Finance Technical Price - Free Essay Example

The subject matter of this examination is based upon the performance of financial markets as its base. In analyzing the logical starting point for the examination of whether behavioural finance, and technical analysis affects share price prediction, the nuances, in a broad sense, of how financial markets work serves as a foundation. As an organized institutional structure for created and exchanging financial assets (Free Dictionary, 2007), financial, and or capital markets trade in stocks, bonds, currency as well as various commodities (Amadeo, 2007) capital markets represent the place where stock shares meet the public and institutional as well as financial investors. In simplistic terms, stock prices are a reflection of the earnings potential of a company over the long-term (United States Department of State, 2006). In a more astute explanation, stock prices are a function of expectations of future real dividend growth, future real interest rates, and future excess returns (Blake and Wohar, 2006). Changes in the future expectations, or excess returns represent a function of revisions in expectations about future real dividend growth, future real interest rates, and future excess returns (Blake and Wohar, 2006). Stock prices are also subject to broader determinants as represented by the state of a countrys economy, external international events, the strength, and or status of other global economies that are interlinked with the host country, the state of political affairs on an international basis, the availability, and supply of raw materials (most noteably oil, gas, steel, and agricultural products), the state of consumer and business demand, key economic indices relating to home building, business spending and expansion, new plant and equipment, interest rates, governmental monetary policies, inflation, and the allocation of capital resources on a national plane (Baumol, 1965, p. 2). In equating the approaches to this examination it is important to consider that the circumstances surrounding as well as inherent in the historical, recent actions, and activities of a companys stock are factors that are behind, and a part of the triggering of interest, and or lack of interest in a companys s tock. The potential for upside gains, in the near and or long term are what triggers interest, led by the possibilities of increased earnings, market share, new product introduction, that is in keeping with earnings, and competitive conditions. These variables were considerations that were a part of this examination to reach a determination as to whether behavioural finance, and technical analysis affect stock price prediction. The following, shall seek to present those findings. Chapter 1 Introduction An illustration of how stock prices can be predicated is provided by the recent stock performances of Apple, Inc., formerly known as Apple Computer. The darling of the computer industry during the 1980s fell upon stagnate times during the 1990s as IBM compatible PCs dominated the market, and eroded Apples position in the market through pricing as well as having more software options as a result of the companys closed architecture system (Finneran, 2002). Apple introduced new computer designs, faster processing times, and innovated in other ways to attempt to increase market share and earnings, but the stock price remained basically flat, as shown on the following chart (Yahoo Finance, 2007). Chart 1- Apple Inc. (Yahoo Finance, 2007) A new line of desktops, along with a redesigned portable line, and new operating software boosted the companys sales in early 2000, but, after the initial market introductions, things again reverted to being flat in terms of the stock price (Briggs, 2000). The long stagnate sales history of the company during the 1990s amid declining, and flat revenues had analysts questioning the direction of the company as it was unable to break out of its loyal user group of buyers and crack open new ground in the larger PC dominated market (Kleinbard, 2000). The announcement by the company that fourth quarter earnings, along with revenues would fall below expectations in 2000 saw a raft of investors pull out of the companys stock as it fell to approximately half of its high during that year (Kleinbard, 2000). The news in September of 2000 coincides with the drop in the stocks price as shown on the Chart. Apples CEO, Steve Jobs, in a press release stated Weve clearly hit a speed bump, which will result in our earning, before investment gains, approximately $110 million rather than the expected $165 million for the September quarter (Kleinbard, 2000). Industry analysts downgraded the company, citing that the revenue shortfall was a company-specific problem, not a sign of a slump for the entire personal computer industry (Kleinbard, 2000). The foregoing represents an example of the earnings forecast facet of a company as an important determinant of stock prices. Evidence pointing in the other direction is provided by the Apple, Inc. example as shown in the following. On 28 April 2003 Apple introduced its iTunes Music Store, a company innovation that emanated out of the basic creativity, and genre theme of the companys computers that were long known for their use in the music, and movie editing businesses (slashdot, 2003). It also unveiled its new product the iPod, a device specifically developed to play music in a highly compact, and thin casing that was light, and would fit inside a shirt pocket, or on ones sleeve without any noticeable weight (slashdot, 2003). The timing came on the heels of the coming explosion in downloadable music that had been brewing in the industry. The introduction proved to be a resounding success, as did the companys new Apple Stores (BusinessWeek, 2002). The move towards a more diversified earning platform also helped to rekindle interest in the companys computer line as a result of broader market exposure to the young generation. The earnings shot up, and so did the companys stock price. Apple followed with successive new iPod introductions, and January of 2007 it introduced the iPhone (Apple, 2007). The earnings forecasts for sales of the new product were estimated to be 1 million over a three-month period after introduction, and the company sold 270,000 in the first 30 hours (engadget, 2007). In less than 1 month Apple sold over 1 million of these phones since the launch on the 29th of June, and the earnings forecasts, along with the stock took off again (gadgettastic, 2007). The foregoing example of Apple has been illustrated to show how stock prices move, and the reasons behind these movements. It shall also serve as an illustration in other examples, although other stock price references will also be utilised. The sphere of capital markets represents the environment in which the context of this examination takes place. It is the locale where the dynamics of the economy of a country, global conditions, national policies, and the performance of companies converge. Stock price prediction is essentially forecasting, which represents seeing in advance, or anticipating, future trends (Haney, 1931, p. 1). Baumol (1965, p. 35) advises that the price of a companys shares