Is Technical Analysis Profitable in Silver Market in the Implication of Efficient Market Hypothesis?

Technical Analysis in the Implication of Efficient Market Hypothesis on Silver Market

The thesis is for the study of simple commonly used technical trading rules, which are applied on silver market. It covers years 1989 to 2005. A famous study carried out by Lakonishok, LebaRon and in year, 1992 has clearly shown that technical analysis can lead to abnormal prices when compared with buy-and-hold strategy. Other studies have been carried out and found out that technical trading rules cannot over-rule passive investment management strategy. The study uses Brock et al.'s methodology. Several trading rules are discussed (Dawson & Steeley 2003).

LITERATURE RIVIEW

In financial theory, efficiency of financial silver market is highly disputed. This has led to many attempts to explain efficiency of silver markets. Eugene.F. Fama formulated the most famous definition in 1970 referred to as the, Efficient Silver market Hypothesis (EHM). The basis of the hypothesis is that a security price always reflects all the information available. Many studies have been undertaken to explore silver market. This has led to two camps; those who believe in silver market efficiency and those who do not believe in it. Those who believe in efficiency argue that silver market has all the information available while those who believe in inefficacy argue that security prices lack important information.

Problem Specification

Although technical analysis is used; it does not have much support within academic circles. The preferred theory is that of silver market efficiency. The purpose of this paper is to contribute to the contradicting ideas between advocates of inefficient silver markets and efficient silver market in silver market. The basic question is whether it is possible to earn a significant better return from buy -- and -- hold strategy using simple technical trading rules when applied on growth and value stock respectively (Dawson & Steeley 2003).

Limitations

To analyze technical trading rules, moving average rules will be considered. This is because it is these rules can be used mechanically and can be tested easily since they produce clear signals. Stock classification into value or growth portfolio is based on book-to-silver market (B/M) ratio and the earnings -- to price (E/P). These are the commonly used measures when classifying stocks. The test period will be limited to a span of 19 years. That is from 1986 to 2004. This period is of interest because 1992 study by BLL shows that use of technical analysis approach earns a higher return as compared to simple buy- and- hold strategy based on DJIA testing from 1897 to 1986.

Theoretical Background

Technical Analysis

Definition

Technical analysis is defined as the study of silver market action using charts to forecast various silver market actions like, volume, price and interest. Technical analysis is based on various premises. These are; price moves in trends, silver market action discounts everything and history repeats itself (Dawson & Steeley 2003). Chartists and technicians believe that everything that has effect on silver market price of a good is contained in the price. There is need therefore to study silver market price by all technicians. On the other hand, chartists are not concerned with price change factors but they react to them. Therefore, chartists want to know whether the price of stock is rising or falling but they have no concern with the actual price. The purpose why chartists have to be analyzed is to identify trends to trade in accordance with the trend. Technical analysts believe that a trend in motion is likely to continue than reverse based on the Newton's first law of motion (Dawson & Steeley 2003). Trends differ in many ways and chartists have divided them into time units. This is what is called the Dow Theory, which is discussed below. This theory states that there are three levels of silver market trends: tertiary, secondary and primary. In other words, there are intermediate trends, secondary trends and long-term trends.

2.1 The Silver market Cycle Model

It indicates that the key to predicting the future is based on past understanding. Therefore, human psychology has an important part to play in technical analysis.

Technical Trading Rules

Principles behind technical analysis are similar. It does not matter whether you focus on long-term or short-term investments, follow a speculative or conservative strategy, the basic principles remain the same. Psychology and mass psychology determine prices and psychology is significant in long -- term charts and short-term charts. In addition, technical analysis principle is the same in spite of investment opportunities and geographical location.

Human actions form silver market and people make same mistakes. Technical analysts exploit the fact that, human nature is less or more constant hence people keep on committing same mistakes since emotional swings keep on recurring. Described below are the most important issues within technical trading.

