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How Can We Predict the Financial
Markets?
Investing in the time of uncertainty
By Dr. Lipa Roitman
IKnowFirst.com
February 2013
2Slide
IKnowFirst.com
I Know First - Israeli Forecasting Technologies is an Israeli start up company. Our main
product is a financial market forecasting algorithm that predicts daily more than 200 markets:
Stocks, world indices, Currencies & Commodities.
The company-I Know first-Introduction
• The system is a predictive
model based on artificial
Intelligence (AI) and
Machine Learning (ML),
and incorporating
elements of Artificial
Neural Networks and
Genetic Algorithms, built
with insights of Chaos
Theory and self-similarity,
the Fractals.
• I Know First
tracks and
predicts the flow
of money from
one market or
investment
channel to
another
I Know First
Predicts 200
investment channels
daily
Tracks the
flow of money Artificial
Intelligence
(AI) and
Machine
Learning (ML),
Artificial
Neural
Networks
Genetic
Algorithms
3Slide
IKnowFirst.com
Lipa Roitman PhD
Entrepreneur, algorithmic trading system developer
20 years in the AI (artificial intelligence) and Machine
Learning Fields
Consultant for startup companies
R&D Chemist with record in computer modeling of
process, new product and process development
 http://iknowfirst.com/Stock-forecast-articles
 l k rc ic he e
4Slide
IKnowFirst.com
"it is exceedingly difficult to make predictions,
particularly about the future"
Niels Bohr.
5Slide
IKnowFirst.com
Old Fallacies About Markets
Efficient: “Markets are efficient and
unpredictable: today’s information is
already reflected in price. No one stock
is a better buy then the other”.
Random walk: “The patterns of stock
market prices are purely random.
Markets are unpredictable because they
are random. Playing markets is a
gamble“
Markets are neither totally efficient, nor totally
random. They are complex and chaotic.
6Slide
IKnowFirst.com
Trading the Stock Market
Fact: big trading houses like
Goldman Sachs, Morgan
Stanley consistently make profit
trading stocks.
Some fail spectacularly, like
Lehman Brothers, when they
make big bets that go wrong,
and we read of them in the
paper.
7Slide
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High-Frequency Trading
High-frequency trading HFT: quick arbitrage (in
milliseconds)
Computer algorithms place orders based on
information that is received electronically, before
human traders can react.
Example: arbitrage from the bid-ask spread
→Supposed to provide liquidity, but sometimes errors in the
algorithms cause huge distortions in prices.
→Example: May 6, 2010 Flash Crash
→January 23, 2013 AAPL plunge
8Slide
IKnowFirst.com
Risk Management: Fat Tailed Distribution
 AAPL Flash
Dump
 High-frequency
trading
algorithms.
 How else could
800,000 shares
worth nearly
$300 million be
sold in 17-second
intervals?
9Slide
IKnowFirst.com
High-Frequency Trading
Profits from high-
frequency trading HFT in
American stocks have
peaked in 2009, and
going down since:
→ $1.25 billion in 2012, down
35 percent from 2011 and
74 percent lower than $4.9
billion in 2009
→The percentage of stock
trades handled by firms
that specialize in H.F.T. fell
to about 51 percent in 2012
from 60 percent in 2009.
10Slide
IKnowFirst.com
High-Frequency Trading
Why HFT is slowing down?
Lower trade volume
retail investor is not back yet
(begins to come back recently)
Mainly institutional investors are participating
Technological costs: financial resources to compete in this
field are now enormous (high latency, proximity to
exchanges, investment in hardware and software)
High competition-low profit
11Slide
IKnowFirst.com
Trading the Stock Market
Stock market is not a walk in the park, but it’s not
a random walk either.
Everyone can get lucky with stock market
sometimes.
To do it consistently requires knowledge of
the market, the market direction and risk
management strategy.
12Slide
IKnowFirst.com
Chaos is the Result of Complexity
 Why do stock prices move?
→news stream constantly injects new information.
→mixed messages
→different market players psychology creates patterns.
13Slide
IKnowFirst.com
Chaos is the Result of Complexity
 Objective factors
→Different valuation
models
Fundamental
valuation
Price momentum, etc.
→Different time horizons
 short time horizon vs.
the longer view.
Reasons for chaotic behavior
14Slide
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Psychology of Trading
Human factor: It is difficult
if not impossible to make a
100% rational decision
during uncertainty.
Prospect theory:
(Kahneman and Tversky,
1979): Losses have more
emotional impact than an
equivalent amount of gains.
Risk aversion.
Reasons for chaotic behavior
15Slide
IKnowFirst.com
Psychology of Trading
Which stock do you check first when you analyze
your portfolio performance daily ?
→Last one?
→First one?
→The largest investment?
16Slide
IKnowFirst.com
Psychology of Trading
Latest news bias
Overreaction: can’t quantify
Anchoring: buying on dips
Trend lovers: herd mentality
Why people lose money
17Slide
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Chaos is the Result of Complexity
18Slide
IKnowFirst.com
There is an Order in the Chaos.
 Basic Money Law: Money
is looking for:
 highest returns with
 lowest risk
 It constantly flows from
one market to another.
→ However the way the flow
occurs is rarely smooth,
but marked with periods of
turbulence.
19Slide
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What is Chaos?
Chaos is a complex non-linear evolving system
that is sensitive to initial conditions. It has
“memory”.
There are times when the path is well defined
and predictable
There are points in time (instability regime)
where a minor perturbation can switch the
future path between two opposite directions.
Chaos can appear as randomness, but it is
not. This is the way we could tell them apart:
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Random behavior can't be learned. It is random.
→Past patterns don't repeat.
Chaotic systems have memory. What happened in
the past affects the future.
→ Can be learned and predicted to some extent (quasi-
deterministic chaos).
What is Chaos?
21Slide
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Chaos is a law of
nature. It appears
in complex dynamic
systems where
each element
affects the others.
Astronomy: many-
bodies system.
Weather
What is Chaos?
22Slide
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Stock Market and Quantum Mechanics
 Double slit experiment
Particle-wave duality
23Slide
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Stock Market and Quantum Mechanics
Five years chart:
Patterns can be seen.
The prices jump from 1 level to another
24Slide
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Stock Market and Quantum Mechanics
One month chart:
No patterns apparent
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Stock Market and Quantum Mechanics
Three days chart
Granularity: Single
transactions
No patterns apparent
Stock market exhibits deterministic chaos,
making the short-term movements of prices
extremely impossible to predict.
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Stock Market and Quantum Mechanics
How is the Stock Market like Quantum Mechanics?
A single photon (a particle of light) or an electron
behave like a particle (quantum), an assembly of
them behaves like a wave.
Discrete quantum levels of electrons in an atom.
Market is an assembly of individual transactions
(quanta). Together they show similar patterns.
• Both are Probabilistic
• Discrete levels
• Granularity: unpredictable on microscopic
scale, but predictable on large scale.
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Chaos Example
What is the pattern?
28Slide
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Financial Bubbles
Big Bubble
iPhone 5 release
29Slide
IKnowFirst.com
Chaos Theory and Financial Bubbles
Feedback mechanisms:
Positive feedback amplifies trends (bubble
formation):
→Crowd mentality, chain reaction: chasing one stock,
→Go with the trend!
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Chaos Theory and Financial Bubbles
Feedback mechanisms:
Negative feedback limits the trend
→bubble bursting,
→range-bound trading
→“price got too high: sell!,
→“price got too low: buy!”
31Slide
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Chaos Example
Negative Feedback
Positive Feedback
32Slide
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Apple Inc. AAPL Bubble Crash
Financial Bubble Detection
33Slide
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Chaos Theory and Financial Bubbles
Randomness is also part of the market:
Occurs when the market is indecisive
→Low volume
→Increased volatility
→Randomness is stronger near turning points
Hidden variables: Albert Einstein VS. Niels Bohr:
"I am convinced God does not play dice"
→A trader placed big order at the low liquidity time
→Computer error
→Market manipulation (HFT program trading, etc)
Increased volatility (randomness) is a warning: stay away
from the market or adjust your tactics
34Slide
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What are financial bubbles?
