Stock Market
Modeling and Forecasting
A System Adaptation Approach
Springer, New York, London,
2013 (¥
click on this link to order ¥)
Lecture Notes in
Control and Information Sciences, 442 xii/161 pages
ISBN
978-1-4471-5154-8 / ISBN 978-1-4471-5155-5 (eBook) / DOI
10.1007/978-1-4471-5155-5
About this book:
Stock
Market Modeling and Forecasting
translates experience in system adaptation gained in an engineering context to
the modeling of financial markets with a view to improving the capture and
understanding of market dynamics. The modeling process is considered as
identifying a dynamic system in which a real stock market is treated as an
unknown plant and the identification model proposed is tuned by feedback of the
matching error. Like a physical system, a stock market exhibits fast and slow
dynamics corresponding to internal (such as company value and profitability)
and external forces (such as investor sentiment and commodity prices)
respectively. The framework presented here, consisting of an internal model and
an adaptive filter, is successful at considering both fast and slow market
dynamics. A double selection method is efficacious in identifying input factors
influential in market movements, revealing them to be both frequency- and
market-dependent.
The
authors present work on both developed and developing markets in the shape of
the US, Hong Kong, Chinese and Singaporean stock markets. Results from all
these sources demonstrate the efficiency of the model framework in identifying
significant influences and the quality of its predictive ability; promising
results are also obtained by applying the model framework to the forecasting of
major market-turning periods. Having shown that system-theoretic ideas can form
the core of a novel and effective basis for stock market analysis, the book is
completed by an indication of possible and likely future expansions of the
research in this area.
Table of Contents
1 Introduction {1} 1.1 Introduction
1.2 Stock Market Analysis
1.3 Contribution of This Work
1.4 Preview of Each Chapter
2 A System Adaptation Framework {13} 2.1 Introduction
2.2 Structure of System Adaptation Framework
2.3 Internal Model Design
2.4 Adaptive Filter Design
2.5 Case Study: Dow Jones Industrial Average
2.5.1 Data Description
2.5.2 Internal Model Estimation
3 Market Input Analysis {29} 3.1 Introduction
3.2 Influential Factor Selection
3.2.1 Causality Tests
3.2.2 Redundant Variable Test
3.3 Influential Factors of Dow Jones Industrial Average
3.3.1 Empirical Selection
3.3.2 Time-Varying Causality Test Results
3.3.3 Nonlinear Causality Test Results
3.3.4 Redundant Variable Test Results
4 Analysis of Dow Jones Industrial Average {43} 4.1 Introduction
4.2 Measure of Predicting Performance
4.3 Adaptive Filter and Predicting Performance
4.3.1 Preliminary Analysis
4.3.2 Adaptive Filter Hyperparameters
4.3.3 Predicting Performance
5 Selected Asian Markets {53} 5.1 Introduction
5.2 Shanghai Stock Exchange Composite Index
5.2.1 Input Selection
5.2.2 Causality Tests
5.2.3 Predicting Performance and Analysis
5.3 Hong Kong Hang Seng Index
5.3.1 Input Selection
5.3.2 Causality Testsi
5.3.3 Predicting Performance and Analysis
5.4 Singapore Straits Times Index
5.4.1 Input Selection
5.4.2 Causality Tests
5.4.3 Predicting Performance and Analysis
6 Market Turning Period Forecasting {91} 6.1 Introduction
6.2 A Frequency Domain Forecasting Technique
6.2.1 Dow Jones Industrial Average
6.2.2 China, Hong Kong and Singapore Markets
6.3 A System Stability-Based Confirmation Method
6.3.1 Dow Jones Industrial Average
6.3.2 Hong Kong Hang Seng Index
6.3.3 Singapore Straits Times Index
6.3.4 Shanghai Stock Exchange Composite Index
7 Technical Analysis Toolkit {123} 7.1 Introduction
7.2 T-TAS Functions
7.2.1 User and Data Management
7.2.2 Online Data Loading System
7.2.3 Technical Analysis
7.2.4 System Adaptation Framework
8 Future Research {149}
References {151}
Index {159}
Link
to the page of Ben M. Chen's Books……