Lade Inhalt...

Technical Analysis - Myth or Magic?

Masterarbeit 2010 80 Seiten

BWL - Bank, Börse, Versicherung

Leseprobe

Table of content

TABLE OF FIGURES

GLOSSARY

1. INTRODUCTION

2. SHARE ANALYSIS AND ITS PREMISES
2.1 Classification and development
2.2 Dow Theory In Correspondence with TA
2.2.1 The Averages Discount Everything (except ‘Acts of God’)
2.2.2 The Three Trends
2.2.3 The History repeats itself.
2.2.4 Criticism of the Dow Theory
2.3 Random Walk Theory in Correspondence with BHS
2.3.1 Criticism of the Efficient Market Hypothesis
2.4 Fundamental Analysis
2.5 Technical Analysis versus Fundamental Analysis

3. METHODOLOGY
3.1 Test conditions
3.2 Tested Indices

4. INSTRUMENTS OF TECHNICAL ANALYSIS
4.1 Indicators
4.1.1 Moving Averages
4.1.2 Moving Average Convergence Divergence
4.1.3 Bollinger Bands
4.2 Oscillators
4.2.1 The dilemma of Oscillators
4.2.2 Momentum
4.2.3 Relative Strength Index
4.2.4 Slow Stochastic
4.2.5 Combination of Indicators and Oscillators
4.3 Approach to performance tests

5. RESULTS/FINDINGS
5.1 BHS
5.2 EMA
5.3 MACD
5.4 Bollinger Bands
5.5 Momentum
5.6 RSI
5.7 Slow Stochastic

6. ANALYSIS
6.1 EMA
6.2 MACD
6.3 Bollinger Bands
6.4 Momentum
6.5 RSI
6.6 Slow Stochastic

7. CONCLUSION

8. APPENDIX

9. REFERENCES

Table of figures

Figure 1: Classification of Share Analysis

Figure 2: Trend Classification DAX 2004 - 2007

Figure 3: Correlation between S&P 500 and DAX Price-Index 1989 - 2010

Figure 4: Correlation between NIKKEI 225 and DAX Price-Index 1989 - 2010

Figure 5: Correlation between NIKKEI 225 and S&P 500 1989 - 2010

Figure 6: EMA (50,10) trading signals DAX July 2009 - June 2010

Figure 7: MACD (12,26,9) trading signals DAX January 2010 - June 2010

Figure 8: BB (20,2) with RSI (80/20) trading signals DAX July 2005 - June 2010

Figure 9: Momentum (3000/-3000, 23) trading signals DAX January 2003 - June 2010

Figure 10: RSI (75/25, 14) trading signals DAX January 2003 - June 2010

Figure 11: Slow Stochastic (80/20, 14/3) trading signals DAX January 2003 - June 2010

Figure 12: Summary of nominal performances

Figure 13: Summary of inflation adjusted performances

Figure 14: NIKKEI 225 - DAX Price Index - S&P 500 by comparison

Figure 15: Classification Bull- and Bear-Markets DAX

Figure 16: Classification Bull- and Bear-Markets S&P 500

Figure 17: Classification Bull- and Bear-Markets NIKKEI 225

Glossary

illustration not visible in this excerpt

1. Introduction

The following paper will outline the suitability of Technical Analysis (TA), regarding selective chosen tools for performance increase versus the classic Buy-and-Hold- Strategy (BHS). These two approaches, beside the Fundamental Analysis (FA), are the foundations used by investors concerning their Investment strategy and differ substantially in their nature. Thereby, this dissertation will investigate whether the application of active TA is a productive approach, yielding to favourable results and having the ability to outperform the passive BHS.

To achieve substantive results, the comparisons of performances will be stretched to 21 years and are based on the following three indices, which differ significantly concerning their location, volume and importance.

- Standard & Poor’s 500 (S&P 500)
- German Stock Exchange (DAX)
- Japanese Nikkei 225 (N225)

However, to reinforce the impression of the analysis, semi-annual and annual performances will also be measured. This is an essential element of the comparisons, as due to the nature of TA, the seed capital of 1.000.000 Sterling will not be invested at all times. In this case, the capital will yield the current base rate of interest of the Bank of England minus 0.5 % per annum. The measurements will be assessed by means of three established Indicators and Oscillators.

Indicators

- Exponential Moving Averages; 200 days and 100 days
- Moving Average Convergence Divergence
- Bollinger Bands

Oscillators

- Relative Strength Index
- Slow Stochastic
- Momentum

TA can be divided into Chartism and the statistical based TA. Although a clear demarcation between these groups is not given in reality, as most proponents of TA combine both techniques. The vast majority of this dissertation will only reflect the latter. This can also be justified, as Chartists predict future price developments based on trend lines, patterns and formations. Murphy (1999) states that all Chartists are Technical Analysts, but not all Technical Analysts are Chartists.1 Due to the lack of standardised price characteristics, Chartism implies a high degree of subjectivity.2 Therefore, the absence of operational ability of this sub-area would not lead to a feasible analysis concerning an increase in performance.

We maintain that financial markets are either moving in boom or bust cycles (Bull or Bear Markets). The classic BHS, based on the Investment-Legend Benjamin Graham, has generated high profits in the past. However, at least since the burst of the “Dot-com- bubble” in March 2000 this approach must be questioned and leads us to believe that timing and behavioural finance, do play an important role concerning an investment strategy. An Investor, following the BHS, can always wait and hope for a comeback of his underlying investment; however, would it not be more profitable and economically efficient to buy at low prices and get out at high prices?