should, ideally, measure the value of the firm whose ownership they represent.. He sides with the contention that most analysts would doubtless agree that the price of a security should be determined, ultimately, by the prospective earnings of the company adding that it is not essentially clear how closely the value of future earnings and share prices correspond in practice (Baumol, 1965, p. 35). Coyne and Witter (2002) tell us when buyers are more anxious to buy than sellers are to sell, share prices rise and that they fall when the reverse happens. In further explaining the nuances involved in stock prices, and trading, Coyne and Witter (2002) advise; When buyers collectively want large amounts of a stock, they have to keep surrendering to successive layers of sellers up the offer curve. Sellers who unload large numbers of shares move along the curve in the opposite direction Large institutional investors as well as large individual private investors can influence stock pricing, however Coyne and Witter (2002) indicate that these buy and sell actions are generally short term occurrences as demonstrated by the following when theBass family of Texas sold its stake in Disney, September 2001, in response to a margin call, Disneys stock fell by 8 percent. They state typically, short-term changes in a companys stock price arent the result of a single big trade (Coyne and Witter,2002). To better understand the significance of the preceding, Coyne and Witter (2002) elaborate further: For the SO companies whose quarterly stock price variations we studied, we consistently found that the majority of unique changes in each companys stock price resulted from the net purchases and sales of the stock by a limited number of investors who traded in large quantities. (By unique changes, we mean those occurring relative to the rest of the market. In other words, they do not include price bumps or falls that coincided with the overall movements of the market or the sector.) Although the number of crucial investors in a company ranged from as few as 30 to (more typically) as many as 100, in each case this set of actors had a dramatic impact on share prices. In the companies we studied, we could attribute from 60 to 80 percent of all unique changes, quarter by quarter, to the net trading imbalances of these investors. From the market efficiency perspective, the tying of stock prices to earnings serves as a key motivator for management to generate performance. Baumol (1965, p. 36) helps to illuminate this by stating if security prices were divorced from earnings potential, the stock market could not be expected to serve as an effective disciplinary force capable of pressing management to maintain the efficiency of company operations. The preceding leads to a discussion of the efficient market hypothesis. Toporowski (2000, p. 39) state that Orthodox finance theory, from Walras, through Miller and Modigliani, to McKinnon and Shaw and efficient market theories, are based on optimizing equilibrium. The efficient market hypothesis represents a theory of investment that states that it is impossible to beat the market because stock market efficiency causes existing share prices to always incorporate and reflect all relevant information. (investopedia, 2007a). The theory states that stocks always trade at their fair value on stock exchanges, and thus it is impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices, thus making it virtually impossible to out guess the market overall (investopedia, 2007a). The efficient market theory is generally credited to being developed by Fama who published The Behavior of Stock Prices, in The Journal of Business in 1963, and later excerpted in The Financial Analysts Journal and The Institutional Investor (Hagstrom, 1999, p. 27). Fama stated Stock prices are not predictable because the market is too efficient. In an efficient market, as information becomes available, a great many smart people (Fama called them rational profit maximizers) aggressively apply that information in a way that causes prices to adjust instantaneously, before anyone can profit (Hagstrom, 1999, p. 27). He added Predictions about the future therefore have no place in an efficient market, because the share prices adjust too quickly (Hagstrom, 1999, p. 27-28). Toporowski (2000, p. 19) provides further insight into this important facet of stock prices, in stating: The buying and selling of existing stock is important in ensuring that quoted firms remain efficient and seek to maximize their profits The stock market encourages efficiency and profitability of firms and thereby benefits the economy in general A well-developed stock market with a high degree of liquidity therefore helps to both increase the volume of new issues and their costs The performance of the stock market also has both a direct wealth effect on expenditure decisions and also an important confidence influence on economic agents. As the real value of shares rise, the wealth and usually the confidence of economic agents are raised, this encourages greater expenditure and investment that can reduce unemployment and contributes to economic growth. If the stock market is performing poorly this tends to lower agents wealth and confidence, and generally has an adverse impact on the economy. The broad ramifications indicated in the preceding are germane to this examination in that it introduces the external spheres that are at play in stock markets, and also serves to provide a look at investor facets that are inherent in behavioral finance and technical analysis. The efficient markets hypothesis has its supportors as well as detractors. Dietl (1998, p. 2) in equating the allocation of capital, suggests that capital markets are not efficient under all circumstances, which, based upon all the variables involved, is not hard to fathom. He makes his point in referring to Grossman and Stiglitz (1976, pp. 246-253) where he points out that (Dietl, 1998, p. 37): capital markets can only be informationally efficient in a strong sense with respect to knowledge generated by costless information. If stock prices already convey all available knowledge, market participants cannot obtain positive returns from investing into information activities. Accordingly, information activities will be limited to the acquisition of costless information and stock prices will remain incompletely arbitraged with respect to knowledge based on costly information. The foregoing economic, stock, capital market and related theories are important underpinnings in the understanding of behavioral finance and technical analysis, both of which are explored in the following segments. Chapter 2 Literature Review 2.0 Behavioural Finance The question, and thus subsequent importance of stock market efficiency is brought up by Schleifer (2000, p. 2) in his book Inefficient Markets: An Introduction to Behavioral Finance. He states that inefficient markets are also viewed as having validity in that: economic theory does not lead us to expect financial markets to be efficient. Rather, systematic and significant deviations from efficiency are expected to persist for long periods of time. He points to the fact that Empirically, behavioral finance both