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A Test of Macd Trading Strategy
A TEST OF MACD TRADING STRATEGY Bill Huang Master of Business Administration, University of Leicester, 2005 Yong Soo Kim Bachelor of Business Administration, Yonsei University, 200 1 PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF BUSINESS ADMINISTRATION In the Faculty of Business Administration Global Asset and Wealth Management MBA O Bill HuangIYong Soo Kim 2006 SIMON FRASER UNIVERSITY Fall 2006 All rights reserved. This work may not be reproduced in whole or in part, by photocopy or other means, without permission of the author. APPROVAL Name: Bill Huang 1 Yong Soo Kim Degree: Master of Business Administration Title of Project: A Test of MACD Trading Strategy Supervisory Committee: Dr. Peter Klein Senior Supervisor Professor, Faculty of Business Administration Dr. Daniel Smith Second Reader Assistant Professor, Faculty of Business Administration Date Approved: SIMON FRASER . UNI~ER~IW~Ibra ry DECLARATION OF PARTIAL COPYRIGHT LICENCE The author, whose copyright is declared on the title page of this work, has granted to Simon Fraser University the right to lend this thesis, project or extended essay to users of the Simon Fraser University Library, and to make partial or single copies only for such users or in response to a request from the library of any other university, or other educational institution, on its own behalf or for one of its users. The author has further granted permission to Simon Fraser University to keep or make a digital copy for use in its circulating collection (currently available to the public at the "lnstitutional Repository" link of the SFU Library website <www.lib.sfu.ca> at: ~http:llir.lib.sfu.calhandlell8921112~)and, without changing the content, to translate the thesislproject or extended essays, if .technically possible, to any medium or format for the purpose of preservation of the digital work. -
Calibration of Bollinger Bands Parameters for Trading Strategy Development in the Baltic Stock Market
ISSN 1392 – 2785 Inzinerine Ekonomika-Engineering Economics, 2010, 21(3), 244-254 Calibration of Bollinger Bands Parameters for Trading Strategy Development in the Baltic Stock Market Audrius Kabasinskas, Ugnius Macys Kaunas University of Technology K. Donelaicio st. 73, LT-44029, Kaunas, Lithuania e-mail: [email protected], [email protected] In recent decades there was a robust boom in "Bollinger plotter" was developed using the most investment sector in Lithuania, as more people chose to popular mathematical toolbox MatLab in order to solve invest money in investment funds rather than keep money in stated problems. Application is capable of charting the closet. The Baltic States Market turnover has increased Bollinger Bands and 6 other technical indicators with from 721 MEUR in 2000 to 978 MEUR in 2008 (with peak desired period of time. This software is not a fully 2603 MEUR in 2005). When difficult period appeared in automated decision making system, as decisions are global markets, a lot of attention was dedicated towards the usually made based on value judgment. managing of investments. Investment management firms in Since the stock returns usually have distributions with Lithuania gain significance in personal as well as in fat tails, then less than 95% of data fit in the Bollinger business section increasingly; even though these firms are trading channels. However the Bollinger bands trading considerably young (the first one in Lithuania was signals were supported by additional indicators (e.g. %b), established in year 2000). so the loss of data is not significant. Successful investment begins with the financial Our calibration results show that short term investor analysis of stock, asset or index, which you are going to should apply 10 days moving average and use a trading invest. -
FOREX WAVE THEORY.Pdf
FOREX WAVE THEORY This page intentionally left blank FOREX WAVE THEORY A Technical Analysis for Spot and Futures Currency Traders JAMES L. BICKFORD McGraw-Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Copyright © 2007 by The McGraw-Hill Companies. All rights reserved. Manufactured in the United States of America. Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher. 0-07-151046-X The material in this eBook also appears in the print version of this title: 0-07-149302-6. All trademarks are trademarks of their respective owners. Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark. Where such designations appear in this book, they have been printed with initial caps. McGraw-Hill eBooks are available at special quantity discounts to use as premiums and sales pro- motions, or for use in corporate training programs. For more information, please contact George Hoare, Special Sales, at [email protected] or (212) 904-4069. TERMS OF USE This is a copyrighted work and The McGraw-Hill Companies, Inc. (“McGraw-Hill”) and its licen- sors reserve all rights in and to the work. Use of this work is subject to these terms. -
