b.tech project report on financial fore castings using neural networks [19, 34, 37]

8/6/2019 B.tech Project Report on Financial Fore Castings Using Neural Networks [19, 34, 37]

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Gram : Electronics EngineeringPhone # : 2307014, 2307011 (off)

2307010 (HOD)Fax : 91-0542-2366758Email : [email protected]

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FINANCIAL FORECASTING USING NEURAL NETWORKS : A COMPARATIVE STUDY

TABLE OF CONTENTS

ABSTRACT

1. Basic terms and Definitions in Finance 4

1.1 Interest rates 41.1.1 Historical Interest Rates 4

1.2 Bonds 5

1.3 Background in Forecasting 6

2. Neural Networks 9

2.1 Background 9

2.2 Why Use Neural Network? 10

2.3 Neural Network v/s Conventional Computers 10

2.4 Basic Architecture of Neural Network 11

2.4.1 Transfer Functions Used 142.4.2 Training Algorithms 15

2.5 Feed Forward Neural Network Analysis 16

2.6 Back-propogation Training 18

3. Time Series Forecasting 21

3.1 Narx Model 21

3.2 Difficulties in Time Series Forecasting 23

4. Pre-processing Techniques Used 24

4.1 Hilbert-Huan Transform 24

4.1.1 Introduction 24

4.1.2 Introduction to EMD and IMF 244.1.3 Empirical Mode Decomposition 25

4.1.4 The Stoppage Criteria 26

4.1.5 Hilbert Spectrum Analysis 28

4.1.6 A Comparative Study 28

5. Approach Taken 30

5.1 Drawbacks Of EMD 31

6. Matlab Codes 32

7. Results And Graphs 57

8. Future Works 66

REFERENCES 67


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66

Future Works

Though we have shown that data pre-processing can significantly improve the performance

of the neural network on noisy and non-stationary data, there is ample scope for future

research. Some of the key issues for the same are listed below:

Effect of number of training given to neural network and also that of amount ofdata for training testing and validation.

Use of other Algorithms other than LM for forecasting.A heuristic approach to mark certain IMFs as the noise.Effect of training the data on a particular IMF or sum of certain IMFs.Use two or more data series simultaneously (like interest rates and Foreign

Exchange or Foreign Reserve, etc.), apply EMD to each and use the

combinations of their IMFs to produce a more fruitful research.

With the understanding of the economy, if we can use IMFs to provide the hintsto the neural network.

A comparative study on the effect of EMD on different types of data.Effect of EMD on neural networks with different architectures and learning

algorithms.Use of Hilbert spectral analysis and then training the neural network by

frequency domain data.


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References

1. Introduction to Financial Forecasting by Yaser S. Abu Mostafa2. Introduction to Neural Network Toolbox from Matlab.3. Introduction To The Hilbert Huang Transform And Its Related Mathematical

Problems by Nordan E. Huang

4. Time Series Prediction based on NARX Neural Networks by Hang Xie, Hao Tang andYu Hae Liao

5. Time Series Forecasting With Feed-Forward Neural Networks: Guidelines AndLimitations by Eric A. Plummer

6. Exchange Rate and Interest Rate data from RBI Treasury Bills, Sensex Data fromYahoo Finance.

b.tech project report on financial fore castings using neural networks [19, 34, 37]

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