涉外船舶与海洋工程英语

张丹 10070312

Literature Review: Of the Value of Art and Morality in The Picture of

Dorian Gray

The paper will summarize the reflection of the value of art and morality of Oscar Wilde in The Picture of Dorian Gray.

There is no doubt that this book caused much controversy and was condemned immoral by many critics for its degenerate plot after its publication. At that time-the Victorian time, this work obviously was incompatible with the time. The people just looked it from a superficial sight. So this work didn?t get much good reflection.

But now, more and more critics began to review this book in an objective way. But criticism on this book was focused on its characteristic of aestheticism for a long time. In the A History of Criticism of English Fiction, it says that the name ?Oscar Wilde? nearly became the synonymy of aestheticism. Even in the A History of English Literature, it says that The Picture of Dorian Gray nearly is the unique aesthetic novel in the English literature history. We can see that many people concentrated on this point and ignored its other significances, such as the value of art and morality.

Oscar Wilde is the representative of “art for art sake”. He also advocated that art is higher than morality. He once said that there is no such thing as a moral or an immoral book. Books are well written, or badly written. That?s all. But it didn?t get rid of morality. In this work, we can find that Oscar Wilde was not trying to abandon the conscience, and what he advocated is instinctive morality with a higher form.

In my part, both art and morality are the essential parts of Wilde?s works. We couldn?t only focus on his aestheticism, but on his traditional moral values.

References

刘茂生：《王尔德创作的伦理思想研究》，华中师范大学出版社，20xx年，第68页。

常耀信：《英国文学通史》（第二卷），南开大学出版社，20xx年，第669页。 殷企平，高奋：《英国小说批评史》，上海外语教育出版社，20xx年，第117页

涉外船舶与海洋工程英语

张丹 10070312

《王尔德全集》（第一卷），荣如德，巴金等译，中国文学出版社，20xx年，第3页。

第二篇：Chapter 2 Literature review

Chapter 2 Literature Review

The objective of this chapter is to review some relevant articles and journals to describe the technical analysis and mechanical trading strategy. The order of the review is following the causal relationships with the technical analysis. The first section is to introduce the origin of the technical analysis and then to explain why this is work, also the efficiency market hypothesis (EMH) will be mentioned. Secondly, this part will discuss the something relevant with mechanical trading strategy. For the last part, some regular trading rules will be introduced.

2.1 The cornerstone of technical analysis

Two hundred years ago, technical analysis was an original form of investment analysis where investors were able to develop fundamental analysis on the basis of financial information, (Reitz, 2005). Traders tried to use different technical analytical method to make the profit. The rationale of the technical analysis is based on three major assumptions. (Murphy John, 1994)

(1). Market behavior includes and digests everything.

“Market behavior includes and digests everything” constitutes the basis of the technical analysis. If this condition is not accepted and understood by the traders, then what we discuss will be meaningless. Chart analysts think that any factor could affect the price of some kind of commodity, fundamentally, politically, mentally or any other factors, these all factors actually are all reflected by the price of the commodity. So research the change of the price is the vital thing we have to do.

The substance of this premise is price changes must be reflects the relationship between the supply and demand. If demand exceeds supply, then the price will increase. If the supply exceeds demand, then the price will fall. This law is complies with all the economic and fundamental prediction. Let’s reverse the procedure of the last sentence, so if the price increases, whatever the specific reason, demand must beyond the supply, it is optimistic from the economic perspective. If the price declines, it must be bearish from the economic perspective. In the final analysis, technical analysis is just simply researching the economic base through the price changes indirectly. Chart itself does not lead to change of the prices. it just shows the different mentality in the market.

(2).Prices tend to move in trends that persist for an appreciable length of time.

The whole meaning of researching charts is to identify the trend at the beginning stage of the trend. Hence, we can ride on or follow the trend. If this assumption is correct, for a trend is shaping up, the next step is always keep going, it is unlikely that the existing trend will change, this is also applied by Newton’s laws. In other words, consistent with a shaping trend until the trend is change. This is the origin of the trend following.

(3).History will repeat itself

Technical analysis and market behavior have an inextricably connection with human psychology. Take price patterns as an example, they were shown by some specific forms. And these patterns reveal the mental activities of people’s attitudes about the market. Hundreds years ago, these patterns have already been known and classified. Since it worked in the past, based on the psychology, it will be also useful in the future.