Dow Theory

Charles Dow, one of the founders of The Wall Street Journal, developed this theory in 1890s and its first editor. He was the first to create stock silver market average in 1897. After his death, his theory was formulated. William Hamilton who succeeded him as editor published more than 250 stock- silver market predictions. He used the theories of Dow. Dow's theory is the starting point in studying technical analysis. This theory tries to identify long-term trends in stock silver market prices. Its basic tenets are:

1. The Income Special discounts everything.

2. The Industry Has Three Styles.

3. Significant Styles Have Three Stages.

4. The Income Must Validate Each Other.

5. Amount Must Validate the Design.

6. A Design Is Considered to Be in Impact Until It Gives Certain Alerts That It Has Reversed.

Figure 2.2 Verification of reversals of the main trend in the DJIA and DJTA

Source: Hirschey & Nofsinger (2005)

The figure above shows three situations. The grayish variety symbolizes the DJTA while the green variety symbolizes the DJIA. At factor 1, the DJTA does not look at the keep trend proven by the DJIA thus, the fluff organizations are unchanged. However, at factor 2, the end of fluff organizations are verified. Fashionable change is verified by the reduced total and the reduced low on the DJTA. Point 3 verifies that the fluff silver market has started again. The greater levels and the greater peaks on both averages indicate this resumption.

Industry Pattern Model

The company cycle is a well-known trend in the economic climate. Economists believe that the economic climate goes in a stroking cycle from growth to the economic downturn. Among technical experts, there is an extensive perception that inventory silver marketplaces also move in rhythmic periods from growth to the economic downturn and returning to growth or, in other conditions they believe that there is a propensity for expenses to move from silver market optimum to silver market trough in a stroking. Cyclical activities are caused by the actual financial and governmental causes and audience actions among people. It can take months or even decades for audience actions to rise to the stage of irrational exuberance, decrease to despondent negativity and returning to unreasonable exuberance.

Some of the well-known periods of unreasonable exuberance in the U.S. took place in the overdue 60's with technological innovation stocks, overdue 80's with energy stocks and finally in the late 90's with technological innovation stocks again. The corresponding levels took place in 1974, 1982 and 1990 when investors where uncommonly gloomy. One of the best-known silver market cycle designs is The Elliot Trend Design, after Ralph Nelson Elliot. He thought audience actions trends and reversals occur in recognizable styles. The process of the Elliot Trend Concept is that stock prices are managed by the Fibonacci figures (1, 2, 3, 5, 8, 13, 21, 34, 55….), and the benefit silver market goes in five surf and three on the disadvantage. Within these waves there can, however, be slight surf and these also display the same pattern.

Figure 2.3 the Elliot Trend Pattern

Source: Own creation with inspiration from Murphy (1986).

As it was the situation in Dow Concept, the surf can be separated according to their size. The major wave chooses the most essential or main trend of the industry and the minor waves the slight trend. As can be seen in determine 2.3, optimum 1 is an aspect of the major trend whereas the first trough, noticeable variety 2, is only remedial and, therefore, only another trend.

Other Specialized Indicators

While cost is the most used indication for technical experts, other signs are also used. Some of these signs are used for verifying the indication produced by the price, but they can also be used as a main indication. These are volume, money moves, silver market breadth and silver market discrepancy.

2.2.3 Simple Dealing Rules

In the following aspect, two basic trading guidelines will be described. The going average trading guidelines, that will be empirically examined, will be highlighted. 2.2.3.1 Moving Average

Probably the most flexible and used trading concept is the going regular trading concept. The concept has been used for at least 50 decades and connected to type of indicators called trend-following signs. These signs are intended to sleek the price pattern of spiders or stocks creating it simpler to recognize origins, end of trends, and recognize the actual trend. The purpose why going regular is so commonly used may be because trade signals can quickly be calculated into a pc. Chart analysis is mostly highly subjective and difficult to evaluate. Specialists may do not agree whether a cost pattern is a head-and-shoulder pattern or a banner pattern while moving averages is a statistical determined pattern making no issues open for controversy. As the phrase indicates, going regular is a strategy where the information of a certain stock or a catalog is averaged over an occasion frame. There are no specific requirements to the length of enough periods, but it has to fit the trading problem. In addition, different expenses can be used. Normally, however, the ending cost is used, but there is no concept that says you cannot use other expenses such as peaks, levels or maybe even a mixture of more expenses.

