Candlestick Forecasting for Investments: Applications, Models and Properties

133 Pages·2021·2.248 MB·English

by  Haibin Xie, Kuikui Fan, Shouyang Wang

Candlestick Forecasting
for Investments
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.
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.
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Candlestick Forecasting for Investments
Applications, Models and Properties
Haibin Xie, Kuikui Fan and Shouyang Wang
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Routledge-Advances-in-Risk-Management/book-series/RM001
Candlestick Forecasting
for Investments
Applications, Models and Properties
Haibin Xie, Kuikui Fan and
Shouyang Wang
First published 2021
by Routledge
2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN
and by Routledge
52 Vanderbilt Avenue, New York, NY 10017
Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2021 Haibin Xie, Kuikui Fan and Shouyang Wang
The right of Haibin Xie, Kuikui Fan and Shouyang Wang to be
identified as the authors of this work has been asserted in accordance with
sections 77 and 78 of the Copyright, Designs and Patents Act 1988.
All rights reserved. No part of this book may be reprinted or reproduced
or utilised in any form or by any electronic, mechanical, or other means,
now known or hereafter invented, including photocopying and recording,
or in any information storage or retrieval system, without permission in
writing from the publishers.
Trademark notice: Product or corporate names may be trademarks or
registered trademarks, and are used only for identification and explanation
without intent to infringe.
British Library Cataloguing-in-Publication Data
A catalog record for this book has been requested
Library of Congress Cataloging-in-Publication Data
A catalog record has been requested for this book
ISBN: 978-0-367-70337-0 (hbk)
ISBN: 978-1-003-14576-9 (ebk)
Typeset in Galliard
by Apex CoVantage, LLC
Contents
List of figures viii
List of tables x
About the authors xii
Acknowledgements xiii
Preface xiv
PART I
Introduction and outline 1
1 Introduction 3
1.1 Technical analysis before the 1970s 3
1.2 Technical analysis during 1990s–2000s 5
1.3 Recent advances in technical analysis 9
1.4 Summary 10
2 Outline of this book 11
PART II
Candlestick 13
3 Basic concepts 15
4 Statistical properties 19
4.1 Propositions 20
4.2 Simulations 21
4.3 Empirical evidence 22
4.4 Summary 23
vi Contents
PART III
Statistical models 25
5 DVAR model 27
5.1 The model 28
5.2 Statistical foundation 29
5.3 Simulations 32
5.4 Empirical results 33
5.5 Summary 33
6 Shadows in DVAR 35
6.1 Simulations 35
6.2 Theoretical explanation 38
6.3 Empirical evidence 43
6.4 Summary 44
PART IV
Applications 45
7 Market volatility timing 47
7.1 Introduction 47
7.2 GARCH@CARR model 48
7.3 Economic value of volatility timing 49
7.4 Empirical results 51
7.4.1 The data 51
7.4.2 In-sample volatility timing 52
7.4.3 Out-of-sample volatility timing 54
7.5 Summary 58
8 Technical range forecasting 62
8.1 Introduction 62
8.2 Econometric methods 63
8.2.1 The model 63
8.2.2 Out-of-sample forecast evaluation 64
8.3 An empirical study 65
8.3.1 The data 65
8.3.2 In-sample estimation 65
8.3.3 Out-of-sample forecast 66
8.4 Summary 68
Contents vii
9 Technical range spillover 69
9.1 Introduction 69
9.2 Econometric method 70
9.3 An empirical study: DAX and CAC40 71
9.3.1 The data 71
9.3.2 Estimation 73
9.4 Summary 74
10 Stock return forecasting: U.S. S&P500 76
10.1 Introduction 77
10.2 Econometric methods 78
10.2.1 The model 78
10.2.2 Out-of-sample evaluation 79
10.3 Statistical evidence 79
10.3.1 The data 79
10.3.2 In-sample estimation 80
10.3.3 Out-of-sample forecast 82
10.4 Economic evidence 84
10.5 More details 87
10.6 Summary 89
11 Oil price forecasting: WTI crude oil 91
11.1 Introduction 91
11.2 Econometric method 92
11.2.1 DVAR model 92
11.2.2 Forecast evaluation 93
11.3 Empirical results 94
11.3.1 The data 94
11.3.2 In-sample model estimation 96
11.3.3 Out-of-sample performance 96
11.4 Summary 98
PART V
Conclusions and future studies 101
12 Main conclusions 103
13 Future studies 104
Bibliography 106
Index 115
Figures
3.1 A typical candlestick 16
3.2 Black and white candlesticks 17
5.1 Information sets and price changes 30
5.2 Granger causality: an illustration 31
6.1 Granger causality test: the histogram of p values when σ = 0.01 39
6.2 Granger causality test: the histogram of p values when σ = 0.05 40
6.3 Granger causality test: the histogram of p values when σ = 0.1 41
6.4 Shadows in DVAR: Granger causality 43
7.1 Time series plots of Parkinson price range and excess return 52
7.2 In-sample volatility forecasting: GARCH@CARR,
GJR-GARCH and EGARCH 55
7.3 Cumulative return using different in-sample volatility timing
strategies: 1983.01–2016.12 57
7.4 Out-of-sample optimal allocation weight on risky Asset:
1997.01–2016.12 59
7.5 Cumulative portfolio return formulated using different out-of­
sample volatility timing strategies: 1997.01–2016.12 60
8.1 Out-of-sample technical range forecasting 68
9.1 Time series plot of log closing prices, DAX and CAC40:
1994.01–2014.12 72
9.2 Time series plot of log technical range, DAX and CAC40:
1994.01–2014.12 73
9.3 Plots of variance decomposition of technical range, DAX and
CAC40 75
10.1 Time series of cumulative squared forecast error: 1995.01–
2015.12 83
10.2 Cumulative squared forecast error for the historical mean bench­
mark forecasting model minus the cumulative squared forecast
error for the competing model: 1995.01–2015.12 84
10.3 Dynamic weights allocated on equities over time: 1995.01–
2015.12 86
10.4 Dynamic cumulative portfolio returns formed by different
trading strategies over time: 1995.01–2015.12 87
Figures ix
10.5 Time series of cumulative squared forecast error over business
cycle 88
10.6 Cumulative squared forecast error for the historical mean bench­
mark forecasting model minus the cumulative squared forecast
error for the DVAR model over business cycle 88
10.7 Dynamic cumulative portfolio return formed by different trading
strategies over business cycle 89
11.1 Time series of monthly WTI crude oil price over 1986.01–
2013.01 95
11.2 Out-of-sample forecasting comparison, ARMA v.s. DVAR over
2001.01–2013.01 98