Candlestick Forecasting for Investments : Applications, Models and Properties book cover
1st Edition

Candlestick Forecasting for Investments
Applications, Models and Properties

ISBN 9780367703370
Published March 12, 2021 by Routledge
132 Pages 26 B/W Illustrations

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Book Description

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.

Table of Contents

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 [email protected] 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

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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.