Simple Moving Average

The most commonly type of regular used is the basic going regular. The calculation of this regular is quite easy. If a 10-day regular is required, the cost of each day for the last 10 days is included and then separated by 10. To make it a moving average, the most ancient declaration is taken and a new is included. To discover out what length the normal should have, reasoning sense must be used. If you need weekly data, 4-week information may seem reasonable. If monthly information is required, a 12-month moving regular is more useful (Fama, 1970).

The simple going regular has, however, two main disadvantages. The first is the fact that it only protects the interval under declaration and limits previously information, which might contain useful details. Secondly, each declaration is given equivalent bodyweight. The most ancient declaration is in other conditions considered just as valuable as the latest. Experts dispute that latest the conclusions should be given more body-weight in the normal. To appropriate this, the linearly heavy going regular and exponential going regular has been used.

The Linearly Weighted Moving Average

The simplest way to appropriate the above-mentioned problem is to use the straight line heavy regular. If a 5-day going regular is used, the observation on the fifth day is increased by five; the declaration on it all day is increased by four etc. The total is included up and separated by the sum of the multipliers (Fama, 1970). The linear weighted going regular strategy does, however, not help with the so-called drop-off effect. To appropriate for both issues, experts must turn to the exponentially smoothed going regular.

The Rapid Moving Average

The exponential going regular is also a heavy regular giving more weight to latest conclusions. The most ancient cost conclusions are never eliminated from the data but the further returning they are the less bodyweight they are given in the computations. The system for the exponential going regular is:

One Moving Average

The going regular is just a variety on a screen or sheet of the document and is not by itself an indication that can be used to make buy or offer choices. To make signals out of the normal, experts standard either one or more against the actual price or each other (Fama, 1970). A sensible way to produce an indication is by using one moving average and evaluates it to the actual cost. The concept behind this is that in an uptrend, the going regular tends to lag the cost action and paths below the expenses. If the actual cost goes above the going regular a buy indication is produced and conversely, if the cost goes below the normal an offer indication is produced.

Figure 2.4 Simple one moving average relating to silver market

As can be seen in the figure, several trade signals for the XM Satellite television Radio stock are produced. The first indication is an offer indication, which happens in overdue Apr 2004.

Two Moving Averages

Technicians also have other opportunities to make better choices besides the use of filter systems. An effective and typical strategy is to use two going averages simultaneously. The averages are of different measures with the least of them used instead of the actual cost and the greatest to recognize the actual trend. There are numerous blends of averages that can be used, but some precise common combinations are the 5- and 20-day averages and 10- and 40-day averages. If the shorter moving regular passes across from below a buy indication are given and if it passes across from above an offer indication is given. The use of two going averages lags the indication a little bit, but the advantage is that it generates less whipsaw than by the use of only one moving regular. It should be observed that it is the basic going regular that lies behind the strategy described above. As can be seen in the determined several trade signals for the XM Satellite television Radio stock are produced. The first indication is an offer indication, which happens in overdue Apr 2004.

Two Moving Averages

Technicians also have other opportunities to make better choices besides the use of filter systems. An effective and typical strategy is to use two going averages simultaneously. The averages are of different measures with the least of them used instead of the actual cost and the greatest to recognize the actual trend. There are numerous blends of averages that can be used, but some precise common combinations are the 5- and 20-day averages and 10- and 40-day averages. If the shorter moving regular passes across from below a buy indication are given and if it passes across from above an offer indication is given. The use of two going averages lags the indication a little bit, but the advantage is that it generates less whipsaws than by the use of only one moving regular. It should be observed that it is the basic going regular that lies behind the strategy described above.