Stock market is a bubble machine on all
time scale levels. Big bubbles can last
years.
A bubble is a very basic part of the
market and can’t be eliminated, but if it
is recognized it could be exploited!
Part of the price discovery process in
the presence of uncertainty.
To get to the “fair” market price the price
has to “overshoot” in both directions.
The market constantly behaves like a drunk driver
35Slide
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The Key to the Market
Markets are chaotic, they alternate between three
regimes: positive feedback, negative feedback, and
randomness.
The three regimes could be present simultaneously at
different time scales
 The one who can recognize these regimes has the key to
the market.
The “buy” or “sell” decision depends on what regime you
think the market is at now, and at what time scale!
36Slide
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The Key to the Market
“For every complex problem there is an answer
that is clear, simple and wrong.”
→– H. L. Mencken
37Slide
IKnowFirst.com
The Key to the Market
Market fallacies:
“The trend is your friend —
(until that nasty bend at the end)”.
“Buy low, sell high”.
The “buy” or “sell” decision depends
on what regime you think the market is
at now, and at what time scale!
38Slide
IKnowFirst.com
The Key to the Market
"I took economics courses in Harvard College
for four years, and everything I was taught
was wrong."
Franklin D. Roosevelt (1882 –1945)
"The established theory has collapsed but we
haven't actually got a proper understanding
of how financial markets operate”.
George Soros
World Economic Forum in Davos 2013
39Slide
IKnowFirst.com
I Know First Algorithmic System
I Know First algorithmic system was developed
to discover the laws of the market that could show
which way the market is going.
40Slide
IKnowFirst.com
I Know First - Israeli Forecasting Technologies is an Israeli start up company. Our main
product is a financial market Forecasting system that predicts daily more than 200 markets:
Stocks, Currencies & Commodities.
The company-I Know first-Introduction
• The system is a predictive
model based on artificial
Intelligence (AI) and
Machine Learning (ML),
and incorporating
elements of Artificial
Neural Networks and
Genetic Algorithms, built
with insights of Chaos
Theory and self-similarity,
the Fractals.
• I Know First
tracks and
predicts the flow
of money from
one market or
investment
channel to
another
I Know First
Predicts 200
investment channels
daily
Tracks the
flow of money Artificial
Intelligence
(AI) and
Machine
Learning (ML),
Artificial
Neural
Networks
Genetic
Algorithms
41Slide
IKnowFirst.com
I Know First Algorithmic System
I Know First Algorithmic System is based on the realization
that a stock value is a function of many factors which interact
in a non-linear way and affect the future trajectory of the stock
creating waves in prices.
Being completely empirical, the I Know First self learning
algorithms analyze the inputs and rank them according to their
significance in predicting the target stock price.
Then they create multiple models, and test them automatically
on the historical data.
The robustness of the model is measured by how it performs
in different market circumstances.
The best predicting models are kept and the rest are rejected.
Such refinement has continued daily as the new market data
is added to the historical pool.
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IKnowFirst.com
I Know First Algorithmic System
Adaptable and balanced algorithm:
→Empirical
does not depend on any human assumptions
self-learning
→learns new patterns daily,
→adapts to new reality,
→but still follows the general historical rules.
43Slide
IKnowFirst.com
I Know First Algorithmic System
The stocks, indexes, commodities and currencies
represent most of the liquid forms of investment
“What if” scenarios:
→Effect of raising or lowering interest rates on the
markets and on real estate prices.
→Effect of currency exchange rates
→Effect of higher or lower oil prices
→Effect of price of oil on oil company stocks
→More….
A Model of the World Economy!
44Slide
IKnowFirst.com
I Know First Algorithmic System
 Many inputs from different sources go into each algorithmic
forecast.
 Up to 15 years of data go into each model.
 Powerful computers process and learn the data, and create
forecast.
45Slide
IKnowFirst.com
I Know First System
running
Cycle
The Product- I Know First System
Basic Principle
Daily stock Data
Get the daily market
update, and add it to
the 15-years database
Run a learning & prediction cycle with new combined
data (Takes about 8 hours per cycle, it runs non-stop
around the clock).
15 years
stock
database
Reporting Module-Software as a
service (SaaS)
The daily prediction for each stock/
currency/ commodity is produced for
the following periods :3 days, 7 days,
14 days, 30 days, 90 days & 365 days
AgenTeam
IQSHIP
Learning &
Prediction
Cycle
Learning &
Prediction
Cycle
Generate
Results”
procedure
Generate
Results”
procedure
PredictionsPredictions
Database
3 Days
prediction
7 Days
prediction
14 Days
prediction
30 Days
prediction
90 Days
prediction
365 Days
prediction
46Slide
IKnowFirst.com
I Know First Algorithmic System
Feature Tech.
analysis
I Know First
Algorithm
 Self learning
 Adaptable
 Learns new patterns daily
 Looks at many different stocks,
indexes, etc
 Signals at different time horizons
 Quantitative
 Predictability indicator
 Artificial Intelligence
 Neural Networks
 Genetic algorithms
No
No
No
No
No
No
No
No
No
No
No
 Yes
 Yes
 Yes
 Yes
 Yes
 Yes
 Yes
 Yes
 Yes
 Yes
 Yes
The system is self learning, which sets it apart
from the technical analysis.
47Slide
IKnowFirst.com
I Know First Algorithmic System-Performance
48Slide
IKnowFirst.com
I Know First Algorithmic System-Performance
49Slide
IKnowFirst.com
The Product- I Know First System
Basic Principle
GTI CHKP X F VNQ
16.69 14.58 13.90 13.42 12.62
0.622 0.528 0.676 0.655 0.679
GT KEY Platinum ^HSI ORBK
11.82 11.80 11.09 9.75 9.45
0.709 0.209 0.591 0.707 0.685
AMD BA IFN WAG DD
9.20 8.77 7.87 7.50 6.76
0.363 0.553 0.575 0.484 0.576
DIS CSCO VUG UN POT
5.68 5.56 5.34 5.31 4.76
0.592 0.642 0.694 0.585 0.551
^VIX ZRAN CSX VPL VXF
4.71 4.55 4.41 4.26 4.01
0.526 0.469 0.643 0.66 0.629
WFR ^TA100 AAUKY VDE BAC
3.86 3.86 3.77 3.73 3.68
0.557 0.731 0.634 0.618 0.463
VTV VGK S&P500 INTC HAS
3.64 3.61 3.38 3.22 2.90
0.605 0.696 0.628 0.595 0.439
DOX WFC RTLX LMT ADM
2.81 2.79 2.36 2.02 1.70
0.448 0.269 0.456 0.39 0.265
EWG TEVA EWC NICE WMT
1.54 1.25 0.68 0.56 0.51
0.59 0.487 0.373 0.411 0.125
EWA JNJ KO VWO T
0.34 0.26 0.24 0.21 0.19
0.741 0.406 0.491 0.707 0.028
K BMY AIP NIS/EUR NIS/GBP
0.09 0.07 0.03 -0.09 -0.09
0.402 0.048 0.614 0.272 0.476
TRP NIS/$US CVS FCX CAT
-0.18 -0.22 -0.29 -0.29 -0.35
0.536 0.485 0.381 0.29 0.583
ENB NSC JY/$US ESLT TKF
-0.38 -0.51 -0.77 -0.78 -2.21
0.625 0.479 0.203 0.594 0.418
OTEX AAPL Crude Oil IRL ISRL
-2.32 -3.19 -5.09 -7.09 -11.64
0.459 0.644 0.668 0.47 0.398
WDC DOW
-12.53 -43.43
0.692 0.602
Negative Negative Positive Postive
++ ++
Signal
Symbol
Predictability
VWO
0.21
0.707
• For each stock/ currency/
commodity the following
data is calculated by the
system:
•Predictability - The
"strength" of the
prediction
•Signal- the movement
direction
(increase/decrease)
•The daily prediction is
produced for the
following periods :3
days, 7 days, 14 days,
30 days, 90 days & 365
days
50Slide
IKnowFirst.com
The Product- I Know First System
Basic Principle
GTI CHKP X F VNQ
16.69 14.58 13.90 13.42 12.62
0.622 0.528 0.676 0.655 0.679
GT KEY Platinum ^HSI ORBK
11.82 11.80 11.09 9.75 9.45
0.709 0.209 0.591 0.707 0.685
AMD BA IFN WAG DD
9.20 8.77 7.87 7.50 6.76
0.363 0.553 0.575 0.484 0.576
DIS CSCO VUG UN POT
5.68 5.56 5.34 5.31 4.76
0.592 0.642 0.694 0.585 0.551
^VIX ZRAN CSX VPL VXF
4.71 4.55 4.41 4.26 4.01
0.526 0.469 0.643 0.66 0.629
WFR ^TA100 AAUKY VDE BAC
3.86 3.86 3.77 3.73 3.68
0.557 0.731 0.634 0.618 0.463
VTV VGK S&P500 INTC HAS
3.64 3.61 3.38 3.22 2.90
0.605 0.696 0.628 0.595 0.439
DOX WFC RTLX LMT ADM
2.81 2.79 2.36 2.02 1.70
0.448 0.269 0.456 0.39 0.265
EWG TEVA EWC NICE WMT
1.54 1.25 0.68 0.56 0.51
0.59 0.487 0.373 0.411 0.125
EWA JNJ KO VWO T
0.34 0.26 0.24 0.21 0.19
0.741 0.406 0.491 0.707 0.028
K BMY AIP NIS/EUR NIS/GBP
0.09 0.07 0.03 -0.09 -0.09
0.402 0.048 0.614 0.272 0.476
TRP NIS/$US CVS FCX CAT
-0.18 -0.22 -0.29 -0.29 -0.35
0.536 0.485 0.381 0.29 0.583
ENB NSC JY/$US ESLT TKF
-0.38 -0.51 -0.77 -0.78 -2.21
0.625 0.479 0.203 0.594 0.418
OTEX AAPL Crude Oil IRL ISRL
-2.32 -3.19 -5.09 -7.09 -11.64
0.459 0.644 0.668 0.47 0.398
WDC DOW
-12.53 -43.43
0.692 0.602
Negative Negative Positive Postive
++ ++
Signal
Symbol
Predictability
VWO
0.21
0.707
Two components to
the stock action
→ Stock-specific action.
