Financial Forecastings using Neural Networks ppt - WordPress.com

muscleblouseAI and Robotics

Oct 19, 2013 (4 years and 2 months ago)

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FINANCIAL FORECASTING
USING NEURAL
NETWORKS

What is Financial Forecasting


Prediction of prices of instruments of
speculation


Stocks


Commodity futures


Exchange Rates


Interest Rates .



Problem : Non linear and non stationary
data

Methods
U
sed


Fundamental Analysis


Understanding the supply demand curve


Involves studying of news and economic
factors



Technical Analysis


Understanding historical price patterns


Tools like moving average, learning systems


Latest Approach: Combine Technical
and Fundamental Analysis



NEURAL NETWORKS


Map some type of input stream of information to an
output stream of data
.


They derive
non
-
linear models

that can be trained to
map past and future values of the input output
relationship .It extracts relationships governing the data
that was not obvious using other analytical tools.


Capability to recognize pattern

and the speed of
techniques to accurately solve complex processes,
exploited exhaustively in financial forecasting.


Trained
without the restriction of a model
to derive
parameters and discover relationships,
driven and
shaped solely by the nature of the data
.





NEURAL NETWORKS V/S CONVENTIONAL
COMPUTERS


Neural networks have the
unique capability of
learning
thus are
adaptive .
This problem solving
tool creates a unique
likeness to the human brain .


Use the interconnectedness of the elements of the
model rather than follow a set of sequential steps
,
that may or may not solve the problem like
computers do.


A different aspect of model building, where the
unique relationships between the variables creates
the model
, rather than trying to force variables to
conform to a theoretical abstract
that may or may
not exist.


NEURAL NETWORKS IN FINANCE



Neural networks are trained
without the restriction of a model
to
derive parameters and discover relationships, driven and shaped
solely by the nature of the data. Thus it has profound implications
and applicability to the finance field.


Some of the fields where it is applied are:



Financial forecasting


Capital budgeting and risk management


Stock market analysis


Used to analyze and
verify Economic hypothesis and theories

which were not possible otherwise.


Govt. predicts interest rates to gauge the
future inflationary situation
of its economy .




Neural Networking and Similarities with the Workings of the Human Brain

A SIMPLE NEURON

VECTOR INPUT TO NEURON

LAYER OF NEURONS

LAYER OF NEURONS …..

MULTIPLE LAYERS

MULTIPLE LAYERS …..

NARX MODEL

TRANSFER FUNCTIONS

TRAINING ALGORITHMS


t
rainlm

: fastest and better for non
-
linear
cases , default for feed
-
forwardnet

.

BACK
-
PROPOGATION


Numerous such input/target pairs are
used to train the Neural Network.

TIME SERIES FORECASTING


Time series
forecasting

or
time series prediction
,
takes an existing series of data and forecasts the
data values. The goal is to observe or model the
existing data series to enable future unknown data
values to be forecasted accurately
.


Done using the
NARX model
or
NAR model
.

DIFFICULTIES


Limited quantity
of data .


Noise in data


It obscures the
underlying pattern of the data .


Non
-
stationarity

-

data that do not have
the same statistical properties (e.g.,
mean and variance) at each point in
time


Appropriate Forecasting Technique
Selection
.

Preprocessing of Training Data


Reason
: Need to understand underlying
patterns.


Tools:


Moving Average


Fast Fourier Transform (FFT)


Hilbert Huang Transform (HHT)

Types Of Data Worked Upon


Interest Rates
(RBI 91 day Govt. Of India Treasury Bills)


Sensex Data
( 2005
-
2010)


Exchange Rates
(Daily Exchange Rates of INR
-
Dollars
2004
-
2011)



All the Data are divided into Three Sets

1.
Training Set

2.
Testing Set

3.
Validation Set

Types Of Preprocessing


No Pre
-
Processing

(Simple NN)


Using FFT
(FFT NN)


Using HHT

(HHT NN)


All the types of data are used on all the types of
preprocessing techniques , therefore generating 9
cases.


Now, we Compare all of them Data
-
Wise.

1. Interest Rates


The interest rate data is applied on all three kinds of preprocessing.
The Error Graphs are as:


Simple NN



FFT NN



HHT NN


2. Sensex Data


The
sensex

data is applied on all three kinds of
preprocessing. The Error Graphs are as
:


Simple NN




FFT NN



HHT NN


3. Exchange Rates


The
Exchange Rate
data is applied on all three kinds of
preprocessing. The Error Graphs are as:


Simple NN



FFT NN



HHT NN


Conclusion from Results



Pre
-
processing can boost the Neural
Network Performance




The performance of Neural Network also
depends on the nature of the data series

Thank You