Figure 2.5 Simple Two Moving Averages

Source: http://finance.yahoo.com, April 22, 2005

In determine 2.5 some of the issues from the one going regular strategy are corrected. The problem with the whipsaws around Aug 2004 is repaired since the shorter going regular does not mixture the longer period going regular and hence, no false signals are given. Below the cost shapes, the oscillator is proven. To make less faults, three going regular strategy is also used.

If the craze is an uptrend, the cost will cross the stage of level of resistance while in a downtrend the assistance stage is surpassed. When one of the lines is surpassed, the tasks of them are changed. What this means is that if the support stage is surpassed from above it becomes the new stage of level of resistance and if the original stage of level of resistance is damaged it becomes the new assistance stage. The purpose for this is that investors have the cost in thoughts. Investors want to get out of losing trades at break-even. In the same way, investors seek to improve successful tasks by buying more stocks at or near the assistance stage.

Another emotional aspect of assistance and level of resistance stages is the role of round numbers as assistance and level of resistance. Circular figures will stop advances or decreases. Investors tend to see round figures such as 50, 100, 1.000 10.000 etc. As cost goals and act accordingly. Hence, round figures often act as emotional assistance or level of resistance stages.

Complex Patterns

Price styles includes two groups, namely change and extension styles. Reversal styles produce signals of treating trends whereas extension patterns are only a brief stop of a trend, maybe to appropriate for overbought or oversold conditions.

Effective silver market hypothesis

Many analyses have been conducted to obtain theoretical groundwork to understand the cost motions on the economical silver market. The analysis has led to a number of different descriptions and speculation of the efficient silver market. Perhaps the most famous and commonly used speculation was described by Fama (1970) and known as the EMH. The EMH states that protection expenses conclusively indicate all available details. Any new details will thereby instantly be included in the cost, creating the quoted inventory cost a reasonable value. You cannot anticipate gaining any irregular revenue for a given danger a bit more duration of time2. The inventory come returning will go up and down at random, while the inventory cost only will reply to any new details. New details are by definition unforeseen, creating the inventory cost and the come returning unforeseen and random (Fama, 1970).

The theory of efficient silver marketplaces is extremely carefully associated with a unique move. The unique move speculation appears in distinct comparison to fundamental analysis. If inventory values follow a unique move, it seems that essential analysis is worthless. Stock expenses will no more be managed by the law of provide and need, but instead be quite unique and unforeseen. The ignoring of the law of supply and need created uncertainty among the instructors and look for crucial proof towards the unique move design speculation was set in movement. However, the unique move design left as many questions an answered as it resolved and it did not take lengthy for the doubters to discover it flaws. They discovered that the weakness of the unique move speculation was the mistake phrase. The assumptions behind the mistake phrase were too limited to explain the inventory cost motions follow a unique move the mistake phrase should, besides being uncorrelated, also be entirely separate with previously period's mistake conditions. Studies soon showed autocorrelation and the unique move speculation was taken down. A new less restrictive design still containing you will of the unique move design was needed. The answer was the martingale design. John Samuelson's (1965) document was the first to develop the link between investment silver market performance and martingales. Unlike the unique move design, the martingale design does not believe absolutely independency between the mistake conditions, but only that they are uncorrelated. Thus, it can be seen that the unique move design is a peculiar situation of the martingale. Fama grabbed the insights from Samuelson and established the EMH.

Amounts of Industry efficiency

Fama (1970) differentiates between three different levels of silver market performance -- labeled poor type, semi-strong type and powerful type respectively. These levels are categorized by the amount of details, which is included in the current expenses.

The poor type includes the smallest challenge that must be met for one to dispute that the stock organizations are efficient. In the poor type the only details showing the current prices is the conventional expenses. What this means is that any evaluation of previous times expenses is redundant and cannot be used to make any irregular revenue. This declaration, due to the meaning of technical analysis, undermines the effectiveness of technical analysis making essential analysis and specialized details the only tools for creating superior revenue.