Proquest Dissertations
INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy sutxnitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indisünct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6” x 9” black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. Bell & Howell Information and Leaming 300 North Zeeb Road. Ann Arbor, Ml 48106-1346 USA 800-521-0600 UMÏ METAPHORS OF EXCHANGE AND THE SHANGHAI STOCK MARKET DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in the Graduate School o f The Ohio State University By Susan Diane Menke, M A ***** The Ohio State University 2000 Dissertation committee: Approved by: Dr. -
Forecasting Direction of Exchange Rate Fluctuations with Two Dimensional Patterns and Currency Strength
FORECASTING DIRECTION OF EXCHANGE RATE FLUCTUATIONS WITH TWO DIMENSIONAL PATTERNS AND CURRENCY STRENGTH A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES OF MIDDLE EAST TECHNICAL UNIVERSITY BY MUSTAFA ONUR ÖZORHAN IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PILOSOPHY IN COMPUTER ENGINEERING MAY 2017 Approval of the thesis: FORECASTING DIRECTION OF EXCHANGE RATE FLUCTUATIONS WITH TWO DIMENSIONAL PATTERNS AND CURRENCY STRENGTH submitted by MUSTAFA ONUR ÖZORHAN in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Engineering Department, Middle East Technical University by, Prof. Dr. Gülbin Dural Ünver _______________ Dean, Graduate School of Natural and Applied Sciences Prof. Dr. Adnan Yazıcı _______________ Head of Department, Computer Engineering Prof. Dr. İsmail Hakkı Toroslu _______________ Supervisor, Computer Engineering Department, METU Examining Committee Members: Prof. Dr. Tolga Can _______________ Computer Engineering Department, METU Prof. Dr. İsmail Hakkı Toroslu _______________ Computer Engineering Department, METU Assoc. Prof. Dr. Cem İyigün _______________ Industrial Engineering Department, METU Assoc. Prof. Dr. Tansel Özyer _______________ Computer Engineering Department, TOBB University of Economics and Technology Assist. Prof. Dr. Murat Özbayoğlu _______________ Computer Engineering Department, TOBB University of Economics and Technology Date: ___24.05.2017___ I hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original to this work. Name, Last name: MUSTAFA ONUR ÖZORHAN Signature: iv ABSTRACT FORECASTING DIRECTION OF EXCHANGE RATE FLUCTUATIONS WITH TWO DIMENSIONAL PATTERNS AND CURRENCY STRENGTH Özorhan, Mustafa Onur Ph.D., Department of Computer Engineering Supervisor: Prof. -
Modeling and Analyzing Stock Trends
Modeling and Analyzing Stock Trends A Major Qualifying Project Submitted to the Faculty of Worcester Polytechnic Institute in partial fulllment of the requirements for the Degree in Bachelor of Science Mathematical Sciences By Laura Cintron Garcia Date: 5/6/2021 Advisor: Dr. Mayer Humi This report represents work of WPI undergraduate students submitted to the faculty as evidence of a degree requirement. WPI routinely publishes these reports on its web site without editorial or peer review. For more information about the projects program at WPI, see http://www.wpi.edu/Academics/Projects 1 Abstract Abstract The goal of this project is to create and compare several dierent stock prediction models and nd a correlation between the predic- tions and volatility for each stock. The models were created using the historical data, DJI index, and moving averages. The most accurate prediction model had an average of 5.3 days spent within a predic- tion band. A correlation of -0.0438 was found between that model an a measure of volatility, indicating that more prediction days means lower volatility. 2 2 Acknowledgments Without the help of some people, it would have been signicantly more di- cult to complete this project without a group. I want to extend my gratitude to Worcester Polytechnic Institute and the WPI Math Department for their great eorts and success this year regarding school and projects during the pandemic. They did everything they could to ensure these projects was still a rich experience for the students despite everything. I would also like to thank my MQP advisor, Professor Mayer Humi for his assistance and guidance on this project, for allowing me to work indepen- dently while always being willing to meet with me or answer any questions, and for continuously encouraging me to do what I thought was best for the project. -
Earth & Sky Trading System
