The Science of Financial Market Trading.

By: Mak, Don KMaterial type: TextTextPublisher: Singapore : World Scientific Publishing Co Pte Ltd, 2003Copyright date: ©2003Description: 1 online resource (261 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9789812796875Subject(s): Capital market -- Forecasting | Investments -- MathematicsGenre/Form: Electronic books.Additional physical formats: Print version:: The Science of Financial Market TradingDDC classification: 332.60151 LOC classification: HG4515.3.M35 2003Online resources: Click to View
Contents:
Intro -- Contents -- Preface -- 1. Introduction -- 1.1 Fundamental Analysis -- 1.2 Technical Analysis -- 1.2.1 Pattern Recognition -- 1.2.2 Indicators -- 1.3 Hybrids -- 2. Is the Market Random ? -- 3. Models of the Financial Markets -- 3.1 Chaos -- 3.2 Complexity -- 3.3 Wave Model -- 3.4 Time Series Analysis -- 3.5 Neural Network -- 3.6 Fractal Geometry -- 3.7 Fuzzy Logic -- 3.8 Wavelet Analysis -- 4. Signals and Indicators -- 4.1 Stochastic Indicator -- 4.2 Momentum Indicator -- 5. Trending Indicators -- 5.1 Simple Moving Average (SMA) -- 5.2 Exponential Moving Average (EMA) -- 5.3 Adaptive Moving Average (AMA) -- 5.4 Trading Rules using Moving Averages -- 6. Oscillator Indicators -- 6.1 Parabolic Velocity Indicator -- 6.2 Parabolic Acceleration Indicator -- 6.3 Cubic Velocity and Acceleration Indicators -- 6.4 Divergences -- 6.4.1 Class A Divergence -- 6.4.2 Class B Divergence -- 6.4.3 Class C Divergence -- 6.5 Head and Shoulders -- 7. Vertex Indicators -- 7.1 Parabolic Vertex Indicator -- 7.2 Cubic Vertex Indicator -- 8. Various Timeframes -- 8.1 Under-sampling -- 8.2 Frequency Characteristics of an Indicator -- 9. Wavelet Analysis -- 9.1 High Wavelet Indicator -- 9.2 Middle Wavelet Indicator -- 9.3 Low Wavelet Indicator -- 10. Other New Techniques -- 10.1 Skipped Convolution -- 10.2 Forecasts -- 11. Trading Systems -- 12. Financial Markets are Complex -- Appendix 1 Time Series Analysis -- A1.1 Autoregressive Moving Average Model -- A 1.2 Autoregressive Integrated Moving Average Model -- A 1.2.1 IBM Stock Price -- Al.2.2 Dow-Jones Utilities Index -- A 1.3 Model in Terms of Previous Data -- Appendix 2 Signals and Systems -- A2.1 Discrete-time Signals -- A2.2 Discrete-time Systems -- A2.3 Frequency Response of Linear Shift-invariant Systems -- Appendix 3 Low Pass Filters -- A3.1 Simple Moving Average (SMA) -- A3.1.1 Two point Moving Average.
A3.1.2 N point Moving Average -- A3.2 Exponential Moving Average (EMA) -- Appendix 4 High Pass Filters -- A4.1 Derivative -- A4.2 Moving Difference -- A4.3 Parabolic Velocity Indicator -- A4.4 Parabolic Acceleration Indicator -- A4.5 Cubic Velocity Indicator -- A4.6 Cubic Acceleration Indicator -- A4.7 Properties of a Cubic Function -- Appendix 5 Vertices -- A5.1 Parabolic Vertex Indicator -- A5.2 Cubic Vertex Indicator -- Appendix 6 Downsampling and Upsampling -- A6.1 Downsampling -- A6.2 Upsampling -- A6.3 Downsampling in the Frequency Domain -- Appendix 7 Wavelets -- A7.1 Fourier Analysis -- A7.2 Wavelet Analysis -- A7.3 Coefficients of the Sine Wavelet Indicators -- A7.4 Other Wavelets -- Appendix 8 Skipped Convolution and Forecasting -- A8.1 Skipped Convolution -- A8.2 Forecasting -- Bibliography -- Index.
Summary: In this book, Dr Mak views the financial market from a scientific perspective. The book attempts to provide a realistic description of what the market is, and how future research should be developed. The market is a complex phenomenon, and can be forecasted only with errors - if that particular market can be forecasted at all. The book reviews the scientific literatures on the financial market and describes mathematical procedures which demonstrate that some markets are non-random. How the markets are modeled - phenomenologically and from first principle - is explained. It discusses indicators, which are quite objective, rather than price patterns, which are rather subjective. Similarities between indicators in market trading and operators in mathematics are noted, and particularly, between oscillator indicators and derivatives in Calculus. It illustrates why some indicators, e.g., Stochastics, have limited usage. Several new indicators are designed and tested on theoretical waveforms to check their validity and applicability. The indicators have a minimal time lag, which is significant for trading purposes. Common market behaviors like divergences between price and momentum are explained. A skipped convolution technique is introduced to allow traders to pick up market movements at an earlier time. The market is treated as a nonlinear phenomenon. Forecasting of when the market is going to turn is emphasized. Contents: Is the Market Random?; Models of the Financial Markets; Signals and Indicators; Trending Indicators; Oscillator Indicators; Vertex Indicators; Various Timeframes; Wavelet Analysis; Other New Techniques; Trading Systems; Financial Markets are Complex. Readership: Investors, traders and undergraduate students of finance.
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Intro -- Contents -- Preface -- 1. Introduction -- 1.1 Fundamental Analysis -- 1.2 Technical Analysis -- 1.2.1 Pattern Recognition -- 1.2.2 Indicators -- 1.3 Hybrids -- 2. Is the Market Random ? -- 3. Models of the Financial Markets -- 3.1 Chaos -- 3.2 Complexity -- 3.3 Wave Model -- 3.4 Time Series Analysis -- 3.5 Neural Network -- 3.6 Fractal Geometry -- 3.7 Fuzzy Logic -- 3.8 Wavelet Analysis -- 4. Signals and Indicators -- 4.1 Stochastic Indicator -- 4.2 Momentum Indicator -- 5. Trending Indicators -- 5.1 Simple Moving Average (SMA) -- 5.2 Exponential Moving Average (EMA) -- 5.3 Adaptive Moving Average (AMA) -- 5.4 Trading Rules using Moving Averages -- 6. Oscillator Indicators -- 6.1 Parabolic Velocity Indicator -- 6.2 Parabolic Acceleration Indicator -- 6.3 Cubic Velocity and Acceleration Indicators -- 6.4 Divergences -- 6.4.1 Class A Divergence -- 6.4.2 Class B Divergence -- 6.4.3 Class C Divergence -- 6.5 Head and Shoulders -- 7. Vertex Indicators -- 7.1 Parabolic Vertex Indicator -- 7.2 Cubic Vertex Indicator -- 8. Various Timeframes -- 8.1 Under-sampling -- 8.2 Frequency Characteristics of an Indicator -- 9. Wavelet Analysis -- 9.1 High Wavelet Indicator -- 9.2 Middle Wavelet Indicator -- 9.3 Low Wavelet Indicator -- 10. Other New Techniques -- 10.1 Skipped Convolution -- 10.2 Forecasts -- 11. Trading Systems -- 12. Financial Markets are Complex -- Appendix 1 Time Series Analysis -- A1.1 Autoregressive Moving Average Model -- A 1.2 Autoregressive Integrated Moving Average Model -- A 1.2.1 IBM Stock Price -- Al.2.2 Dow-Jones Utilities Index -- A 1.3 Model in Terms of Previous Data -- Appendix 2 Signals and Systems -- A2.1 Discrete-time Signals -- A2.2 Discrete-time Systems -- A2.3 Frequency Response of Linear Shift-invariant Systems -- Appendix 3 Low Pass Filters -- A3.1 Simple Moving Average (SMA) -- A3.1.1 Two point Moving Average.