In the semi-strong type all community available details is within the cost. It means that besides conventional details everything read in the news, heard on the radio, seen on the television and Internet is already included in the cost (Fama, 1970). This has the consequence that essential analysis no more can produce irregular revenue, leaving the use of specialized details the only way to generate improved revenue than a simple buy-and-hold strategy.

In the powerful type performance all details community as well as personal details is incorporated in the existing expenses. This implicates that it is not possible by any indicates, not even specialized details, to continually produce greater revenue. Note that the details set are progressively increasing throughout the three types. This means that if the industry is efficient in the powerful type it is of course also efficient in the poor and partial powerful type. However, if the industry is efficient in its weak type, it is not actually efficient in the partial and powerful type. As implicated earlier, the industry cannot be efficient in its poor type, if technical analysis works and the industry cannot be semi-strong or powerful if it is not capable of the poor type. 3.4 Difficulties to the EMH

The proven reality that the EMH only is a calculation of how the actual life acts has of course led many people to try to confirm that the concept is inadequate or wrong. These challenges include flaws which can be described as the methodical actions of investors which is not using the EMH (Stracca, 2004). The major flaws in the silver marketplace are the schedule effects, small company effect, winner's effect and value top quality challenge.

METHODOLOGY

Data and Analysis

For most scientists the Center for Research in Security Prices (CRSP) has been the main resource for information removal mainly because of the broad variety of information and its high stability. However, this dissertation uses Thomson DataStream (TDS) especially because of the great degree of functionality and quick accessibility. Well conscious that user friendliness and quick accessibility must not overrule the stability of the information; TDS has been recognized as an excellent provider of value information, although researchers might encounter examining issues (Ince & Porter, 2004). Some of these screening restrictions have been experienced in the information collating stage of this thesis (Fama, 1970). As an example, proper examining between stocks from AMEX and NYSE and is not easy since they have identical ticker identifier5. Similar, all dead stocks in U.S. are assigned a six-digit variety regardless of source (NYSE, AMEX or NASDAQ). Furthermore, TDS does not provide examining requirements for common stock and it is not possible to recognize stocks with financial season in Dec. While rummaging through the NYSE homepage has fixed the three first issues, Compustat has been used to recognize organizations with financial season in Dec. be more upwards sloping for the value profile than for the development profile. The number of "buy-days" is continually greater than was the situation for the growth portfolio. For the value profile "buy-days" surpasses "sell-days" with approximately 50%, whereas the same rate was only roughly 35% for the development profile. Notice also that portion of appropriate signals following a "buy-signal" is greater manpower to generate a come returning that is as excellent as or better than the standard. 8 Significances of Findings

The results achieved are after several studies are in a variety with previously analysis. First of all they confirm what was determined by BLL assisting the use of simple technical trading rules by creating come returning above and considerably different come returning from the buy-and-hold strategy

Previously it was stated that the come returning sequence did not adhere to a unique move. However, in the EMH the unique move is originally used on inventory values. If inventory values adhere to a random move then cost changes are white-noise (Fama, 1970). Therefore, examining whether returns are white-noise is comparative to examining for unique move in the inventory values. One-way of taking advantage of sequential connection in return series is the use of technical analysis. It has been suggested that silver market performance could be separated into three stages, weak, semi-strong and powerful type performance. It was also suggested that if the empirical tests proven that the technical trading guidelines were able to outshine a buy-and-hold 9 Conclusion

Since the ingredients of the Effective Industry Hypothesis many efforts have been made to ignore it. A well-known evaluate of silver market performance has been to evaluate whether the use of technical trading guidelines allows investors to continually generate unwanted come returning. If this indeed is the situation the industry can be considered as ineffective. The use of technical trading guidelines to evaluate for silver market performance is appropriate basically because that the approach is commonly used in practice.