Investors use TA to benefit from movements through an accurate determination of buy- and sell signals. Clearly, the aim of this investment approach is to beat the classic BHS and the FA. The FA is based on the economical power of supply and demand, which leads to rising, declining or stagnating prices. The determining factor is the intrinsic value of the underlying asset. In case the value is below the current price, the asset is overpriced and should be sold. If the price lies under the ‘fair’ intrinsic value, the asset should be bought.3 This approach is very time demanding as many further factors, for example domestic and foreign political and economic events and government policies must be considered as well. This approach is widely used by ‘Wall Street Players’ (including the Oracle of Omaha - Warren Buffet) and professional analysts.4

The TA stands in stark contrast to FA. The TA confines itself concerning profitable buy- or sell decisions only on the analysis of the current price of the underlying asset. Intrinsic values are considered as ineffective. Deployment of this assumption, the technical analyst attempts to beat the fundamental based proponent, through the provision of future price developments using historical data. In other words, the FA studies the causation of market movements, while the TA analyse their impacts.5

In contrast, the statistical based TA only uses computer-assisted trading signals, which are based and generated by an objective and unemotional trading system. These systems are composed of Indicators and Oscillators and eliminate any emotions from trading. This is crucial to set up an empirical and clearly comprehensible analysis that market participants are able to generate an excess return through TA. Unlike the BHS, TA also attempts to benefit from trend-less times where prices languish in no-man’s land. Sophisticated short-selling techniques in short term or secondary downtrends, as a result of retracements and volatility impulses, can lead to substantial profits during times when the market oscillates between a certain range. Therefore, back-tests will demonstrate that TA enables investors to generate accurate buy/sell signals, complied with a consistent trading approach.

The key aims of this dissertation can be illustrated as follows:

- Investigate the evidence that financial markets are not efficient.
- Evaluate if TA with the selected instruments during the chosen time period outperformed the BHS.
- Identify that TA will lead to different results on different markets despite a high grade of correlation.
- Explore and appraise the results and their significance.

The paper is divided into a theoretical and a practical part. To understand the results of the practical part, it is essential to comprehend the functionality and meaning of the discussed Indicators & Oscillators. These will be explained in detail in the appendix.

The first chapter of the paper will focus on an in-depth literature review. Therefore, I will investigate the share analysis and its premises, including a critical review of their strengths and weaknesses. In addition, I will look into detail if TA and its tools are theoretically suitable for the attempt to outperform the classic BHS. Furthermore, I will point out that financial markets are not perfectly efficient and the hypothesis of random walk can be seriously questioned. Evidence that these hypotheses can be falsified, would clearly underpin the theory that TA can lead to an increase in performance versus the classic BHS.

The third Chapter will illustrate and outline the methodology, while justifying and explicitly defining the test conditions and tested indices. Chapter four will explain the three intentionally selected Indicators and Oscillators and clarify the chosen set-up. Generally, users of TA can adjust their set-up to some extent. The aim is to visualise to the reader how Indicators & Oscillators can be combined to read and analyse a chart and how to adopt those in current trades. Moreover, it will be explained and commented on why the analysis of TA in this paper applies to the three chosen Stock Exchanges.

In Chapter five, the practical analysis will take place. Therefore, the three selected markets will be evaluated concerning their performance using the TA and the classic BHS.

The sixth Chapter will examine the findings of the former chapter in more detail. This section will shed light on, why one of both trading techniques (TA vs. BHS) has achieved a better performance within a certain period and on different markets. An empirical comparison will show which of the different instruments of TA has lead to the most reliable and profitable outcomes.

Chapter seven will briefly summarise the dissertation and critically answer the research question consistent with a discussion on the limitations of the conclusion. Furthermore, possible further future research will be alluded and give an outlook of trends and development potentialities of TA.

2. Share Analysis and its Premises

2.1 Classification and development

Share analysis can be fragmented into the Single Value Oriented Method and the Capital Market Oriented Method6. The former, more traditional approach, assesses shares unrelated to the portfolio. This method is divided into the FA and the market analysis.7. The market analysis can be classified into the TA and the Random Walk Theory (RWT).

The Capital Market Oriented Method is basically a modern technique, used by financial analysts to value banks, based on the their share prices. In contrast to the Single Value Oriented Method, this approach does not assess the shareholder value of financial institutions within the framework of FA, but derives indirectly the value of the underlying from external factors, which are based on the estimation of market participants.8 This method will not be addressed furthermore in this paper.

Share analysis (Cf.: Appendix 1) is as old as stock trading itself. The FA is the most widely used method to analyse shares.9 Benjamin Graham and David Dodd are considered to be the mentors of this method, which became the basis of the legendary Value Investing. Clearly, this ‘easy’ but time demanding approach aided some investors to make a fortune. Both have published a book in 1934, named Security Analysis with the first approach to assess the profitability and book value of companies for accurate investment decisions.

