Ratio

Controls the Probability ofMaking the Right Decision

Claude ShannonNational Medal of

Science, 1966

Proved

Merlyn.AIAccepts This

Re-Discovery Not Necessary

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Think Outside of the Box

Someplace to Start Designed for Performance

Matched Filter Theory

Design for OptimumSignal-to-Noise Ratio

J. H. Van VleckNoble Prize, 1977

Merlyn.AIAccepts This

Re-Discovery Not Necessary

Differential Signal Processing

Removes Common Mode Noise

(Relative Strength)

Wheatstone Bridge

Samuel H. ChristieRoyal Society 1836

5 Years Full Span

Merlyn.AIAccepts This

Re-Discovery Not Necessary

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Sectors Provide Power Strokes

Market Cycle

Economic Cycle

Merlyn.AIAccepts This

Re-Discovery Not Necessary

StormGuard-ArmorDetect the Onset of Bad Markets

Know When the Market is Safe: Risk-On vs Risk-Off

Merlyn.AIAccepts This

Re-Discovery Not Necessary

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StormGuard-ArmorDetect the Onset of Bad Markets

These Guys

Got us Here

Is There More?

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What About Selection Bias?Who Needs

XLV-Healthcare and XLE-Energy?

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Merlyn.AI Is a Genetic Algorithm

Layered on Top of a Strategy

Why?To Remove Hindsight Selection Bias

Why?To Evolve its Set of Funds Each Month

Why?To Achieve Better Future Performance

Genetic Algorithm on Top

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Merlyn.AI Is a Genetic Algorithm

Layered on Top of a Strategy

This MeansYou Must Submit to a Higher Power

This MeansFund Selection is Automated

Merlyn.AI

Genetic Algorithm on Top

What is Artificial Intelligence?AI Definition

WikipediaArtificial intelligence (AI) is the ability of a machine to perceive its environment and take actions to maximize its chance of success at some goal.

Applied to InvestmentCan a machine perceive its environment (sector rotation, bull / bear markets) and take action to maximize returns and minimize risk? We say YES!

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Artificial Intelligence AlgorithmsTypes of machine learning algorithms[edit]

•Almeida–Pineda recurrent backpropagation

•ALOPEX

•Backpropagation

•Bootstrap aggregating

•CN2 algorithm

•Constructing skill trees

•Dehaene–Changeux model

•Diffusion map

•Dominance-based rough set approach

•Dynamic time warping

•Error-driven learning

•Evolutionary multimodal optimization

•Expectation–maximization algorithm

•FastICA

•Forward–backward algorithm

•GeneRec

•Genetic Algorithm for Rule Set Production

•Growing self-organizing map

•HEXQ

•Hyper basis function network

•IDistance

•K-nearest neighbors algorithm

•Kernel methods for vector output

•Kernel principal component analysis

•Leabra

•Linde–Buzo–Gray algorithm

•Local outlier factor

•Logic learning machine

•LogitBoost

•Manifold alignment

•Minimum redundancy feature selection

•Mixture of experts

•Multiple kernel learning

•Non-negative matrix factorization

•Online machine learning

•Out-of-bag error

•Prefrontal cortex basal ganglia working memory

•PVLV

•Q-learning

•Quadratic unconstrained binary optimization

•Query-level feature

•Quickprop

•Radial basis function network

•Randomized weighted majority algorithm

•Reinforcement learning

•Repeated incremental pruning to produce error reduction (RIPPER)

•Rprop

•Rule-based machine learning

•Skill chaining

•Sparse PCA

•State–action–reward–state–action

•Stochastic gradient descent

•Structured kNN

•T-distributed stochastic neighbor embedding

•Temporal difference learning

•Wake-sleep algorithm

•Weighted majority algorithm (machine l

Supervised learning

•AODE

•Artificial neural network

•Association rule learning algorithms

• Apriori algorithm

• Eclat algorithm

•Case-based reasoning

•Gaussian process regression

•Gene expression programming

•Group method of data handling (GMDH)

•Inductive logic programming

•Instance-based learning

•Lazy learning

•Learning Automata

•Learning Vector Quantization

•Logistic Model Tree

•Minimum message length (decision trees, decision graphs, etc.)

• Nearest Neighbor Algorithm

• Analogical modeling

•Probably approximately correct learning (PAC) learning

•Ripple down rules, a knowledge acquisition methodology

•Symbolic machine learning algorithms

•Support vector machines

•Random Forests

•Ensembles of classifiers

• Bootstrap aggregating (bagging)

• Boosting (meta-algorithm)

•Ordinal classification

•Information fuzzy networks (IFN)

Bayesian[edit]

Bayesian statistics

•Bayesian knowledge base

•Naive Bayes

•Gaussian Naive Bayes

•Multinomial Naive Bayes

•Averaged One-Dependence Estimators (AODE)

•Bayesian Belief Network (BBN)

•Bayesian Network (BN)

Decision tree algorithms[edit]

Decision tree algorithm

•Decision tree

•Classification and regression tree (CART)

•Iterative Dichotomiser 3 (ID3)

•C4.5 algorithm

•C5.0 algorithm

•Chi-squared Automatic Interaction Detection (CHAID)

•Decision stump

•Conditional decision tree

•ID3 algorithm

•Random forest

•SLIQ

Linear classifier[edit]

Linear classifier

•Fisher's linear discriminant

•Linear regression

•Logistic regression

•Multinomial logistic regression

•Naive Bayes classifier

•Perceptron

•Support vector machine

Unsupervised learning[edit]