One of the most well-known analyses of technical trading guidelines on silver market was conducted by Brock, Lakonishok & LeBron and released in 1992. This study proven that it was possible to produce a come returning that was above and considerably different from the buy-and-hold return through the use of simple trading guidelines. BLL were the first to incorporate the bootstrap process with evaluate of technical trading guidelines (Dawson & Steeley 2003). The use of this procedure helps to get over the issues connected with the presumptions behind conventional tests when examining on come returning sequence.

This dissertation studies the productivity of eight simple technical trading guidelines applied on growth and value stocks.

The results display that the trading guidelines are able to recognize times with good and negative earnings. For both investment portfolios the mean come returning following buy signals is positive for all trading guidelines while it is adverse following an offer indication. Furthermore, sell periods are recognized by greater motions than buy times. This is continuous with the make use of effect. More essential is it that the use of simple technical trading gives 120 a better come returning when in comparison to a buy-and-hold strategy also when modifying for danger. This is legitimate for all trading guidelines and on both investment portfolios. For the development profile, three of the eight trading guidelines produce a come returning that is above and mathematically significantly different from the buy-and-hold at the five percent importance stage using a two-tailed test. These three guidelines are all without group. For the value profile five of the trading guidelines produce a come returning that is above and mathematically considerably different from the buy-and-hold strategy (Dawson & Steeley 2003). Again, the guidelines without group are excellent. The value top quality is obvious in the investment portfolios. The come returning produced by the value portfolio is above and considerably different from the come returning produced by the growth portfolio. Actually, the come returning of the development profile is reduced than what can achieved if there is a special rate for investment. b. It can therefore be determined that the use of technical trading guidelines might help improve earnings when in comparison to a buy-and-hold strategy but when in comparison to what can be gained on other investment portfolios the use of technical trading guidelines allows only little. This makes it unrelated to further talk about the application of technical trading guidelines on this profile. The factor that the value portfolio clearly outperforms is an abuse of the EMH. It has been suggested that an abnormality is present but it must be inquired whether it is possible to manipulate this continually. The only person that comes to thoughts who has done so is the well-known value investor Warren Food (Dawson & Steeley 2003).

As it is well-known that resource earnings exists as a certain variety of functions that violates the presumptions behind the t-test, bootstrap models, as used in BLL, are conducted to check whether the previous answers are due to these functions. This is highly denied for all the four zero designs examined, even though functions such as autocorrelation, motions clustering and the make use of effect are existing in the return series. Thus, the outcomes are in common continuous with those revealed by BLL for the DJIA; verifying so simple technical trading guidelines have of a routine energy for value stocks.

the trading guidelines with the least lengthy going regular without group, basically, due to the proven reality that they produce more signals (Fama, 1970). Following this disagreement, it can be concluded that the trading guidelines using a group are preferred. However, the core finding is that release of cope pre-uses that none of the trading guidelines produce a come returning that is mathematically significantly different from the buy-and-hold come returning. What this means is that investors cannot anticipate earning an irregular come returning by using the examined trading guidelines on stocks categorized as value stocks. The irregular come returning produced by the trading guidelines may basically be due to genuine fortune.

The conclusions of this dissertation have several implications for the EMH. The fact that at least some predicting energy is recorded, need not to be an abuse of the EMH. The release of cope expenses causes that equivalent rights, between the return generated by the trading guidelines and the come returning acquired through a buy-and-hold strategy cannot be denied. Thus, the poor type performance cannot be denied for value stocks.

Thus, it can be determined that the use of simple technical trading guidelines does not enable investors to continually generate irregular earnings when used on value and growth stocks

The only reason as to a change can take place from t to t+1 is as a result of arrival of unpredicted events and arrival of news. Estimated errors +1 +1 +1 = ? t t ? PEP are supposed to be zero on average. The latter is called rational expectations element of EMH and are represented in this manner:

(3-1) +1 +1 +1 = + t t PEP ?