explains the evidence that appears anomalous from the efficient markets perspective, and generates new predictions that have been confirmed in the data (Schleifer, 2000, p. 2). In delving into his examination of behavioral finance Schleifer (2000, p. 2) begins by noting that the theoretical foundations of efficient capital markets is an important facet in the understanding of behavioural finance (Schleifer, 2000, p. 2). This same underlying foundation has also been brought forth by Mitchell and Utkus in their book Pension Design and Structure: New Lessons from Behavioral Finance (2004, p. 14). They assert an efficient capital market will compensate investors only for the aggregate market risk they endure, so there will be no single-s tock investments on the efficient frontier (Mitchell and Utkus, 2004, p. 14). Schleifer (2000, p. 13) states that The central argument of behavioral finance states that, in contrast to the efficient markets theory, real-world arbitrage is risky and therefore limited. Within the frontier of finance, and in terms of how people make decisions related to the preceding, Mitchell and Utkus (2004, p. 3) inform us that such entails a combination of psychology, finance, economics, and as they state, even sociology . The preceding is based upon the recognition that people seek to maximise their self interest , however the foregoing is often influenced by what Michell and Utkus (2004, p. 3) term as bounded reality, whereby the variables inherent in reaching a decision entails factors, and considerations that might be too involved as well as complex for them to engage in (Mitchell and Utkus, 2004, p. 3). The fact is, one cannot eliminate the truth that individuals employ their emotions, historical understandings and what Statman (2005) calls cognitive biases in their investment decisions. In explaining behavioral finance, Statman (2005) opens by stating that it is a framework that augments some parts of standard finance and replaces other parts. He cont inues that it, behavioral finance, represents investor behavior as well as those of managers and describes the outcomes of interactions between these two in financial and capital markets (Statman, 2005). In explaining his point, he refers to modern portfolio theory, which is comprised of four foundational blocks (Statman, 2005): rational investors, efficient markets, investors who construct their portfolios in accordance with the rules of Mean Variance Portfolio Theory, and that the expected returns represent a function of risk Statman (2005) advises that the foundation of modern portfolio theory can be found in the Modigliani and Miller hypothesis. Ramrattan and Szenberg (2004) addthe market value of any firm is independent of its capital structure, and is given by capitalizing its expected return at the rate appropriate to its class. The Miller and Modigliani hypothesis goes on to add an investor can buy and sell stocks and bonds in such a way as to exchange one income stream for another the value of the overpriced shares will fall and that of the under priced shares will rise, thereby tending to eliminate the discrepancy between the market value of the firm (Ramrattan and Szenberg, 2004). In fact, there are two core propositions in the hypothesis, which are that the type of instrument used to finance an investment is irrelevant to the question of whether or not the investment is worth while and rate of return on common stock in companies whose capital structure includes some debt (Ramrattan and Szenb erg, 2004). In continuing with his explanation of behavioral finance, Statman (2005) also refers to Harry Markowitzs mean variance portfolio theory. It, the mean variance portfolio theory, showed how investors should pick assets if they care only about the mean and varianceor equivalently the mean and standard deviationof portfolio returns over a single period (Campbell and Viceira, 2002, p. 2). In continuing his discussion on behavioral finance, Statman (2005) explains that William Sharpe developed his capital asset pricing theory as an adaptation of the mean variance portfolio theory in describing investor behaviour. The capital asset pricing model represents a formula that describes the relationship between risk and expected return, which is usually employed when higher risk securities are being considered (investopedia, 2007b). The formula for the preceding is represented by (investopedia, 2007b): Figure 1 CAPM (investopedia, 2007b) The general idea behind CAPM is as follows (Hagstrom, 1999, p. 26): stocks carry two distinct risks. One risk is simply the risk of being in the market, which Sharpe called systemic risk. Systemic risk is beta and it cannot be diversified away. The second type, called unsystemic risk, is the risk specific to a companys economic position. Unlike systemic risk, unsystemic risk can be diversified away by simply adding different stocks to the portfolio. In equating the results, and effect of Sharpes postulation, Peter Bernstein, the editor of the Journal of Portfolio Management, arrived at the inescapable conclusion that the efficient portfolio is the stock market itself. He went on to add that No other portfolio with equal risk can offer a higher expected return; no other portfolio with equal expected return will be less risky (Hagstrom, 1999, p. 26). Statman (2005) tells us that behavioral finance represents an alternative concept for each of the foundation blocks of standard finance. Under the theorem, behavioral finance, investors are considered as normal as opposed to being rational, and markets are seen as not being efficient despite the fact that they are hard to beat (Statman, 2005). Behavioural portfolio theory represents the directive these investors follow (Statman, 2005). Behavioural portfolio theory is consistent with a reluctance to hold margined, and short positions, an inverse relation between the bond/stock ration and portfolio riskiness , hone bias, and the utilisation of such terms as growth, and income (Shefrin and Statman, 1997). Investors utilising the behavioural portfolio theory model are categorised as having non-standard preferences that are termed as being modeled using experimental evidence from psychology (Campbell and Viceira, 2002, p. 9). It, behavioural finance, is regarded as a promising area in that it is attempting to explain the varied types of investor behaviour (Campbell and Viceira, 2002, p. 9). Behavioural portfolio theory links how portfolios are designed, and how they are constructed (Shefrin and Statman, 1997). Under the Behavioural portfolio theory, investors approach the building of their portfolios in terms of asset pyramids via a layer by layer approach, whereby the layers are associated with particular goals, and particular attitudes towards risk (Shefrin and Statman, 1997). They, Shefrin and Statman (1997), advise pension funds typically utilise this methodology, beginning with asset allocation that is structured to define the layers, or classes. In Pension Design and Structure: New Lessons from Behavioral Finance, by Mitchell and Utkus (2004, p. 67) they refer to Shefrin and Statman (1997) in stating that the behavioural portfolio theory is an alternative to the mean-variance portfolio theory, a