Investments Based on Technical Analysis Master Thesis

Masaryk University Faculty of Economics and Administration

Study Program: Finance and Accounting

Supervisor: Author: Ing. Luděk Benada B.ASc. Vukman Manić

Brno, February 2017 Masaryk University Faculty of Economics and Administration

Department of Finance Academic Year: 2016/2017

Assignment

Name: MANIĆ Vukman Department: Finance Topic: Investments Based on Technical Analysis

Principles

The aim of the thesis: To present the profitable option of investing on selected technical market analysis on the main financial markets and formulate recommendation for novice investors.

Methods used to support the thesis conclusion: 1. History, definition and characteristics of technical market analysis 2. Features of technical patterns and indicators 3. Research of retail manual and automated traders’ performance that use only technical analysis 4. Testing and creating defined results of technical trading strategies (system) 5. Limitation and Conclusion

Methods: analysis, description, comparison, research, deduction and probability

Graphic representations: the nature of the matter is in need of vast graphic illustrations Extent: 60-70

List of Special Literature:

 BODIE, Zvi; KANE, Alex; MARCUS, Alan J. Investments. 5th ed. Boston: McGraw- Hill/Irwin, 2002. 1016 s. ISBN 0-390-32002-1.  MURPHY, John J. Technical Analysis of the financial markets: A Comprehensive Guide To Trading Methods And Applications. 1st ed. New York: NYIF, 1999. ISBN 0-7352- 0066-1.  NISON, Steve. Beyond Candlesticks: More Japanese Charting Techniques Revealed. New York: Wiley, 1994. Print.  NISON, Steve. Japanese Candlestick Charting Techniques: A Contemporary Guide to the Ancient Investment Techniques of the Far East. New York: New York Institute of Finance, 1991. Print.  REILLY Frank K. and BROWN Keith C: Investment Analysis and Portfolio Management, 10th Edition. 2012. ISBN-10: 0538482389

Supervisor: Ing. BENADA Luděk

Thesis created on: December 30th, 2016

______Head of Department Dean

Name and Surname of the Author: Vukman Manić Title of the Master Thesis: Investments Based on Technical Analysis Title in Czech Language: Investovani založene na technicke analize Department: Finance Supervisor: Ing. Luděk Benada Year of Defense: 2017

Annotation:

The master thesis on this topic “Investments based on Technical Analysis” distinguishes the possibility of investing based on technical analysis and its indicators. Furthermore, it investigates if there is a opportunity of being profitable on financial market based only on technical analysis. Also, in the last chapter, chosen technical strategies will be tested. All things considered, conclusion and recommendation, will be presented for novice investors.

Keywords: Technical market analysis, financial markets, investments, indicators, retail trading, leveraged trading, financial performance, candlestick patterns, trends, market technical strategy

I hereby declare that I worked on my Master Thesis on my own and that all the information sources I used are listed in bibliography paragraph. Moreover, I state that all used literary and other non-scholarly sources are in accordance with law. I agree with storing my thesis in the Library of the Faculty of Economics and Administration, Masaryk University, for the study purposes.

______Vukman Manić 5

Acknowledgments

I would like to show appreciation for people helping me and contributing in writing this thesis. Firstly, I express the gratitude for overall brilliant assistance and very useful comments to my mentor and supervisor professor Ing. Luděk Benada. In addition, I am thanking the Faculty of Economics and Administration for being part of their institution for the past two and a half years.

I would also like to express tremendous gratitude to a doctoral student, and my friend Mirjana Rupar, who from the beginning assisted me and providing me valuable insights on the empirical part of the thesis. Additionally, thank you Mirjana for the final reviewing too.

Special thanks to my beloved girlfriend Veronika Klodnerová, for fully supporting and translating the important academic material in Czech language, that was used as a literature background in this thesis.

Last but not the least, I would like to thank for absolute support to my beloved brothers: Igor, Marko and Vuk (twin). Also, thanks for unconditional love and support to my lovely mother Verica and my great dad Predrag.

In the end, I would like to express gratitude to all my friends and colleagues, in particular to Vildana, Nemanja, Vana&Trajan, Marija&Aldin and Uros who kept me float when some moments were hard.

Content

Content ...... 7 1. Overview ...... 8 1.1 Introduction ...... 8 1.2 Objectives and Steps ...... 9 1.3 Methodology ...... 10 2. Theoretical Background - Financial Markets ...... 12 2.1 Financial Markets ...... 14 2.2 Financial Instruments ...... 16 2.3 Role in the Economy ...... 22 2.4 Fundamental Analysis ...... 23 3. Technical Analysis ...... 24 3.1 History ...... 24 3.2 Chart Evolution ...... 25 3.3 Candlesticks ...... 27 3.4 Charting Patterns ...... 28 3.5 Indicators ...... 41 4. Empirical Evidence ...... 48 4.1 First Part of the Research ...... 49 4.2 Second Part of the Research ...... 61 4.3 Comparison: Empirical Evidence...... 69 5. Limitation and Further Research ...... 72 6. Conclusion ...... 72 7. Bibliography ...... 74 8. List of Figures ...... 77

9. Appendix ...... 78

1. Overview

1.1 Introduction

Over the last decade, financial markets became tremendously accessible to vast majority of people. In addition, due to an internet expansion, financial markets tool has adapted to this technological changes that allowed access to many people to participate on the financial markets (Chen and James, 2010). Furthermore, today we have sophisticated trading platforms that are easy to learn and use for anyone having basic computer literacy. Anybody with an excess to the internet is able to invest today. As a result, many brokerage firms offer huge leverage possibilities to traders. This technological progress allowed individuals, even with a small amount of fund, going lowest of $100, to be able to invest on the financial markets. This practice decade ago, would simply not be possible. Also, there are new researches showing that technical analysis is being used more frequently. In one of the earlier research, Smidt (1965) found that vast majority of investors (80%) on the US commodity futures market, used technical analysis. More recently, Lui and Mole (1998) evidence revealed that more than 85% of foreign exchange investors surveyed (Hong Kong’s FX market) relied heavily on technical analyses. Frankel and Froot (1990) stressed that investment analysts fully include technical analysis for forecasting the financial markets. These evidences suggest that over the past decades, the use of technical analysis slowly started to grow in the practice. Retail leveraged investors, due to optimization of their profits created many strategies and made huge innovations in technical market analysis, both in pattern recognitions and statistical indicators. Growing presence of technical analysis on the financial markets in the recent years motivated the need to explore deeper its advantages in technical market analysis. In like manner, great participation of many ordinary people on the market, motivated me to analyze technics of Technical Market Analysis (TMA) and its probabilities of generating profits in most financial instruments. Second, I wanted to explore, if technical analysis (TA) can be the primary tool used to generate optimized profit. Thus, I conducted empirical evidence showing that “Investment Based on Technical Analysis” can be the only analysis used to generate profitable financial positions on the market.

Finally, the research question: “Is it possible to make profitable investment decision only based on Technical Analysis?” was answered by the results of the thesis research. In brief, the meaning of my research thesis is to point out that due to an innovation in TMA, today many investors, both institutional and retail, more and more use the trading systems based on TA. Therefore, I wanted to prove that we are entering in a new investment analysis period of investing. Thereupon, my aim is to show empirical evidence that using technical analysis, for analyzing the financial markets, can give to investors profitable and consistent performance on their investment accounts.

1.2 Objectives and Steps

The main objective of the thesis is to provide relevant evidence whether investment decisions, based on technical analysis, can generate consistent and substantial profits. It investigates the well-established technical patterns and most of the indicators. Moreover, it introduces the test statistics or backtesting methodology in order to provide evidence on the profitability of those technical systems. Another additional input to the research, is analysis on profitability of consistent long-term investors, in order to explore real market situation profitability of technical investors. There are four aims of the thesis’s research: The first aim of my research is to define and characterize financial investments, but also to explain the types and components of investments. In this part, I focus on defining the financial markets, its constituents, participants and what role in the economy investments has. To understand better advantages of technical analysis, it is necessary for one to extend knowledge on other investment analysis that are currently being used on the market. Thus, in the second part of the thesis, to give some knowledge to the readers what is also used today in conducting financial analysis besides from technical analysis. It will be briefly introduced different types of investment analysis that have empirical evidence of being profitable. The third objective of my research will be detailed explanation of technical analysis and its indicators. The chapter starts with the suppositions, necessary for understanding the mechanisms of technical analysis. Hence, this part of the theoretical research will be detailed. Moreover, whoever uses this thesis in educational purpose, will be able to gain exceptional understanding of

technical analysis. The first part of this impartial will start from history of the technical analysis, its introduction to the west, and continue defining its main characteristics. Crucially, it will be rich in illustrations due to its nature. Furthermore, it will explain most of the charting patterns and indicators used. After all conditions will be thoroughly explained and background reached, I will choose most recent representative sample of backtested technical indicator from two different researches, in order to provide complementary findings. Another part of the research paper’s empirical evidence will be aslo based on the top traders and 200 sample of consistent profitable technical investors, on the public systems: tradingview and myfxbook. However, importantly to state, this paper is not about investigating the profitability of novice investors or the ratio between profitable and non-profitable traders. It investigates the profitability of experienced traders whose investments system is only based on TA, to prove that there are investors generating consistent profit based only on the mentioned analysis. It is clear that, the purpose of the empirical evidence, is to show in practice that there is reliable evidence that investors can be profitable by using in question technique. The fourth aim is to address limitation and conclusion based on empirical evidence that will emerge from findings in this research paper, both from backtesting of indicators and from real- market situation analyzed by technical manual and automated systems investors.

1.3 Methodology

To achieve the goal of the thesis, I used next methodologies: analysis, description, deduction, synthesis, comparison and statistical probability. To elaborate, probability analysis method is used to explore opportunity of profitability, based only on technical analysis. To put it differently, probability method in this argument, is a statistical occurrence of profitability of investor (or group of investor or strategies). The use of essential method of “analysis” means creating constituent elements, with identified features and relationship between the fragments from unravelling the complex data input. This method was used to analyze the complexity of the financial markets, its structure and entities

involved. This method of analysis is of particular interest for technical indicators and patterns, as they are a complex matter, thus separated into smaller understandable segments. Another method used in my thesis is “description”. This method served for detailed characteristics of all the topics given in this thesis. Moreover, description is heavily used in first three chapters, chapters on investments, financial markets and theory of technical analysis. In specific, where it was important to describe technical history, evolution and background of testing, the whole chapter of technical analysis is based on descriptive method. The method of “deduction” applies the logical significances of assumed premises or general premises that is going to derive logical conclusions. This method is used in the fourth chapter to deduct the results and issues of strategy and pattern testing. When analyzed in this method, testing results will form statistical outputs from advanced automated mathematical system platforms, matatrader trading platform, myfxbook and tradingview. Thus, it is a need to deliver more logical and easy-understandable conclusions of those testing’s results. The other coin of the analysis method is the “synthesis” that links the constituent elements or entities into the whole complex data output. This method is used for the last part of the thesis, where findings reached during the research of the thesis are synthesized and elaborated. One of the most common used method in research is “comparison”. In simple, it is the estimate of the similarities and differences on subject matter. This method is mainly used in the chapter where I compare results strategies based on tested indicators and included individual subjects test from mentioned investment systems. In addition to those methods, I also included “statistical probability”, which provides the probability of occurrence, in this matter occurrence of having profitable trading positions compare to losing ones. In other words, the likelihood of investor being profitable, an investor that in my research will only use technical analysis. Additionally, this thesis’s research includes a lot of charts, statistics and figures that are used to constract tables, to summarize data, and to explain the complex matter. Likewise, charts presented are methodology itself, as they provide numerical analyses of psychology (irrational forces that move prices) of the market of the financial instruments. Meaning, because this thesis is about technical market analysis, which is charting analysis system, charts are explanatory statistical apparatuses that provide analyzed output.

