AN INTERACTIVE TOOL FOR THE
STOCK MARKET RESEARCH
USING RECURSIVE NEURAL
NETWORKS
Master Thesis
Michal
Trna
michal.trna@gmail.com
= Overview =
•
Introduction to RNN
•
Demo of the tool
•
Application on the chosen domain
AN INTERACTIVE TOOL
FOR THE
STOCK MARKET RESEARCH
USING RECURSIVE NEURAL
NETWORKS
= Introduction to NN & RNN =
•
Motivation of the NN
Brain contains 50
–
100 billion
neurons
1000 trillion synaptic connections
S
olves
complex problems
R
ecognition
of complex forms
F
orms
well

founded predictions
↑ Contours
of the human brain
Drawing of neurons from the
cerebellum of a pigeon by
Ramón y
Cajal
(1911) →
= Introduction to NN & RNN =
•
Non

local connection
•
Plasticity, synaptic learning
•
Creation and atrophy of the connections
Axon
Nucleus
Dendrites
Axon terminal
Action potential
1

100m/s
= Introduction to NN & RNN =
Hebb’s law:
When an axon of cell A is near enough to excite
cell B and repeatedly or persistently takes
part in firing it, some growth process or
metabolic change takes place in one or both
cells such that A's efficiency, as one of the
cells firing B, is increased.
i.e.:
Cells that fire together, wire together.
Donald
O.
Hebb
,
1949
•
Hebbian learning
/
Synaptic
learning
•
Anti

Hebbian learning
= Introduction to NN & RNN =
•
Mathematical model of neuron
Summing
junction
Activation
function
Σ
Output
x
j
.
.
.
f
Bias
Inputs
Neuron j
Synaptic
weights
Recipients
of the output
= Introduction to NN & RNN =
•
Artificial neural networks
A neural network is a massively parallel distributed
processor that has a natural propensity for storing
experiential knowledge and making it available for use.
It resembles the brain in two respects:
1.
Knowledge is acquired by the network through a
learning process.
2.
Interneuron connection strengths known as synaptic
weights are used to store the knowledge.
= Introduction to NN & RNN =
•
Artificial neural network
•
Properties
–
Adaptability
–
Fault tolerance
–
Knowledge representation, context
–
Non

linearity
–
I/O mapping
= Introduction to NN & RNN =
•
Hebbian theory
•
For
p
patterns of length
n
:
= Introduction to NN & RNN =
•
Feed

forward neural networks
•
Recursive neural networks
= Introduction to NN & RNN =
•
Perceptron
Summing
junction
Activation
function
Σ
Output
x
j
.
.
.
f
Bias
Inputs
Neuron j
Synaptic
weights
= Introduction to NN & RNN =
•
Perceptron
–
Separability, linear classifier
–
XOR problem
↑ Linear separation of logical AND, logical OR and logical XOR
= Introduction to NN & RNN =
•
Multilayer perceptron
= Introduction to NN & RNN =
•
Multi

layer perceptron
•
Learning algorithm = back

propagation
–
generate the output
–
propagates back to produce deltas of all output and
hidden layers
–
gradient of weights
–
modify the weight in the (opposite) direction of
grad.
= Introduction to NN & RNN =
•
Single

layer and Multi

layer perceptron
Single layer
Two layers
Three layers
Arbitrary set
XOR

like set
= Introduction to NN & RNN =
•
Recurrent networks (RNN)
•
Simple RNN: Elman/Jordan network
•
Fully connected: Hopfield network
= Introduction to NN & RNN =
•
Elman network
Context layer
= Introduction to NN & RNN =
•
Jordan network
Context layer
= Introduction to NN & RNN =
•
Hopfield Networks
•
Dynamic equation
= Introduction to NN & RNN =
•
Synaptic potential, threshold
•
Mode of operation
–
Synchronous
–
Asynchronous
–
Deterministic
–
Non

deterministic
•
Energy
•
Autoassociative memory
–
Capacity: 0.15 N
= Graph Approach =
•
Graph approach
–
Acquiring pattern
ξ
:
–
Hopfield network:
= Graph Approach =
•
Coloring
Red component
Blue component
= Graph Approach =
•
Tetrahedral property
= Graph Approach =
•
Tetrahedral property
•
Four possible configurations
1
1
1
1
1
1
0
0
0
1
1
1
0
0
1
–
1
0
0
–
1
–
1
–
1
–
1
–
1
–
1
= Graph Approach =
•
Parameters
= Graph Approach =
•
Energy point, projection to 2D
•
Energy lines
–
classes
•
Scalar energy
•
Control of the convergence
= Graph Approach =
•
Relative weight of neuron
–
contribution of this neuron to the component I or O
•
Deviation
•
“a hash function”
= Graph Approach =
•
Thresholds
= Tool =
•
Time for a demo
–
http://msc.michaltrna.info/markers/index.html
↑ Typical convergence path
•
Outlooks, future lines
–
To use deviation for discrimination of parasitic
states
–
Quantify the results
–
Application on automatic trading
Thank you for your attention!
Time for your questions
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