→ Stock action related to
general market action.
 A stock is a part of the
market and responds to
the general market news.
 The forecast table
shows how the stock
forecast is positioned
relatively to other stocks
forecast.
51Slide
IKnowFirst.com
The Product- I Know First System
Basic Principle
GTI CHKP X F VNQ
16.69 14.58 13.90 13.42 12.62
0.622 0.528 0.676 0.655 0.679
GT KEY Platinum ^HSI ORBK
11.82 11.80 11.09 9.75 9.45
0.709 0.209 0.591 0.707 0.685
AMD BA IFN WAG DD
9.20 8.77 7.87 7.50 6.76
0.363 0.553 0.575 0.484 0.576
DIS CSCO VUG UN POT
5.68 5.56 5.34 5.31 4.76
0.592 0.642 0.694 0.585 0.551
^VIX ZRAN CSX VPL VXF
4.71 4.55 4.41 4.26 4.01
0.526 0.469 0.643 0.66 0.629
WFR ^TA100 AAUKY VDE BAC
3.86 3.86 3.77 3.73 3.68
0.557 0.731 0.634 0.618 0.463
VTV VGK S&P500 INTC HAS
3.64 3.61 3.38 3.22 2.90
0.605 0.696 0.628 0.595 0.439
DOX WFC RTLX LMT ADM
2.81 2.79 2.36 2.02 1.70
0.448 0.269 0.456 0.39 0.265
EWG TEVA EWC NICE WMT
1.54 1.25 0.68 0.56 0.51
0.59 0.487 0.373 0.411 0.125
EWA JNJ KO VWO T
0.34 0.26 0.24 0.21 0.19
0.741 0.406 0.491 0.707 0.028
K BMY AIP NIS/EUR NIS/GBP
0.09 0.07 0.03 -0.09 -0.09
0.402 0.048 0.614 0.272 0.476
TRP NIS/$US CVS FCX CAT
-0.18 -0.22 -0.29 -0.29 -0.35
0.536 0.485 0.381 0.29 0.583
ENB NSC JY/$US ESLT TKF
-0.38 -0.51 -0.77 -0.78 -2.21
0.625 0.479 0.203 0.594 0.418
OTEX AAPL Crude Oil IRL ISRL
-2.32 -3.19 -5.09 -7.09 -11.64
0.459 0.644 0.668 0.47 0.398
WDC DOW
-12.53 -43.43
0.692 0.602
Negative Negative Positive Postive
++ ++
Signal
Symbol
Predictability
VWO
0.21
0.707
Customized forecast
Choosing stocks from
particular industry into
the table composition
52Slide
IKnowFirst.com
The Product- I Know First System
Short term predictions: 3 days, 7 days, 14 days
Example
53Slide
IKnowFirst.com
The Product- I Know First System
Long term predictions: 30 days, 90 days & 365 days
54Slide
IKnowFirst.com
I Know First Algorithmic System
More uses for algorithms:
Forecasting demand for products and services
Modeling complex chemical processes
Global climate trends
Agricultural forecasting: crops, water demand
More….
Send us requests for your forecasting needs
iknowfirst@iknowfirst.com
55Slide
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What are the stocks for 2013?
56Slide
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December performance
57Slide
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December 2012 performance-aggressive
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Recent performance
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October 2012 performance
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Google Stock Forecast: Case Study
 http://iknowfirst.com/Google-stock-forecast-case-study
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Google Stock Forecast: Case Study
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Google Stock Forecast: Case Study
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Signal Analysis of a Group of Stocks
The following chart shows a combined signal of all
stocks in the system, calculated for each of the
six time ranges.
http://iknowfirst.com/S-P-500-May-12-2013
Send us requests for your forecasting needs
iknowfirst@iknowfirst.com
64Slide
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Signal analysis of a group of stocks
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Signal analysis of a group of stocks
The stocks and indices in the I Know First
system are a good representation of a
broader market.
The forecast for the plurality of stocks in the
I Know First system can serve as a proxy for
the S&P500 forecast.
66Slide
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Predictability: Measuring Chaos
Predictability is the indicator that tells predictable
chaos from randomness.
Just like the market rises and falls in waves, so
does the predictability. And the waves are not
synchronous.
The focus shifts between gold, stocks, oil, bonds.
Some become more predictable, while the others
retreat to randomness.
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Predictability: Measuring Chaos
By monitoring predictability one can get advance
warning that the market paradigm change is in
progress.
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Which stocks can be predicted
 Most of the major stocks in the S&P 500 index are
predictable to some extent.
 Start-Ups are Unpredictable
→ Investor hopes: ‘This is going to be the new Google, the new
Facebook.’
→ Some start-ups make it, some don’t — nobody knows in
advance
 But the main reason our algorithms can’t forecast start-
ups: no history. No way to predict future moves.
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Risk Management
So, is it a trading system that could make money
consistently?
Most of the time yes, with the right risk
management strategy and a bit of luck!
Luck factor
We don’t know all risk factors.
81Slide
IKnowFirst.com
Risk Management
●
There are:
●
“known knowns; there are things we know that we
know.
●
known unknowns; that is to say there are things
that, we now know we don't know.
●
But there are also unknown unknowns – there are
things we do not know we don't know”.
●
—Donald Rumsfeld
●
United States Secretary of Defense
82Slide
IKnowFirst.com
Risk Management: Normal Distribution
Normal
distribution
applies to
many
random
events,
but it is not a
rule in the
markets, but
rather an
exception.
83Slide
IKnowFirst.com
Risk Management: Fat Tailed Distribution
Fat Tailed
distribution is very
common in the
markets.
Large swings (3 to 6
standard deviations
from the mean)
Far more frequent
than the normal
distribution
Power Law
84Slide
IKnowFirst.com
The Danger of Fat Tails
The uncertainty about price distribution makes
“rational” decision making impossible.
One could be right about the market direction, but
lose money or miss an opportunity.