The roots of the TA first date back to 1884, as Charles H. Dow attempted to illustrate the economic development of the USA through self-created stock indices, named the Dow Jones Transportation Index and the Dow Jones Industrial Index.10

The simple idea behind this, was to project the performance of the underlying shares onto the total market and economy. Dow has set a milestone for the innumerable range and variety of indices all around the world. His findings were then published in the Wall Street Journal and summarized in the book The ABC of Stock Speculation by S.A. Nelson after his death.11 That coined the term Dow Theory, which is still regarded as the fundament of all TA, despite the sophisticated computer technology nowadays.

The RWT contrasts strongly with the TA on the level of the Single Value Oriented Method. It was first noticed by the British physicist Lord Rayleigh in 1880 and was firstly analysed and published in 1900 in Louis Bachelier’s PhD Thesis Théorie de la spéculation. 12 Naturally, this hypothesis does not enjoy great popularity among institutional investors, as ultimate evidence of this would question and imperil the existence of fund managers. Nevertheless, many academic authors still support this theory.

2.2 Dow Theory In Correspondence with TA

The Dow Theory is basically a cut together of articles, which were published for more than 27 years. Edwards et al (2007) call it fondly ‘the granddaddy of all technical analysis’.13 In essence, it bases on three principles:

2.2.1 The Averages Discount Everything (except ‘Acts of God’)

The market consists of buyers and sellers and the pricing is a result of the supply and demand of market participants. All indices and their underlying assets are a reflection of the behaviour of their buyers and sellers, which means that all information and determining factors are already discounted in their day-to-day fluctuations, as a result of adequate attitudes of the investors.14

‘All information’ in this context, refers to everything foreseeable, everything known and even natural disasters as they would immediately be appraised and discounted.15 There is no need to add or work out any indices like adjustments of Commodities-Indices, or fluctuations in the exchange rate as the market already considers these actions.16

That means that technicians only study the current price and attempt to predict its future value with the assistance of a chart and supportive Indicators/Oscillators. The Technical Analyst is aware that there must be reasons why the market is moving. However, the crucial point is that it is simply not important for him to know the reason of a price formation to forecast a future value change.17

2.2.2 The Three Trends

The definition is without doubt one of Dow’s main achievements during his lifetime. Devoid of a clear concept of a trend, TA would not exist in its present form. The ‘Trend is your friend’ or ‘Always trade with the trend’ are age-old stock market adages, which owe their rights to exist to their architect. Markets are located in one of three possible stages/trends.18 Most of the TA Instruments are based on a trend-following approach, that is, they perform best if the market is moving in either an uptrend or a downtrend.19 However, Loh (2007) suggests to combine trend indicators with confirming indicators to filter noisy signals and improve forecasting power.20 Wong et al (2003) go even further and state that the best mix of trend followers and counter-trend indicators is the combination of Moving Averages (MA’s) and the Relative Strength Index (RSI). They found evidence - at least on the Singapore Stock Exchange (SES) - that traders generated excessive returns using that configuration.21

An uptrend possesses a pattem of increasing price climaxes and minimums. A downtrend works in contrary; the supply outweighs the demand and therefore price climaxes and minimums are decreasing. A trendless/sideward market is the result of equally high/low climaxing/minimum prices.22 Dow found out that primary trends are divided in three stages. During the accumulation-phase, farsighted investors begin to buy, with the ulterior motive that the market assimilated all the bad news (Bull Market); the shares are marching from weak hands to strong hands. The second phase is determined by increasing prices, enhanced financial reports and the public getting on board; mostly much too late though.23 Edwards et al (2007) argue that “It is during this phase that the technical trader normally is able to reap his best harvest ofprofits.”24 In times where the sentiment is at its peak and the financial news is better than ever, the former investors start to distribute their shares to anticipate other market participants; vice versa, the shares hand off from strong hands to weak hands.25

Due to this phenomenon, the relatively new term ‘Behavioural Finance’ was coined after intense research by several academics. This science explores the psychology of investors and their herd instincts and doubts that markets are efficient. It outshined the glorified Homo economicus and demonstrated that investors act irrationally. Warren Buffet said once “The dumbest excuse to buy a stock is when the price is going up”.26 TA is the ideal tool to make Behavioural Finance usable for trading, which is basically the theoretical-psychological fundament of TA.27

Furthermore, Dow categorised trends in their duration. Basically, each investor could find thousands of trends, be it in one minute or in yearly-based charts. The amount of possible patterns in line with the modern computer chart programmes do not have set limits. Nevertheless, according to Dow, the market always swings in three trends; tide, wave and ripple.28

The primary trend lasts for at least a year, though usually for a longer period. Edwards et al (2007) suggest that, “(...) the primary is the only one of the three trends with which the true long-term investor is concerned.”29 In other words, it is this trend that defines Bull- and Bear-Markets. The secondary trend interrupts the primary trend and retraces at least one third, but can be up to two thirds of the former gains (Bull Market) or losses (Bear Market); a guideline says that the corrections mostly stop after 50 %. 30 This trend drags on from three weeks to several months. The minor trend, according to Dow, is meaningless and normally ranges in a time spread of less than three weeks being the only trend, which can be manipulated.31

Figure 2 in the appendix illustrates an example.

illustration not visible in this excerpt

In contemporary context of TA, especially related to Futures-Markets, many investors adjust their time horizon. This can be justified by the fact that the investing period has dramatically shortened due to the entry of new market participants like Scalp-, Swing-, Day- or Position-Traders32, but also Hedge funds. Murphy (1999) argues that nowadays a primary trend can be determined after 6 months.33