2. Theoretical Background - Financial Markets

First of all, if we want to understand any kind of financial analysis, we need to define what is investment and investing. Investment is an asset that is purchased and will generate income in the future (Jeff, 2011). In an economic sense, an investment is a future consumed created wealth, that was generated by purchased today’s goods. In term of finance, an investment is a monetary asset bought for a reason that the asset will provide income or capital gain in the future, or it will be sold at a higher price for a profit (Bodie, Kane, Marcus, 2002) Thus, from definition, investment is an asset to create wealth. It is not for consumption but to generate capital gain or profit for period of time. On the other hand, investing is an action of endeavor of expecting an additional income or profit by binding your own capital (Bogle, 2007). Subsequently, these purchases of assets or items that will generate wealth are traded on the, so called, financial markets. However, to understand financial market we first need to get understanding of financial system, its components and types of financial markets and instruments that are traded. The following sub-chapters are brief explanations about: Financial System, financial markets’ components and types.

Financial System

The key stimulating economic growth, affecting economic warfare and influencing economic performance is done through by financial system. This system substructure is achieved by entities which have funds to allocate to those who have potentially more efficient and effective way to use those funds (Vesela, 2011). In other words, financial system is system where we have flow of the wealth for mutual benefit of all involved parties. There are six elements of financial system:  The ultimate lender (surplus economic unit) and borrowers (deficit economic unit). o Household, Corporate (Business), Government and Foreign  Financial intermediaries, which make the process of lending and borrowing. o Due to a conflict between lenders and borrowers (term, information, volume, etc.), fin. Intermediaries resolve these conflicting requirements. 12

 Financial Instruments (assets), which are issued/created by the borrowers or fin. institutions.  Creation of money (bank deposits), the demand for new bank credit.  Financial Markets, an institution that arranges issuing and trading of the financial instruments. o All financial instruments are issued in primary and secondary market.  Price discovery, the establishment in the market of the valuation; price of money, interest on debt, and price of equity. o The prices of financial instruments are discovered in the financial market by the interplay of demand and supply. (Valdone, 2010)

In the Figure 1, we can see visual representation of the financial system.

Figure 1: Financial System, source Valdone (2010)

All in all, those circulations of fund are arranged primarly by finanical instutions or intermediaries. They are the center of this mutially benefital exchanges. Thus, the following chapter is about financial markets and its components.

2.1 Financial Markets

Components of Financial Markets

As previously stated, financial markets are institutions, that arrange the issuing and trading of the financial instruments. There are two types of Financial Markets: Primary and Secondary. A primary market issues new securities on the behalf of the companies or governments bodies in order to obtain financing through debt-based or equity-based securities (Oldrich, 2008). Primary markets are facilitated by underwriting groups consisting of investment banks that have a power to the first price for the given security and process its sale to other interested investors. Correspondingly, the secondary market is just the opposite. Once the initial sale is complete, further trading is conducted on demand and supply of the given security through so called secondary market, thus the bulk of exchange trading occurs each day (Vesela, 2010).

Figure 2: Primary and Secondary Markets, source Valdone (2010)

Secondary market has two main categories: over-the-counter (OTC) and exchange-driven one. Foreign Exchange and money markets are OTC, and they are fundamentally in the sphere of the well capitalized banks. While the share and bond markets are exchange-driven markets. And under both categories fall derivative instruments. (Valdone, 2010) Besides understanding primary and secondary markets and their main characteristics, without understanding security types that they exchange, we would not have a clear picture of financial system. Subsequent sub-chapter chapter gives us understanding components of the financial

systems, or its security types that are traded, and what affect it has on the macrolevel in the country.

Security Types /Types of Financial Markets

One of main subdivision of financial markets is based on security types. There are five main security types: Debt market instruments, equity market, derivative markets, tangible markets (real estate, commodities, art, antique and other), and markets of indirect investment instruments (Ludvik, 2008). Debt markets are used to raise funds for long-term purposes by national governments and companies. Notably, bonds are long-term borrowing instruments for issuer. Equity market is a financial market where long-term financial instruments are traded. Investing in equities, and as such, providing funds to the corporation, equity holder is granted residual claim on company’s income, and becomes one of the owner respectfully to proportion of the shareholding. Thus, another rising of funds by companies is trough issuing shares (Valdone, 2010). Derivative markets create financial leverage and allow the investors to multiply the rate of returns on the underlying asset. In addition, derivative instruments are based on some underlying asset. And importantly, the price of the derivative has lower price than price of the underlying asset. Thus, this leverage creates two possibilities for investors and explain why investors invest in derivative markets. The first is speculative or taking advantage of specific profit opportunity. The second possibility with derivatives is hedging portfolio or financial instrument against risk. There are five different types of derivatives: options, forwards, futures, swaps contracts and various forms of bonds (Jeff, 2011). “Figure 3 Debt vs. Equity” explains some of the advantages and disadvantages investing in two main trading markets.

Figure 3: Debt vs. Equity, source Valdone (2010)

The chapter 5, provide us with detailed explanation of main financial instruments that are traded. Due to their differences features, but also their similarities it is important to have some background of each instruments that are traded.

2.2 Financial Instruments

Equites

“Stocks are equity investments, which means that buying even one share of a company’s stock means you are a part-owner”. (Investing 101, 2011 no author’ name) retrieved from: www.wallstreetsurvivor.com/pdf/Investing101_eBook.pdf In other words, it is a type of a security that indicates ownership in a corporation and represents a claim on the company’s earnings and assets. We have two types of stocks: Common stock that allows the holder to vote at shareholder’s meeting and to receive dividend. Second type of the stocks is preferred one, that generally holder does not have rights to vote, but has a higher claim on the assets and earnings. Another key point is that stocks are foundation of every investment portfolio (Root, 2015). “Stock exchange is actually a misnomer; securities exchange 16

is more technically correct. Along with equity securities (stocks), stock exchanges also facilitate trading of options, bonds, pooled investment products (such as mutual funds), investment trusts, commodity futures and some of the other financial products” (Investing 101, 2011) Underlying value of stocks, in plain English, is that when business is doing good, making a lot of money, the price of the equity rise. The opposite is true when business is doing poorly, the price of the equity declines. As mentioned in previous chapters, stock exchange is the place where you can buy and sell the shares of stock. In the United States, there are three main stock exchanges: New York Stock Exchange (NYSE) located on the Wall Street, and the National Association of Securities Dealers Automated Quotations (NASDAQ). Finally, the third is American Stock Exchange that was acquired by NYSE, and merged in 2009 with Euronext. Also, the exchanges provide liquidity, helping to ensure that buyers get possible the lowest price of the stocks, and sellers get the highest price possible. There are two ways how investors generate capital gain on the stocks. The first one is through rise in price of the stock, and another way of generating wealth is through dividend income (Jeff, 2011). Over time stocks achieved consistently higher return on investment (ROI) compared to Treasury bonds. From 1900 - 2000, stocks returned 9.2% on average per year (US stocks performed 10%), whereas, bonds generated 4.4% and short-term Treasuries (Cash) returned even lower, 4.1% on average per year. (Investing 101, 2011) The illustration 1 compares the $100,000.00 of investment by two investors over 20 years, one investing in stocks and other in bonds.

Illustration 1: ROI comparison- Bonds vs. Stocks Source: Investing 101 (2011)

Investor investing in bonds ended up with 114% return, or exactly $214.567. However, investor investing in stocks ended up with a 433% return, or in dollar values: $ 532, 590. In this case, investing in stock over long term period, based on evidence, we could make profit but also, “If you had only one year to invest in the stock market, depending on the year you invested (over the last 100 years) your earnings range might have swung between 61% down to -39% - quite risky. If you had a longer investment period, say 10 years, it averages out much more positively, ranging somewhere between 19% and 0.50% per year, with a likely end return of 11.10%.” (Investing 101, 2011). Illustration BBB illustrates the mentioned:

Illustration 2: Stock Return average based on 100 years return. Source: Investing 101 (2011)

Bonds

Bonds are debt instruments. When you purchase bond, in effect you are loaning the bond issuer money, which they repay with the interest. Lender (investor) typically gives to the company or government money, upon which the government or company promises to pay a certain interest rate every year. This process is called coupon rate, and then after the maturity (maturity date) of the bond, barrower party gives back the loaned value (Bailey, 2005). Bonds are different from stocks in the way that bonds provide stated earnings rate (regular cash flows). Also, the value of the bond is contributed by cash flows and affects the true yield (earnings rate) holders receive. On the exchanges, bonds price (face value) changes due to the forces of the demand and the supply. Initially, return on bonds is comprised by the risk of the bond issuers,

but also the rate of return is effected by the interest rates, where if we have an increase of the interest rates, bond’s price will drop (Valdone, 2011). Conversely, if we have decrease of interest rate, then coupon rate of the bond starts looking attractive, and investors will bid the price of the bond higher. When we have trading bond above its face value, it is said its traded at a premium. On opposite, when bond is traded below its face value, it is said its traded on a discount. (Bailey, 2005) We have three common types of bonds: Treasuries, Corporate Bonds, and Municipal Bonds. US Treasury bonds. “US. Treasury bonds carry the full faith and credit of the U.S. federal government, eliminating much of the risk associated with investments. As you can imagine, in return for this minimized risk, your earnings rate will be less than more “exotic” investment choices.” (2011) retrieved from Government bonds are sometimes quoted as “risk-free rate of return”, particularly 3-months Treasury bills (T-bills). T-bills do come close to it, but in real world there is no true risk-free rate financial instrument. Corporate bonds are generally secure, but can also be sometimes risky. For example, most bonds offered by the US automaker returned good levels of security. However, when in 2009 GM and Chrysler bankrupted, this bonds became risky. Historically, corporate stability can change over time, and their inherent value is significantly determined by the creditworthiness (Jeff, 2011). Municipal bonds have primary advantages to investors are tax benefit (offers interest earnings that are exempt from federal tax) and security. The reason why States (US), cities and other local governments issues bonds is to raise money to fund infrastructure projects or services they provide (Oldrich, 2008).