theory that is founded on the expected utility theory. The following explains the concept of expected utility (Mitchell and Utkus, 2004, p. 67): the principles of which are illustrated in the observation that most people prefer a sure $1 over a gamble with the same expected value, such as one that offers a 50 percent chance to win $2 and a 50 percent chance to win nothing. Expected utility theory says that the utility of money increases at a lower rate than its amount The core facet of the behavioural portfolio theory is represented by the fact the portfolio is not viewed as a whole, which is the technique utilised by the mean-variance theory, but as layers that are distinct via an asset pyramid construction (Mitchell and Utkus, 20004, p. 71). The individual layers represent association with goals as set by the investor, along with varying attitudes and or approaches to risk (Mitchell and Utkus, 20004, p. 71). These layers might represent what Mitchell and Utkus (2004, p. 71) refer to as downside protection , which is designed to protect investors from being poor, and another layer might represent upside potential that is designed to give investors a chance at being rich. In commenting on the investor reaction (in general) to the aforementioned, Mitchell and Utkus (2004, p. 71) state that investor attitude regarding the downside protection is negative, while as would be expected their reaction to the upside potential is positive. The behaviou r theory sees the asset pyramid utilisation as being consistent in terms of investment advice (Mitchell and Utkus, 2004, p. 71). In further understanding the foundation of the behavioural theory of investment (finance), the mean-variance theory points to investors having a singular attitude toward risk as opposed to attitudes toward risk as represented by the behavioural theory that utilises the layered approach (Mitchell and Utkus, 2004, p. 73). Key to the preceding is the understanding of the following relatively complex, yet summary statement of these two forms that set them into context (Mitchell and Utkus, 2004, p. 73): Mean-variance investors construct the mean-variance efficient frontier by identifying portfolios with highest level of expected wealth for each level of standard deviation. The counterpart in behavioral portfolio theory to standard deviation in mean-variance portfolio theory is the probability that wealth might fall below the aspiration level. Behavioral investors construct the behavioral efficient frontier by identifying the portfolios with the highest level of expected wealth for each probability that wealth would fall below the aspiration level. In all investing, downside risk represents the factor to be minimised. Under the behavioral finance theory the central argument states that in contrast to the efficient markets theory, real-world arbitrage is risky and therefore limited (Shleifer, 2000, p. 13). For the sake of clarity, arbitrage represents the simultaneous purchase and sale of an asset in order to profit from a difference in the price this usually takes place on different exchanges or marketplaces Investopedia, 2007c). Shleifer (2000, p. 23) tells us that on the basic level that behavioural finance represents the study of human fallibility in competitive markets. They also point out that its intent is not to put forth, and or deal(s) with the observation that people are stupid, confused, or biased, instead placing the aforementioned into competitive financial markets where the arbitrageurs are rational (Shleifer, 2000, p. 23-24). Behavioural finance as in competitive markets with respect to human fallibility, is based upon two key foundations Shleifer (2000, p. 24): Limited arbitrage represents the first foundation, whereby this function, is less than perfect in reality. The preceding is based upon the fact that in many cases securities do not have either perfect, or good substitutes. The foregoing makes the process of arbitrage therefore fundamentally risky, even in those instances where there are good substitutes. In addition, the opportunities are limited as the prices to not tend to converge in terms of fundamental values on an instantaneous basis. The foregoing is evidenced by the fact that stock prices do not necessarily react in the right amount to information, and on the opposite side they can, and do often react to non-information as demonstrated by un-uniform changes in demand. The preceding provides the explanation as to why arbitrage is thus limited. 2. Investor sentiment represent the second foundation, which is the theory of how investors form their beliefs and valuations and thus their demand for securities. Shleifer (2000, p. 24) makes the statement that Combined with limited arbitrage, a theory of investor sentiment may help generate precise predictions about the behavior of security prices and returns. Shleifer (2000, p. 24) make the observation that without investor sentiment, there are no disturbances to efficient prices (thus) prices do not deviate from efficiency. The foregoing is critical. It explains why stock prices rise, and fall before and after information as well as in periods where no real information is present. Instances such as the believe in a new product that was lagging in the market will take off, or that the economic climate is shifting, and or that consumer confidence levels are either on the upswing or reverse are some examples of the preceding. Shleifer (2000, p. 25) goes on to add that it is the theories of limited arbitrage as well as investor sentiment that permits the predictions concerning security prices to be made. He goes onto add that some predictions can be made as a result of the recognition of limited arbitrage without investor sentiment, however, he explains that in order to make more precise predictions, the understanding of investor sentimen t, meaning how they form their beliefs, is needed. From the preceding, it can be deduced that the unexplained swings in the market are a factor of investor sentiment as opposed to limited arbitrage. Shleifer (2000, p. 25) advise us that in terms of behavioral finance, there is not one singular unifying theory. Sharpe and Alexander (1990, pp. 55) in their definition of arbitrage stated that it is the simultaneous purchase and sale of the same, or essentially similar, security in two different markets for advantageously different prices. In purely theoretical terms, it, arbitrage, thus requires no capital and also does no involve risk. Shleifer (2000, p. 28) tells us that arbitrage aids in bringing prices to fundamental values, thus helping to keep market efficient. As investor sentiment represents a critical facet in behavioural finance, more understanding with respect to its nuances is in order. 