However, a trend is intact until the trend line is breached and a turnaround occurres. According to Dow’s Theory, this is significant if the trend-break arises at the Industrial Index as well as for the Transport Index. The signals do not have to appear simultaneously, although the shorter the time-period the more convincing the signal. A divergence, that is an absence of a confirmation, implies a continuation of the existing trend.34 From my point of view, this confirmation can be neglected in today’s context. Firstly, the quantity of members in indices has risen significantly. Secondly, the main indices are largely diversified and thirdly, markets feature in any case a certain grade of correlation. Moreover, Dow suggested to ‘ride the trend’ until it is intact and justified its behaviour with the physical law that an item tends to keep moving in a direction until external forces provoke an alternation of fluctuation.35

A further doctrine to examine a trend constitutes the increasing volume of trade. In a Bull Market, the volume should increase when prices rise and subsequently shrink as prices decline. On the other hand, in a Bear Market, vice versa.36 This is also valid for a secondary trend, although to a lesser degree. Stöttner (1989) argues that increasing prices combined with a low volume can be considered sceptical.37 Nowadays, as a result of the existence of Future Markets, the volume is determined by the volume itself, the price and the open interest38.39 Technical Analysts believe that the volume precedes the price, which means that a deprivation of pressure in a trend occurs, even before the reversal is taking place.40

2.2.3 The History repeats itself

Concerning TA and the study of market fluctuations, much is based on human psychology and has shown that past price performance41 repeats steadily.42 Murphy (1999) argues that patterns in charts reflect specific optimistic and pessimistic conducts of market participants and players react similarly in comparable situations, whereby particular price performances continuously occur.43 The human psyche does not tend to alter, therefore, the key of the comprehension of the future lies in the study of the past; respectively the future is just a repetition of the past.44

Cowles (1933 and 1944) and Spano (1954) provided evidence that trading according to Dow’s Theory clearly performed better versus randomly picked stocks.45

Hamilton, the most famous chartist at that time and strong protectionist of Dow’s conclusions, kept a track record from 1902-1929, showing that he was able to forecast bull and bear market moves, under the terms of Dow’s principles.46

2.2.4 Criticism of the Dow Theory

Like every theory in practise, Dow’s approach has its weaknesses and points of criticism. Would it not be like that, we would all roll in money and set for life. Murphy (1999) criticises that if the theory is strictly followed, an investor misses out on average 20-25 % of a fluctuation, before a signal is generated.47 Normally, a Dow-Theory-Signal occurs in the second phase of a trend (more or less at that point, when trend-following Indicators/Oscillators converge as well) and aims to catch the large central part of a Bull- or Bear Market.48 We have to bear in mind, that Dow’s intention was not the utopic fact to buy at the bottom-price and sell at the peak-price, but rather ride an intact trend as long as it does not breach. However, this is exactly the dilemma of being too late. “The Dow Theory is a sure fire system for depriving the investor of the first third and the last third of every Major Move, and sometimes there isn’t any middle third.”49 It is true, that the risk-reward ratio for Dow proponents sometimes refrain them from entering a position, particularly if a price draws nearer decisive support/resistance lines. However, this mental attitude is in sharp contrast to the fundamental concept that all the statistics and news are discounted.50 Nervousness, impatience and heightened emotions lead to a rebellion of active traders, in particular when the trading-range is tight, in line with the fact that Dow’s Theory does warn of changes in an Intermediate Trend.51

2.3 Random Walk Theory in Correspondence with BHS

The RWT, with the assumption that prices move randomly, stands in contradiction to TA and Behavioural Finance. This is based on the presumption that markets are strongly efficient and investors cannot gain abnormal returns through the study of historical charts and patterns. The author Burton G. Malkiel is deemed to be one of its main proponents and made the case that “(...) the market prices stocks so efficiently that a blindfolded chimpanzee throwing darts at the Wall Street Journal can select a portfolio that performs as well as those managed by the experts.”52

To comprehend the RWT we have to distinguish the basic understanding of the three efficient-market theories, which derive from the Efficient Market Hypothesis, launched by Miller and Fama in 1970.53 Both authors were firmly convinced that prices are always right, investors are always rational and well informed, and because of the fact that prices are random, then no one can predict market’s future direction.54 Murphy (1999) criticises this argument, saying that it is unrealistic that all price fluctuations are randomly based and he doubts the assumption that the BHS leads to better results than any effort to beat the market.55 He also asserts that many academics (such as Miller and Fama), were/are simply incapable revealing systematic structures in price fluctuations.56 Perridon and Steiner (1995) suggest that the price of a stock is determined by the average of all past news and future expectations, and the different assessment of values by market participants is based on the fact that the intrinsic value can never be defined exactly.57 Thus, stocks fluctuate randomly and even if these spreads are systemic, the attempt by market participants to benefit from those will lead to a neutralisation of the systemic influence.58

The weak form efficiency implicates that prices express all historical information and future prices cannot be predicted, thus TA is not able to generate abnormal returns.59

Run, autocorrelation and trading rules-tests, based on market-generated information like transactions by exchange specialists, block trades and odd-lot transactions, which support this form of efficiency, but have shown that they are not able to outperform the BHS.60 Although studies have demonstrated evidence to this form, it is crucial to mention that the more recent tests that were undertaken, the less evidence was found that capital markets are weak form efficient.61