Gold and Other Precious Metals

Precious metals are attractive investments to many experienced investors. It is more challenging then investing in stocks. Moreover, they can fluctuate in values as rapidly as common stocks, but also have higher returns. “Investing prudently in precious metals is much

more complicated since it is simultaneously a global commodity, a hedge against inflation, interest rates, and “end-of-the-world” scenarios. That being said, many advisors are recommending that up to 10% of one’s portfolio should be invested in precious metals. (Investing 101, 2011) On the other hand, precious metals offer protection against inflation. As well as they carry no credit risk, precious metals cannot be inflated (the scarcity, you cannot print them more). As investor’s standpoint, investment in precious metals provide low or negative correlation to other asset classes. This means that having precious metals in your investment portfolio will reduce both volatility and risk (Katz, 2008).

Foreign Exchange (FOREX)

Foreign exchange market, precisely, speaking is not a market. Lending and borrowing does not always take place on the market. Residence can lend or borrow the currencies offshore. Technically, speaking foreign exchange is happening also without institutions. Essentially, as far as financial investment is concern, forex market is a channel (Frankel, J. A., 2008). Moreover, for every international or local exchange of investment in goods or services we have an exchange (demand and supply) of currencies (money). The following illustration 3 is the demand and supply of Forex:

Figure 3: Demand and Supply Entities Source: O’Keefe (2010) created table illustration

These groups of supply and demand make up the balance of payments (BoP) and all the sources of the data are always available. The main source of the BoP is the exchange rate against the vehicle currency US dollar.

To summarize, Forex market is a decentralized channel of private and public institutions, banks, retailers, speculators. Also, central banks are key party in the system of selling and buying. When we look deeper, trading between traders represents one of the highest turnover, thus Forex owns the largest liquidity of all financial, both equity and commodity markets. Another characteristic of the Forex market, it is a spot market, which means trading takes at the current market price and it is determined by supply and demand within the channels of trade, marketplace (O‗Keefe, R., 2010).

Accordant to BIS (Bank for International Statements) FX markets averaged $5.1 trillion per day in April 2016. On the other hand, it’s the first the time since 2001 recession, we had decline from 5.4 to 5.1 trillion. Even though FX spot trading has declined, we have FX derivatives continued to increase. Trading in OTC interest rate derivatives averaged $2.7 trillion per day in April 2016, up from $2.3 trillion in April 2013. [Bank for International Settlements 2016]. Based on data, even with the recent decrease of the volume, still FX is, the volume per day, largest financial market in the world.

Forex is an over-the-counter market (OTC), which means that the financial instrument- currencies are traded between two parties, without intervention of intermediaries or regulations. Considering the lack of a central exchange, there is no single price for a given instrument - currency pair. Although, there is no single price given, the prices are very close to one another; however, the result would cause an arbitrage opportunity by any noteworthy price difference (Frankel, J. A., 2008). Overall, the Forex market exist to facilitate the trade (demand and supply) of the currencies to customers who intend to take delivery of the currencies; however, in term of investment, the majority of forex trading is done by speculators seeking nothing more than profit (O‘Keefe, R., 2010) The forex financial instrument that are traded are in currency pairs: The major (AUD/USD, EUR/USD, GBP/USD, NZD/USD, USD/CAD, USD/CHF and USD/JPY and cross pairs (EUR/JPY, EUR/GBP, EUR/CHF, GBP/CHF, GBP/JPY “not involving USD”), also there are exotic pairs (other countries) with little liquidity and limited dealing (due to a spread “broker’s fee” they are not attractive investment instruments) (O‘Keefe, R., 2010).

Futures, Forwards, Options, Swaps and Other (Weather, Credit, etc.)

These instruments, or products, (Futures, Forwards, Options, Swaps and Other) “derived” from something. And the something/s are financial market instruments and indices. In essence, derivatives cannot exist on their own. On the other hand, there are derivatives that are linked on the other derivatives. For example, options on futures and swaps (O‗Keefe, R., 2010). In this chapter, I will not go detailly on each of one, as in general, those instruments, can be explained briefly, as contracts between two parties to buy, or sell, or exchange (obligatory or optional- options), a standard or non-standard quantity and quality of the cash flow or asset at a determined price. The value of the derivatives are based on the underlying security or index, and as spot instrument that underlines derivative changes, derivatives changes continuously (Santamero and Babbel, 2001). In summary, we reached the point where we have a basic understanding of financial instruments and their characteristics. Thus, giving us knowledge what investors speculate on and make profits. Importantly to note, all those mentioned financial instruments’ prices are presented through charts, therefore on them technical analysis is applicable.

2.3 Role in the Economy

Both on macro and micro economical level, financial system plays key role. As mentioned before, entities with funds allocate those funds to those who have potentially more efficient and effective way to invest those funds. Moreover, financial systems create more low-transaction cost of exchange, leading to efficient transfer of funds. Financial markets eliminate asymmetry problem and inefficient allocation of the financial resources. As one entity of the transaction may hold superior information or innovative venture that can create economic growth greater than the other entity (Bailey, 2005). Furthermore, financial system facilitates balance between those with funds and those needing funds (Valdone, 2011). Indeed, financial system stimulate economic growth, and both affecting economic welfare and economic performance of the actors. Given these points, it is clear that financial markets facilitate the flow of funds from lenders to borrowers in order to borrowers: individuals, corporations and governments finance or invest borrowed funds that will in the future create more capital gain (Valdone, 2011).

2.4 Fundamental Analysis

Before jumping to the Technical Analysis, we need elementary knowledge of opposite main analysis in investment. This section of the thesis provides the basic information about Fundamental Analysis. Fundamental analysis is considered, to be the cornerstone of investing. Some would argue that investing if means you are performing fundamental analysis. The biggest part of fundamental analysis (excluding forex fundamental analysis) involves investigating into the financial statements. Correspondingly, known as quantitative analysis, this analysis involves analyzing entities: revenue, expenses, assets, liabilities and all the other financial aspects. The fundamental approach analyses historical information (profits, sales, and dividend rates) to forecast financial company's future performance. Fundamental analysis can be a valuable tool if used as analysis method for long-term predictions, but not for analyzing day-today price movement (Graham and Dodd, 2009). There are two general approaches to the valuation process: 1. Top-down, three-step approach. 2. Bottom-up, stock valuation, stock picking approach. Importantly, both of these approaches can be implemented by technician analyst too. Advocates of the top-down, believe that the economy, the market and the industry have a significant impact on the total returns for individual stocks. In contrast, those who favor the bottom-up (stock-picking approach) cope that it is possible to find financial instruments that are undervalued relative to their market price. Thereupon, investing in these instruments will provide greater returns regardless of the market and industry outlook (Reilly and Brown, 2012). In general, fundamental analysis provides the investigation, in result the information, of the value of the financial instrument. As perceived, if we know that there is an indication that there is a going to be an increase the value of the instrument, it is attractive to invest in it, as in the future, the growth of the instrument will increase in value, subsequently, will increase our capital invested. Other approach, is that we attained information that the instrument is overvalued or undervalued, thus indicating the increase or decline in the instrument. It is important to understand fundamental analysis from the perspective of this thesis, as the “opportunity cost” of using technical analysis or vice-versa. 23

3. Technical Analysis

3.1 History

“A chart is like a map, the more information each one provides, the better the chance of reaching your destination safely” (Nison, 1994) The first to use charting technique for the market analysis were the Japanese in 1600s. Together with the first use for the first world futures market. In that period, after the century of internal warfare among the dynamo (Japanese feudal lords), when the General Tokugawa won the war, the battle that helped unified the Japan (Battle of Sekigahara in 1600). It led that General Tokugawa became the Shogun (Emperor) of all Japan. His main trade was rice, and the tax system was in rice too. Thus, the demand and supply for the rice was not just for food supplies but also for tax purposes. On the other hand, dynamos and Shogun lived very luxury life. To maintain that kind of life, sometimes from their warehouses they were selling future harvest of the rice. And then, we have a first world’s futures rice market. The warehouses would issue receipts for the future rice. They were called empty rice contracts (“empty rice contracts” since the rice was not in anyone’s physical possession), and they were sold on the secondary market. (Nison, 1994). The key point of creation of the technical market analysis was in the 1700s. When famous trader of the rice future, Homma realized that there was more to a speculation, as he perceived it is not just about demand and supply that move the price of the rice, but also emotion-driven movement of the prices by the traders of the futures. Moreover, he discovered that market of the futures was strongly influenced by the emotion of the market participant. Additionally, “He reasoned that studying the emotions of the market could help in predicting prices. In other words, he understood that there was a difference between the value and the price of rice. This difference between price and value is as valid today with stocks, bonds, and currencies, as it was with rice centuries ago” (Nison, 1994). In the contrast, the candlesticks charting (also Japan, 1700s) was introduced by Stive Nison in late 80s. What is fascinating, the technique used centuries ago is still valid today. To better understand the charting, we need to look first at the charting evolution. Nowadays charting system, needed many changes and progress to reach the charting we use today. The

following sub-chapters explain charting progress, and how small changes in features provided investors better understanding of the financial markets.

3.2 Chart Evolution

Stopping Chart was the earliest type of chart and they were driven by only joining closing prices. Thus, to draw a stopping chart, you would connect only the closing prices of the session (Minute, hour, day, week, month). The following Chart 1. shows the example of the stopping chart:

Chat 1: Stopping Chart Source, Nison (1994)

Next evolution of the charting, was the pole charts. It name is driven from the visual of the lines that look like poles. In this progress of the charting technique, evolution of the charting added extra information imparted by the showing the range in the session of highs and lows (high and low price of the session-hours, days, weeks, months). Moreover, they don’t just show the direction of the price but also the extent of the move for each of the session (Nison, 1994).

The chart below shows how the pole chart extended extra information, the highs and lows of the price chnages during session:

Chart 2: Pole Chart- High and Low Source: Nison (1994)

The third evolution of the charting was bar charts and anchor charts. Bar charts were the combination of the stopping and pole charts. On the other hand, anchor chart technique had important influence in the subsequent evolution of the charting technique. With existence of the anchor charting, the opening price was now added and for the first time we have chart showing open, high, low, and close of the price during a session. The chart 3 and 4 shows the mentioned charts techniques.