Technical analysis assumes that the stock price is determined by

demand and supply, and the information of the specific stock is all reflected in the price, (Witkowska & Marcinkiewicz, 2005). However, some stock prices responding sluggishly from variety of investors’ opinion and information set due to the inefficiency of the information spread. Both public and private information shift the demand and the supply to a new equilibrium price, (Kwon & Kish, 2002). As indicated by the technical analysts, in terms of the delayed movement toward equilibrium, they can benefit from the observation of patterns or trends of past price, (Kidd & Brorsen, 2004),besides , they can also conclude the information about the potential fundamentals, (Reitz).As a result, it is the detail of price movements that attracts the attentions of technical analysts instead of the fundamental information, including financial statements, company policies, or any financial information about the company. The markets’ strength and direction are determined by the various indicators, including charts and patterns, etc., (Witkowska & Marcinkiewicz, 2005).

There is an assumption that the released information can’t promptly influence the prices to a full extent, based on which the disequilibrium models were proposed by Beja & Goldman, (1980) and Grossman & Stiglitz, (1980). In their point of view, the major

traders begin to operate without completely facilitating the equilibrium trend of price, resulting from the risk aversion, capital constraints, or position limits that lead to the trends of price, from which the technicians can benefit after detection. Meanwhile, there are two contradictory explanations put forward by Brock et al. (1992) for the predictability of technical analysis: (1) the inefficiency of market leads to the fluctuation of prices on their fundamental values, and (2) the efficiency of market results in the explanation of predictable variation by time-varying returns of equilibrium.

Therefore, what stated above has proved the importance of technical analysis, and due to the insufficiency of market statistics, some information is still payable for all investors (Blume et al. 1994). Thus, the effectiveness of technical analysis is contradictory with the Efficiency Market Hypothesis (EMH).

2.2 Technical analysis and Efficiency Market Hypothesis The random walk, as related to the Efficiency Market Hypothesis (EMH), indicates that the information flow is unpredictable and arbitrary, which enable the comprehensive and immediate reflection on the stock. Moreover, the changes of price can make

known the coming information with releasing the previous information. As a result, the purchase of a diversified portfolio can enable a investor of little experience obtain a rate of returns the same as those obtained by experts. The market, in an efficient sense, provides both the above-average returns and risks for the earning of investors. The spurious correlation between financial variables, data snooping and lucky can lead to a lot of trading regulations for technical earnings in an efficient market. In the affirmation of advocators of efficient market, sufficient time and massaging of data series can enable a man to tease most of patterns from data sets (Malkiel, 2003). On the basis of EMH, the fundamental analysis takes in the weak form of EMH without considering the semi-strong form. On the other hand, the fundamental analysis takes in the semi-strong form of EMH without considering the weak form, that is to say, apart from the influence on investment, the fundamental principles and factors of economy can be seen everywhere with a reaction on the price (Leigh et al. 2002).

The effectiveness of random-walk theory, put forward by Fama (1965), was analyzed by Mandelbrot and Hudson (2004). In the claim of Fama, had the random-walk theory been a precise account

of reality, the technical or chartist procedure at all levels would be useless for predicting the stock prices.

Despite the contradictions existed in both of the technical analysis and the EMH, there remains the truth that he market is hard to precisely efficient, with the insiders of market having no motivations to obtain the information as soon as possible (Grossman & Stiglitz, 1980). To be frank, technical analysis is closely related to the efficient market. The rules of technical trading can be more profitable resulting from equilibrium market and information reactions, influenced by potential factors including the decreasing cost of information and computation and increasing application of technical analysis (Kidd & Brorsen, 2004). Currently, the technical analysis has been found related to the efficient market by some scholars.