The semi-strong form claims that prices amend quickly to the disclosure of all public information, including all non-market information like political news, economy news, stock splits, price -book value ratios, dividend-yield-ratios, price-to-earnings ratios and earnings and dividends announcements.62 The range of accomplished tests, based on event studies and return prediction studies, is numerous and leads to mixed results.63 Franks et al (1977) also assert that the capacity to absorb information, and thus the return to the equilibrium price, is strongly linked with the trade-frequency of a security and that potential mergers are anticipated in the price approximately three months before the pronouncement.64

The strong form efficiency assumes that prices reflect all information, no matter if it is publicly or private; therefore, no one would be able to make abnormal returns from trading.65 Would this be true, a coherent consequence is that markets are perfect, information would not be chargeable, but freely available to everybody at all times. Several tests were based on the performance of professional money managers, security analysts, stock exchanges specialists and corporate insiders and have generated mixed results; showing that the two last-mentioned groups do have access to unique information and using it to outperform ‘common’ investor’s performance.66

Nevertheless, Beechey et al (2000) indicate that actively managed funds (compromised with management costs) regularly underperform the market and investors cannot benefit from ‘hot tips’ due to the speed which new information incorporates into prices.67 Therefore, we maintain that the semi-strong and strong form is not in line with the beliefs of FA.

Hence, it is obvious that in case of validity of the Efficient Market Hypothesis, the attempt to forecast share prices would serve no purpose. Even the weak form stands in strong contradiction to TA, due to the fact, that the establishment of a trend is only based on past prices and if it were not possible suggesting future development, then TA would lose its right to exist. However, nowadays it is without controversy that prices (indices) fluctuate in trends. The semi-strong form embodies that even intensive research of securities, in terms of FA, would not lead to the possibility of making abnormal returns. In other words, fund managers and analysts work was futile and meaningless.

Fama, recognised as the father of modern finance68, found evidence in his PhD dissertation (1964) that chart reading and TA is an interesting pastime, but not of any real value for investors and the RWT cannot be challenged.69 The author is clear about the fact that perfectly efficient markets do not exist in reality. To measure efficiency, Fama (1970) implies:70

- Operational efficiency: Transaction costs need to be as low as possible.
- Pricing efficiency: Prices for securities are always regarded as fair, as they reflect all past as well as future information.
- Allocational efficiency: As a result of pricing efficiency, funds are made available to the most profitable businesses.

2.3.1 Criticism of the Efficient Market Hypothesis

Fama (1970) was blatantly amazed by his apparently inviolable hypothesis and even stated, that “(...) the evidence in support of the efficient markets model is extensive, and (somewhat uniquely in economics) contradictory evidence is sparse.”71 However, in the course of time, his findings were contested by several authors.

If trends do not exist, how is it possible to distinguish between Bull- and Bear-Markets? In addition, how can a Bull, Bear-, or Trendless-Market be justified, negating the existence of trends, as even these expressions implicate a trend. Leonard (2009) assert that “Using the S&P Index, and going back to January 1926, there have been 23 separate bear and bull markets. The average bear market lasted 11 months, while the average bull market lasted 32 months (...). ”72

Ball and Brown (1968) and Chan et al (1996) have illustrated, that in opposition to Fama’s theses, share prices do not only react to announcements immediately; in fact they keep on fluctuating for some time.73,74

Reinganum (1983) found evidence that small firms outperform large firms, no matter if the risk is greater for the bigger companies. 75 Roll (1988) affirmed Reinganum’s findings and justified the fact, saying that stocks of small caps are traded less frequently and as a result the systemic risk for those daily returns are biased downward.76 Keim (1983) explains this phenomenon by the fact, that many investors sell their shares in December to establish capital losses for income tax purposes and buy them back in January.77 This ‘Turn-of-the-year-effect’ is confirmed among other authors.

Nevertheless, Mishkin and Eakins (2009) among others, mistrust this theory, as institutional investors (like Pension Funds) are not affected by income taxes and could therefore generate abnormal returns, by buying disproportional amounts of shares in December.78 Anderson et al (2007) state that among authors, the January effect ranges from and up to; changes of investor psychology, risk premia, increased year-end liquidity, market microstructure effects, tax-loss selling and window dressing.79 Cheng and Singal (2004) note that window dressing cannot be blamed for this effect, as fund managers report quarterly and this pattern only exists in January.80 Keim (1983) also evidenced that almost half of the yearly performance of listed shares of small firms were generated in January and the first five trading days in January make 50 % of January’s total performance (especially the first trading day).81 Although, Schwert (2002) provided evidence that the January effect has deteriorated recently, he could not prove the ultimate absence of it. 82

A further point of criticism by academics is the frequent occurrence of market overreactions. De Bondt and Thaler (1987) demonstrated pricing errors, which are not consistent with Fama’s Efficient Market Hypothesis, stating that “(...) as a consequence of investor overreaction to earnings, stock prices may also temporarily depart from their underlying fundamental values.”83 Shiller (1981) attested their findings and justified this incident by saying the cause was because of excessive volatility. This underlines the fact that prices cannot only be driven by fundamental date, like new information about future dividends.

