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
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