Mathematical techniques in financial market trading:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hackensack, N.J.
World Scientific
c2006
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Includes bibliographical references (p. 297-300) and index Cover -- Contents -- Preface -- 1. Introduction -- 2. Scientific Review of the Financial Market -- 2.1 Econophysics -- 2.1.1 Log-Normal Distribution of Stock Market Data -- 2.1.2 Levy Distribution -- 2.1.3 Tsallis Entropy -- 2.2 Non-Randomness of the Market -- 2.2.1 Random Walk Hypothesis and Efficient Market Hypothesis -- 2.2.2 Variance-Ratio Test -- 2.2.3 Long-Range Dependence? -- 2.2.4 Varying Non-Randomness -- 2.3 Financial Market Crash -- 2.3.1 Log-Periodicity Phenomenological Model -- 2.3.2 Omori Law -- 3. Causal Low Pass Filters -- 3.1 Ideal Causal Trending Indicator -- 3.2 Exponential Moving Average -- 3.3 Butterworth Filters -- 3.4 Sinc Function n = 213; -- 3.5 Sinc Function n = 413; -- 3.6 Adaptive Exponential Moving Average -- 4. Reduced Lag Filters -- 4.1 "Zero-lag" EMA (ZEMA) -- 4.2 Modified EMA (MEMA) -- 4.2.1 Modified EMA (MEMA) with a Skip 1 Cubic Velocity -- 4.2.2 Modified EMA (MEMA) with a Skip 2 Cubic Velocity The present book contains much more materials than the author's previous book "The Science of Financial Market Trading". Spectrum analysis is again emphasized for the characterization of technical indicators employed by traders and investors. New indicators are created. Mathematical analysis is applied to evaluate the trading methodologies practiced by traders to execute a trade transaction. In addition, probability theory is employed to appraise the utility of money management techniques. The book: identifies the faultiness of some of the indicators used by traders and accentuates the potential of wavelets as a trading tool; describes the scientific evidences that the market is non-random, and that the non-randomness can vary with respect to time; demonstrates the validity of the claim by some traders that, with good money management techniques, the market is still profitable even if it were random; and analyzes why a popular trading tactic has a good probability of success and how it can be improved |
| Beschreibung: | 1 Online-Ressource (xvi, 304 p.) |
| ISBN: | 9789812774064 9812774068 9812566996 9789812566997 1281379107 9781281379108 |
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| 100 | 1 | |a Mak, Don K. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Mathematical techniques in financial market trading |c Don K. Mak |
| 264 | 1 | |a Hackensack, N.J. |b World Scientific |c c2006 | |
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| 500 | |a Includes bibliographical references (p. 297-300) and index | ||
| 500 | |a Cover -- Contents -- Preface -- 1. Introduction -- 2. Scientific Review of the Financial Market -- 2.1 Econophysics -- 2.1.1 Log-Normal Distribution of Stock Market Data -- 2.1.2 Levy Distribution -- 2.1.3 Tsallis Entropy -- 2.2 Non-Randomness of the Market -- 2.2.1 Random Walk Hypothesis and Efficient Market Hypothesis -- 2.2.2 Variance-Ratio Test -- 2.2.3 Long-Range Dependence? -- 2.2.4 Varying Non-Randomness -- 2.3 Financial Market Crash -- 2.3.1 Log-Periodicity Phenomenological Model -- 2.3.2 Omori Law -- 3. Causal Low Pass Filters -- 3.1 Ideal Causal Trending Indicator -- 3.2 Exponential Moving Average -- 3.3 Butterworth Filters -- 3.4 Sinc Function n = 213; -- 3.5 Sinc Function n = 413; -- 3.6 Adaptive Exponential Moving Average -- 4. Reduced Lag Filters -- 4.1 "Zero-lag" EMA (ZEMA) -- 4.2 Modified EMA (MEMA) -- 4.2.1 Modified EMA (MEMA) with a Skip 1 Cubic Velocity -- 4.2.2 Modified EMA (MEMA) with a Skip 2 Cubic Velocity | ||
| 500 | |a The present book contains much more materials than the author's previous book "The Science of Financial Market Trading". Spectrum analysis is again emphasized for the characterization of technical indicators employed by traders and investors. New indicators are created. Mathematical analysis is applied to evaluate the trading methodologies practiced by traders to execute a trade transaction. In addition, probability theory is employed to appraise the utility of money management techniques. The book: identifies the faultiness of some of the indicators used by traders and accentuates the potential of wavelets as a trading tool; describes the scientific evidences that the market is non-random, and that the non-randomness can vary with respect to time; demonstrates the validity of the claim by some traders that, with good money management techniques, the market is still profitable even if it were random; and analyzes why a popular trading tactic has a good probability of success and how it can be improved | ||
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| 650 | 4 | |a Finance |x Mathematical models | |
| 650 | 4 | |a Speculation |x Mathematical models | |
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Datensatz im Suchindex
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| author | Mak, Don K. |
| author_facet | Mak, Don K. |
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| author_sort | Mak, Don K. |
| author_variant | d k m dk dkm |
| building | Verbundindex |
| bvnumber | BV042966012 |