Chart 3: Bar Chart Chart 4: Anchor Chart Sources: (Nison, pg 17)

Finally, the last improvement is candlesticks charting. They probably started in mid-19th century, in Meiji period, Japan. The use of the colors (black and white or red and green), made analyzing visually easier to determine the underlying supply and demand, where compared to other charting techniques. Chart 5 illustrates the candlestick chart:

Chart 5: Candlestick Chart. Source: (Nison, 1991)

3.3 Candlesticks

“With the arrival of the candle charts, Japanese technical analysis flowered as people started thinking in terms of signals and trading strategies. Patterns were developed and market prediction became more important. Trying to forecast the market took on extra importance in the 1870s when the Japanese stock market opened” (Nison, 1994). Illustration 3 shows why these charts are named candlestick charts. As illustrated, we can see a rectangular part which is named “real body” and represents open and close. Depending if the price is going down, we have a color of the body part in the black or red. And if the market price is going in up-direction then we have color of the body part of the chart in white or green. Also, the lines, in down and up part of the real body are named upper and lower shadows, presenting highest and lowest point of prices during the session (Kahn, 2010).

Illustration 3: Candlestick Chart. Source: (Nison, 1994) 27

To better understand the differences and some analysis between bar charts and candlestick charts, we can do visual comparison of those charts in chart 6. As we can see, candlestick charting is easier to grasp the price action behavior, especially it’s easy to notice where the price is going. Its more visually clear of not just open, close, high and low, but also the direction of the market. Charting technique is heavily visual technique.

Chart 6: Bar and Candlestick Chart Source: (Nison, 1994)

3.4 Charting Patterns

In following nine sub-chapters will be explained candlesticks patterns that are applicable to all the financial market instruments including: equities, bonds, forex, commodities, ETFs, and other (Kahn, 2010). In this research paper, I will use Candlesticks charts, as they are the most advanced charting system and used by most of the investors. Moreover, they are easy to understand and read, even for a novice investor who just started using for the first time the charting system of analysis. 28

The reason why using technical market analysis Nison (1994) explains in his book: “The importance of technical analysis is five-fold. First, while fundamental analysis may provide a gauge of the supply-demand situations, price-earnings ratios, economic statistics, and so forth, there is no psychological component involved in such analysis. Yet the markets are influenced at times, to a major extent, by emotionalism. An ounce of emotion can be worth a pound of facts. As John Manyard Keynes stated: there is nothing so disastrous as a rational investment policy in an irrational world."~ Technical analysis provides the only mechanism to measure the "irrational" (emotional) component present in all markets.” Thus, we want to analyze behavior of the prices and to perceive, based on the session, where is market moving the financial instrument. Many information is not known to ordinary public, or it comes too late. However, demand and supply of the market cannot hide itself. It’s clear why from this perspective technical market analysis is so important.

Reversal Patterns

Reversal patterns are price clues that can alert that trend of the instrument is shifting in market psychology. Moreover, the reversal signal implies that the prior trend is likely to change, but not necessary to reverse (Murphy, 1999). “Recognizing the emergence of reversal patterns can be a valuable skill. Successful trading entails having both the trend and probability on your side. The reversal indicators are the market's way of providing a road sign, such as "Caution-Trend in Process of Change." In other words, the market's psychology is in transformation. You should adjust your trading style to reflect the new market environment.” (Nison, 1991).

Single-Candlestick Patterns

Hammer

Illustration 11 shows a single candlestick pattern with long lower shadow and small real body. Importantly to understand, this single pattern needs to be near the support of a trend. Furthermore, all the reversal single, double or triple patterns are near the important levels, like support/resistance or trendlines. (Nison, 2008). In fact, real small body explains us that change in open and close price is small, demand and supply are not moving the price, but most importantly when we also add the lower shadow, lowest price of the price action during the session, we can then realize that price went down by 2-3 times compared to open, then it was pulled up (close), reaching almost the open price. In other words, price was going lower and lower, and then there is strong up-swing back to the almost original start price. Showing that bulls (demand) are gaining the control, and pushing prices up. Importantly, this reversal pattern to work, it must be happening near the important price action levels, like support in this case (Kahn, 2010).

Illustration 4: Hammer Source: Onlinetradingconcepts.com

We can likewise analyze that technical analysis and its patterns are applicable on indices, commodities and forex. The following charts examples will be also diverse in different types of investment. Purposely, showing us that technical market analysis is useful in all financial markets. The following example is shown in chart 7: Hammer Pattern (Index) 30

Chart 7: Index S&P 500, 2H, April-May 2015 Source: Charts on Tradingview

Shooting Star

Shooting star or inverted hammer has the same logic behind constructing as hammer but in opposite market direction. As we learn hammer formation needs to be near the support while shooting star needs to be on the price level where we have strongest resistance level. It has long upper shadow and small real body. (Nelly, 1997)

Illustration 5: Shooting Star Source: Onlinetradingconcepts.com 31

Accordingly, shooting star pattern has same underlying “behind the scene” price action. Hence, the logic is just reverse. We have price of the fin. Instrument going in up direction, and in one particular session near the resistance level we have a pull back of the prices by 2/3 or more and closing price is near the open price (Nison, 2008). This pull back is supported by the bears (supply) of the market and possibly, significant retrace of the demand. The example of this pattern Shooting Star is Illustrated in chart 8.

Chart 8: Index FTSE 100, D, 2015-2016 Source: Tradingview Charts

DOJI

Doji is a single candlestick pattern, many argue one of the most important single candlestick pattern. A doji session has instead of the budy just horisontal line. And this happens when the session open and close are the same. In same cases, also chartists consider doji when they are almost the same. Also, doji as reversal, is near price action level where is support and resistance respectivly (Kahn, 2010). Illustration 6 shows the characteristics of the doji pattern.

Illustration 6: Examples of Doji Source: onlinetradingconcepts Examples of Doji, represented in chart 9. Moreover, when we analyze further, this pattern shows that in the session, we have market confusion, meaning that prices (looking highs and lows) might went higher then open or close or lower than open and close and came back to its open (or very near it) and closed. In essence, this single candle pattern in combination with other signals in more than two sessions can indicate strong reversal signal.

Chart 9: AUDUSD, D, Sep, 2015 – Jan, 2016 Source: Tradingview Charts

Double and Triple Candlesticks Patterns

Most candlesticks patterns are combination of two or more. Moreover, we cannot just look in one session what has happened with price action and based on that made our investment decision. Thus, we need to analyze more sessions and see bigger picture of the market trend and its reversals. It’s good to have multiple signals that will confirm our analysis (Pring, 1991).

Bullish Engulfing Pattern

The engulfing patterns are first of the multiple candlesticks patterns. It’s a major reversal signal of two candlestick patterns. We have Bullish and Bearish Engulfing patterns. The bullish engulfing pattern composes of white bullish real body wraps around, or engulfs, the prior smaller black real body. Illustration 7 shows a bullish engulfing patter. This pattern explains that buying pressure has overwhelmed selling pressure. Thus, we have a higher demand than supply, leading the prices to push up. (Bartels, Marry and Fred, 2007)

Illustration 7: Bulish Englufing Pattern Source: Nison (2008)

Bearish Engulfing Pattern

Bearish engulfing pattern is a reversal signal of an uptrend, thus real black body engulfs the small real white body. Furthermore, it’s the same logic behind as bullish engulfing pattern, but in this case, selling pressure has overwhelmed the buying pressure.

Illustration 8: Bearish Englulfing Pattern Source: Nison (2008)

The following example in chart 10 emphases on both Bullish&Bearish Engulfing Patterns. Engulfing patterns when looked in two sessions, have the same background explanation of the price reversals as shooting star and hammer patterns. In essence, when we analyze the price movement of those patterns, they are the same with the engulfing feature of second candle underlying the explanation of the price action.

Chart 10: Walmart (Equity), 1H, 2016 Source: Charts on Tradingview

Dark Cloud and Piercing Patterns

Illustration 10 and 11 shows how the dark cloud is formed. Furthermore, “A dark cloud shows, as the Japanese express it, that the market has a poor chance of rising. The dark cloud cover's first candle is a strong white session. During the next session, there is buying pressure left over and the market opens higher, but later in that session, prices decline as the market closes under the center of the previous session. This pattern reflects a period in the market when the upward power of the tall white candle has been dissipated by next session's weak black candle” (Nison, 1991).

Illustration 9: Blended canlde

In this example I want to introduce the behind logic of the charts. If we look “Dark Cloud” closely, two session creates it, but if we combine these two session in one, let’s say from 1H, to 2H then we can see that those two 2H session dark cloud candlesticks are simply one blended candlestick, the shooting star. Illustration 9 explains the mentioned. Thus, clearly all the patterns shows the same logic, as in essence they are the same underlying demand-supply endeavor.

Illustration 10: Dark Cloud Illustration 11: Piercing Both dark cloud cover and piercing patters are composed similarly as engulfing patterns but the difference is that the subsequent reversal candle must reverse in opposite direction by more than 50% of the previous candle. This pattern shows us that the advance of the market is

exhausted, and possible for reversing. And the name of the dark cloud comes from Japanese expression that, the market has a poor chance of rising (Murphy, 1999).

Chart 11: Gold/USD, 2H, 2016 (dark cloud cover and piercing patters) Source: Tradingview

Limitation and Summary of Chart Patterns

There are many other patterns, but in essence, both reversal and continuation patters are constructed by the same structures of movement of the prices by the demand and supply. One would argue that the underlying psychology of the market for technical patterns is the same, just knowing to recognize when those different patterns form. However, due to a limitation of the text, other patterns will not be included, such as marubozu, spinning top, three white soldiers, three black crows, hikkake pattern. Also, there are pattern strategies that will not be included too: Head and shoulders, cup and handle, double top and double bottom, triple top and triple bottom, broadening top, price channels, wedge pattern, triangle (chart pattern), flag and pennant patterns, the island reversal and gaps. They are important, but on the basics, the presented charting patterns are enough to provide needed knowledge to understand also the other one too. Because

of that, it is not necessary to include them. On the other hand, the analyzed patterns give us basic understanding of the price action of the financial instruments. To sum, what is pattern as financial story of the for example equity. If we know that during the last weeks we are in the upper trend, prices constantly are pushing upper and upper, and last three days prices fell, for 23.8% and last day of those three days, we had retracement of the prices by 80% reaching the opening of the prices during that day, we have a pattern of hammer, showing us the reversal and continuation of the prior trend. That’s one of the possible stories how the patterns can be explained trough narrative nature of the price action. To understand trend and what’s 23.8 retracement, we need to learn other part of the technical analyst. The following chapters will explain in detail about support and resistance, Fibonacci and final indicators.

