An interactive tool for the stock market research using recursive neural networks
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AN INTERACTIVE TOOL FOR THE STOCK MARKET RESEARCH USING RECURSIVE NEURAL NETWORKS. Master Thesis. Michal Trna [email protected] = Overview =. Introduction to RNN Demo of the tool Application on the chosen domain.

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AN INTERACTIVE TOOL FOR THE STOCK MARKET RESEARCH USING RECURSIVE NEURAL NETWORKS

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An interactive tool for the stock market research using recursive neural networks

AN INTERACTIVE TOOL FOR THE STOCK MARKET RESEARCH USING RECURSIVE NEURAL NETWORKS

Master Thesis

Michal Trna

[email protected]


Overview

= 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

= Introduction to NN & RNN =

  • Motivation of the NN

  • Brain contains 50–100 billion neurons

  • 1000 trillion synaptic connections

  • Solvescomplex problems

  • Recognition of complex forms

  • Forms 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 rnn1

Axon terminal

Nucleus

Axon

Dendrites

= Introduction to NN & RNN =

  • Non-local connection

  • Plasticity, synaptic learning

  • Creation and atrophy of the connections

Action potential

1-100m/s


Introduction to nn rnn2

= 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.

Donald O. Hebb, 1949

i.e.:

  • Cells that fire together, wire together.

  • Hebbian learning / Synaptic learning

  • Anti-Hebbian learning


Introduction to nn rnn3

Bias

Neuron j

Summing junction

Output

Σ

f

xj

Inputs

.

.

.

Activation function

Synaptic weights

Recipients of the output

= Introduction to NN & RNN =

  • Mathematical model of neuron


Introduction to nn rnn4

= 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 rnn5

= Introduction to NN & RNN =

  • Artificial neural network

  • Properties

    • Adaptability

    • Fault tolerance

    • Knowledge representation, context

    • Non-linearity

    • I/O mapping


Introduction to nn rnn6

= Introduction to NN & RNN =

  • Hebbian theory

  • For p patterns of length n:


Introduction to nn rnn7

= Introduction to NN & RNN =

  • Feed-forward neural networks

  • Recursive neural networks


Introduction to nn rnn8

Bias

Neuron j

Summing junction

Output

Σ

f

xj

Inputs

.

.

.

Activation function

Synaptic weights

= Introduction to NN & RNN =

  • Perceptron


Introduction to nn rnn9

= Introduction to NN & RNN =

  • Perceptron

    • Separability, linear classifier

    • XOR problem

↑ Linear separation of logical AND, logical OR and logical XOR


Introduction to nn rnn10

= Introduction to NN & RNN =

  • Multilayer perceptron


Introduction to nn rnn11

= 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 rnn12

Single layer

Three layers

Two layers

Arbitrary set

XOR-like set

= Introduction to NN & RNN =

  • Single-layer and Multi-layer perceptron


Introduction to nn rnn13

= Introduction to NN & RNN =

  • Recurrent networks (RNN)

  • Simple RNN: Elman/Jordan network

  • Fully connected: Hopfield network


Introduction to nn rnn14

Context layer

= Introduction to NN & RNN =

  • Elman network


Introduction to nn rnn15

Context layer

= Introduction to NN & RNN =

  • Jordan network


Introduction to nn rnn16

= Introduction to NN & RNN =

  • Hopfield Networks

  • Dynamic equation


Introduction to nn rnn17

= 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 =

  • Graph approach

    • Acquiring pattern ξ:

    • Hopfield network:


Graph approach1

Red component

Blue component

= Graph Approach =

  • Coloring


Graph approach2

= Graph Approach =

  • Tetrahedral property


Graph approach3

1

0

0

–1

0

1

1

–1

1

0

0

–1

1

0

1

–1

1

1

1

1

0

–1

–1

–1

= Graph Approach =

  • Tetrahedral property

  • Four possible configurations


Graph approach4

= Graph Approach =

  • Parameters


Graph approach5

= Graph Approach =

  • Energy point, projection to 2D

  • Energy lines

    • classes

  • Scalar energy

    • Control of the convergence


Graph approach6

= Graph Approach =

  • Relative weight of neuron

    • contribution of this neuron to the component I or O

  • Deviation

    • “a hash function”


Graph approach7

= Graph Approach =

  • Thresholds


An interactive tool for the stock market research using recursive neural networks

= Tool =

  • Time for a demo

    • http://msc.michaltrna.info/markers/index.html

↑ Typical convergence path


An interactive tool for the stock market research using recursive neural networks

  • Outlooks, future lines

    • To use deviation for discrimination of parasitic states

    • Quantify the results

    • Application on automatic trading


An interactive tool for the stock market research using recursive neural networks

Thank you for your attention!

Time for your questions


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