2.1 Technical Analysis Technical analysis represents research conducted concerning market dynamics (meta quotes, 2007). It is primarily accomplished through the utilisation of charts, along with the forecasting of the development of future pricing (meta quotes, 2007). Inherent in the preceding are three basics that analysts utilise, these are (meta quotes, 2007): The movement of the market considers everything: Aspects as represented by economic trends, political factors, as well as psychological are areas that need to be considered as their near, intermediate, and or long term ramifications can have an influence, and or impact on the performance of a stock. The foregoing is generally included in the price chart, and price changes incorporate the preceding into movements. Through analyzing price charts in conjunction with other indices and indicators, the technical analyst arrives at the conclusions provided by this information whereby the market shows them the trend that follows. Stock prices move with the trend Under this assumption the market moves in conjunction with trends that have been, and or can be analyzed. The foregoing foundation has two effects. The first of which is that the present trend has a high probability of continuing, and the second is that said trend will continue until an opposite trend establishes itself. History repeats itself. The similarity that technical analysis has with behavioral finance is that is closely related to the studies of human psychology (meta quotes, 2007). By the preceding, it is meant that studies on an historical basis covered long period will reveal trends, and characteristics that are termed as the psychological state of the market. Said charts will indicate the moods prevailing in the market in terms of being either bullish or bearish (meta quotes, 2007). Studies have shown the correlation of historical trends and present market trends thus supporting the preceding from a number of variables. Technical analysis provides information that permits the making of better investment decisions. In addition, it provides a better reference point concerning the true value of the underlying asset (Brunnermeir, 2001, p. 98). It, technical analysis, is also defined as the forecasting of market prices by means of analysis of data generated by the process of trading (technicalanalysis.org, 2006). Technical analysts are interested in the movement in the market based upon price as opposed to fundamental analysts that look at a companys characteristics in the view of obtaining an estimate of that companys value (Janssen et al, 2006). In reviewing a wealth of sources that offer differing types of charts, methods and techniques for the process, technical analysis Janssen et al (2006) state that the process really entails the supply along with demand of a market to attempt to reach a determination of the trend / direction that will manifest itself in the future. Murphy (2000, p. 427) defines technical analysis as a technique that generally uses past price and volume patterns to evaluate investments. He continues that technical analysis is primarily used to is to select investments that are not priced correctly, and that will earn abnormal returns in excess of the required returns, where required returns can be measured using the CAPM equation (Murphy, 2000, p. 427). Murphy (2001, p. 427) classifies technical analysis into three areas: 1. trend following systems, systems that estimate changes in valuation ranges, and systems that estimate changes in required returns. Murphy (2000, p. 427) is in agreement with Janssen et al (2006) in that he states that while there are technical analysts that utilise complex computerized systems, many good technical analysts rely solely on simple systems and judgment. Brown and Jennings (1989, pp. 527-552) define technical analysis as the inference of information from past prices. The preceding, past prices, always provides information in a setting with asymmetric information (Brunnermeir, 2001, p. 99). The process of technical analysis is beneficial when it (Brunnermeir, 2001, p. 99): results in an improvement the choices a trader will make, and or winds up in adding to information that has already been revealed on the present price. In technical analysis, when a trader obtains new information, he has to make a determination if that data is already included in the present stock price (Brunnermeir, 2001, p. 100). If said information is included, then a trader attempts to determine how much said information is reflected in the present stock price. Signal jamming is when a trader is privy to information they received in advance of others, and attempts to utilise it to manipulate the price before this information is available in general (Brunnermeir, 2001, p. 102). This type of trader usually buys on what are termed as rumors, and positions himself to sell on news (Brunnermeir, 2001, p. 102). In more fundamental terms, technical analysis seeks to buy assets that they perceive are being purchased by informed fundamental analysts (and sell assets being sold by informed fundamental analysts) and thereby jump on the bandwagon of arbitrage profits (Murphy, 2000, p. 428). In some instances technical analysts utilise just o ne type of trading system (such as a trend-following system, a system estimating support and resistance levels, or a system of estimating movements in required returns), some utilize various systems simultaneously (Murphy, 2000, p. 428). A trend following system attempts to locate those assets whereby the price is being either bid upwards, or is being sold down to an intrinsic value by a fundamental analyst (Murphy, 2000, p. 428). Under trend following systems, technical analysts seek to determine the upward trend via the use of moving averages that has measured the price over a previous number of specified days (Murphy, 200, p. 428). When the trend is up, meaning over its 200 day moving average, the decision is usually to buy, and in the reverse situation, the recommendation is to sell. The preceding is a standard measure used in technical analysis. The general rule is that the time frames are in general, short, typically 100 days (Lunde and Timmermann, 2004). In looking at trends, sometimes the data can be overwhelming clear, while in others, it is relatively obscure. Figure 2 Trend Analysis 1 (Janssen et al, 2006) Figure 3 Trend Analysis 2 (Janssen et al, 2006) The trends in the second chart do not reflect the upward movement as shown in the first, yet the trend is there, it is represented by a series of highs and lows. Trends can manifest themselves in a number of types, such as 1). uptrends, 2). downtrends, and 3). Sideways trends, as shown by the following (Janssen et al, 2006). Figure 4 Trends (Janssen et al, 2006) The concept of support and resistance is embedded in trend directions. It means those things, and movement that are supporting the stock to get it to move upwards, and those inputs that are seeking to see the stock move in the opposite direction, such as short sellers. The following Figure, reveals this concept: Figure 5 Support and Resistance (Janssen et al, 2006) Support and resistance represent the markets laws of supply and demand (Janssen et al, 2006). Key in the understanding of support is round numbers. Supporters buy when a stock starts falling toward a major round number such as $50.00, they will buy shares to prevent that from occurring, thus making is hard for the stock to reach that plateau and yield to short sellers, this is termed support (Janssen et al, 2006). However, once the resistance price has been broken, then it becomes the resistance level for upward movement (Janssen et al, 2006). Figure 6 Resistance and Support (Janssen et al, 2006) Volume represents an important indicator of the activity of a stock in many terms. High activity, which is over the usual average daily trading amount is accompanied by some type of news, and or event that directly is impacting, and of influence in share trading. Changes in the average daily volume usually occur over short time periods. Trading charts indicate the volume, which is another means via which technical analysis is conducted (Janssen et al, 2006). There are cyclical stocks, which have higher sales, and or shipping incidents at certain period in the year, such as Christmas, news, sales, economic conditions will reflect themselves in trading direction as well as volume to provide a picture of trends (Janssen et al, 2006). Figure 7 Trends and Volume (Janssen et al, 2006) Volume is an indicator that precedes price (Janssen et al, 2006). Charts indicate the closing prices when viewed in historical terms, and other charts show the intraday trading activity as broken down into hours, and or time periods. The points on these charts represent information that aid in the analysis of trends in motion, or new trends that are developing. In analyzing charts, a number of differing types offering the same data can be utilised to spot, and or confirm a trend. Longer yearly and monthly charts reveal patterns that shorter time period charts can aid in defining trends within those patterns. An important trend is represented by moving averages, which are the average price of a security over a set amount of time (Janssen et al, 2006). Through the plot of the average price, the movement in price is smoothed to reveal the moving average through the removal of day-to-day fluctuations (Janssen et al, 2006). Moving average have a number of differing types, with the three most common represented by simple, linear and exponential (Janssen et al, 2006). In a simple moving average, which is the most common type utilised to calculate prices, the sum of past price closes is divided by the number of prices in the time period being used. The following is an illustration of the foregoing: Figure 8 Moving Averages (Janssen et al, 2006) The linear weighted average is the least common, and is utilised in the problem termed as equal weighting (Janssen et al, 2006). It is calculated through the taking of the sum of closing prices as represented during a specified period of time. Then multiplying them via the data point position, which is then divided by the sum of the number of periods (Janssen et al, 2006). The third of most common type is the exponential moving average that utilises a smoothing factor to place a higher weight on recent data points and is regarded as much more efficient than the linear weighted average (Janssen et al, 2006). Generally, a charting package performs all of the calculations, thus most traders do not understand the how of the calculations, only the what, and why (Janssen et al, 2006). Exponential moving averages are more responsive to new information relative to the simple moving average., which makes it the moving average most traders prefer as a result of this sensitivity (Janssen et al , 2006). Figure 9 Exponential Moving Average (Janssen et al, 2006) Moving averages are utilised in spotting trends in stocks, and also serve as the informational input to determine support and resistance levels (Janssen et al, 2006). As previously stated, a moving average measures the average price over the previous n days (Murphy, 2000, p. 428). The following Figure helps to illustrate a trend identified via a moving average. Figure 10 Moving Average Trending (Janssen et al, 2006) Figure 3 The preceding chart showed the upward trend moving into a reversal. Janssen et al (2006) tell us that the first indicator of a reversal is when the price of a stock moves through and important moving average, as discussed under support and resistance, this is shown below. Figure 11 Moving Average / Support and Resistance (Janssen et al, 2006) Figure 4 Murphy (2000, p. 429) summarises the process succinctly; Besides possibly indicating the direction of fundamental trades, the relationship between price and its moving average might also have a psychological effect on future trades. For instance, a price for an asset above its moving average implies that the average recent buyer has earned a profit on the purchase, and might be optimistic enough to buy more. A price below the moving average implies that the average recent buyer has lost money and is now pessimistic enough to want to sell (especially if the price recovers slightly so the average buyer can sell without a loss). Technical analysts also often attempt to decipher which individual assets are being purchased most strongly within a market by using relative strength indicators. Relative strength is computed by comparing the return on the asset by the return on all assets trading in its particular market over the previous n days. For example, stocks that have earned returns that exceed those of the some stock index (such as the SP 500) over the previous n days are said to exhibit strong relative strength, which may result from accumulation by fundamental analysts buying below value. Chapter 3 Methodology The methodology employed in this examination to reach a determination as to whether behavioural finance, and technical analysis affect share price prediction entailed a comprehensive examination of a broad variety of books, journals, articles, and Internet sources. The utilisation of secondary source material represented the broadest technique in gathering data as it permitted the scanning of a large base of material to obtain differing views that could be correlated, and thus increase the degree of objectivity as well as quality (Patzer, 1995, p. 3). Secondary data is increasingly being used more extensively as it broadens the field of possibilities in terms of viewpoints, and offers correlation on theories, points and approaches that could be either overlooked, and or not discovered via primary research (Patzer, 1995, p. 11). The nature of the examination lent itself particularly well to the use of secondary research Qualitative research formed the basis for this examination in that it seeks out the why as opposed to the how of quantitative research (QSR International, 2007). It, qualitative research looks into the why as well as how connected to the making of decisions as opposed to the what / where / when of quantitative research (Daymon and Holloway, 2002, pp. 7-10). However, aspects of the examination did call for the what / where / when of quantitative research, and thus were included to augment, and or support the research uncovered. Table 1 Features of Qualitative and Quantitative Methods (Silverman, 2006) Qualitative Quantitative Soft Hard Flexible Fixed Subjective Objective Political Value free Case Study Survey Speculative Hypothesis testing Grounded Abstract The combination of methodologies that utilised quantitative research to a lesser degree, thus aided in understanding key components of the question in terms of market occurrences, and historical facets. Daymon and Holloway (2002. p. 8) provided further insight as to the benefits of quantitative research in terms of its applicability