Support & Resistance

Support and resistance define the effect of how market prices tend to “bounce” off from historical price levels (Renato Di, 2013). To elaborate to the statement above, support level in chart forms due to the presence of the buyers (bulls) on specific price levels. Moreover, these prices level can be horizontal or in diagonal price levels, when it is a trend. It is recognizable, because prices tend to bounce from that level. On the other hand, when the support level is broken (prices went lover to the support area), a financial instrument is free to move lower due to the absence of buyers and demand (Renato Di, 2013). Furthermore, when the support price level is broken, it becomes resistance area, as we have more sellers than buyers. Chart 12 and 13 represent both horizontal and diagonal support price levels:

Chart 12: Horizontal Support Chart 13: Trend Support

The resistance price level is opposite of the support levels, but also important as the other side of the coin. Resistance level is formed at an area where prices seems do not want to move higher from that level. Moreover, these price levels bounce prices back lower, and one of the underlying explanation is that these levels represent overbought price levels, meaning the buyers exit the positions and sellers step in due to highest prices of the period, looking to short high (Xin, 2011). Furthermore, there are two western indicators, Moving Averages and Fibonacci Retracement that provide support and resistance levels too. As we can see from illustrations below, a moving average is a constantly changing line that smooths out past price data. Moreover, we can also identify support and resistance on moving averages line (Kahn, 2010). Notice on the chart 14, how the price of the asset finds support at the moving average when the trend is up, and how it finds resistance when the trend is down. Finally, the Fibonacci retracement tool clearly identify levels of potential support and resistance. Both indicators will be explained in subsequent chapters. (Murphy, Investopedia, 2016)

Chart 14: Moving Averages (S&R) Chart 15: Fibonacci Retracement (S&R)

3.5 Indicators

Moving Averages (MA)

A moving average is an arithmetic moving average calculated by adding mostly closing price of the security for a number of time period and dividing its total by the number of time periods. In conclusion, the MA gives us the average price of the security over the period. We also have smoothed moving average, that mainly smooths out volatility, and makes it easier to see price trend of the financial instrument. If the MA points price up, it shows us that the prices are going up, or they are in uptrend (Pring, 1991). On the other hand, when MA points down, the security is in the downtrend. Most investor use MA in analysis is using it to easily recognize if a security is in uptrend or downtrend. Additionally, MA are used in a pair, where we use two different covering time frames simple moving averages. If a shorter-term simple moving average is above a longer-term average, an uptrend is expected. Then again, a long-term average above a shorter- term average signals a downward movement in the trend (Nelly, 1997). The figure 4 represents uses of two moving averages. “if a shorter term moving average crosses over the longer term moving average, it is viewed as a bullish sign. The Japanese call

such a crossover a golden cross. Dead cross is a bearish indication that occurs when some short- term moving average crosses under the longer term moving average” (Nison, 1991).

Figure 4: Use of two moving averages (Golden Cross& Dead Cross) Source : (Nison, 1991)

Correspondingly, an exponential moving average (EMA) is like a simple moving average, except that more weight is given to the latest data. It's also known as the exponentially weighted moving average. By using EMA reacts faster to recent price changes. Exponential Moving Averages (EMA) (Nelly, 1997).

Relative Strength Index (RSI)

The Relative Strength Index, or RSI, was created by J. Welles Wilder in 1978. It compares the strength and magnitude of a stock’s gains and losses in recent time periods. The formula converts

this winning and losing data into a number ranging from 0 to 100. There are RSI ’s three factors: Relative Strength, Average Gains, and Average Losses (Park and Irwin, 2004). The basic formula is: 100 – (100/RS + 1) Where relative strength is the average gain divided by the average loss over the period. The important levels of RSI are 30 (oversold, or undervalued) and 70 (overbought, or overvalued) levels. In combination with candlesticks patters, this levels signals good buy and sell forecast. Figure 5 bellow in short shows us the levels 30 and 70, and how it looks when is overbought and oversold regions:

Figure 5: RSI Source: Metatrader 4 platform, data extracted.

Moving Average Convergence Divergence (MACD)

MACD tool shows the difference between fast and slow moving average of a financial instrument’s prices. Moreover, it is design to identify significant trend changes and price forecast signals. MACD is constricted that there is signal line, mostly the 9 period EMA line of the MACD line. Second, we have a histogram which represent the visual representation of the differences between MACD line and signal line. Finally, MACD line is the difference between the 12 and 26 period of the EMA. Thus, the crossovers of the MACD line and signal line are the signals of buy or sell. Moreover, its indicators that shows the changes of the momentum, which

is key to identifying the trading opportunities around support and resistance levels. Figure 6 illustrate the mentioned above (Barters, Marry and Fred, 2007).

Figure 6: MACD Source: Metatrader 4 platform, data extracted.

Another method of using MACD in investing, is the divergence. Divergence is when security price diverges from MACD, it signals the end of the current trend. Also, when MACD rises dramatically, in other words, the short moving average pulls away from the longer moving average- the security is in overbought level, which signals it will soon be return to the normal levels (Root, Investopedia, 2016).

Fibonacci

Fibonacci number is also called as a golden number. The number name was give by the mathematician of the Middle Ages, Leonardo Fibonacci, who invented it. The Fibonacci number appears in sequences, starting with 0 and 1, after which every third number is the sum of previous two numbers and sequence continues infinitely. From the sequences we have, the Fibonacci ratios — 23.6%, 38.2%, 50%, 61.8%, and 100% — that show the mathematical relationship between the numbers, and are important to investors (Lorenzo, 2013). Those ratios found the implementation in investment. The Fib ratios would be a percentage corrections of the primary trend, thus giving reversal signals. “The Fibonacci sequence of numbers is as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, etc. One of the remarkable characteristics of this numerical sequence is that each number is approximately 1.618 times greater than the preceding number. This common relationship 44

between every number in the series is the foundation of the common ratios used in retracement studies.” (Murphy, What Is Fibonacci Retracement, 2016). The Fib ratios of 23.6, 28.2, 50, 61.8 are the most used retraces ratios in charting. These ratios represent support and resistance levels, but also importantly they are levels of retracement. Nevertheless, break of those levels are continuation of the trend. The level of 61.8 represent strong level of any trend. Moreover, whever we have strong up or down trend, after a period of time, there should be a price correction, (price in normal enviroment cannot just go up and up or versa) and most of the time, this correction of the price is a retracement, mostly of the levels of 61.8 or 50 (which is not a Fibonacci level) and then continuing its trend (Lorenzo, 2013).

Figure 7: Fibonacci Retracement Source: StockCharts.com

This charting technique is mostly used for to identify the end of the correction or a counter-trend bounce. Whenever we have a strong price move, often there is a portion of the retracement, or the correction. On the other hand, these corrections, when ended, we have a reversal signal for a buy or sell, and significantly when we enter these signals we are also in the direction of the market (retracement occur when the price of the financial instrument is trending).

Stochastics

Figure 8: Stochastic Source: Source: Metatrader 4 platform, data extracted.

It helps to determent when the security is overbought or oversold. To state, based on stochastics, that financial instrument is overbought or oversold, it simply mean that the buying or selling pressure has run out of steam (Chen, 2010). When we have occurrence of overbought or oversold, for the investor it means the reversal in price can happen more likely. Figure 8 explains how can we read the stochastics. The level 80 on stochastic is overbought region and the level 20 is oversold region. When we have the break of those regions respectively, we can read it as an overbought or oversold likely reversal (Lorenzo, 2013).

Bollinger Bands

“In the 1980s, financial analyst John Bollinger developed a new technical analysis tool to measure the highs and lows of a security price relative to previous trade data. These trading bands help investors track and analyze the bandwidth of stock prices over a period.” ("Investing 101,2013) The objective of the Bolliner Bands is to provide the investor the highs and lows of prices over the period. Moreover, as we examine the bandwidth of a security, we can notice variations on both sides, which along provides as trends too. Also, due to variations we can measure standard deviations or in chart reading volatility of the financial instrument over the time. (Bartels, Marry and Fred, 2007)

In other words, the Bollinger Band is designed by two standard deviations away from moving average. So that, closer the prices move to the upper band, the more the overbought the market is, and the closer the prices move to the down band, the more the oversold is the market. Another use of this tool is that when there is “squeeze”, when the bands come closer together near the moving average. This period that is called “squeeze” signals the low volatility of the market, and is consider to be potential sign of the high volatility, or that there is going to be significant move up or down respectively. Another read of the tools is when there is a breakout of the band, which indicates major event. The figure bellow shows how the tool looks on the chart (Park and Irwin, 2004).

Figure 9: Bollinger Band

Indicators Summary

Technical indicators are mathematical calculations based on historical prices, volume and other. The main characteristic of indicators is that they read processed market information to forecast financial market direction. They are based, as charting technique, on the price reading, thus they are just other part of the technical analysis. It is clear from examples given, we can understand that indicators, overlay on the price chart data to indicate where the market price is going, or when the financial instrument is in an overbought or oversold condition.

4. Empirical Evidence

Research- Introduction There are substantial number of published articles and literature on technical market analysis. However, on academic level there is still no consensus regarding benefits of using technical analysis. Moreover, there are many studies concluding that technical analysis in investing does not work. When we look at books written many years ago (Fama, 1970; Friedman, 1953), they have very controversial, negative stance on technical analysis as forecasting tool. Nevertheless, there are many research papers and empirical evidence showing that specific technical strategies do work. Nowadays, most retail profitable investors do use technical analysis only or as complement in their trading. Earlier studies stressed that technical analysis is useless (Ackemann and Keller, 1977, Bohan, 1981, Levy, 1967, Van Horne and Parker 1967, 1968), arguing that strategies based on technical markets cannot provide us better returns than compare to a simple trading system of buy and hold (Jacobs and Levy, 1988, Alexander, 1961, Brush and Boles, 1983). Moreover, some studies that considered transaction costs (Fama and Blume 1966, Ball 1978, Jensen and Benington 1970) demonstrated that returns can be negative due to many trades (overtrading) in comparison with the buy and hold strategy. Only recently, in last two decades, as technology progressed, there are more and more empirical papers concluding that technical analysis is a profitable trading strategy tool. Studies (Brock et al., 1992, Hudson et al., 1996, Gunasekarage and Power 2001) suggested that technical market analysis showed superior returns in stock markets. Also, Park and Irwin (2007) found that, because of the progress of the computing power, advancement in electronic price platforms, and recent innovations in technical analysis, provided significant study that trading through pattern recognitions and indicators, can give investors signals for profitable trading on the market. Furthermore, according to Park and Irwin (2007) meta-analysis, 95 studies, 56 studies found positive results, while only 20 studies showed negative results, and 19 showed neutral results in technical analysis.

In conclusion, the subject of technical market analysis remains a controversy among scholars and interested parties. That is one of the main reason why I wanted to conduct this research thesis on profitability of technical analysis.