2.4 Fundamental Analysis

FA is much more widespread than TA, with the basic assumption that in due course, prices of securities are fluctuating around its intrinsic value.84 Therefore, a rational investor following FA would only buy the underlying if its price is below its ‘real value’ and vice versa. To determine the intrinsic value, investors can choose between the Asset Value Method and the Present Value Method. Both methods involve numerous parameters such as; an estimation of a firm’s sales levels, corporate tax rates, depreciation policies and the sources and cost of its capital required. These have to be discounted to get net present values.85 Due to the limitation of this paper, I will not go into further detail about the assessment of intrinsic values.

2.5 Technical Analysis versus Fundamental Analysis

Most traders/investors classify themselves either as a technician or a fundamentalist, although in reality there are a plenty of overlaps.86 Taylor and Allen (1992) determine that 90 % of foreign exchange market dealers combine both methods.87 Unlike the prejudice, proponents of TA and FA completely ignore the facts of the ‘counterparty’, although both do understand the elementary aims of the differing approaches. Murhpy (1999) hits the bull’s eye, saying that technicians do not controvert the fundamental background of price movements, but assume that fluctuations, due to rational and irrational decisions of market participants, tend to precede fundamental data.88

Because FA is an active method, as a result of different estimated dividend yields and discount factors, this approach is not free of emotions and mistrust. Welcker and Audörsch (1994) assert that if FA would be the only trading method, share prices would always range around the estimation of the intrinsic value by the majority and the confidence of an investor in his (deviated) calculated value must be enormous if he trusts himself more than the majority.89 This leads to the fact that FA proponents not only possibly suffer losses from an own wrong estimation of the intrinsic value, but also from a right valuation, where the price moves in the adverse selection as a result of a misestimation of other market participants.90 In contrast, TA acts passively and only responds using its instruments and determined premises of price fluctuations to the outcome of expectations.

These findings confirm the issue of time-problems. Apart from the difficulties of the provision of information and data, an adequate calculation is very time demanding and requires a basic understanding of the interaction of altering influencing variables. Even though investors acquire appropriate data, these are out of date for at least one to three months.91 Therefore, most private investors hark back to ‘professional” recommendations of analysts which are published in the form of target prices. These prices differ substantially from each analyst. However, TA is able to provide an analysis all the time and very rapidly. Databases, that are mostly freely available, render daily open-, peak-, low-, and closing-prices, which can be adapted to the desired trading period. This easy and quick trading method is responsible for the massive growth of computer-based technical trading approaches used by fund managers.92 It can be criticised that TA is reliant on past data and sometimes, in case of new issued shares or indices, the attempt to gain excess returns through TA will not be successful. This can be justified by the fact that the absence of trends and patterns stands in conflict with TA and Indicators/Oscillators refuse their services.

Protectionists of FA also criticise TA and its subgroup Chartism because of its deficiency of doubtless and explicit determinable analysis criteria. Due to the fact that Chartism is extremely subjective and based on a framework of patterns and trends, it is impossible to standardise and generalise the observations.93

[...]


1 Cf.: Murphy, J.J. (1999), Technical Analysis of Financial Markets, New York Institute of Finance, p. 30.

2 Cf.: Stöttner, R. (1989), Grundlagen der markttechnischen Analyse, R. Oldenburg Verlag München, p.1.

3 Cf.: Murphy, J.J. (1999), Technical Analysis of Financial Markets, New York Institute of Finance, p. 38.

4 Cf.: Covel, M.W. (2006), Trend Following, Prentice Hall, p. 7.

5 Cf.: Granger, C.W.J. and O. Morgenstern, (1970), Predictability of Stock Market Prices, Heath Lexington Books, p. 88

6 The most common Capital Market Oriented Methods are Price/Earning-Ratio, Price/Book-Ratio, Price/Earning-Growth-Ratio, Price/Cash-Flow-Ratio and EV/EBITDA-Ratio.

7 Cf.: Steiner, M. and C. Bruns (2002), Wertpapiermanagement, Handelsblatt Bücher, Schäffer-Poeschel, p. 227.

8 Cf.: Holzamer, M. (2004), Shareholder-Value-Management von Banken, Munich Business School, Finance Research Series, p. 68.

9 Cf.: Thomsett, M.C. (1998), Mastering Fundamental Analysis, Kaplan Business, p. 2.

10 Cf.: Steiner, M. and C. Bruns (2002), Wertpapiermanagement, Handelsblatt Bücher, Schäffer-Poeschel, p. 108.

11 Cf.: Murphy, J.J. (1999), Technical Analysis of Financial Markets, New York Institute of Finance, p. 42.

12 Cf.: http://www.e-m-h.org/historv.html. 5.6.2010.

13 Cf.: Edwards, R.D., J. Magee and W.H.C. Basetti (2007), Technical Analysis of Stock Trends, 9th Edition, CRC Press, p. 13

14 Cf.: Ibidem, p. 15.

15 Cf.: Edwards, R.D., J. Magee and W.H.C. Basetti (2007), Technical Analysis of Stock Trends, 9th Edition, CRC Press, p. 15.

16 Cf.: Hamilton, W.P. (2006), The Stock Market Barometer: A Study of Its Forecast Value Based on Charles H. Dow’s Theory, Cosimo Classics, pp. 40-41.