A3.1.2 N point Moving Average -- A3.2 Exponential Moving Average (EMA) -- Appendix 4 High Pass Filters -- A4.1 Derivative -- A4.2 Moving Difference -- A4.3 Parabolic Velocity Indicator -- A4.4 Parabolic Acceleration Indicator -- A4.5 Cubic Velocity Indicator -- A4.6 Cubic Acceleration Indicator -- A4.7 Properties of a Cubic Function -- Appendix 5 Vertices -- A5.1 Parabolic Vertex Indicator -- A5.2 Cubic Vertex Indicator -- Appendix 6 Downsampling and Upsampling -- A6.1 Downsampling -- A6.2 Upsampling -- A6.3 Downsampling in the Frequency Domain -- Appendix 7 Wavelets -- A7.1 Fourier Analysis -- A7.2 Wavelet Analysis -- A7.3 Coefficients of the Sine Wavelet Indicators -- A7.4 Other Wavelets -- Appendix 8 Skipped Convolution and Forecasting -- A8.1 Skipped Convolution -- A8.2 Forecasting -- Bibliography -- Index.

In this book, Dr Mak views the financial market from a scientific perspective. The book attempts to provide a realistic description of what the market is, and how future research should be developed. The market is a complex phenomenon, and can be forecasted only with errors - if that particular market can be forecasted at all. The book reviews the scientific literatures on the financial market and describes mathematical procedures which demonstrate that some markets are non-random. How the markets are modeled - phenomenologically and from first principle - is explained. It discusses indicators, which are quite objective, rather than price patterns, which are rather subjective. Similarities between indicators in market trading and operators in mathematics are noted, and particularly, between oscillator indicators and derivatives in Calculus. It illustrates why some indicators, e.g., Stochastics, have limited usage. Several new indicators are designed and tested on theoretical waveforms to check their validity and applicability. The indicators have a minimal time lag, which is significant for trading purposes. Common market behaviors like divergences between price and momentum are explained. A skipped convolution technique is introduced to allow traders to pick up market movements at an earlier time. The market is treated as a nonlinear phenomenon. Forecasting of when the market is going to turn is emphasized. Contents: Is the Market Random?; Models of the Financial Markets; Signals and Indicators; Trending Indicators; Oscillator Indicators; Vertex Indicators; Various Timeframes; Wavelet Analysis; Other New Techniques; Trading Systems; Financial Markets are Complex. Readership: Investors, traders and undergraduate students of finance.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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