4.1 First Part of the Research

Data Analysis, Strategies and Optimization

As mentioned before, today we have many technical indicators that can show the trend, sentiment, signals when to buy or sell, volume and all other statistical and mathematical tools that can help us in respect to our investment strategy. There are automated trading systems (ATS) on almost all trading platforms, which state that they are all profitable and by using all combinations of indicators they can reach the most optimal profitability on the market. Thus, as they are purely technical, I will include most of them in my research, as my colleagues Marian Hockicko (2014) and Federico Lovison (2014) included in their master thesis too. On the other hand, almost all traditional institutions, excluding high frequency trading ones, do not use ATS. The problem is that they work at some time period, however as we live in very dynamic political-economic environment these automated systems fails. As in recent research (Hockicko, 2014; Lovison, 2014) stated that majority of ATS systems, that are available on internet, declare they are profitable, but in practice they don’t work, or only in specific situations. All in all, this thesis also includes apart from ATS, empirical evidence of investors who uses only technical analysis and are consistently profitable. Thus, besides technical statistical tools- indicators, I will also include testing investors who use technical analysis, with specific accent on their own systems and human psychology touch in trading. To reach the goal of my thesis, that is to provide empirical evidence that investing based on technical market can be profitable and to show that specific trading strategy can be optimal in trading and later recommendations for novice traders, further stapes will be taken. First, there is a need of backtest of those strategies and indicators. Importantly, due to personal financial constrains, I will use Hockicko’s data (2014) on those ATS and Lovison’s findings (2014). Speficically, there is no necessity of using my own resources (inefficiently) for a Metatrade platform system, and having the same results. However, their results will be thoroughly 49

explained and elaborated in new light. I will address the advantages in flaws of ATS and provide suggestions for further research based on comparative analysis. Second, I will provide empirical evidence based on couple of real technical investors performance (out of approximately 200, that are used in statistics of the research, but not in the study itself, where all features provided are not possible due to a limitation of the text) and 10 summarized manual investment systems, both in regards of reaching factual market outcome to the technical market discussion, if it by real evidence works. Thus, the practical part of the thesis will be constructed from two parts. The first following part is data based on Hockico’s and Lovison’s research and the second part my own empirical evidence.

Indicators and Patterns: Essential strategy parameters need to be well-defined to have proper backtest. As follow: . Financial Instrument (Equity, bonds, foreign-exchange, cfd, etc) . Timeframe (Minute, Hour, Day, and Week) . Trades include, both buy and sell (stop loss, target profit, and trailing stop) . Position size (depends on money management, only 3% risk “possible loss” per trade)

Investors subject tested: Regarding empirical evidence on other investors that invest based on TA, due to some lack of availability of data, in this part perimeter focuses on: . Which financial market (subjected investors are trading) . Timeframe . Patterns strategies they use for their signals when to buy or sell . And, if possible, their profit to risk ratios It is required to provide some precise feature of the boundaries of this analysis, of parimiters that are not general and are more specific, prone to be important, as they are the main factor to some general inputs and outputs provided by the research. The following three chapter are about the mentioned.

Software and Web Platforms

To collect, analyze and interpret data input, it is impossible without software and web- platform that collect individual investment statistics and performances. Thus, for backtesting ATS, it will be used metatrader complex trading software for forex and commodity market (Hocicko’s data), then CoolTrade automated stock software for equity market. Importantly to state, due to vast complexity and space-time consuming, in this thesis there will be not included all types of financial instruments, only the main one. Second, apart from softwares, in my research data input will be also included web-platforms. These platforms that will be used are myfxbook and tradingview web-platforms, and they provide important database, that I will be able to acquire data of investors using only technical analysis and evaluate their performance.

Data Analysis and Backtesting

The given indicators strategies will be tested by applying strategy on real-time historical data, that will as an output give us evaluation of profitability and other elements. This system, the backtesting, is a process of how to optimize trading strategies and to evaluate them before implementing them. Prons: . Time speed of testing (even historical data of 5 years can be tested in approximately 20 sec) . Disregarding irrational choices by human errors and psychological aspects Based on data you can include as many variables (indicators) to reach optimal strategy (Hockicko, 2012)

Cons: . As charting, in general it is not exact science, without human touch in most cases, as it will be shown empirically, do not optimize profits . It is required advance IT literacy (programming)

. Over time, it does not work (financial environment changes) Overtrading (leading ultimately in higher losses) (Hockicko, 2012)

There are many indicators and investors, however it is not possible to include them all in my research. Thus, I will use only indicators that are used most widely and have very positive feedback of performance. Actually, the indicators will be chosen based on Waller (2012) article “What Are The Most Popular Technical Indicators Worldwide?”, where author used the most famous Ciana’s data regarding investing. Moreover, the same recommended indicators can be found on Forbs articles too. On the other hand, recommended investors strategies and their performances will be chosen from mentioned web-platforms. Given that, the aim of this thesis is the evidence that by using only technical analysis trading can be profitable, tests will be done on the investors that trade on the mentioned system analysis and will be chosen current top 10 systems.

Risk Management

Money management, or risk side of investment, is a system of preventing the capital loss. It is a measure and management of loss (risk). When looked from other side, it is a system how to utilize the most efficient one’s capital. In plain English, if we have smaller losses we reached the efficiency of using our capital. It is very important that there is a discipline when trading and experiencing losses. As stated before, investing strategies are not exact science, thus you cannot ever be having 100% being correct and profitable on the market. Thus, losses are part of the business and its management or system of risk is very important. Elder (2002) states that we should never have more than 2% equity exposure to the risk of loss. On the other hand, due to volatility of the market many other investors consider that efficient stop loss is 3-8% of equity risk. The prices can fluctuate (breath) but still stay on the right trend perceived, and due to a money management (stop-loss) the position due to a fluctuation can be automatically exited, and still the trade itself correct.

Moving Averages Trading Strategy

Inputs values: . Currency- EUR/USD . 22 period (optimized period, usual is 12 period) . Stop-loss- in this case 2% of equity . Buy signal- when price level crosses above MA line . Sell signal- when price level crosses below MA line . Time frame: 01.2009-12.2013, H1, 0.1 lot, $10.000.00 . X- number of trades, y-total capital

Source: Matatrader Backtesting Illustration (Hockicko, 2012)

MA strategy report:

Source: Output data from Metatrader (Hockicko, 2012)

In this testing, gethered from Hockicko’s ATS data, we have a positive performance, gaining $5053. We had total of 224 trades (where if we had period of 12, we would have 199 trades) signals. The biggest loss trade was $216,3 which correspond to our risk management. Due to its good consistent performance, I would recommend this trading strategy. On the other hand, if we had not optimal input of period 12 (Hocicko, 2012) then moving averages are mostly used as a indication of trend but as we can see from this testing, when optimized they can be a good trading system. When we look at Lovison’s test on MA period 21 on commodity market, crude oil. It again shows the use of technical indicators can work and create profit on other markets too. The figure bellow shows the data from the test: Mode: Stop-loss (trailing) $500; Target-profit $1000

Source: Equtiy Curve Line, MA- 21 (Lovison, 2014)

In his testing, Lovison got annual return of 37.30% and had 48.55% of profitability of total trades (Lovison, 2012). And one of the reasons, why this testing showed very good performance is good risk-reward ratio of 2:1 or $1000:$500. When we analyse this profitability curve, we can see nice consistency too, which indicates that this strategy tested is very attractive to use.

Previously we discussed about price fluctuation or breathing. The following data on the same MA but with higher Target-profit (TP) and Stop-loss SL we can see enormously better performance in the same environment. Mode: SL (trailing) $1500; TP $3000

Source: Equtiy Curve Line, MA- 21, Higher SL and TP (Lovison, 2014)

Just understanding of psychology of the prices and their flactuations, this trading with higher SL and TP pruduced profit of 86.05% per annum (Lovison, 2014). This risk management allowed our open position to have higher “breathing”. This means, we canot easly be stopped out, as we have higher stop loss, and on average, prices do tend not to go in such a huge fluctuations (daily change of -+ of 1500$, in this case crude oil), thus we cannot be stopped out of trade so easly. Again, in this investing system, we see consistency of generating profits for period of 8 years. This two examples showed us the importance and relevance of risk management in investing.

RSI Trading Strategy

Inputs values: . RSI signal line period- 14 . Overbought line 70 . Oversold line 30 . Stop-loss 0.5% . 2.5:1 profit-loss ratio

Source: Metatrader Backtesting Illustration (Hockicko, 2012)

RSI Optimized strategy report:

Source: Output data from Matatrader (Hockicko, 2012)

By this inputs values for RSI, we have a positive performance of $9,312.25. By analyzing it, we can see that there are higher loss trades of 64% (due to a very tight stop-loss) in this case. Also, having higher profits because we put higher profit to loss ratio and better signal of overbought

and oversold, our performance drastically changed. We have consistent profit for 5 years of 93%, or per annum of 18.6%. In conclusion, equity curve has consistent profits during all 5 years. Furthermore, including that forex is very volatile market, this results show amazing efficiency of using resources (limited risk, based on the data) Lenny Connors is investor who mastered the RSI trading strategy. His famous RSI 2 strategy for stock market has been fantastic tool used by many investors. In his book “Short Term Trading Strategies that Work” he provided empirical evidence that in last 14 years his technique of RSI 2 is still reliable (Connors, 2009).

MACD Trading Strategy

Input values: . MA-17; Open level- 3; Close level- 6 . Buy signal- when MACD line crosses above zero level or when it crosses above the signal line. . Sell signal- when MACD line crosses below zero level or when it crosses below the signal line . Target profit- when new signal or when it reaches target profit to risk ratio . Stop-loss of 250 price index points . Time frame: 01.2011-10.2016, H1, 0.1 lot, $10,000.00

Source: Metatrader Backtesting Illustration (Hockicko, 2012) MACD strategy report:

Source: Output data from Matatrader (Hockicko, 2012)

Strategy resulted in profit of 5% per annum. When we look for 5 years our initial investment of $10,000.00, our capital increased for $24,762 (Hockicko, 2012). When we analyze the performance curve, we can see due to environment, first two and a half years were vibrant, not consistent. But following three years show consistency in gains. This example, is a case where it shows that automated technical systems, due to a lack of human, do sometimes overtrade most probably the consolidation of prices (where is recommended not to trade, only experienced investors)

Bollinger Bands (BB) Trading Strategy

Input Values: . MACD slow line- Exponential MA 14 . MACD fast line- EMA 12 . BB- 14 . Standard deviation -2 . Buy/Sell signals when averages of MACD crosses above/bellow the band . SL- 2% TP- when another signal reaches . 01/2009-12/2013, H1, 0.1 lot size, capital: $10,000.00

Source: Metatrader Backtesting Illustration (Hockicko, 2012)

BB+MACD strategy report:

Source: Output data from Matatrader (Hockicko, 2012)

Again, using BB strategy we have positive performance of 11.63% per annum. Moreover, technical analysis and its disciplined view on risk, we can see that even with 49.5% of profitable trades, we still have generated capital gain for 5years of 58%.

