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

Candlestick Forecasting for Investments
Applications, Models and Properties

  • Available for pre-order. Item will ship after March 11, 2021
ISBN 9780367703370
March 11, 2021 Forthcoming by Routledge
134 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 chart. 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 Vector Autoregressive (DVAR) modelling and empirical datasets, the book investigates the power of candlestick forecasting and establishes the statistical foundation of candlestick forecasting.

Table of Contents

I INTRODUCTION AND OUTLINE. 1 INTRODUCTION. 1.1 Technical Analysis Before 1970s. 1.2 Technical Analysis Over 1990s-2000s. 1.3 Recent Advances in Technical Analysis. 1.4 Summary. 2 OUTLINE OF THIS BOOK II CANDLESTICK. 3 BASIC CONCEPTS. 4 STATISTICAL PROPERTIES. 4.1 Propositions. 4.2 Simulations. 4.3 Empirical Evidence. 4.4 Summary. 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. 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. 7.4.1 The Data. 7.4.2 In-Sample Volatility Timing. 7.4.3 Out-of-sample Volatility Timing. 7.5 Summary. 8 TECHNICAL RANGE FORECASTING. 8.1 Introduction. 8.2 Econometric Methods. 8.2.1 The Model. 8.2.2 Out-of-Sample Forecast Evaluation. 8.3 An Empirical Study .8.3.1 Data. 8.3.2 In-Sample Estimation. 8.3.3 Out-of-Sample Forecast. 8.4 Summary. 9 TECHNICAL RANGE SPILLOVER 9.1 Introduction. 9.2 Econometric Method. 9.3 An Empirical Study: DAX and CAC40. 9.3.1 Data. 9.3.2 Estimation. 9.4 Summary. 10 STOCK RETURN FORECASTING: U.S. S&P500. 10.1 Introduction. 10.2 Econometric Methods. 10.2.1 The Model. 10.2.2 Out-of-sample Evaluation. 10.3 Statistical Evidence 10.3.1 The Data. 10.3.2 In-Sample Estimation. 10.3.3 Out-of-Sample Forecast. 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. 11.2.1 DVAR model. 11.2.2 Forecast Evaluation. 11.3 Empirical Results. 11.3.1 Data 11.3.2 In-Sample Model Estimation. 11.3.3 Out-of-Sample Performance. 11.4 Summary. V CONCLUSIONS AND FUTURE STUDIES. 12 MAIN CONCLUSIONS. 13 FUTURE STUDIES.

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Haibin Xie is Associate Professor at 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 Academy of Mathematics and Systems Science, Chinese Academy of Sciences.