85Slide
IKnowFirst.com
The Danger of Fat Tails
Risk management:
allocation of capital traditional allocation model)
allocation of risk.
86Slide
IKnowFirst.com
Risk Management
87Slide
IKnowFirst.com
Strategies for Success
Watch the signals daily, but act only on strong ones.
To minimize risk stay out of the market until you see a
great opportunity: a strong signal, extreme price.
When predictability is high, invest on strong signals.
When predictability goes down, expect a storm.
When the signal disappears or weakens, reduce your
exposure.
For a stable portfolio invest in non-correlated securities.
→Caveat: during times of global financial crisis all assets become
positively correlated, because they all move (down) together.
88Slide
IKnowFirst.com
Risk Management: Fat Tailed Distribution
What happened to Apple (AAPL) in the last
minute of trading Friday, January 23, 2013
89Slide
IKnowFirst.com
Risk Management: Fat Tailed Distribution
AAPL Flash
Dump
High-
frequency
trading
algorithms.
How else
could 800,000
shares worth
nearly $300
million be sold
in 17-second
intervals?
:
90Slide
IKnowFirst.com
What Causes Fat Tails
●
Extreme risks of "high consequence", but of low
probability. The risks of
●
terrorist attack, major earthquakes, accidents
●
hurricanes, a volcanic-ash cloud grounding all
flights for a continent,
●
HFT trading algorithms
the frequency and impact of totally unexpected events is
generally underestimated
91Slide
IKnowFirst.com
Self-Similarity: Fractals.
Chaotic systems and fractals.
Fractals are objects which are "self-similar" in the
sense that the individual parts are related to the
whole. Mandelbrot.
→The detail looks just about the same as the whole.
 Market patterns are the same on all time scales,
except the shortest times (quanta).
92Slide
IKnowFirst.com
Fractals and the Power Laws
 The main attribute of power laws that makes them interesting is their
scale invariance. Given a relation f(x) = ax^k, scaling the argument x
by a constant factor c causes only a proportionate scaling of the
function itself. That is,
→ f(c x) = a(c x)^k = c^{k}f(x) is proportional to f(x).
 That is, scaling by a constant c simply multiplies the original power-
law relation by the constant c^k. Thus, it follows that all power laws
with a particular scaling exponent are equivalent up to constant
factors, since each is simply a scaled version of the others. This
behavior is what produces the linear relationship when logarithms are
taken of both f(x) and x, and
 the straight-line on the log-log plot is often called the signature of a
power law.

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I Know First Presentation (February 2013)

  • 1. How Can We Predict the Financial Markets? Investing in the time of uncertainty By Dr. Lipa Roitman IKnowFirst.com February 2013
  • 2. 2Slide IKnowFirst.com I Know First - Israeli Forecasting Technologies is an Israeli start up company. Our main product is a financial market forecasting algorithm that predicts daily more than 200 markets: Stocks, world indices, Currencies & Commodities. The company-I Know first-Introduction • The system is a predictive model based on artificial Intelligence (AI) and Machine Learning (ML), and incorporating elements of Artificial Neural Networks and Genetic Algorithms, built with insights of Chaos Theory and self-similarity, the Fractals. • I Know First tracks and predicts the flow of money from one market or investment channel to another I Know First Predicts 200 investment channels daily Tracks the flow of money Artificial Intelligence (AI) and Machine Learning (ML), Artificial Neural Networks Genetic Algorithms
  • 3. 3Slide IKnowFirst.com Lipa Roitman PhD Entrepreneur, algorithmic trading system developer 20 years in the AI (artificial intelligence) and Machine Learning Fields Consultant for startup companies R&D Chemist with record in computer modeling of process, new product and process development  http://iknowfirst.com/Stock-forecast-articles  l k rc ic he e
  • 4. 4Slide IKnowFirst.com "it is exceedingly difficult to make predictions, particularly about the future" Niels Bohr.
  • 5. 5Slide IKnowFirst.com Old Fallacies About Markets Efficient: “Markets are efficient and unpredictable: today’s information is already reflected in price. No one stock is a better buy then the other”. Random walk: “The patterns of stock market prices are purely random. Markets are unpredictable because they are random. Playing markets is a gamble“ Markets are neither totally efficient, nor totally random. They are complex and chaotic.
  • 6. 6Slide IKnowFirst.com Trading the Stock Market Fact: big trading houses like Goldman Sachs, Morgan Stanley consistently make profit trading stocks. Some fail spectacularly, like Lehman Brothers, when they make big bets that go wrong, and we read of them in the paper.
  • 7. 7Slide IKnowFirst.com High-Frequency Trading High-frequency trading HFT: quick arbitrage (in milliseconds) Computer algorithms place orders based on information that is received electronically, before human traders can react. Example: arbitrage from the bid-ask spread →Supposed to provide liquidity, but sometimes errors in the algorithms cause huge distortions in prices. →Example: May 6, 2010 Flash Crash →January 23, 2013 AAPL plunge
  • 8. 8Slide IKnowFirst.com Risk Management: Fat Tailed Distribution  AAPL Flash Dump  High-frequency trading algorithms.  How else could 800,000 shares worth nearly $300 million be sold in 17-second intervals?
  • 9. 9Slide IKnowFirst.com High-Frequency Trading Profits from high- frequency trading HFT in American stocks have peaked in 2009, and going down since: → $1.25 billion in 2012, down 35 percent from 2011 and 74 percent lower than $4.9 billion in 2009 →The percentage of stock trades handled by firms that specialize in H.F.T. fell to about 51 percent in 2012 from 60 percent in 2009.
  • 10. 10Slide IKnowFirst.com High-Frequency Trading Why HFT is slowing down? Lower trade volume retail investor is not back yet (begins to come back recently) Mainly institutional investors are participating Technological costs: financial resources to compete in this field are now enormous (high latency, proximity to exchanges, investment in hardware and software) High competition-low profit
  • 11. 11Slide IKnowFirst.com Trading the Stock Market Stock market is not a walk in the park, but it’s not a random walk either. Everyone can get lucky with stock market sometimes. To do it consistently requires knowledge of the market, the market direction and risk management strategy.
  • 12. 12Slide IKnowFirst.com Chaos is the Result of Complexity  Why do stock prices move? →news stream constantly injects new information. →mixed messages →different market players psychology creates patterns.
  • 13. 13Slide IKnowFirst.com Chaos is the Result of Complexity  Objective factors →Different valuation models Fundamental valuation Price momentum, etc. →Different time horizons  short time horizon vs. the longer view. Reasons for chaotic behavior
  • 14. 14Slide IKnowFirst.com Psychology of Trading Human factor: It is difficult if not impossible to make a 100% rational decision during uncertainty. Prospect theory: (Kahneman and Tversky, 1979): Losses have more emotional impact than an equivalent amount of gains. Risk aversion. Reasons for chaotic behavior
  • 15. 15Slide IKnowFirst.com Psychology of Trading Which stock do you check first when you analyze your portfolio performance daily ? →Last one? →First one? →The largest investment?
  • 16. 16Slide IKnowFirst.com Psychology of Trading Latest news bias Overreaction: can’t quantify Anchoring: buying on dips Trend lovers: herd mentality Why people lose money
  • 17. 17Slide IKnowFirst.com Chaos is the Result of Complexity
  • 18. 18Slide IKnowFirst.com There is an Order in the Chaos.  Basic Money Law: Money is looking for:  highest returns with  lowest risk  It constantly flows from one market to another. → However the way the flow occurs is rarely smooth, but marked with periods of turbulence.
  • 19. 19Slide IKnowFirst.com What is Chaos? Chaos is a complex non-linear evolving system that is sensitive to initial conditions. It has “memory”. There are times when the path is well defined and predictable There are points in time (instability regime) where a minor perturbation can switch the future path between two opposite directions. Chaos can appear as randomness, but it is not. This is the way we could tell them apart:
  • 20. 20Slide IKnowFirst.com Random behavior can't be learned. It is random. →Past patterns don't repeat. Chaotic systems have memory. What happened in the past affects the future. → Can be learned and predicted to some extent (quasi- deterministic chaos). What is Chaos?
