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Neural network time series forecasting of financial markets

Neural network time series forecasting of financial markets (Loan 11 times)

Material type
단행본
Personal Author
Azoff, E. Michael (Eitan Michael)
Title Statement
Neural network time series forecasting of financial markets / E. Michael Azoff.
Publication, Distribution, etc
Chichester ;   New York :   Wiley ,   1994.  
Physical Medium
xiii, 196 p. : ill. ; 24 cm.
Series Statement
Wiley finance editions.
ISBN
0471943568 :
Bibliography, Etc. Note
Includes bibliographical references (p. [181]-190) and index.
Subject Added Entry-Topical Term
Time-series analysis --Mathematical models. Neural networks (Computer science). Futures market --Forecasting --Mathematical models.
000 00921camuuu200265 a 4500
001 000000023428
005 19981117095151.0
008 931029s1994 enka b 001 0 eng
010 ▼a 93042336
020 ▼a 0471943568 : ▼c $56.00
040 ▼a DLC ▼c DLC ▼d DLC
049 1 ▼l 111024515
050 0 0 ▼a HG6024.A3 ▼b A96 1994
082 0 0 ▼a 332.64/5 ▼2 20
090 ▼a 332.64 ▼b A996n
100 1 ▼a Azoff, E. Michael ▼q (Eitan Michael)
245 1 0 ▼a Neural network time series forecasting of financial markets / ▼c E. Michael Azoff.
260 ▼a Chichester ; ▼a New York : ▼b Wiley , ▼c 1994.
300 ▼a xiii, 196 p. : ▼b ill. ; ▼c 24 cm.
440 0 ▼a Wiley finance editions.
504 ▼a Includes bibliographical references (p. [181]-190) and index.
650 0 ▼a Time-series analysis ▼x Mathematical models.
650 0 ▼a Neural networks (Computer science).
650 0 ▼a Futures market ▼x Forecasting ▼x Mathematical models.

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/Western Books/ Call Number 332.64 A996n Accession No. 111024515 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents


CONTENTS
Preface = xi
Acknowledgements = xv
1 Introduction = 1
 1.1 Neural Networks = 2
 1.2 Literature Overview = 5
2 The Multilayer Perceptron : A Tutorial = 11
 2.1 The Multiconnectivity MLP = 12
 2.2 The Backpropagation Algorithm = 15
 2.3 Exclusive OR Example = 18
3 The Preprocessing Stage = 21
 3.1 Data Integrity = 21
 3.2 Data Representation = 22
  3.2.1 The Random Walk Indicator = 23
  3.2.2 The Volatility Indicator = 24
 3.3 Input Normalisation = 24
 3.4 Target Selection and Normalisation = 27
 3.5 Dimension Reduction = 28
  3.5.1 Data Compression = 28
  3.5.2 Principal Component Analysis = 29
  3.5.3 The Wavelet Transform = 31
 3.6 Decorrelation = 32
 3.7 Noise Filtering = 32
 3.8 Spectral Analysis for Nonstationary Signals = 33
4 Designing the Neural Network = 37
 4.1 Training and Sampling = 37
 4.2 Cost Functions = 38
 4.3 Convergence Metrics = 39
  4.3.1 Percentage Accuracy = 40
  4.3.2 Mean Square Error = 41
  4.3.3 Threshold Error = 42
  4.3.4 Contingency Tables = 42
  4.3.5 Correlation = 44
 4.4 Generalisation and Testing = 45
  4.4.1 The True Error = 46
  4.4.2 Re-sampling Techniques = 47
  4.4.3 Time Series = 47
 4.5 Network Structure and Complexity = 49
 4.6 Neuron Transfer Functions = 51
 4.7 The Autocorrelated Error Neuron = 55
 4.8 The Leaming Coefficient = 57
 4.9 Training with Noise = 58
 4.10 Robust Cost Functions = 58
 4.11 Weight Pruning = 59
 4.12 Input Sensitivity Analysis = 60
 4.13 Design Aspects for Forecasting = 62
  4.13.1 Prediction Look-Ahead Period = 63
  4.13.2 Multistage Networks = 64
  4.13.3 Postprocessing = 65
5 Random Time Series and Market Prices = 67
 5.1 The Random Walk = 67
 5.2 Market Correlations = 76
 5.3 Median Forecast Method = 81
 5.4 Market Time Series = 84
6 Benchmarks = 91
 6.1 Exclusive OR = 92
 6.2 Problems in Chaos = 94
  6.2.1 Henon Map = 94
  6.2.2 Mackey-Glass Equation = 97
 6.3 Sunspot Number Series = 102
 6.4 S&P 500 Futures = 113
 6.5 BP/USD Currency Futures = 118
7 Futures Forecasts Implementation 125
 7.1 Overview of Commodity and Financial Futures = 125
 7.2 Futures Time Series = 130
 7.3 Trading Systems = 132
 7.4 Risk Management = 136
  7.4.1 Risk of Ruin = 136
  7.4.2 Capital Allocation = 139
  7.4.3 Diversification = 140
8 Further Reading and Information = 143
 Appendix A Backpropagation = 149
  A.1 Steepest Descent Method = 149
  A.2 Conjugate Gradient and Quasi-Newton Methods = 153
   A.2.1 Shanno Method = 153
   A.2.2 BFGS Method = 155
  A.3 Second Order Analysis = 156
 Appendix B Benchmark Weights = 159
  B.1 XOR Function = 159
  B.2 Herion Map = 160
  B.3 Mackey-Glas-Equation = 160
  B.4 Sunspot Numbers = 161
 Appendix C Recall Mode MLP Simulator = 163
Bibliography = 181
Index = 191


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