Earth & Sky Trading System By Pierre Du Plessis 1 Copyright © Forex Mentor Pro 2011. All rights reserved. Any redistribution or reproduction of part or all of the contents in any form is prohibited other than the following: You may print or download to a local hard disk copies for your personal and non- commercial use only; You may not, except with our express written permission, distribute or commercially exploit the content. Nor may you transmit it or store it in any other website or other form of electronic retrieval system. Risk Disclosure Statement The contents of this e-Book are for informational purposes only. No part of this publication is a solicitation or an offer to buy or sell any financial market. Examples are provided for illustration purposes only and should not be construed as investment advice or strategy. All trade examples are hypothetical. No representation is made that any account or trader will or is likely to achieve profits or loses similar to those discussed in this e-Book. By purchasing this e-Book, and/or subscribing to our mailing list you will be deemed to have accepted these and all other terms found on our web page ForexMentorPro.com in full. The information found in this e-Book is not intended for distribution to, or use by any person or entity in any jurisdiction or country where such distribution or use would be contrary to the law or regulation or which would subject us to any registration requirement within such jurisdiction or country. CFTC RULE 4.41 HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN LIMITATIONS. -
© 2012, Bigtrends
1 © 2012, BigTrends Congratulations! You are now enhancing your quest to become a successful trader. The tools and tips you will find in this technical analysis primer will be useful to the novice and the pro alike. While there is a wealth of information about trading available, BigTrends.com has put together this concise, yet powerful, compilation of the most meaningful analytical tools. You’ll learn to create and interpret the same data that we use every day to make trading recommendations! This course is designed to be read in sequence, as each section builds upon knowledge you gained in the previous section. It’s also compact, with plenty of real life examples rather than a lot of theory. While some of these tools will be more useful than others, your goal is to find the ones that work best for you. Foreword Technical analysis. Those words have come to have much more meaning during the bear market of the early 2000’s. As investors have come to realize that strong fundamental data does not always equate to a strong stock performance, the role of alternative methods of investment selection has grown. Technical analysis is one of those methods. Once only a curiosity to most, technical analysis is now becoming the preferred method for many. But technical analysis tools are like fireworks – dangerous if used improperly. That’s why this book is such a valuable tool to those who read it and properly grasp the concepts. The following pages are an introduction to many of our favorite analytical tools, and we hope that you will learn the ‘why’ as well as the ‘what’ behind each of the indicators. -
Technical-Analysis-Bloomberg.Pdf
TECHNICAL ANALYSIS Handbook 2003 Bloomberg L.P. All rights reserved. 1 There are two principles of analysis used to forecast price movements in the financial markets -- fundamental analysis and technical analysis. Fundamental analysis, depending on the market being analyzed, can deal with economic factors that focus mainly on supply and demand (commodities) or valuing a company based upon its financial strength (equities). Fundamental analysis helps to determine what to buy or sell. Technical analysis is solely the study of market, or price action through the use of graphs and charts. Technical analysis helps to determine when to buy and sell. Technical analysis has been used for thousands of years and can be applied to any market, an advantage over fundamental analysis. Most advocates of technical analysis, also called technicians, believe it is very likely for an investor to overlook some piece of fundamental information that could substantially affect the market. This fact, the technician believes, discourages the sole use of fundamental analysis. Technicians believe that the study of market action will tell all; that each and every fundamental aspect will be revealed through market action. Market action includes three principal sources of information available to the technician -- price, volume, and open interest. Technical analysis is based upon three main premises; 1) Market action discounts everything; 2) Prices move in trends; and 3) History repeats itself. This manual was designed to help introduce the technical indicators that are available on The Bloomberg Professional Service. Each technical indicator is presented using the suggested settings developed by the creator, but can be altered to reflect the users’ preference. -