Summary on First Research

The research data from 2012 and 2014 (a recent one), provided us conclusion that even through automated trading systems, that are fully technical, we can have consistent capital gains. On the other hand, the limitation is that automated systems, researched in first part, have some flaws, the simple is, that there is an example of not being profitable first two and a half year, and 59

then growth (even high, still two year not constantly profitable and even few time losses on the capital). It is high likely, in human nature, that after 6 months we will bail this system as it will show for that period of not being profitable. Another one is, considering that most people are risk averse, there is high risk with their profit performance, their occurrence of profitability, not the final result that might be luck by sake of argument, which there might be a probability considering statistical probability. Moreover, on average they have 46% of winning trades, indicating that, again having this statistic with possibility of having oscillations and non- consistencies of generating profit through two years and a half (even very high profitability on the capital, highest 86% per annum), it is very highly unlikely that some investor will stay with the system for long. However, still this research, provides us with historical data that even with automated system, showed that, still including the probability of profitability on average 50%, we were profitable (good risk management). The best three strategies and recommended ones are: MA 21 on crude oil- high SL and TP performed: 86% profit p. a. MA 21 on crude oil- low SL and TP performed: 37% profit p. a. RSI on currency – low SL and TP performed: 18.5% profit p. a.

Whether we like the ATS or not, investment on this technical system (automated investment positions based on technical analysis) is attractive, considering that opportunity cost, range from 37-86% of profit in this case. Maximum drawdown (decrease of capital) for RSI strategy was 10%, and this risk compared to gain is good management decision to invest in this strategy. When we compare that for strategy of MA 21 for crude oil, there is an amazing gain, while using the strategy “buy and hold”, for the same period we would have loss of capital of 19% (Lovison, 2014). Altogether, it can be concluded that if we want to have better confidence and some bigger picture of having more stable return on capital, backtesting is tool for it. Moreover, we can change parameters and combine with other technical indicators and patterns.

4.2 Second Part of the Research

Data Analysis and Findings

Research on both given data have crucial missing point - simulations. When we look at the analyzed reports from Hockicko and Lovison research, these reports provided us with more than once tested optimal strategy, leading to the best performance. In other words, inputs were few times changed to reach the optimal profitability on historical data. In reality, we cannot simulate future market data. It is clear, that it is not enough evidence to prove that investing on the market can be profitable using only optimal technical analysis (automated) can be profitable. Therefore, in this chapter, I will include automated verified trading systems that were active in last 3 to 5 years. Thus, we will have real profitability and not the simulated one. Second, I will also include manual verified trading systems (technical patterns and indicators recognized and executed by person) again active in last 2-5 years. Both automated and manual technical investment strategies will provide further evidence for answering the research question - is it possible being profitable on the financial market using only technical analysis. Moreover, this data will provide tangible evidence of having profitability based on using mentioned analysis.

Essential data input parameters need to be defined to have proper data (investment accounts) results: . Track record and trading privileges verified . Real account (real money invested) . Manual or technical . Time-period of investing (All evidences, test subjects, include these points)

Also, another important output parameters to have proper analyzed data, are as follow: . Profit factor (shows how many times the gross profit exceeds the gross loss. The higher the value, the better)

. Standard deviation (is a statistical measure of volatility, that shows how much variation or dispersion there is from the mean) . Sharp ratio (is used to characterize how well the return of an asset compensates the investor for the risk taken. The higher the value, the better) . Z-score (is used for calculating the ability of a trading system to generate wins and losses in streaks. It enables us to see if the streaks generated are of a random pattern or not.) . Expectancy (what can be expected to make (win or lose) on every trade, the $ value is what is expected on each trade to make) . AHPR (Arithmetic Average Holding Period, is the average holding period return) . GHRP (Geometric Average Holding Period, is the geometric holding period return) . Profitability (How many trades were won) . Consistency (All outputs calculated for the given test subject data was automatically done by the platform)

Data Evidence 1: FX Viper Live Master Account (Manual)

General data statistics

Source: myfxbook, account FX Viper Live Master Account

(http://www.myfxbook.com/statements/616112/statement.pdf)

Advance data statistics

Source: myfxbook, account: FX Viper Live Master Account

Manual account, based on the trading statement (616112, pdf), is an example of technical trader using scalping technic of TA.. This trader has small but very consistent profits, on average 8 price index points (pips) per trade. Moreover, from equity curve we can see not just consistency, but also unbelievable profitability ratio of 95% of being profitably consistent. To better understand, this trader out of 100 total trades, there would be 95 won trades. For three years, he had 2759 trades, where only 5% of total trades were closed in loss. In term of investment, this is remarkable investment strategy (based on TA). There is probability of success of being profitable and consistent for last 3 or more years, compared to, for example, banking sector or other investment funds’ returns. Fortunately, also for this account, we have public information about the person behind this account, which is Mr. Jeffrey Shear from Toronto, Canada. This person is managing fund over 100 million and over 1000 investors. (the relevance of identity, further confirms the validity of account, even though all subjected accounts are verified).

Data Evidence 2: Managers (Real, Manual)

General data statistics

Source: myfxbook, account: Managers (http://www.myfxbook.com/statements/1739950/statement.pdf)

Advance data statistics

Source: myfxbook, account: Managers

For this account, we have 85% chance of profitability. Out of 470 trades for year and a half, there were only 70 trades closed in losses (15%). On the other hand, there is bigger gain of 306.99%. The profit factor analysis of data, shows smaller value of 2.83 compared with the previous account value of 3.63 with gain of 114.89%. Thus, good trading system is not only

based on how much you gain, but also on consistency and probability of profitability too. Still, all other analysis shows stable investment strategy. Based on the data given, from both accounts, one would argue that both are very good strategies, having great profit track. However, when they would be compared only based on capital gain, many would invest in Managers account. Although, that would be plausible decision, taking into consideration people’s understanding of investment and risks that goes with it, choosing Viper account instead due to its better consistency of equity curve and very low risk would be reasonable. Data Evidence 3: GTS Risk Management System General data statistics

Source: myfxbook, account: GTS Risk Management System (http://www.myfxbook.com/statements/1038905/statement.pdf)

Advance data statistics

Source: myfxbook, account: GTS Risk Management System 65

For this account, we have 81% chance of profitability. Out of 214 total trades for almost two years of investing, there were only 40 trades closed in losses (or 19%). The absolute gain is low compared to “normal” gain, because this trader withdraws from the account $31194.96, which is the reason why we have abs. gain of 23.95%. When we check this trading strategy, against other two manual trading technical accounts, this one shows lower profitability but also low probability of profitability too. Even though, compared to other trading accounts in this empirical finding, still it is a very good trading account. When we look closer, this trading account for two years had only 214 traders, where there were earned 35,118 pips compared to other two accounts 9,380.3 and 8,088.7 respectively. This technical trader, based on data analyzed from the advanced statistics, would be stated that this trader is experienced technical trader for long-term. His average trading positions on the market are 7 days while other two accounts, only 1 to 2 days. All in all, we can conclude that he is doing long-term technical analysis that gains higher pips per traded (326.77) than, for example Viper who is using scalping technique (7 pips per trade). There are 503 profitable trading manual accounts on the myfxbook platform, profiting from as low as 5% to 1000% (Source: Trading Systems/ myfxbook). Moreover, there are vast data, however due to a limitation of text for this subject matter, it is not possible to look all analysis of each specific one. On the other hand, there are analysis of three manual accounts that represent three main important analyses when looking for optimal strategies: probability of being profitable, consistency of gains over period of time, and frequency of trading positions. All those points are important, when choosing strategy and its future possible profit generating. It is clear, that there are good number of individual investment accounts (503), that are investing based on technical analysis, and are profitable.

Data Evidence 4: Summary: 10 best Automated Trading Systems

Due to enormous data, thus limitation in presenting all statistics, the following are the top 10 manual (ATS) accounts from the list of 503 found trading systems on the known platform (myfxbook). Another reason why the data is only summarized, is because we have detailed explanations about ATS and its features in data from previous chapter. The difference of this 66

empirical evidence is that this trading technical systems have all the necessary criteria, proving they are trading in real time and capital. Moreover, they are not simulations, which is the main reason for their inclusion into evidence that confirms benefits of using technical analysis. In other words, we have a real data on profitability. Bellow table shows consistency in all of the automated accounts growth of capital, averaging 612% gains. Those accounts are live in last year, and as we can see max-drawdown was 45.97% and lowest 7.5%.

Gain Drawdown Account System Trading Performance

System

Mt4-2906567 +742.56% 23.12 Real Technical Automated

MFM7 +697.86% 36.73 Real Technical Automated

Elite PAMM +679.05% 30.01 Real Technical Automated

AlgoTradeSoft Safe Scalper +678.87% 7.57 Real Technical Automated

v 8.90

FXDiverse_1 +677.88% 30.16 Real Technical Automated

FX7 +677.88% 30.16 Real Technical Automated

System_9 +677.88% 30.16 Real Technical Automated

BachaTICEA +664.42% 32.39 Real Technical Automated

Alpari PAMM Sportloto1 +663.6% 21.89 Real Technical Automated

Bl - 1 +640.96% 45.97 Real Technical Automated

Source: myfxbook, accounts: Top Automated Systems

The limitation of this data on ATS is that the strategy and parameters used are unknown to the public. There is a general knowledge available but not sufficient for profound knowledge on the features of the automated system. Moreover, on the platform it can be read that these ATS use programmed language, providing computational recognition of patterns and indicators signals for

buy and sell. To elaborate, they use intelligent computer programs that recognizes technical analysis’s patterns and western indicators as a signal, where the program based on those signals automatically trade. On the other hand, this is still the optimal point from this data, compared with the data in previous chapter about ATS. However, regarding the thesis goal, this data is efficient, as we can see the profitability of those ATS based on technical analysis. Thus, I can state that the above table, provides us sufficient necessary data output, that confirms the profitability of those trading automated systems, and at the end comply with our hypothesized answer to our question.