  • 21. 21Slide IKnowFirst.com Chaos is a law of nature. It appears in complex dynamic systems where each element affects the others. Astronomy: many- bodies system. Weather What is Chaos?
  • 22. 22Slide IKnowFirst.com Stock Market and Quantum Mechanics  Double slit experiment Particle-wave duality
  • 23. 23Slide IKnowFirst.com Stock Market and Quantum Mechanics Five years chart: Patterns can be seen. The prices jump from 1 level to another
  • 24. 24Slide IKnowFirst.com Stock Market and Quantum Mechanics One month chart: No patterns apparent
  • 25. 25Slide IKnowFirst.com Stock Market and Quantum Mechanics Three days chart Granularity: Single transactions No patterns apparent Stock market exhibits deterministic chaos, making the short-term movements of prices extremely impossible to predict.
  • 26. 26Slide IKnowFirst.com Stock Market and Quantum Mechanics How is the Stock Market like Quantum Mechanics? A single photon (a particle of light) or an electron behave like a particle (quantum), an assembly of them behaves like a wave. Discrete quantum levels of electrons in an atom. Market is an assembly of individual transactions (quanta). Together they show similar patterns. • Both are Probabilistic • Discrete levels • Granularity: unpredictable on microscopic scale, but predictable on large scale.
  • 29. 29Slide IKnowFirst.com Chaos Theory and Financial Bubbles Feedback mechanisms: Positive feedback amplifies trends (bubble formation): →Crowd mentality, chain reaction: chasing one stock, →Go with the trend!
  • 30. 30Slide IKnowFirst.com Chaos Theory and Financial Bubbles Feedback mechanisms: Negative feedback limits the trend →bubble bursting, →range-bound trading →“price got too high: sell!, →“price got too low: buy!”
  • 32. 32Slide IKnowFirst.com Apple Inc. AAPL Bubble Crash Financial Bubble Detection
  • 33. 33Slide IKnowFirst.com Chaos Theory and Financial Bubbles Randomness is also part of the market: Occurs when the market is indecisive →Low volume →Increased volatility →Randomness is stronger near turning points Hidden variables: Albert Einstein VS. Niels Bohr: "I am convinced God does not play dice" →A trader placed big order at the low liquidity time →Computer error →Market manipulation (HFT program trading, etc) Increased volatility (randomness) is a warning: stay away from the market or adjust your tactics
  • 34. 34Slide IKnowFirst.com What are financial bubbles? Stock market is a bubble machine on all time scale levels. Big bubbles can last years. A bubble is a very basic part of the market and can’t be eliminated, but if it is recognized it could be exploited! Part of the price discovery process in the presence of uncertainty. To get to the “fair” market price the price has to “overshoot” in both directions. The market constantly behaves like a drunk driver
  • 35. 35Slide IKnowFirst.com The Key to the Market Markets are chaotic, they alternate between three regimes: positive feedback, negative feedback, and randomness. The three regimes could be present simultaneously at different time scales  The one who can recognize these regimes has the key to the market. The “buy” or “sell” decision depends on what regime you think the market is at now, and at what time scale!
  • 36. 36Slide IKnowFirst.com The Key to the Market “For every complex problem there is an answer that is clear, simple and wrong.” →– H. L. Mencken
  • 37. 37Slide IKnowFirst.com The Key to the Market Market fallacies: “The trend is your friend — (until that nasty bend at the end)”. “Buy low, sell high”. The “buy” or “sell” decision depends on what regime you think the market is at now, and at what time scale!
  • 38. 38Slide IKnowFirst.com The Key to the Market "I took economics courses in Harvard College for four years, and everything I was taught was wrong." Franklin D. Roosevelt (1882 –1945) "The established theory has collapsed but we haven't actually got a proper understanding of how financial markets operate”. George Soros World Economic Forum in Davos 2013
  • 39. 39Slide IKnowFirst.com I Know First Algorithmic System I Know First algorithmic system was developed to discover the laws of the market that could show which way the market is going.
  • 40. 40Slide IKnowFirst.com I Know First - Israeli Forecasting Technologies is an Israeli start up company. Our main product is a financial market Forecasting system that predicts daily more than 200 markets: Stocks, Currencies & Commodities. The company-I Know first-Introduction • The system is a predictive model based on artificial Intelligence (AI) and Machine Learning (ML), and incorporating elements of Artificial Neural Networks and Genetic Algorithms, built with insights of Chaos Theory and self-similarity, the Fractals. • I Know First tracks and predicts the flow of money from one market or investment channel to another I Know First Predicts 200 investment channels daily Tracks the flow of money Artificial Intelligence (AI) and Machine Learning (ML), Artificial Neural Networks Genetic Algorithms
  • 41. 41Slide IKnowFirst.com I Know First Algorithmic System I Know First Algorithmic System is based on the realization that a stock value is a function of many factors which interact in a non-linear way and affect the future trajectory of the stock creating waves in prices. Being completely empirical, the I Know First self learning algorithms analyze the inputs and rank them according to their significance in predicting the target stock price. Then they create multiple models, and test them automatically on the historical data. The robustness of the model is measured by how it performs in different market circumstances. The best predicting models are kept and the rest are rejected. Such refinement has continued daily as the new market data is added to the historical pool.
  • 42. 42Slide IKnowFirst.com I Know First Algorithmic System Adaptable and balanced algorithm: →Empirical does not depend on any human assumptions self-learning →learns new patterns daily, →adapts to new reality, →but still follows the general historical rules.
  • 43. 43Slide IKnowFirst.com I Know First Algorithmic System The stocks, indexes, commodities and currencies represent most of the liquid forms of investment “What if” scenarios: →Effect of raising or lowering interest rates on the markets and on real estate prices. →Effect of currency exchange rates →Effect of higher or lower oil prices →Effect of price of oil on oil company stocks →More…. A Model of the World Economy!
  • 44. 44Slide IKnowFirst.com I Know First Algorithmic System  Many inputs from different sources go into each algorithmic forecast.  Up to 15 years of data go into each model.  Powerful computers process and learn the data, and create forecast.
  • 45. 45Slide IKnowFirst.com I Know First System running Cycle The Product- I Know First System Basic Principle Daily stock Data Get the daily market update, and add it to the 15-years database Run a learning & prediction cycle with new combined data (Takes about 8 hours per cycle, it runs non-stop around the clock). 15 years stock database Reporting Module-Software as a service (SaaS) The daily prediction for each stock/ currency/ commodity is produced for the following periods :3 days, 7 days, 14 days, 30 days, 90 days & 365 days AgenTeam IQSHIP Learning & Prediction Cycle Learning & Prediction Cycle Generate Results” procedure Generate Results” procedure PredictionsPredictions Database 3 Days prediction 7 Days prediction 14 Days prediction 30 Days prediction 90 Days prediction 365 Days prediction
  • 46. 46Slide IKnowFirst.com I Know First Algorithmic System Feature Tech. analysis I Know First Algorithm  Self learning  Adaptable  Learns new patterns daily  Looks at many different stocks, indexes, etc  Signals at different time horizons  Quantitative  Predictability indicator  Artificial Intelligence  Neural Networks  Genetic algorithms No No No No No No No No No No No  Yes  Yes  Yes  Yes  Yes  Yes  Yes  Yes  Yes  Yes  Yes The system is self learning, which sets it apart from the technical analysis.
