TECHNICAL CHARTING ANALYSIS

Technical Charting Analysis as Statistically Feasible For Individual Stocks in UK Stock Market

Acknowledgement

I would take this opportunity to thank my research supervisor, family and friends for their support and guidance without which this research would not have been possible.

Table of Contents

Chapter 1: Introduction4

Chapter 2: Literature Review8

Moving Averages13

Chapter Three: Methodology25

Research Method28

Initial data analysis32

Chapter 4: Discussion & Data Analysis34

Implementing Moving Averages35

Equivolume Charting36

Volume adjusted moving average (VAMA)38

Ease of movement (EMV)42

Relative Strength Index (RSI)44

Neural network model50

Generalised regression neural network (GRNN)51

Neural network modeling55

Performance measures59

Chapter 5: Conclusions75

References78

Technical Charting Analysis as Statistically Feasible For Individual Stocks in UK Stock Market

Chapter 1: Introduction

Technical charting has been a part of financial practice for many decades (Lo, Mamaysky, & Wang, 2000). It is considered by many to be the original form of investment analysis, dating back to the 1800s (Brock, Lakonishok, & Lebaron, 2002). As opposed to fundamental analysis, which is the study of economic, industry, and company conditions in an effort to determine the intrinsic value of a company' stock (Cutler, Poterba, & Summers, 2001), Technical charting studies the historical data surrounding price and volume movements of the stock by using charts as the primary tool to forecast future price movements (Murphy, 2002). Technical charting normally uses two techniques to evaluate the stock prices. The first Technical charting technique uses charts to study the movement of stock prices. The use of technical charting indicators is another technique that includes calculations or mathematic equations to investigate the trading decisions. The aim of this research work is to evaluate the effectiveness of technical charting analysis as statistically feasible for individual stocks in UK stock market.

Charting has attracted an increasing amount of attention from finance researchers, exhaustively reviewed by Park and Irwin (2004). As well as surveys showing the popularity of charting, there are theoretical explanations based on rational feedback models ([De Long et al., 1990]and [Shliefer and Summers, 1990]), noisy rational expectation models and others. The majority of studies, though, focus on the possibility of achieving excess returns using charting methods. This is an important area of research because demonstrations of this directly contradict the Efficient Markets Hypothesis (EMH) assumption that prices contain all known information and that abnormal returns cannot be made either by fundamental or technical analysis ([Fama, 2000], [Fama, 1991] and [Malkiel, 2003]). Tests of EMH consistently present conflicting results (Zuckerman, 2004 and Preda, 2007a) for an explanation based on structural incoherence). Park and Irwin (2004) show that of 92 studies carried out from 1988 to 2004, 58 report that charting methods consistently generated economic profits, but Park and Irwin suggest that these studies remain methodologically flawed. Their review highlights that there is, as yet, insufficient evidence to determine the efficacy of charting methods one way or the other.

Many British financial advisors practice technical analysis. Charles Dow developed the original Dow Theory for technical analysis in 1884, and Edwards and Magee (2004) wrote a modern version. Charting, a technique of technical analysis, compares market price and volume history to archetypal chart patterns and predicts future price behaviour based on the degree of ...