Haibin Xie, Kuikui Fan, Shouyang Wang

Candlestick Forecasting for Investments

Applications, Models and Properties

• Gebundenes Buch

Produktbeschreibung

Candlestick charts are often used in speculative markets to describe and forecast asset price movements. This book is the first of its kind to investigate candlestick charts and their statistical properties. It provides an empirical evaluation of candlestick forecasting. The book proposes a novel technique to obtain the statistical properties of candlestick charts. The technique, which is known as the range decomposition technique, shows how security price is approximately logged into two ranges, i.e. technical range and Parkinson range. Through decomposition-based modeling techniques and empirical datasets, the book investigates the power of, and establishes the statistical foundation of, candlestick forecasting.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Routledge
• Seitenzahl: 134
• Erscheinungstermin: 12. März 2021
• Englisch
• Abmessung: 240mm x 161mm x 12mm
• Gewicht: 375g
• ISBN-13: 9780367703370
• ISBN-10: 0367703378
• Artikelnr.: 61322461

Autorenporträt

Haibin Xie is Associate Professor at the School of Banking and Finance, University of International Business and Economics. Kuikui Fan is affiliated with the School of Statistics, Capital University of Economics and Business. Shouyang Wang is Professor at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences.

Inhaltsangabe

PART I INTRODUCTION AND OUTLINE 1. Introduction 1.1 Technical analysis
before the 1970s 1.2 Technical analysis during 1990s-2000s 1.3 Recent
advances in technical analysis 1.4 Summary 2. Outline of this book PART II
CANDLESTICK 3. Basic concepts 4. Statistical properties 4.1 Propositions
4.2 Simulations 4.3 Empirical evidence 4.4 Summary PART III STATISTICAL
MODELS 5. DVAR model 5.1 The model 5.2 Statistical foundation 5.3
Simulations 5.4 Empirical results 5.5 Summary 6. Shadows in DVAR 6.1
Simulations 6.2 Theoretical explanation 6.3 Empirical evidence 6.4 Summary
PART IV APPLICATIONS 7. Market volatility timing 7.1 Introduction 7.2
GARCH@CARR model 7.3 Economic value of volatility timing 7.4 Empirical
results 7.5 Summary 8. Technical range forecasting 8.1 Introduction 8.2
Econometric methods 8.3 An empirical study 8.4 Summary 9. Technical range
spillover 9.1 Introduction 9.2 Econometric method 9.3 An empirical study:
DAX and CAC40 9.4 Summary 10. Stock return forecasting: U.S. S&P500 10.1
Introduction 10.2 Econometric methods 10.3 Statistical evidence 10.4
Economic evidence 10.5 More details 10.6 Summary 11. Oil price forecasting:
WTI Crude Oil 11.1 Introduction 11.2 Econometric method 11.3 Empirical
results 11.4 Summary PART V CONCLUSIONS AND FUTURE STUDIES 12. Main
conclusions 13. Future studies