  • 47. 47Slide IKnowFirst.com I Know First Algorithmic System-Performance
  • 48. 48Slide IKnowFirst.com I Know First Algorithmic System-Performance
  • 49. 49Slide IKnowFirst.com The Product- I Know First System Basic Principle GTI CHKP X F VNQ 16.69 14.58 13.90 13.42 12.62 0.622 0.528 0.676 0.655 0.679 GT KEY Platinum ^HSI ORBK 11.82 11.80 11.09 9.75 9.45 0.709 0.209 0.591 0.707 0.685 AMD BA IFN WAG DD 9.20 8.77 7.87 7.50 6.76 0.363 0.553 0.575 0.484 0.576 DIS CSCO VUG UN POT 5.68 5.56 5.34 5.31 4.76 0.592 0.642 0.694 0.585 0.551 ^VIX ZRAN CSX VPL VXF 4.71 4.55 4.41 4.26 4.01 0.526 0.469 0.643 0.66 0.629 WFR ^TA100 AAUKY VDE BAC 3.86 3.86 3.77 3.73 3.68 0.557 0.731 0.634 0.618 0.463 VTV VGK S&P500 INTC HAS 3.64 3.61 3.38 3.22 2.90 0.605 0.696 0.628 0.595 0.439 DOX WFC RTLX LMT ADM 2.81 2.79 2.36 2.02 1.70 0.448 0.269 0.456 0.39 0.265 EWG TEVA EWC NICE WMT 1.54 1.25 0.68 0.56 0.51 0.59 0.487 0.373 0.411 0.125 EWA JNJ KO VWO T 0.34 0.26 0.24 0.21 0.19 0.741 0.406 0.491 0.707 0.028 K BMY AIP NIS/EUR NIS/GBP 0.09 0.07 0.03 -0.09 -0.09 0.402 0.048 0.614 0.272 0.476 TRP NIS/$US CVS FCX CAT -0.18 -0.22 -0.29 -0.29 -0.35 0.536 0.485 0.381 0.29 0.583 ENB NSC JY/$US ESLT TKF -0.38 -0.51 -0.77 -0.78 -2.21 0.625 0.479 0.203 0.594 0.418 OTEX AAPL Crude Oil IRL ISRL -2.32 -3.19 -5.09 -7.09 -11.64 0.459 0.644 0.668 0.47 0.398 WDC DOW -12.53 -43.43 0.692 0.602 Negative Negative Positive Postive ++ ++ Signal Symbol Predictability VWO 0.21 0.707 • For each stock/ currency/ commodity the following data is calculated by the system: •Predictability - The "strength" of the prediction •Signal- the movement direction (increase/decrease) •The daily prediction is produced for the following periods :3 days, 7 days, 14 days, 30 days, 90 days & 365 days
  • 50. 50Slide IKnowFirst.com The Product- I Know First System Basic Principle GTI CHKP X F VNQ 16.69 14.58 13.90 13.42 12.62 0.622 0.528 0.676 0.655 0.679 GT KEY Platinum ^HSI ORBK 11.82 11.80 11.09 9.75 9.45 0.709 0.209 0.591 0.707 0.685 AMD BA IFN WAG DD 9.20 8.77 7.87 7.50 6.76 0.363 0.553 0.575 0.484 0.576 DIS CSCO VUG UN POT 5.68 5.56 5.34 5.31 4.76 0.592 0.642 0.694 0.585 0.551 ^VIX ZRAN CSX VPL VXF 4.71 4.55 4.41 4.26 4.01 0.526 0.469 0.643 0.66 0.629 WFR ^TA100 AAUKY VDE BAC 3.86 3.86 3.77 3.73 3.68 0.557 0.731 0.634 0.618 0.463 VTV VGK S&P500 INTC HAS 3.64 3.61 3.38 3.22 2.90 0.605 0.696 0.628 0.595 0.439 DOX WFC RTLX LMT ADM 2.81 2.79 2.36 2.02 1.70 0.448 0.269 0.456 0.39 0.265 EWG TEVA EWC NICE WMT 1.54 1.25 0.68 0.56 0.51 0.59 0.487 0.373 0.411 0.125 EWA JNJ KO VWO T 0.34 0.26 0.24 0.21 0.19 0.741 0.406 0.491 0.707 0.028 K BMY AIP NIS/EUR NIS/GBP 0.09 0.07 0.03 -0.09 -0.09 0.402 0.048 0.614 0.272 0.476 TRP NIS/$US CVS FCX CAT -0.18 -0.22 -0.29 -0.29 -0.35 0.536 0.485 0.381 0.29 0.583 ENB NSC JY/$US ESLT TKF -0.38 -0.51 -0.77 -0.78 -2.21 0.625 0.479 0.203 0.594 0.418 OTEX AAPL Crude Oil IRL ISRL -2.32 -3.19 -5.09 -7.09 -11.64 0.459 0.644 0.668 0.47 0.398 WDC DOW -12.53 -43.43 0.692 0.602 Negative Negative Positive Postive ++ ++ Signal Symbol Predictability VWO 0.21 0.707 Two components to the stock action → Stock-specific action. → Stock action related to general market action.  A stock is a part of the market and responds to the general market news.  The forecast table shows how the stock forecast is positioned relatively to other stocks forecast.
  • 51. 51Slide IKnowFirst.com The Product- I Know First System Basic Principle GTI CHKP X F VNQ 16.69 14.58 13.90 13.42 12.62 0.622 0.528 0.676 0.655 0.679 GT KEY Platinum ^HSI ORBK 11.82 11.80 11.09 9.75 9.45 0.709 0.209 0.591 0.707 0.685 AMD BA IFN WAG DD 9.20 8.77 7.87 7.50 6.76 0.363 0.553 0.575 0.484 0.576 DIS CSCO VUG UN POT 5.68 5.56 5.34 5.31 4.76 0.592 0.642 0.694 0.585 0.551 ^VIX ZRAN CSX VPL VXF 4.71 4.55 4.41 4.26 4.01 0.526 0.469 0.643 0.66 0.629 WFR ^TA100 AAUKY VDE BAC 3.86 3.86 3.77 3.73 3.68 0.557 0.731 0.634 0.618 0.463 VTV VGK S&P500 INTC HAS 3.64 3.61 3.38 3.22 2.90 0.605 0.696 0.628 0.595 0.439 DOX WFC RTLX LMT ADM 2.81 2.79 2.36 2.02 1.70 0.448 0.269 0.456 0.39 0.265 EWG TEVA EWC NICE WMT 1.54 1.25 0.68 0.56 0.51 0.59 0.487 0.373 0.411 0.125 EWA JNJ KO VWO T 0.34 0.26 0.24 0.21 0.19 0.741 0.406 0.491 0.707 0.028 K BMY AIP NIS/EUR NIS/GBP 0.09 0.07 0.03 -0.09 -0.09 0.402 0.048 0.614 0.272 0.476 TRP NIS/$US CVS FCX CAT -0.18 -0.22 -0.29 -0.29 -0.35 0.536 0.485 0.381 0.29 0.583 ENB NSC JY/$US ESLT TKF -0.38 -0.51 -0.77 -0.78 -2.21 0.625 0.479 0.203 0.594 0.418 OTEX AAPL Crude Oil IRL ISRL -2.32 -3.19 -5.09 -7.09 -11.64 0.459 0.644 0.668 0.47 0.398 WDC DOW -12.53 -43.43 0.692 0.602 Negative Negative Positive Postive ++ ++ Signal Symbol Predictability VWO 0.21 0.707 Customized forecast Choosing stocks from particular industry into the table composition
  • 52. 52Slide IKnowFirst.com The Product- I Know First System Short term predictions: 3 days, 7 days, 14 days Example
  • 53. 53Slide IKnowFirst.com The Product- I Know First System Long term predictions: 30 days, 90 days & 365 days
  • 54. 54Slide IKnowFirst.com I Know First Algorithmic System More uses for algorithms: Forecasting demand for products and services Modeling complex chemical processes Global climate trends Agricultural forecasting: crops, water demand More…. Send us requests for your forecasting needs iknowfirst@iknowfirst.com
  • 60. 60Slide IKnowFirst.com Google Stock Forecast: Case Study  http://iknowfirst.com/Google-stock-forecast-case-study
  • 63. 63Slide IKnowFirst.com Signal Analysis of a Group of Stocks The following chart shows a combined signal of all stocks in the system, calculated for each of the six time ranges. http://iknowfirst.com/S-P-500-May-12-2013 Send us requests for your forecasting needs iknowfirst@iknowfirst.com
  • 65. 65Slide IKnowFirst.com Signal analysis of a group of stocks The stocks and indices in the I Know First system are a good representation of a broader market. The forecast for the plurality of stocks in the I Know First system can serve as a proxy for the S&P500 forecast.