to the subject matter in this examination: Other features of quantitative methods are that they tend to be large-scale with a focus on specific factors which are studied in relation to specific other factors. This requires researchers to isolate variables from their natural context in order to study how they work and their effect Both techniques were utilised to form a more balanced assessment of the examination, as quantitative research entails that the researcher does not participate in, and does not influence the subject matter under study, whereas in qualitative research the researcher tends to be immersed in the subject matter to learn about the situation(s) (Newman and Benz, 1998, p. 14). The preceding approach is supported by Newman and Benz (1998, p. 14) who state qualitative and quantitative strategies are almost always involved to at least some degree in every research study. Further amplification as to qualitative research is provided by Denzin and Lincoln (1994, p. 2) who provide the following generic definition: Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Qualitative research involves the studied use and collection of a variety of empirical materialscase study, personal experience, introspective, life story, interview, observational, historical, interactions, and visual texts the described routine and problematic moments and meanings in individuals lives. Patton (1990, p. 22) adds that qualitative data is: detailed descriptions of situations, events, people, interactions, observed behaviours, direct quotations from people about their experiences, attitudes, beliefs, and thoughts and excerpts or entire passages from documents, correspondence, records, and case histories. Kerlinger (1964, p. 14) refers to quantitative research as hypothesis testing that usually begins with statements of theory whereby the research hypothesis is derived. The advantages as well of limitations of both forms thus dictated a mixture of the two methodologies. Given the large number of sources available on these two subjects, as well as the sub-categories contained therein, the limitation of time did not permit a review of all sources that could and or might be relevant to the examination. As such, it is possible that other viewpoints and or pertinent data might have not been accessed. The broad ramifications contained within these two categories also meant that all of the sub topics could not be explored. The preceding meant that the better known topic areas were selected, which also might have eliminated other important findings of a newer and developing nature. The technical nature of the study dictated that the empirical analysis was best served by a review of varied sources to formulate the context of that examination. This approach was undertaken so as to eliminate the possibility of either bias that might have been included due to the limitations of the research. Through the use of sources, a broader perspective was gained. In addit ion, sources in the empirical analysis were almost completely devoted to the investigation of the subject matter, thus affording a deeper insight. Chapter 4 Empirical Analysis 4.0 Behavioural Finance Empirical research with regard to finance has uncovered that there are two facets, or families of pervasive regularities apparently inconsistent with weak, and semi-strong form market efficiency (Shleifer, 2000, p. 112). The foregoing are termed as underreaction and overreaction (Shleifer, 2000, p. 112). Underreaction refers to the fact that prices of securities, in general, underreact to information such as earnings announcement news (Shleifer, 2000, p. 25). Typically, if the announcement represents good news the stock price trends upward after said news on the initial reaction, or trend down if the news is negative, depending naturally on the degree of severity (Shleifer, 2000, p. 112). Thus, news, and the relative important of it in terms of its impact, and or effect on the company, does contain the power to affect stock price movements, even when such is after the fact as represented by quarterly earnings, or other such announcements that represent events that occurred in the pas t. The preceding usually falls in the area of underreaction as not all investors receive, and or are aware of the news at the same time, thus they buy into, and or upswing the stock in increments over a short period of time that lessens the one time upswing of prices (Shleifer, 2000, p. 112). Overreaction is a phenomenon that usually occurs over a long time period horizon of three to sometimes five years (Shleifer, 2000, p. 112). The foregoing is evidenced by overreaction to what are termed as consistent patterns of news pointing in the same direction (Shleifer, 2000, p. 112). The preceding means that those companies that have had a long record of good earnings, and or revenue news generally become overpriced relative to said news, and tend to wind up being overpriced (Shleifer, 2000, p. 112). In terms of empirical evidence, a model of investor sentiment with regard to an experiment in psychology is utilised. Tversky and Kahneman (1974, p. 1127) conducted a study in this area on behavioural heuristics that is termed representatives. The foregoing represents the tendency of the subjects (experimental) to view events as typical or representative of some specific class and to ignore the laws of probability in the process. They, the subjects think they see patterns in trul y random sequences (Tversky and Kahneman, 1974, p. 1127). Edwards (1968, pp. 145-149) study into this area generated what he termed as conservatism, which represented the slow updating of models in the face of new evidence. The model under discussion works in the following manner, as explained by Shleifer (2000, p. 113-114): Investors have some prior views about the company in question. When they receive earnings news about this company, they tend not to react to this news in revaluing the company as much as Bayesian statistics warrants, because they exhibit conservatism. This behavior gives rise to underreaction of prices to earnings announcements, and to short horizon trends. At the same time, when investors are hit over the head repeatedly with similar newssuch as good earnings surprisesthey not only give up their old model but, because of representativeness, attach themselves to a new model, in which earnings trend. In doing so, they underestimate the likelihood that the past few positive surprises are the result of chance rather than of a new regime. This gives rise to overreaction Evidence of the preceding is found in the example of Apple, Inc. In 2000, the company introduced a new lone of computers with innovative features and a new operating software (Briggs, 2000). The news resulted in an upward move of the stock to a new 15 year high (Yahoo Finance, 2007). However, when the sales failed to gain the company any appreciable gains in market share, the stock returned to prior levels on the announcement that the earnings projections would not meet expectations (Yahoo Finance, 2007). Such instances are voluminous (Shleifer, 2000, p. 114). Jones and Winters (1999) in an examination of extreme past winners on the NASDAQ stock market formed the basis of a study