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Daily Forecast Based on an Advanced Self-Learning Algorithm
 I Know First provides daily investment forecast based on an
advanced self-learning algorithm (time frames: 3 days, 7 day...
I Know First Team
Yaron Golgher - Co-Founder and CEO
• Previously division manager at OIC with over 15
years of experience...
How It Works – Daily Prediction Process
The results are constantly improving as the algorithm learns from its successes an...
• Every day the algorithm generates heat maps demonstrating the
overall direction of the markets in the 6 time frames.
• T...
XOMA returned 61.45% in
1 month from this forecast
Two indicators:
• Signal – Predicted movement of the asset
• Predictabi...
7
Performance
signal strength (relative on a given day) weakest 10% middle 80% strongest 10% all S&P500 constituents
avg d...
8
Performance – Systematic Trading
Custom Strategy
Rules
Strong Signal
High Predictability
Level
$9,000.00
$9,500.00
$10,0...
Debt/
Equity
PTBV
EPS
Growth
9
P/E
Bottom-up approach:
• Investment analysis starts with and focuses on individual stocks
...
I Know First Customized Solutions for
Financial Institutions
From out of 3000 financial assets that I Know First is tracki...
11
IKF Predictive
Algorithm
Prediction
Services
Institutional
Clients
Extended access to
algorithmic
predictions
Customiza...
Thank you!
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I Know First Presentation (May 2016)

I Know First is a financial tech company that provides daily market forecasts to investors based on an advanced self-learning algorithm.

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I Know First Presentation (May 2016)

