PRESENTED BY ,

AMIT JAIN 07000519RANJEET RANJAN 07000537PUNEET GUPTA 07000534

FINANCIAL FORECASTING USING NEURAL

NETWORKS

What is Financial Forecasting

Prediction of prices of instruments of speculationStocksCommodity futuresExchange RatesInterest Rates .

Problem : Non linear and non stationary data

Methods Used Fundamental Analysis

Understanding the supply demand curveInvolves studying of news and economic

factors Technical Analysis

Understanding historical price patternsTools 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 trainlm : 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 AverageFast 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 Sets1. Training Set2. Testing Set3. 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