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| ctrlnum | (OCoLC)614464083 (DE-599)BVBBV042966012 |
| dewey-full | 332.6401/513 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
| dewey-raw | 332.6401/513 |
| dewey-search | 332.6401/513 |
| dewey-sort | 3332.6401 3513 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| format | Electronic eBook |
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| id | DE-604.BV042966012 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:13:58Z |
| institution | BVB |
| isbn | 9789812774064 9812774068 9812566996 9789812566997 1281379107 9781281379108 |
| language | English |
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| spelling | Mak, Don K. Verfasser aut Mathematical techniques in financial market trading Don K. Mak Hackensack, N.J. World Scientific c2006 1 Online-Ressource (xvi, 304 p.) txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references (p. 297-300) and index Cover -- Contents -- Preface -- 1. Introduction -- 2. Scientific Review of the Financial Market -- 2.1 Econophysics -- 2.1.1 Log-Normal Distribution of Stock Market Data -- 2.1.2 Levy Distribution -- 2.1.3 Tsallis Entropy -- 2.2 Non-Randomness of the Market -- 2.2.1 Random Walk Hypothesis and Efficient Market Hypothesis -- 2.2.2 Variance-Ratio Test -- 2.2.3 Long-Range Dependence? -- 2.2.4 Varying Non-Randomness -- 2.3 Financial Market Crash -- 2.3.1 Log-Periodicity Phenomenological Model -- 2.3.2 Omori Law -- 3. Causal Low Pass Filters -- 3.1 Ideal Causal Trending Indicator -- 3.2 Exponential Moving Average -- 3.3 Butterworth Filters -- 3.4 Sinc Function n = 213; -- 3.5 Sinc Function n = 413; -- 3.6 Adaptive Exponential Moving Average -- 4. Reduced Lag Filters -- 4.1 "Zero-lag" EMA (ZEMA) -- 4.2 Modified EMA (MEMA) -- 4.2.1 Modified EMA (MEMA) with a Skip 1 Cubic Velocity -- 4.2.2 Modified EMA (MEMA) with a Skip 2 Cubic Velocity The present book contains much more materials than the author's previous book "The Science of Financial Market Trading". Spectrum analysis is again emphasized for the characterization of technical indicators employed by traders and investors. New indicators are created. Mathematical analysis is applied to evaluate the trading methodologies practiced by traders to execute a trade transaction. In addition, probability theory is employed to appraise the utility of money management techniques. The book: identifies the faultiness of some of the indicators used by traders and accentuates the potential of wavelets as a trading tool; describes the scientific evidences that the market is non-random, and that the non-randomness can vary with respect to time; demonstrates the validity of the claim by some traders that, with good money management techniques, the market is still profitable even if it were random; and analyzes why a popular trading tactic has a good probability of success and how it can be improved Investissements / Mathématiques Finances / Modèles mathématiques Spéculation / Modèles mathématiques BUSINESS & ECONOMICS / Investments & Securities / General bisacsh Finance / Mathematical models fast Investments / Mathematics fast Speculation / Mathematical models fast Mathematik Mathematisches Modell Wirtschaft Investments Mathematics Finance Mathematical models Speculation Mathematical models http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=210793 Aggregator Volltext |
| spellingShingle | Mak, Don K. Mathematical techniques in financial market trading Investissements / Mathématiques Finances / Modèles mathématiques Spéculation / Modèles mathématiques BUSINESS & ECONOMICS / Investments & Securities / General bisacsh Finance / Mathematical models fast Investments / Mathematics fast Speculation / Mathematical models fast Mathematik Mathematisches Modell Wirtschaft Investments Mathematics Finance Mathematical models Speculation Mathematical models |
| title | Mathematical techniques in financial market trading |
| title_auth | Mathematical techniques in financial market trading |
| title_exact_search | Mathematical techniques in financial market trading |
| title_full | Mathematical techniques in financial market trading Don K. Mak |
| title_fullStr | Mathematical techniques in financial market trading Don K. Mak |
| title_full_unstemmed | Mathematical techniques in financial market trading Don K. Mak |
| title_short | Mathematical techniques in financial market trading |
| title_sort | mathematical techniques in financial market trading |
| topic | Investissements / Mathématiques Finances / Modèles mathématiques Spéculation / Modèles mathématiques BUSINESS & ECONOMICS / Investments & Securities / General bisacsh Finance / Mathematical models fast Investments / Mathematics fast Speculation / Mathematical models fast Mathematik Mathematisches Modell Wirtschaft Investments Mathematics Finance Mathematical models Speculation Mathematical models |
| topic_facet | Investissements / Mathématiques Finances / Modèles mathématiques Spéculation / Modèles mathématiques BUSINESS & ECONOMICS / Investments & Securities / General Finance / Mathematical models Investments / Mathematics Speculation / Mathematical models Mathematik Mathematisches Modell Wirtschaft Investments Mathematics Finance Mathematical models Speculation Mathematical models |
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