Technical Analysis: Technical Indicators
Chapter 2.3 Technical Analysis: Technical Indicators 0 TECHNICAL ANALYSIS: TECHNICAL INDICATORS Charts always have a story to tell. However, from time to time those charts may be speaking a language you do not understand and you may need some help from an interpreter. Technical indicators are the interpreters of the Forex market. They look at price information and translate it into simple, easy-to-read signals that can help you determine when to buy and when to sell a currency pair. Technical indicators are based on mathematical equations that produce a value that is then plotted on your chart. For example, a moving average calculates the average price of a currency pair in the past and plots a point on your chart. As your currency chart moves forward, the moving average plots new points based on the updated price information it has. Ultimately, the moving average gives you a smooth indication of which direction the currency pair is moving. 1 2 Each technical indicator provides unique information. You will find you will naturally gravitate toward specific technical indicators based on your TRENDING INDICATORS trading personality, but it is important to become familiar with all of the Trending indicators, as their name suggests, identify and follow the trend technical indicators at your disposal. of a currency pair. Forex traders make most of their money when currency pairs are trending. It is therefore crucial for you to be able to determine You should also be aware of the one weakness associated with technical when a currency pair is trending and when it is consolidating. -
A Linear Process Approach to Short-Term Trading Using the VIX Index As a Sentiment Indicator
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 July 2021 Article A Linear Process Approach to Short-term Trading Using the VIX Index as a Sentiment Indicator Yawo Mamoua Kobara 1,‡ , Cemre Pehlivanoglu 2,‡* and Okechukwu Joshua Okigbo 3,‡ 1 Western University; [email protected] 2 Cidel Financial Services; [email protected] 3 WorldQuant University; [email protected] * Correspondence: [email protected] ‡ These authors contributed equally to this work. 1 Abstract: One of the key challenges of stock trading is the stock prices follow a random walk 2 process, which is a special case of a stochastic process, and are highly sensitive to new information. 3 A random walk process is difficult to predict in the short-term. Many linear process models that 4 are being used to predict financial time series are structural models that provide an important 5 decision boundary, albeit not adequately considering the correlation or causal effect of market 6 sentiment on stock prices. This research seeks to increase the predictive capability of linear process 7 models using the SPDR S&P 500 ETF (SPY) and the CBOE Volatility (VIX) Index as a proxy for 8 market sentiment. Three econometric models are considered to forecast SPY prices: (i) Auto 9 Regressive Integrated Moving Average (ARIMA), (ii) Generalized Auto Regressive Conditional 10 Heteroskedasticity (GARCH), and (iii) Vector Autoregression (VAR). These models are integrated 11 into two technical indicators, Bollinger Bands and Moving Average Convergence Divergence 12 (MACD), focusing on forecast performance. The profitability of various algorithmic trading 13 strategies are compared based on a combination of these two indicators. -
Trading in the Australian Stockmarket Using Artificial Neural Networks
Bond University DOCTORAL THESIS Trading in the Australian Stockmarket Using Artificial Neural Networks Vanstone, Bruce J Award date: 2005 Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. School of Information Technology Bond University Trading in the Australian Stockmarket using Artificial Neural Networks by Bruce James Vanstone Submitted to Bond University in fulfillment of the requirements for the degree Doctor of Philosophy November 2005 Abstract This thesis focuses on training and testing neural networks for use within stockmarket trading systems. It creates and follows a well defined methodology for developing and benchmarking trading systems which contain neural networks. Four neural networks and consequently four trading systems are presented within this thesis. The neural networks are trained using all fundamental or all technical variables, and are trained on different segments of the Australian stockmarket, namely all ordinary shares, and the S&P/ASX200 constituents. Three of the four trading systems containing neural networks significantly outperform the respective buy-and-hold returns for their segments of the market, demonstrating that neural networks are suitable for inclusion in stockmarket trading systems.