Discussion of Empirical Evidence

Empirical Evidence: 1, 2, 3, and 4 (two out of top ten ATS accounts used in comparison as example) GTS Risk Investment FX Viper Live Management Accounts Master Account Fx-Managers System Elite PAMM Bl – 1 Trading style Manual Manual Manual Automated Automated System Technical Technical Technical Technical Technical Gain 114.89% 306.99% 145.85% 679.05% 640.96% Pips 8088.7 9380.8 35118 5885.4 790.8 Drawdown 13.81% 37.43% 37.09% 30.01% 45.97% Monthly 1.81% 9.63% 3.37% 6.64% 8.09% Deposits $100,000.00 $1,398.00 $34,979.42 $39,746.06 Trades 2759 470 214 393 2370 Profitability 95% 85% 81% 68% 72% Longs Won 402 162 99 109 645 Shorts Won 2230 238 75 158 1072 Best Trade (Pips): 79.9 2275 11300 110.6 89.2 Worst Trade (Pips): -844.1 -1667 -9100 -55.5 -185.9 Avg. Trade Length 2d 1d 7d 5h 38m 11h 31m Profit Factor 3.63 2.83 5.02 1.94 1.75 Standard Deviation $179.14 $13.82 $83.25 $878 $969.12 Sharpe Ratio 0.24 0.23 0.35 0.02 0.12 Z-Score -3.43 -3.95 -3.74 -14.81 -5.13 Source: Own illustration 68

Crucially, what the new data provided, can be explains as follows. When we compare new data (both manual and automated) with simulated one, the new data provided true analytics that technical analysis work. And the main parameter of this is, when we check “the probability of occurrence” The old data provided that winning trades ratios are: Strategies: MA12- 55.8%, MA22 oil- 48%, RSI – 36,16%, MACD- 56,77% and BB- 49,5% of winning trades. This data having such results, can be explained or argued also that this was just simple the theory of probability. As prices, can go only up or down, and you can be only correct or wrong (50:50), having many financial positions can be by chance having 50% winning trades. It simply, doesn’t provide stable backed (all strategies averaged 49.2% winning trades) empirical evidence that investment analysis works. On the other hand, based on my data only manual technical investors have on average 87% winning trades. It is also the similar result, when we include top 50 manual investors into the research, they provide on average 86% winning trades. What is interesting there are manual traders that provide even 95% or more winning chance, for example Viper. In comparison, the automated systems from new empirical evidence showed smaller performance compared to manual, but still relevant. Top ten automated technical investors’ accounts on average have 83.4%.

4.3 Comparison: Empirical Evidence

The aim of thesis was to provide empirical evidence of benefits of using technical analysis. In the first part of the thesis, I provided detailed analysis of financial markets by describing its importance for generating wealth. More importantly, I showed the highest (retail) possibility proven (by the research given) of generating that wealth. Further, I addressed until now obtained literature on technical analysis (e.g., Barnes, 2007; Bogle, 2007; Bodie, 2002; Connors and Alvarez, 2009; Fama, 1970; Kahn, 2010). This extensive literature review showed that there is no consensus in the research showing that using technical analysis can be profitable.

Moreover, showed that there is a high demand for gaining more empirical evidence on the profitability of using technical analysis. Therefore, in the second part of the thesis I wanted to contribute to solving this literature dilemma and give inputs for people that aim in using technical analysis for their investments. In the first part of the empirical evidence I used automated system trading, and in the second part I analyzed manual investing by investors based on technical analysis. First, by using the data on both equity and currencies and another one on commodity (Hocicko, 2012; Lovison, 2014) I demonstrated that using automated system trading gives optimal (retail) investing profitability. The evidence showed, by three most optimal (retail) investing automated strategies, that resulted in annual return of 86%, 37% 18.5% is, compared to investment in mutual funds or Russell 2000 Value Index, that had growth of 7.5% for the year 2016, or another example, S&P SmallCap 600 Index 22.43% growth for the same year. However, despite demonstrating optimal investing profitability, automate trading system does not represent fully realistic profitability, due to not being consistent in generating profits. The main flaw of this technique is that program works in a way that searchers the best past strategy until it obtains best probability which offers to investor, but in reality, it is not possible to test on future data and when obtain the most optimal strategy, then to have better results, better optimal profitability. In example of second best automated trading system in the research, annual return on average was 37%, but in first two years and a half, performance were in deviations of both profits and losses. It is evident, that this solution (from the first part of the research) is not realistic one. Moreover, wide public does not have access to the high frequency trading and highly expensive automated efficient systems (which again work on the optimization). On the other hand, second part of the research also includes those automated real-market systems that are optima, just as an variable to the research and to not be excluded from the comparison of the result. Therefore, to demonstrated the real evidence of profitability of technical analysis and to show it is a useful investing strategy for wide public, I further analyzed the manual investing based on technical analysis. In the second part of empirical evidence, I included data from Viper, Mangers, GTS risk management and briefly summarized 503 manual investing accounts. Data findings included explanatory parameters for better understanding of given investment strategies, such as

probability of profitability (occurrence), standard deviation, z-score, sharp ratio, small-to-large, consistency of gains over period of time, and frequency of trading positions. The data showed that, investors were highly profitable by using technical analysis and most importantly they showed consistency in profitability. As for example, a technical retail investor Jeffrey Shear’s performance that, was in the research, his data showed high yearly gains of 43% on average for 5 years consistently. What research shows importantly on the Jeffrey’s performance is that, he traded 2759 trades (2011- 2016), and out of that total number, 2621 financial positions were winning trades (95% being right to 5% being wrong) using only technical analysis. Not that he is technical analyst, but the analyst that uses scalping technical technique, analyzing charts on very short time period, like 5-10 min charts, which is highly technical. This consistency in gaining profit is crucial perimeter for proving and providing evidence that there is an investor that has such a result on the financial market using only technical analysis. The research shows, that not only he has such a performance, but also trading technical account name Managers, has 85% winning trades on the market. Moreover, another important perimeter, that all presented in the research manual technical investors, also show on their equity curve, almost align dispersity of profits over the time period. In other words, there are not such a high deviation from the equity curve (graphical representation of gains on capital over time). All these data contributed further to the evidence of profitability of using technical analysis and demonstrated that real life market investors (from the research) are profitable by using technical analysis. Important to note, I used verified accounts’ performance and not the simulated one demonstrating the realistic picture of manual investing based on technical analysis. Finally, with this empirical part I provided better, more reliable evidence, that exposes high reality of being profitable by analyzing market only via technical analysis. The thesis has theoretical and practical contribution. On one hand, it contributes to solving the dilemma about profitability of using technical analysis (Ackemann and Keller, 1977, Bohan, 1981, Brock et al., 1992, Gunasekarage and Power 2001, Hudson et al., 1996 Levy, 1967, Van Horne and Parker 1967, 1968,). On the other hand, it shows that manual investing based on technical analysis is profitable and also available for being used by wide public.

5. Limitation and Further Research

Despite the new input into the data research, including real investment accounts, with both study of manual and automated investing based on technical analysis, the research still faces with two important limitations. The first one is the limitation of subject tests’ information. There is a vast data on the advanced statistics output of the tests, such as the performance of the technical strategies. However, the main lack of on the subjected tests was the knowledge of how those investment systems are consistently profitable. Therefore, there is a need for different research that would address these questions and complement this study. The second important limitation is that same data could have been analyzed by all technical strategies, or chosen ones, on the real investing account for all main markets (equity, commodity, forex, and other). This would provide exact vast data that would give comprehensive result on the technical analysis, as we could compare the profitability by using different strategies. However, despite the fact that limitations, the research shows that there are real-verified investment accounts that use technical analysis as their trading tool, and are profitable. Likewise, the final finding of the empirical evidence does still positively meets the thesis question. Limitations of this research thesis, are great milestones for additional research, and they should be addressed in future studies. This would complete the overall validity and profound investigation of being profitable by investing based only on technical analysis

6. Conclusion

The primary aim of this thesis was to demonstrate that it is possible to be profitable on the financial market through the use of technical analysis. In order to accomplish the goal of the thesis and to provide empirical evidence to the question of the thesis, there were different partial aims separated into chapters. First was to define and characterize financial investments, and to explain the types and components of investments. Also, the focus was on defining the financial markets, its constituents, participants and what role in the economy investments has. Second, was to detailly provide studies, and how in practice, technical analysis is constructed. There were strong graphical representations of the charting patterns and indicators, used when analyzing

financial market in wish to perceive the markets movements. Beside academic works on the theory of the technical analysis, there were included three different research findings on the similar subject, to provide comparable conclusion from coherent new research findings. The thesis new variable to the research, in a way opposed to other two findings, in terms of their incomplete reliability (they were done based on simulations) and provided better more valid results that were based on real live trials. Therefore, the discovery of added new research answered copiously that it is factual that there are investors investing based on technical analysis and are profitable. Moreover, it also added the optimality of performance compared to other two findings. Altogether, I can conclude that using technical analysis as the only tool of investment analysis can create consistent profits. On the other hand, due to a limitation of the research, the study can be improved upon in the future. Moreover, in conclusion, the subsequent research can be conducted with added missing inputs, and provide empirical evidence with insignificant limitations.

7. Bibliography

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8. List of Figures

1. Figure 1: Financial System 2. Figure 2: Primary and Secondary Markets 3. Illustration 1: ROI comparison- Bonds vs. Stocks 4. Illustration 2: Stock Return average based on 100 years return 5. Chart 1: Stopping Chart 6. Chart 2: Pole Chart- High and Low 7. Chart 3: Bar Chart 8. Chart 4: Anchor Chart 9. Chart 5: Candlestick Chart 10. Illustration 3: Candlestick Chart 11. Chart 6: Bar and Candlestick Chart 12. Illustration 4: Hammer 13. Chart 7: Index S&P 500, 2H, April-May 2015 14. Illustration 5: Shooting Star 15. Chart 8: Index FTSE 100, D, 2015-2016 16. Illustration 6: Examples of Doji 17. Chart 9: AUDUSD, D, Sep, 2015 – Jan, 2016 18. Illustration 7: Bulish Englufing Pattern 19. Illustration 8: Bearish Englulfing Pattern 20. Chart 10: Walmart (Equity), 1H, 2016 21. Illustration 9: Blended canlde 22. Illustration 10: Dark Cloud 23. Illustration 11: Piercing 24. Chart 11: Gold/USD, 2H, 2016 (dark cloud cover and piercing patters) 25. Chart 12: Horizontal Support 26. Chart 13: Trend Support 27. Chart 14: Moving Averages (S&R) 28. Chart 15: Fibonacci Retracement (S&R)

29. Figure 4: Use of two moving averages (Golden Cross& Dead Cross) 30. Figure 5: RSI 31. Figure 6: MACD 32. Figure 7: Fibonacci Retracement 33. Figure 9: Bollinger Band 34. Table 1: Matatrader Backtesting MA 35. Table 2: Output data from Metatrader MA 36. Chart 16: Equtiy Curve Line, MA- 21 37. Chart 17: Equtiy Curve Line, MA- 21, Higher SL and TP 38. Table 3: Metatrader Backtesting RSI 39. Table 4: Output data from Matatrader RSI 40. Table 5: Metatrader Backtesting MACD 41. Table 6: Output data from Matatrader MACD 42. Table 7: Metatrader Backtesting BB+MACD 43. Table 8: Output data from Matatrader BB+MACD 44. Chart 18: Account: FX Viper Live Master Account 45. Table 9: Account: FX Viper Live Master Account 46. Chart 19: Account: Managers 47. Table 10: Account: Managers 48. Chart 20: Account: GTS Risk Management System 49. Table 11: Account: GTS Risk Management System 50. Table 12: Accounts: Top Automated Systems 51. Table 13: Empirical Evidence: 1, 2, 3, and 4

9. Appendix

All thesis appendixes or research data can be found on the Information System of Masaryk University Archive of Thesis/Dissertation Vukman Manić ESF N-FU FINA, učo 440130