  • 66. 66Slide IKnowFirst.com Predictability: Measuring Chaos Predictability is the indicator that tells predictable chaos from randomness. Just like the market rises and falls in waves, so does the predictability. And the waves are not synchronous. The focus shifts between gold, stocks, oil, bonds. Some become more predictable, while the others retreat to randomness.
  • 67. 67Slide IKnowFirst.com Predictability: Measuring Chaos By monitoring predictability one can get advance warning that the market paradigm change is in progress.
  • 68. 68Slide IKnowFirst.com Which stocks can be predicted  Most of the major stocks in the S&P 500 index are predictable to some extent.  Start-Ups are Unpredictable → Investor hopes: ‘This is going to be the new Google, the new Facebook.’ → Some start-ups make it, some don’t — nobody knows in advance  But the main reason our algorithms can’t forecast start- ups: no history. No way to predict future moves.
  • 80. 80Slide IKnowFirst.com Risk Management So, is it a trading system that could make money consistently? Most of the time yes, with the right risk management strategy and a bit of luck! Luck factor We don’t know all risk factors.
  • 81. 81Slide IKnowFirst.com Risk Management ● There are: ● “known knowns; there are things we know that we know. ● known unknowns; that is to say there are things that, we now know we don't know. ● But there are also unknown unknowns – there are things we do not know we don't know”. ● —Donald Rumsfeld ● United States Secretary of Defense
  • 82. 82Slide IKnowFirst.com Risk Management: Normal Distribution Normal distribution applies to many random events, but it is not a rule in the markets, but rather an exception.
  • 83. 83Slide IKnowFirst.com Risk Management: Fat Tailed Distribution Fat Tailed distribution is very common in the markets. Large swings (3 to 6 standard deviations from the mean) Far more frequent than the normal distribution Power Law
  • 84. 84Slide IKnowFirst.com The Danger of Fat Tails The uncertainty about price distribution makes “rational” decision making impossible. One could be right about the market direction, but lose money or miss an opportunity.
  • 85. 85Slide IKnowFirst.com The Danger of Fat Tails Risk management: allocation of capital traditional allocation model) allocation of risk.
  • 87. 87Slide IKnowFirst.com Strategies for Success Watch the signals daily, but act only on strong ones. To minimize risk stay out of the market until you see a great opportunity: a strong signal, extreme price. When predictability is high, invest on strong signals. When predictability goes down, expect a storm. When the signal disappears or weakens, reduce your exposure. For a stable portfolio invest in non-correlated securities. →Caveat: during times of global financial crisis all assets become positively correlated, because they all move (down) together.
  • 88. 88Slide IKnowFirst.com Risk Management: Fat Tailed Distribution What happened to Apple (AAPL) in the last minute of trading Friday, January 23, 2013
  • 89. 89Slide IKnowFirst.com Risk Management: Fat Tailed Distribution AAPL Flash Dump High- frequency trading algorithms. How else could 800,000 shares worth nearly $300 million be sold in 17-second intervals? :
  • 90. 90Slide IKnowFirst.com What Causes Fat Tails ● Extreme risks of "high consequence", but of low probability. The risks of ● terrorist attack, major earthquakes, accidents ● hurricanes, a volcanic-ash cloud grounding all flights for a continent, ● HFT trading algorithms the frequency and impact of totally unexpected events is generally underestimated
  • 91. 91Slide IKnowFirst.com Self-Similarity: Fractals. Chaotic systems and fractals. Fractals are objects which are "self-similar" in the sense that the individual parts are related to the whole. Mandelbrot. →The detail looks just about the same as the whole.  Market patterns are the same on all time scales, except the shortest times (quanta).
  • 92. 92Slide IKnowFirst.com Fractals and the Power Laws  The main attribute of power laws that makes them interesting is their scale invariance. Given a relation f(x) = ax^k, scaling the argument x by a constant factor c causes only a proportionate scaling of the function itself. That is, → f(c x) = a(c x)^k = c^{k}f(x) is proportional to f(x).  That is, scaling by a constant c simply multiplies the original power- law relation by the constant c^k. Thus, it follows that all power laws with a particular scaling exponent are equivalent up to constant factors, since each is simply a scaled version of the others. This behavior is what produces the linear relationship when logarithms are taken of both f(x) and x, and  the straight-line on the log-log plot is often called the signature of a power law.

Editor's Notes

  1. עץ או פלי
  2. Coin game Next question How is that two people, one buying and one selling both think they are right ?
  3. טענות מופרכות שווקים הם יעילים ואינם ניתנים לחיזוי הליכה אקראית הדפוסים של מחירים טענות מופרכות ישנות על שווקים הימור
  4. עובדה: בתי השקעות גדולים כמו גולדמן זאקס GS , מורגן סטנלי MS רווחיות. באופן עקבי מניות למסחר רווח. שוק המניות הוא לא טיול בפרק, אבל גם לא הליכה אקראית כל אחד יכול בר מזל עם שוק המניות לפעמים, אבל לעשות את זה באופן עקבי לוקח ידע.
  5. מסחר בתדירות הגבוהה: במהירות (באלפיות שניים) לקנות ולמכור מניות מוצעות על ידי משקיעים איטיים: (ארביטראז'). רווחים מ HFT מסחר בתדירות גבוהה במניות אמריקניות הגיעו לשיאו בשנת 2009 , ויורדים מאז:  
  6. נפח מסחר נמוך משקיעים מוסדיים בעיקר משתתפים משקיע הקמעונאי עדיין לא חזר עלויות טכנולוגיות של HFT . חסם הכניסה
  7. עובדה: בתי השקעות גדולים כמו גולדמן זאקס GS , מורגן סטנלי MS רווחיות. באופן עקבי מניות למסחר רווח. שוק המניות הוא לא טיול בפרק, אבל גם לא הליכה אקראית כל אחד יכול בר מזל עם שוק המניות לפעמים, אבל לעשות את זה באופן עקבי לוקח ידע.
  8. למה מחירי המניות משתנים? זרם חדשות כל הזמן מזריק מידע חדש. פסיכולוגיה משפיע על שוק משתתפים ויוצר דפוסים.     גורמים אובייקטיביים שיטות הערכה שונות: מה שנראה מוערך בחסר על פי מודל אחד יכול להופיע overvalued למשנו. אופק זמן: מה שנראה מנופח באופק הזמן הקצר יכול להופיע המעיט בתצוגה הארוכה יותר.
  9. למה מחירי המניות משתנים? זרם חדשות כל הזמן מזריק מידע חדש. פסיכולוגיה משפיע על שוק משתתפים ויוצר דפוסים.     גורמים אובייקטיביים שיטות הערכה שונות: מה שנראה מוערך בחסר על פי מודל אחד יכול להופיע overvalued למשנו. אופק זמן: מה שנראה מנופח באופק הזמן הקצר יכול להופיע המעיט בתצוגה הארוכה יותר.
  10. גורם אנושי כלכלה התנהגותית תאורית פרוספקט הישרדות מנטליות עדר
  11. גורם אנושי תגובה מוגזמת לכמת כלכלה התנהגותית התנגדות מנטליות עדר
  12. תוצאות: דפוסים שונים, גלים במחירים.
  13. יש סדר בבלגן. כסף מחפש תשואות הגבוהות ביותר עם סיכון הנמוך ביותר, זה כל זמן זורם משווק אחד למשנו. עם זאת דרך הזרימה מתרחשת רק לעתים נדירות חלקה, אבל מסומן בתקופות של אי שקט.
  14. כאוס יכול להופיע כאקראי, אבל זה לא . כיצד ניתן לדעת: שווקים יש זיכרון. מה שקרה בעבר משפיע על העתיד. התנהגות אקראית לא ניתן ללמוד. זה אקראי. את דפוסי העבר לא יחזרו.
  15. כאוס יכול להופיע כאקראי, אבל זה לא . כיצד ניתן לדעת: שווקים יש זיכרון. מה שקרה בעבר משפיע על העתיד. התנהגות אקראית לא ניתן ללמוד. זה אקראי. את דפוסי העבר לא יחזרו.