Considering the expectation of (3-1) and the expected value of the predicted results to (3-2) ( ) 0-1 1-1 1-1? t t+ t t+ t t+ t t+ t t+ E ? EPEP

The fact that the predicted value of forecast error is zero, it means that the foremost error is zero meaning the forecast of t+1 p is fair. At times the EMH is applied on stock returns and not stock prices and as a result of close link between stock prices and returns in stock (3-2) is written as

(3-3) 0-1 1-1 = ? = t t+ t t+ t t+ E ? ERER

EMH is known to as a fair game property because one can't earn profit that is abnormal through buying and selling of stock.

Martingale difference which is fair game is shown by: A stochastic process tx is fair in relation to a series of information sets t ? If t X has the chattels: (3-4) t t t EX ? = X + ( ) 1

The characteristics of Martingale show that the return should be zero on condition t ?. On the other hand if tx is Martingale then ttty=x-x+1+1 is supposed to be a fair game.

Compiling the fair game property with an ordinally model where stocks do not pay any dividends and at the same time investors are ready to hold stocks for as far as it is predicted to get a constant return, k:

(3-6) ER k t t = +1 k > 0

The above form is referred to as the fair game of surplus return. In a situation where returns are steady but stock pays dividends, in such a situation the expected returns are

Described as:

(3-1) +1 +1 +1 = + t t PEP ?

For further discussion on EMH a model can be brought up as a starting point through incorporating C-CAPM in the RVF for stock prices. Paradox was at first influenced by the disadvantages of the random walk theory.

Table 7.2 Traditional Test Results for the Trading Rules for the Growth Portfolio

Daily mean Return

Standard deviation

Shape Ratio

Signals

MA (1-10-0)

0.0374

0.60663

0.03174

MA (1-20-0)

0.0226,

0.9369

0.61554

0.00709

MA (1-20-0)

0.0423

2.1136

0.59630

0.04045

MA (1-20-1)

0.0190

0.7338

0.59829

0.00136

MA (1-30-0)

0.0399

0.59397

0.03662

MA (1-30-1)

0.0217

0.8943

0.60250

0.00590

88

MA (1-50-0)

0.0277

1.2429

0.60685

0.01566

MA (1-50-1)

0.0174

0.6318

0.61961

-0.00131

76

Buy and hold

0.0066

1,02984

-0.01126

19

In situations where the results for growth portfolio were good the ones for value portfolio were even more remarkable. Out of the eight trading strategies five of then bring about a bigger return than the hold and buy return. As in growth portfolio those trading rules that do not use bands perform best (Fama, 1970). All of them obtain a return that is distinct from the strategy of buy and hold. However there is only one of the trading rules with bands that obtain a return that is different from those generated through the strategy of buy and hold.

Table 7.3 Results for Value Portfolio

Observations N (Buy) N (Sell) Mean Buy Return Mean Sell Return

MA (1-10-0)

4958 2998 1960 0.11646 -0.05453

(58.8) (47.3) 0.61982 0.93848

MA (1-10-1)

4958 3027 1931 0.10092 -0.03233

(58.7) (47.1) 0.62523 0.94020

MA (1-10-2)

(57.6) (44.6) 0.65265 0.88628

MA (1-20-0)

4958 3088 1870 0.11156 -0.05425

(59.2) (48.2) 0.60862 0.96333

MA (1-20-1)

4958 3134 1824 0.09849 -0.03598

(58.8) (47.6) 0.62146 0.95971

MA (1-30-0)

4958 3154 1804 0.10824 -0.05451

(59.3) (48.6) 0.61616 0.96600

MA (1-30-1)

4958 3159 1799 0.10169 -0.04346

(59.1) (48.1) 0.61911 0.96528

MA (1-50-0)

4958 3206 1752 0.10506 -0.05352

(59.4) (48.7) 0.62071 0.96973

MA (1-50-1)

4958 3123 1835 0.09411 -0.02772

(58.7) (47.2) 0.63173 0.94760

In table 7.4 the figures that are comparable to the buy-and-hold strategy are presented.

