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                                                                                                  UNIVERSITY OF TARTU  
                                                                             School of Economics and Business Administration 
                                                                                             Chair of Finance and Accounting 
                                                                                                                            
                                                                                                                            
                                                                                                                            
                                                                                                             Martin Promen 
                                                                                                                            
                                              TRADING TRENDS USING FIBONACCI CORRECTION 
                                            LEVELS AND JAPANESE CANDLESTICK PATTERNS IN 
                                               THE EXAMPLE OF STANDARD & POOR'S 500 INDEX  
                                                                                                                            
                                                                                                            Bachelor Thesis 
                                                                                                                            
                                                                                                                            
                                                                                                                            
                                                                                          Thesis supervisor: Allan Teder, MA 
                                                                                                                            
                                                                                                                            
                                                                                                                            
                                                                                                                            
                                                                                                                 Tartu 2018 
                                            
           
          Recommended for defense ...................................................................... 
                          (supervisor’s signature) 
           
           
           
          Accepted for defense “    “ ............................... 2018 
           
          Head of Chair, Chair of Finance and Accounting ............................................................ 
                                      (Head of Chair’s name and signature) 
           
           
           
           
           
           
          I have written the Bachelor thesis independently. All works and major viewpoints of the 
          other authors, data from other sources of literature and elsewhere used for writing this 
          thesis have been referenced. 
           
          .............................................................................. 
                 (Author’s signature)  
           
                                   
            TABLE OF CONTENTS 
             
            INTRODUCTION ............................................................................................................ 5 
            1.  CANDLESTICK  PATTERNS  AND  FIBONACCI  CORRECTION  LEVELS  IN 
            INVESTMENT STRATEGIES ........................................................................................ 8 
             1.1 Candlestick Pattern Formations in Technical Analysis ........................................... 8 
             1.2. Fibonacci Correction Levels in Technical Analysis ............................................. 16 
            2. EMPIRICAL TESTING OF FIBONACCI RETRACEMENTS AND CANDLESTICK 
            PATTERNS WITH A STRATEGY EXAMPLE ........................................................... 24 
             2.1. Data and Methodology ......................................................................................... 24 
             2.2. Testing the Candlestick Pattern Based Strategy ................................................... 28 
             2.3. Testing the Candlestick Pattern Strategy in Conjunction with Fibonacci Correction 
             Levels .......................................................................................................................... 31 
            CONCLUSIONS ............................................................................................................. 39 
            REFERENCES ................................................................................................................ 42 
            APPENDIXES ................................................................................................................ 45 
             Appendix 1. ................................................................................................................. 45 
             Appendix 2. ................................................................................................................. 52 
             Appendix 3. ................................................................................................................. 53 
            KOKKUVÕTE ................................................................................................................ 54 
             
             
             
             
             
             
             
            INTRODUCTION 
             
            In the times of computer trading and abundant information, banks and investment funds 
            use intricate computer algorithms to predict price movements on the markets. These 
            institutions are in a great need to constantly improve their trading systems and strategies 
            which can outperform the market not only marginally but produce a sizeable profit for 
            their stakeholders. At the same time, non-institutional investors (namely investors trading 
            self-accrued  funds)  who  are  far  less  researched  are  avid  users  of  complex  charting 
            analysis to either generate or confirm their trading decisions (Roscoe et al., 2009). The 
            testing  and  evaluation  of  such  a  charting  strategy  is  the  aim  of  this  research.  Such 
            algorithm, if proved profitable, can help traders around the world, both institutional and 
            private, to develop their own custom trading systems based on current market trends and 
            psychology. 
            Mayall (2006) has divided charting-based trading (sample of non-professionals) into four 
            rough categories which range from “scientific” system where traders try to eliminate as 
            much human contact with trading decisions as possible to “trading as an art” where visual 
            observations  and  trader  intuition  play  a  central  role  in  decision  making.  This 
            nomenclature  was  later  formalized  by  Roscoe  and  Howorth  (2009)  along  with 
            conclusions that the interpretative activity by traders and investors plays an important role 
            in the efficacy of technical analysis (charting). They also suggested that charting has 
            power and importance to users as a heuristic device, regardless of its effectiveness in 
            generating profits.  
            The trading method tested in this work explores the non-interpretative charting style 
            (decisions based on a computer algorithm) and is based on two seemingly very different 
            indicators. One of these indicators is Fibonacci correction levels, which are based on the 
            works of Leonardo Fibonacci, namely his renowned work “The Book of Calculation” 
                                  5