  1. 1. Daily Forecast Based on an Advanced Self-Learning Algorithm
  2. 2.  I Know First provides daily investment forecast based on an advanced self-learning algorithm (time frames: 3 days, 7 days, 14 days, 1 month, 3 months and 1 year)  Technology based on artificial intelligence & machine learning, incorporating elements of artificial neural networks and genetic algorithms, developed to analyze and predict financial markets.  The algorithm is self-Learning, adaptable and scalable, is applied to discover best investment opportunities or as a decision support system of an existing investment process  Tracks over 3000 assets (Stocks, World Indexes, ETFs, Interest rates) and growing  Customized algorithmic solutions (extended tailored access to algorithmic predictions, integration of additional markets/securities)  Clients: Family Offices, WM Companies, Advisors, Retail Clients – grown by 400% during 2013-2015 Predictive Algorithm Artificial Intelligence (AI) Genetic Algorithms (GA) I Know First predicts 3000 securities daily 2 Joint Venture: I Know First is launching a fund this year with a financial agency in Israel
  3. 3. I Know First Team Yaron Golgher - Co-Founder and CEO • Previously division manager at OIC with over 15 years of experience in managing and leading consulting projects for industrial and financial institutions. • EMBA from Ben Gurion University, B.Sc. in Industrial Engineering from Tel-Aviv University Dr. Lipa Roitman - Co-Founder and CTO: • Over 20 years of research and experience in artificial intelligence and machine learning fields • Concept of IKF’s algorithm has crystallized following years of his prior research into the nature of chaotic systems • Head of IKF’s R&D Team • Ph.D. from the Weizmann Institute of Science 3 & • R&D Team: professionals with backgrounds in computer science, applied mathematics, and finance • Operations Team: finance and marketing
  4. 4. How It Works – Daily Prediction Process The results are constantly improving as the algorithm learns from its successes and failures Daily data added to our 15 years historical file Run a learning & prediction cycle with new combined data. Daily predictions for each stock, currency, commodity, etc.. 4
  5. 5. • Every day the algorithm generates heat maps demonstrating the overall direction of the markets in the 6 time frames. • The algorithm outputs a predicted trend as an absolute number (not a percentage) known as signal strength • Table is ordered by signal strength. Example: A bullish asset would be indicated by a green “buy” signal at the top of the table. A bearish asset would be denominated by a red “sell” prediction at the bottom of the table. Algorithmic Forecast Daily Market Heatmap 5
  6. 6. XOMA returned 61.45% in 1 month from this forecast Two indicators: • Signal – Predicted movement of the asset • Predictability Indicator – the fitness function (simplified: a correlation based quality measure of the signal)  Key to identify and focus on most predictable markets and securities, enhancing the overall performance 6 Algorithmic Forecast Signal & Predictability
  7. 7. 7 Performance signal strength (relative on a given day) weakest 10% middle 80% strongest 10% all S&P500 constituents avg daily close-to-close return 0.012% 0.054% 0.139% 0.122% annualized (252 business days) 3.19% 14.59% 41.75% 35.94% signal strength (relative on a given day) weakest 10% middle 80% strongest 10% all S&P500 constituents avg daily close-to-close return 0.005% 0.074% 0.250% 0.122% annualized (252 business days) 1.29% 20.50% 87.63% 35.94% Focusing on a higher level of predictability further improves the returns for stronger signals: Predictability filter  Good foundation for systematic trading
  8. 8. 8 Performance – Systematic Trading Custom Strategy Rules Strong Signal High Predictability Level $9,000.00 $9,500.00 $10,000.00 $10,500.00 $11,000.00 $11,500.00 $12,000.00 $12,500.00 $13,000.00 $13,500.00 Daily trading - rankings based on the short-term signals, filtered predictability S&P500_equity IKF_Top20_pure IKF_Top20_acc_cons_filter IKF_Top20_consist_streak IKF_Top20_pure_cons_comb IKF_Top20_trend_avg_signal +3.77% +19.97% +23.97% +30.97% +25.98% +27.71% S&P 500
  9. 9. Debt/ Equity PTBV EPS Growth 9 P/E Bottom-up approach: • Investment analysis starts with and focuses on individual stocks • IKF’s algorithmic predictions are integrated: a) To discover additional opportunities and/or b) To perform algorithmic screening in parallel Top-down approach: • merged “By Industry” and major “World Indexes” forecasts within (macro-) economic analysis • identifying most promising markets/industries (i.e. sub-universes) •  focus on those sub-universes and go deeper from there, integrating the individual stocks forecasts (as DSS or opportunities identifier) Fundamentally healthy, reasonably priced sub-universe SWN MUR REGN PXD MNST 114.82 62.46 60.97 59.66 54.96 0.3 0.39 0.33 0.45 0.24 CNX CHK ALXN PCLN OKE 54.47 53.12 50.40 50.20 49.40 0.37 0.28 0.29 0.3 0.36 CPB PRGO GAS PCG AAL -2.89 -3.08 -3.28 -3.41 -4.22 0.19 0.36 0.06 0.11 0.13 KMB DPS CLX TE HRB -7.11 -7.41 -7.81 -9.08 -17.19 0.09 0.11 0.09 0.04 0.2 + Algorithmic screen (pattern recognition in historical trading data) Constructing Final Portfolio Forecast Utilization: Different Investment Approaches
  10. 10. I Know First Customized Solutions for Financial Institutions From out of 3000 financial assets that I Know First is tracking, the predictions universe can be tailored according to client’s investment focus. Various filtering criteria: • Asset classes - Stocks, ETFs, Interest Rates etc. • Sector/Industry specific forecasts, “merged” signals • Local markets/exchanges, e.g. DAX 30 companies and the German market • Market Capitalization, Liquidity • Risk (volatility) • Dividend paying stocks • Fundamental key ratios, e.g. P/E, P/S, EPS Growth etc. • Reported insider trades • Custom universe, received from client – can be integrated if a) enough historical data and b) sufficient predictability level We work closely with clients and partners to customize the prediction table for best performance with your portfolio, with hardware designed to keep the system optimized to their trading universe. 10
  11. 11. 11 IKF Predictive Algorithm Prediction Services Institutional Clients Extended access to algorithmic predictions Customization, Integration of additional securities Retail Clients Best Picks (predefined sub- universes) Robo-Advisor Platform Fund Management In partnership with institutional clients Involved in launching IKF’s own fund Other Industries Incoming Requests for: - Automotive (lots of sensors’ data -> Real time prevention of engine/parts failures; sales forecasts) - Financing (loan risk based on socioeconomic factors, age, payment patterns etc.) - Insurance (e.g. car insurance policies based on driving patterns) Business Model and Solutions We Offer
  12. 12. Thank you!

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