Table 7.4 Traditional Test Results for the Trading Rules for the Value Portfolio

Daily Mean Return Standard Deviation Sharpe Ratio Signals

MA (1-10-0)

0.0776 0.48430 0.12273 284

2.2220

MA (1-10-1)

0.0689 0.49016 0.10353 101

1.5415

MA (1-20-0)

0.0767 0.48239 0.12134 175

2.1542

MA (1-20-1)

0.0693 0.49556 0.10314 74

1.5648

MA (1-30-0)

0.0759 0.49330 0.11690 128

2.0733

MA (1-30-0)

0.0719 0.49576 0.10826 56

1.7618

MA (1-50-0)

0.0748 0.50080 0.11311 91

1.9855

MA (1-50-1)

0.0665 0.50266 0.09608 47

1.3416

Buy-and-Hold

0.0490 0.76618 0.04025 19

In situations where the results for growth portfolio were good the ones for value portfolio were even more remarkable. Out of eight trading rules 5 of them generate a higher return compared to the strategy of buy and hold. The greatest return is obtained by 1-10-0 rule of trading meaning the mean return that is obtained by this rule is

0.0776% or 20.25% on an average yearly rate. The same as with the situation of growth portfolio, rules of trading that do not incorporate bands are the ones that perform best (Dawson & Steeley 2003). The only case that makes this different from the growth portfolio is that those rules of trading with bands bring about a return that is distinct from the return that are brought about by buy and hold strategy.

Volatility is notably less for the value of portfolio compared to that of growth portfolio. However it is exciting to note that sharp Ratio for the strategy of buying and selling is greater than the sharp ratio for nine technical trading rules out of the ten in growth portfolio (Dawson & Steeley 2003). Another way used to determine lower volatility is through studying the number of signals. All rules of trading bring about fewer signals when second-hand on the value portfolio than to growth portfolio.

Figure 7.1 Descriptive Statistics on Silver Market

Taking into consideration the histogram above return series do not follow a distribution that is normal. The test of Jarque -- Bera on normality does not support the null hypothesis on normality. Splitting the Jarque- Bera test makes the series meet negative high kurtosis and skewness. The high kurtosis is mainly the key cause for non-normality. Crashes are the main cause of high kurtosis. A good example that can never be forgotten is the black Monday which was on 19th in October. (Watsham & Parramore, 2002) the stock silver market went through the biggest decline in one day in the recorded of stock.

Development and Value stocks

Portfolio professionals often decide what type of stocks to purchase and strategy to adhere to in regards to value making an investment. One can either purchase value stocks or adhere to a value economical commitment strategy or they can decide to use inventory economical commitment strategy by making an investment in growth stocks.

Value Stocks

The common concept in the value economical commitment strategy is to recognize investments that are temporarily overlooked or unpopular for various reasons. Value investors are, so to speak, looking out for discounts where the cost of a protection has been defeated down unfairly. They concentrate on whether the rate is below the approximated economic value of the concrete and intangible resources of the organization (Dawson & Steeley 2003). To evaluate the economic value investors look at quickly considerable concrete resources such as plants, equipment, property and typical inventory or economical holdings in subsidiaries etc. When value investors discover an inventory where the existing rate is below a conservative calculate of the concrete resources a great cope can be created and the larger the gap between the rate of an inventory and industry of its tangible assets the more eye-catching the economical commitment is (Fama, 1970). Value investors look at certain actions when knowing whether an inventory is promoting at a discount. The most typical used actions are P/E and P/B percentages and dividend yields. They look for percentages below the conventional stage of the organization and silver market average or stocks with above-average results generate. This often causes value investors having a personal preference for business stocks or organizations in the financial service or programs industry. However, one must know that a cope is not always the low cost stocks. The factor that an inventory is cheap does not instantly mean that it is plenty. The organization behind must be a quality company promoting at a low price compared with the above-mentioned requirements, not a bad organization promoting at a low price.