17 Cf.: Murphy, J.J. (1999), Technical Analysis of Financial Markets, New York Institute of Finance, p. 23.

18 Cf.: Edwards, R.D., J. Magee and W.H.C. Basetti (2007), Technical Analysis of Stock Trends, 9th Edition, CRC Press, p. 15.

19 Cf.: Murphy, J.J. (1999), Technical Analysis of Financial Markets, New York Institute of Finance, p. 65.

20 Cf.: Loh, E.Y.L. (2007), An alternative test for weak form efficiency based on technical analysis, Applied Financial Economics, No. 17, Routledge Taylor & Francis Group, p. 1004.

21 Cf.: Wong, W.K., M. Menzur and Chews, B.K. (2003), How rewarding is technical analysis? Evidence from the Singapore stock market, Routledge Taylor & Francis Group, p. 543.

22 C-f.: Murphy, J.J. (1999), Technical Analysis of Financial Markets, New York Institute of Finance, p. 63.

23 Cf.: Ibidem, p. 44.

24 Cf.: Edwards, R.D., J. Magee and W.H.C. Basetti (2007), Technical Analysis of Stock Trends, 9th Edition, CRC Press, p. 18.

25 Cf.: Ibidem, p 18.

26 Cf.: http://www.zdiaz.com/2009/07/follow-warren-buffett%E2%80%99s-signal-on-when-to-buy- stocks-part-3/, 7.6.2010.

27 Cf.: http://www.everling.de/?p=820, 7.6.2010.

28 Cf.: Edwards, R.D., J. Magee and W.H.C. Basetti (2007), Technical Analysis of Stock Trends, 9th Edition, CRC Press, p. 15.

29 Cf.: Edwards, R.D., J. Magee and W.H.C. Basetti (2007), Technical Analysis of Stock Trends, 9th Edition, CRC Press, p. 15.

30 Cf.: Ibidem, p. 16.

31 Cf.: Ibidem, p. 16.

32 Scalp- and Swing-Traders attempt to gain from tiny fluctuations, while Day- and Position-Traders hold a position for one or for several days.

33 Cf.: Murphy, J.J. (1999), Technical Analysis of Financial Markets, New York Institute of Finance, p. 67.

34 Cf.: Welcker, J. and J. Audörsch (1994), Technische Aktienanalyse, 7. Auflage, Zürich, p. 28.

35 Cf.: Magee, J. (1994), Analyzing Bar Charts for Profit, Dearborn Financial Publications, p. 24.

36 Cf.: Edwards, R.D., J. Magee and W.H.C. Basetti (2007), Technical Analysis of Stock Trends, 9th Edition, CRC Press, p. 21.

37 Cf.: Stöttner, R. (1989), Grundlagen der markttechnischen Analyse, R. Oldenburg Verlag München,

p.18.

38 Open Interest relates to the total amount of outstanding or still not liquidated Futures Contracts at the end of the trading day regarding to a specific index.

39 Cf.: Murphy, J.J. (1999), Technical Analysis of Financial Markets, New York Institute of Finance, p.

40 Cf.: Ibidem, p. 172.

41 Cf.: Ibidem, p. 24.

42 Cf.: Ibidem, p. 24

43 Cf.: Ibidem, p. 24

44 Cf.: Ibidem, p. 24

45 Cf.: Granger, C.W.J. and O. Morgenstern, (1970), Predictability of Stock Market Prices, Heath Lexington Books, p. 88

46 Cf.: Brown, S.J., W.N. Goetzmann and A. Kumar (1998), The Dow Theory: William Peter Hamilton’s Track Record Reconsidered, The Journal of Finance, Vol. LIII, No. 4, p. 1330.

47 Cf.: Murphy, J.J. (1999), Technical Analysis of Financial Markets, New York Institute of Finance, p. 48.

48 Cf.: Ibidem, p. 48.

49 Cf.: Edwards, R.D., J. Magee and W.H.C. Basetti (2007), Technical Analysis of Stock Trends, 9th Edition, CRC Press, p. 41.

50 Cf.: Ibidem, p. 43.

51 Cf.: Ibidem, p. 44.

52 Cf.: Malkiel, B.G. (2007), A Random Walk Down Wall Street, W.W. Norton & Company Ltd, p. 17.

53 Cf.: Covel, M.W. (2006), Trend Following, Prentice Hall, p. 125.

54 Cf.: Ibidem, p. 125.

55 Cf.: Murphy, J.J. (1999), Technical Analysis of Financial Markets, New York Institute of Finance, p. 37.

56 Cf.: Ibidem, p. 37.

57 Cf.: Perridon, L. and M. Steiner (1995), Finanzwirtschaft der Unternehmen, 8. Auflage, Franz Vahlen München, p. 193.

58 Cf.: Ibidem, p. 193.

59 Cf.: Watson, D. and A. Head (2004), Corporate Finance, 3rd Edition, Prentice Hall, p. 35.

60 Cf.: Brown, K.C. and F.K. Reilly (2009), Investment Analysis Portfolio Management, 9th Edition, Thomson South Western, p. 157.

61 Cf.: Beechey, M., D. Gruen and J. Vickery (2000), The efficient market hypothesis: a survey, Research Discussion Paper, Economic Research Department, Reserve Bank of Australia, p. 17.