  16. כאוס יכול להופיע כאקראי, אבל זה לא . כיצד ניתן לדעת: שווקים יש זיכרון. מה שקרה בעבר משפיע על העתיד. התנהגות אקראית לא ניתן ללמוד. זה אקראי. את דפוסי העבר לא יחזרו.
  17. איך שוק המניות כמו מכניקת קוונטים? פוטון בודד מתנהג כמו חלקיק (קוונטים), פוטונים רבים מתנהגים כמו גל. שוק הוא הרכבה של עסקות בודדות (קוונטים). יחד הם מציגים דפוסים גליים. שניהם הסתברותית גרעיניות: בלתי צפויה בקנה מידה מיקרוסקופי, אך צפויה בקנה מידה גדול.
  18. כאוס כרוך מנגנוני משוב, מגמה משוב חיובי מגביר מגמות (היווצרות בועה):   (מנטליות קהל, תגובת שרשרת: רודף אחרי מנייה אחת, נוהג את המחיר). משוב שלילי מקטין מגמות (בועה מתפוצצת): (טווח נפוח: "קבל מחיר גבוה מדי: תמכרו!") שוקים יש משוב חיובי ושלילי.
  19. כאוס כרוך מנגנוני משוב, מגמה משוב חיובי מגביר מגמות (היווצרות בועה):   (מנטליות קהל, תגובת שרשרת: רודף אחרי מנייה אחת, נוהג את המחיר). משוב שלילי מקטין מגמות (בועה מתפוצצת): (טווח נפוח: "קבל מחיר גבוה מדי: תמכרו!") שוקים יש משוב חיובי ושלילי.
  20. כאוס כרוך מנגנוני משוב, משוב חיובי מגביר מגמות (היווצרות בועה):   (מנטליות קהל, תגובת שרשרת: רודף אחרי מנייה אחת, נוהג את המחיר). משוב שלילי מקטין מגמות (בועה מתפוצצת): (טווח נפוח: "קבל מחיר גבוה מדי: תמכרו!") שוקים יש לך משוב חיובי ושלילי.
  21. . שוק המניות הוא בועת מכונה בכל רמות סולם הזמן. בועות גדולות יכולות להימשך שנים. בועה היא חלק מאוד בסיסי של השוק ולא ניתן למנוע, אבל ניתן לזהות ונצלה! חלק מתהליך גילוי המחיר בנוכחות של אי הוודאות. כדי להגיע למחיר השוק "האמיתי" אתה צריך "להחטיא את המטרה" בשני הכיוונים.
  22. שווקים הם כאוטי, הם חלופיים בין שלושה משטרים: משוב חיובי, משוב שלילי, ואקראיים. מי שיכול לזהות את המשטרים האלה יש את המפתח לשוק. ההחלטה "לקנות" או "למכור" תלויה במה שאתה חושב משטר השוק הוא בבית עכשיו!
  23. שווקים הם כאוטי, הם חלופיים בין שלושה משטרים: משוב חיובי, משוב שלילי, ואקראיים. מי שיכול לזהות את המשטרים האלה יש את המפתח לשוק. ההחלטה "לקנות" או "למכור" תלויה במה שאתה חושב משטר השוק הוא בבית עכשיו!
  24. מערכת האלגוריתמית הראשונה שאני מכיר שפותחה כדי לגלות חוקי שוק לחזות תנועת המחיר.
  25. המערכת היא למידה עצמית, שמייחדת אותו מהניתוח הטכני. אלגוריתם להתאמה ומאוזן: לומד דפוסים חדשים מדי יום, מסתגל למציאות חדשה, אבל עדיין פועל לפי החוקים ההסטוריים הכלליים.
  26. המערכת היא למידה עצמית, שמייחדת אותו מהניתוח הטכני. אלגוריתם להתאמה ומאוזן: לומד דפוסים חדשים מדי יום, מסתגל למציאות חדשה, אבל עדיין פועל לפי החוקים ההסטוריים הכלליים.
  27. ודל של כלכלת העולם! מה אם תרחישים: לדמות את התוצאה של פעולות ממשלה: השפעה של העלאה או הורדת ריבית על השווקים ועל מחירי הנדל"ן. השפעת שינויים בשערי חליפין של מטבע השפעה של מחירי נפט גבוהים יותר או נמוכים יותר השפעה של מחיר הנפט על מניות חברות נפט יותר ....
  28. ודל של כלכלת העולם! מה אם תרחישים: לדמות את התוצאה של פעולות ממשלה: השפעה של העלאה או הורדת ריבית על השווקים ועל מחירי הנדל"ן. השפעת שינויים בשערי חליפין של מטבע השפעה של מחירי נפט גבוהים יותר או נמוכים יותר השפעה של מחיר הנפט על מניות חברות נפט יותר ....
  29. ודל של כלכלת העולם! מה אם תרחישים: לדמות את התוצאה של פעולות ממשלה: השפעה של העלאה או הורדת ריבית על השווקים ועל מחירי הנדל"ן. השפעת שינויים בשערי חליפין של מטבע השפעה של מחירי נפט גבוהים יותר או נמוכים יותר השפעה של מחיר הנפט על מניות חברות נפט יותר ....
  30. ודל של כלכלת העולם! מה אם תרחישים: לדמות את התוצאה של פעולות ממשלה: השפעה של העלאה או הורדת ריבית על השווקים ועל מחירי הנדל"ן. השפעת שינויים בשערי חליפין של מטבע השפעה של מחירי נפט גבוהים יותר או נמוכים יותר השפעה של מחיר הנפט על מניות חברות נפט יותר ....
  31. ודל של כלכלת העולם! מה אם תרחישים: לדמות את התוצאה של פעולות ממשלה: השפעה של העלאה או הורדת ריבית על השווקים ועל מחירי הנדל"ן. השפעת שינויים בשערי חליפין של מטבע השפעה של מחירי נפט גבוהים יותר או נמוכים יותר השפעה של מחיר הנפט על מניות חברות נפט יותר ....
  32. בדיוק כמו בשוק עולה ויורד בגלים, כך גם את יכולת החיזוי. והגלים הם לא סינכרוני. מוקד תשומת לב של שוק כל זמן נע בין מקומות שונים, בין אם זה זהב, מניות, נפט, איגרות חוב. מדדים מסוימים הופכים ליותר צפויים, בעוד האחרים לסגת לאקראיות.
  33. בדיוק כמו בשוק עולה ויורד בגלים, כך גם את יכולת החיזוי. והגלים הם לא סינכרוני. מוקד תשומת לב של שוק כל זמן נע בין מקומות שונים, בין אם זה זהב, מניות, נפט, איגרות חוב. מדדים מסוימים הופכים ליותר צפויים, בעוד האחרים לסגת לאקראיות.
  34. מערכות ופרקטל כאוטי. פרקטלים הם אובייקטים שהם "עצמיים דומים" במובן זה שהחלקים הבודדים מתייחסים לכל. מנדלברוט. הפרט נראה פשוט בערך כמו כולה.   דפוסי שוק הם זהים בכל טווחי הזמן, למעט הזמנים הקצרים ביותר (קוונטים).
  35. התכונה העיקרית של חוקי הכח שגורמים להם מעניינים היא אינווריאנטיות קנה המידה שלהם. בהתחשב ביחס f ( x ) = ax ^ k , דרוג x הטיעון על ידי ג גורם קבוע גורם רק קנה מידה יחסית של הפונקציה עצם. כלומר,    f ( CX ) = ( CX ) ^ k = c ^ {יא} f (x ) הוא פרופורציונלי ל f (x ). כלומר, על ידי שינוי קנה מידה קבוע C פשוט מכפיל את יחס כוח החוק המקורי על ידי ג התמידי ^ k . לפיכך, מסקנה הוא כי כל חוקי החשמל עם מעריך קנה מידה מסוים הם שווים ערך לגורמים קבועים, שכן כל אחד הוא פשוט גרסה מוקטנת של אחרים. התנהגות זו היא מה שמייצר את קשר לינארי כאשר לוגריתמים נלקחים משני f (x ) ו- X , ו הקו ישר על מגרש יומן, היומן נקרא לעתים קרובות את חתימתו של חוק חזק.