involving return-momentum, earnings-momentum, and value strategies. In a study of common characteristics it was found that the stocks that met the aforementioned criteria produced abnormal positive returns that had a duration that averaged one year on the New York Stock Exchange, and an average of two years on the NASDAQ Exchange (Jones and Winters, 1999). They stated (Jones and Winters, 1999): The more prolonged accumulation of abnormal returns in the NASDAQ stocks relative to the NYSE and Amex stocks and the lack of any observable return reversal support delayed reaction as an explanation for the abnormal returns. In addition, we find the institutional response is quicker in the NYSE/Amex sample than in the NASDAQ sample. We interpret this evidence as consistent with institutions contributing to the gradual correction of underreaction in these stocks. The magnitude of the abnormal returns and their association with institutional following also implies that the returns to momentum and value strategies reported in the recent literature are at least in part due to delayed reaction. The study referred to Jegadeesh and Titman (1993, pp. 71) who found that winning New York Stock Exchange and AMEX stocks that experienced return-momentum over the prior two quarters, earned a mean abnormal return of 9.5 percent in the subsequent year. The preceding was attributed to underrreaction correction to information Jegadeesh and Titman (1993, pp. 73). Bernard and Thomas (1989, pp. 1-36) found that stock prices underreact to news about future earnings contained in current earnings announcements. The underreaction to post earning announcements was correlated in stocks that had favorable earnings surprises that lasted for approximately three quarters after the news was released (Bernard and Thomas, 1989, pp. 1-36). The foregoing was corroborated in a more recent study conducted by Giambona et al (2005). That study involved the long term effects of stock repurchases conducted in the open market with regard to Real Estate Investment Trusts (Giambona et al, 2005). In an investigation of underreaction, they found there was what they termed as strong support for the undervaluation hypothesis (Giambona et al, 2005). In this instance, the news was the repurchase of stock by the REITs. Initially, the market reaction to the news of the stock repurchase program was skeptical. As a result, the stock prices remained undervalued (underreaction), thus benefiting the repurchasers (Giambona et al, 2005). In equating overreaction Shleifer (2000, p. 120) advises that when there had been a series of positive news the general mood was that investors became optimistic that this same occurrence would be the case in the future. The net result was that over long periods, the stock price rose to levels that were what Shleifer (2000, p. 120) terms as unduly high . He points out that the idea is that trading on stale information, in this case a series of good or bad news, can earn superior returns. (Shleifer (2000, p. 121). The empirical evidence relating to this phenomenon, just as in the case of underreaction, is extremely large. Shleifer (2000, p. 121) refers to studies conducted by Fama and French (1988, pp. 3-25), as well as Poterba and Summers (1988, pp. 27-59), and Cutler et al. (1991, pp. 529-546). All of the preceding investigations uncovered that over a three to five year period that the auto correction for the overvaluation as a result of the overreation was slight (Shleifer, 2000, p. 121). This phenomenon was more pronounced when the good news series involved firms that had poor returns prior to the new series. In an study conducted by De Bondt and Thaler (1985, pp 781) over a fifty year period that dated back to 1933 they uncovered that stocks that had recorded extremely poor returns during a three year period preceding the good news series, drastically outperformed the stocks of companies that had a series of good news during that same three year period, and continued to have good news (Bondt and Thaler, 1985, pp 781 785). 4.1 Technical Analysis The Gann Studies (Kuepper, 2004) were conducted in 1908 on what he termed the market time factor. He was known as one of the most successful forecasters, and still carries that reputation today. His studies regarding the technical analysis of price movements were based upon three foundations (Kuepper, 2004): That price, time and range are the only three factors to consider, That the markets are cyclician in nature, and The markets are geometric in design and in function Using these three premises, Gann devised strategies that revolved around these pillars. Under the price study Gann utilised support and resistance, pivot points as well as angles (Kuepper, 2004). In the time study he looked at dates that reoccurred from an historical perspective, and in the pattern study, he looked at the swings in the market via the use of trendlines, along with reversal patterns (Kuepper, 2004). The following figures show the building blocks utilised in Ganns studies. Figure 12 Gann Study Illustrations (Kuepper, 2004) Ganns work showed that technical analysis could predict stock price movements with accuracy. Fama (1995) in his article Random Walks in Stock Market Prices discussed the behavioural side of stock prices. Within this realm are the chartists as he termed them, which are the technical theories, and the fundamentalists, which is also known as intrinsic values (Fama, 1995). Technical theory is based upon the assumption that history tends to repeat itself, meaning that past patterns regarding prices in companies have a tendency to reoccur (Fama, 1995). The theories utilised by technical analysis and fundamentalist factors, the market analyst, in principle stated Fama (1974) will be able to determine whether the actual price of a security is above, or below its intrinsic value. The theory of random walks generally departs from the premise that major changes in securities represent good examples of efficient markets (Fama, 1995). It represents Famas (1995) empirical look into technical analysis. Under this theory, Fama (1995) asserts that price movements will not follow any patt erns or trends and that past price movements cannot be used to predict future price movements (onvestorhome.com, 2006). It represents a means to aid in the prediction of stock prices. Chapter 5 Conclusion The adage that one cannot beat the market is proven to be an oft-used phrase that is broad in its context. Beating the market, in total, has a meaning that is hard to define. One can select varied stocks that have differing categories of risk, are showing upward trending at certain times, and thus win on these predictions consistently based upon timing. This requires an analysis of the factors, and concentration of specific stocks for a time duration. The market, as a whole will trend either up or down, however, there are stocks within those trends that run contrary to the market movement, thus the rationale for that statement concerning the market as a whole. Recent evidence supports the preceding statement that stock processes do in fact contain predictable elements (Lo and MacKinlay, 2001, p. 17). Predictability in stock prices via behavioural finance as well as t