62 Cf.: Brown, K.C. and F.K. Reilly (2009), Investment Analysis Portfolio Management, 9th Edition, Thomson South Western, p. 152.

63 Cf.: Ibidem, p. 166.

64 Cf.: Franks, J., J. Broyles and M. Hecht (1977), An industry study of the profitability of mergers in the United Kingdom, Journal of Finance, Vol. 32. p. 1525.

65 Cf.: Watson, D. and A. Head (2004), Corporate Finance, 3rd Edition, Prentice Hall, p. 35.

66 Cf.: Brown, K.C. and F.K. Reilly (2009), Investment Analysis Portfolio Management, 9th Edition, Thomson South Western, p. 169.

67 Cf.: Beechey, M., D. Gruen and J. Vickery (2000), The efficient market hypothesis: a survey, Research Discussion Paper, Economic Research Department, Reserve Bank of Australia, p. 16.

68 Cf.: http://www.chicagobooth.edu/facultv/bio.aspx7person id=12824813568, 16.6.2010.

69 Cf.: Fama, E.F. (1965), The Behaviour of Stock-Market-Prices, Journal of Business, Vol. 38 Issue 1, p. 34.

70 Cf.: Watson, D. and A. Head (2004), Corporate Finance, 3rd Edition, Prentice Hall, p. 34.

71 Cf.: Fama, E.F. (1970), Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance, Vol. 25 Issue 2, p. 416.

72 Cf.: Leonard, S.A. (2009), U.S. Equity Returns After Stock Market Crashes, Journal of Financial Planning, Vol. 22 Issue 10, p. 84.

73 Cf.: Ball, R. and P. Brown (1968), An Empirical Evaluation of Accounting Income Numbers, Journal of Accounting Research 6, p. 177.

74 Cf.: Chan, L.K.C., N. Jegadeesh and Lakonishok, J. (1996), Momentum Strategies, The Journal of Finance, Vol. LI, No. 5, p. 1709.

75 Cf.: Reinganum, M.R. (1983), The Anomalous Stock Market Behaviour of Small Firms in January: Empirical Tests of Tax Loss Selling Effects, Journal of Financial Economics 12, p. 89.

76 Cf.: Roll, R. (1981), A possible explanation of the small firm effect, Journal of Finance 36, p. 879.

77 Cf.: Keim, D. (1983), Size related anomalies and stock return seasonality: Further empirical evidence, Journal of Finance Economics 12, p. 13.

78 Cf.: Mishkin, F.S. and S.G. Eakins (2009), Financial Markets and Institutions, 6th Editon, Prentice Hall, p. 136.

79 Cf.: Anderson, L.R., J.R. Gerlach and F.J. DiTraglia (2007), Yes, Wall Street, There Is A January Effect! Evidence from Laboratory Auctions, The Journal of Behavioural Finance, Vol. 8, No. 1, p. 1.

80 Cf.: Cheng, H. and V. Singal (2004), All Things Considered, Taxes Drive the January Effect, Journal of Financial Research, Fall 27,3, p. 351.

81 Cf.: Keim, D. (1983), Size related anomalies and stock return seasonality: Further empirical evidence, Journal of Finance Economics 12, p. 13.

82 Cf.: Schwert, G.W. (2002), Anomalies and Market Efficiency, The Bradley Policy Research Center, Financial Research and Policy, Working Papter No. FR 02-13, University of Rochester, p. 47.

83 Cf.: De Bondt, W.F.M and R.H. Thaler (1987), Further Evidence On Investor Overreaction and Stock Market Seasonality, The Journal of Finance, Vol. XLII, No. 3, p. 557.

84 Cf.: Steiner, M. and C. Bruns (2002), Wertpapiermanagement, Handelsblatt Bücher, Schäffer-Poeschel, p. 25.

85 Cf. Malkiel, B.G. (2007), A Random Walk Down Wall Street, W.W. Norton & Company Ltd, p. 110.

86 Cf.: Murphy, J.J. (1999), Technical Analysis of Financial Markets, New York Institute of Finance, p. 25.

87 Cf.: Taylor, M and H. Allen (1992), The use of technical analysis in the foreign exchange market, Journal of International Money and Finance 11, p. 304.

88 Cf.: Murphy, J.J. (1999), Technical Analysis of Financial Markets, New York Institute of Finance, p. 25.

89 Cf.: Welcker, J. and J. Audörsch (1994), Technische Aktienanalyse, 7. Auflage, Zürich, p. 6.

90 Cf.: Ibidem, p. 6.

91 Cf.: Ibidem, p. 6.

92 Cf.: Murphy, J.J. (1999), Technical Analysis of Financial Markets, New York Institute of Finance, p. 35.

93 Cf.: Stöttner, R. (1989), Grundlagen der markttechnischen Analyse, R. Oldenburg Verlag München, p.11.

Details

Seiten
80
Jahr
2010
ISBN (eBook)
9783640797509
ISBN (Buch)
9783640797691
Dateigröße
1.2 MB
Sprache
Englisch
Katalognummer
v163846
Institution / Hochschule
Sheffield Hallam University – Sheffield Business School
Note
1
Schlagworte
Technische Analyse Random Walk Theorie Indikatoren Dow Theorie Oszillatoren

Autor

Teilen

Zurück

Titel: Technical Analysis - Myth or Magic?