Unsupervised learning

•Expectation-maximization algorithm

•Vector Quantization

•Generative topographic map

•Information bottleneck method

Artificial neural networks[edit]

Artificial neural network

•Feedforward neural network Logic learning machine

•Self-organizing map

Association rule learning[edit]

Association rule learning

•Apriori algorithm

•Eclat algorithm

Semi-supervised learning[edit]

Semi-supervised learning

•Active learning – special case of semi-supervised learning

Generative models

•Low-density separation

•Graph-based methods

•Co-training

•Transduction

Deep learning[edit]

Deep learning

•Deep belief networks

•Deep Boltzmann machines

•Deep Convolutional neural networks

•Deep Recurrent neural networks

•Hierarchical temporal memory

•Generative Adversarial Networks

•Deep Boltzmann Machine (DBM)

•Stacked Auto-Encoders

Other machine learning methods and problems[edit]

•Anomaly detection

•Association rules

•Bias-variance dilemma

•Classification

• Multi-label classification

•Clustering

•Data Pre-processing

•Empirical risk minimization

•Feature engineering

•Feature learning

•Learning to rank

•Occam learning

•Online machine learning

•PAC learning

•Regression

•Reinforcement Learning

•Semi-supervised learning

•Statistical learning

•Structured prediction

• Graphical models

• Bayesian network

• Conditional random field (CRF)

• Hidden Markov model (HMM)

•Unsupervised learning

•VC theory

How SumGrowth Will Use AITo Perceive the environment and take action to maximize success.

FWPT: Forward WalkProgressive Tuning

Adaptively changing the algorithm based on the past character of the data. Walks through out-of-sample data for its buy/sell decisions.

StormGuard - Armor

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

FWPP: Forward WalkProgressive Picking

Uses a Genetic Algorithm to evolve the candidate funds in a populationof momentum strategies to eradicate remnants of hindsight selection bias.

Old

Old

NewMerlyn.AI

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Removing Hindsight Selection Bias

Employ a Genetic Algorithm“Forward-Walk Progressive Picking”

Funds Picked with In-Sample Data, But Used with Out-of-Sample Data

How Our Genetic Algorithm Works

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How Our Genetic Algorithm Works

Genetic Evolution:MutationCrossover

Human

Consider This Analogy to Humans

Strategy

What are the Gene Mutations

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Where do the Gene Options Come From?

Scraped From ETF.com

How Our Genetic Algorithm Works

Each month mutant and crossover children are made.

Children

Population of 12

Only children with better “fitness” than their parents

survive by replacing the parent.

Population of 12

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Evolution History for Member-1

How Our Genetic Algorithm Works Evolution History for Member-6

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AIGenetic Momentum Portfolio Manager

Monthly Choice Made by SOS Vote(Like a Strategy-of-Strategies)

300x Time Lapse

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Merlyn’s Magic Portfolio

Merlyn’s Magic Portfolio

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(Play Merlyn video from desktop now)

But First…

This is SectorSurfer

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Needs vs Wants?

Why are you Here Today?1. Afraid Current Savings Won’t Be Enough?2. Advisors Only Set Me Up for Average -1%3. I Only Trade for the Fun and Challenge4. If I Had an ETF that did 15% Bull or Bear

and I could chase kids and travel I would not be here or look at screens all the time.

ETF Tax Efficiency?

An Exchange In-Kind Is a Non-Taxable EventOriginally Designed for Moving an Accounts to Another Brokerage

Market Makers Exchange A Basket of Stocks for ETF SharesIndividuals Buy and Sell Only ETF Shares – It is Not Like Mutual Fund Ownership

Market Makers and Exchange In-Kind for the Current Basket When Shares are Created or Destroyed.

Thus Trades Occur Within the ETF Change the Basket, BUT the Investor Gets Long Term Tax Treatment.

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

Merlyn.AISGS License

InvestorsETF Sponsor

SolactiveCalculatorPublisher

MarketingCable CNBC

Advisor SharesG.Adwords

Articles

SGSSectorSurferAlphaDroidMAI Indexes

Alpha ArchitectETF Advisor

Exemptive ReliefWeb ServicesCompliance

US BankCustodian

RBCMarket Maker

NYSE

QuasarDistributor

Mktg. Approval

FINRASEC

Bull-Rider Bear-Fighter

Index

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MAI Bull-Rider Bear-Fighter Index

Solactive – Index Publisher

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MAI Indexes(Bull-Rider Bear-Fighter Will Soon be Here)

Bull-Rider Bear-Fighter IndexGoogle Search – Stay Up To Date

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Why are AI Funds M.I.A. on Wall Street?

hello

Oct. 2017

June 2018

Why are AI Funds M.I.A. on Wall Street?

AIEQ$119M

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Why are AI Funds M.I.A. on Wall Street?

AIIQ$3.7M

Why are AI Funds M.I.A. on Wall Street?

BUZRIP Mar. 2019

Used AI to Find Patterns in Twitter Messages

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hello

August 2018

Why are AI Funds M.I.A. on Wall Street?

A Distinction Without a Difference

$7.2M AUM Ave. After 18mo.

Why are AI Funds M.I.A. on Wall Street?

Too Many Simultaneous Problems to Solve!

• Discover that Signal-to-Noise Ratio is the Problem & Solve It.

• Learn How to Detect a Bear Market & How to Treat it Differently

Neural Network Future Leaks Into the Past

• Neural Networks are a very complex matrix of connections

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We Will Go Live After BreakTo See How Things Really Work

(Do A Live Online Demo of Everything)

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Merlyn.AI Prudent Investing Just Got Simpler and Safer

by Scott JudsSeptember 2019