In 1989, Brorsen and his partners found that the price control would be sped up by the broadened technical trading, so would the variance and kurtosis of price movements. And from 1962 to 1996, Kwon and Kish applied moving average, moving average with momentum, and moving average with volume testing NYSE and NASDAQ indices (2002), whose results suggest that there is a

significant weakening over various trading rules with time going by. It is also proved by Fang & Xu, who in 2003 combined technical analysis and conventional time series to forecast testing three Dow Jones Averages over the past century. The results showed that forecasting ability of technical trading rules has declined in the last several decades. In the in the UK market, we also can find some similar results. In 2005, Bokhari et al. made a research. The group selected 100 companies from FTSE 100, FTSE 250, and FTSE Small Cap at random and tested these companies over the period of 1987 to 2002 through moving average and trading range breakout. The results provide the powerful evidence to show the decline of predictive ability over time. With the pattern recognition algorithms in US dollar/ British pound spot foreign exchange data over the period of 1989 to 1997, Dempster and Jones (2002) found that there is some links between pattern’s attributes and profitability. However, the results failed to be used for trading rules developing. As for other proofs, in 2004, Chang (2004) and his group investigated over 1559 trading rules and discovered that there do exist in some so-called forecasting power, but only a few rule could generate positive excess return when considered as the transaction costs. Since the forecasting power is well known and adopted, it has been eliminated for the usual trading rules.

The above conclusions can be demonstrated by several factors, including the increasing access to and speed of the obtainment of relevant news (as news from the Internet), the up-rising interest in public stocks, the advanced computing technology, the application of searching engines and the influences exerted by popular models of market behaviors (Summers et al. 2004; Bokhari et al. 2005). The changes of prices have seen more random or strange movements caused by these factors. Consequently, the technical trading has been gradually required to drive the market to equilibrium (Kidd & Brorsen, 2004). Moreover, the historical data has discovered the individual stocks, the pricing of which is determined by the predictable patterns or irrationalities that are impossible to insist and fail to offer a significant return (Malkiel, 2003). The market can be more efficient under this step. The value of technical analysis is future stated to be effective by more literatures. As indicated by Dawson & Steeley (2003), the technical trading patterns collected in the UK stock data from 1986 to 2001 are proved to be good for market efficiency without any doubt. The rules of head-and-shoulder are applied to many spot exchange rates for several periods with an assumption that the technical trading rules are failure in normal circumstance.

Technical analysis, being a conventional method to analyze investment, offers unlimited set of tools and signals to analyze the market in an interesting way, (Zielonka, 2004), which, therefore, is still widely applied in practice regardless of the fact that in the academic field, technical trading rules enjoy much weak evidence. Technical trading should be justified to some extent in the case that a lucky trader picks up patterns without being detected by rational expectation models (Fyfe et al. 1999). What’s more, the EMH is seemed to be violated and the abnormal returns are likely to be earned by the abnormal phenomena of firm size effect, January effect, Monday effect, and so on.

Furthermore, an artificial neural network is applied by Sosvilla-Rivero, Gonzalez-Martel and Fernandez-Rodriguez (2000) to the Madrid Stock Market, which leads to the discovery that it is in the ‘bear’ market and ‘stable’ market episodes not in a ‘bull’ market that a buy-and-hold strategy is always inferior to the technical trading rule in the absence of trading costs. One criticism I claim is that if one shows no interest in finding a signal that will be developed into a complete system, then it seems to be of slight importance to beat the market in the absence of the costs. Then,

the existence of ‘bull’ and ‘bear’ markets seems to be a na?ve premise.

Genetic programming is used by Neely and Weller (2001) to demonstrate that in the process of US foreign exchange intervention, profits can be gained from technical trading rules. Prevailed dynamic strategies for asset allocating are compared extensively trough simulation by Cesari and Cremonini (2003), which results in the finding that technical analysis performs well in Pacific markets. A questionnaire survey was conducted in February 1995 on the use of fundamental and technical analyses by foreign exchange dealers in Hong Kong, the results of which are reported by Lui and Mole (1998), containing the findings that more than 85% of respondents depend on both methods and, again at shorter time horizons, technical analysis was more popular.

It is shown by these results that larger abnormal returns are generally produced by the technical trading systems instead of by the passive buy-and-hold policy. Different mechanical technical trading systems are tested in these studies, evidence consistent with technical analysis is found to be capable of identifying trends for profitable trading.

Technical analysis is just a part of trading, more importantly, self-discipline is also required. Hence, you need to keep an eye on your trading, and another on yourself to be a mechanical trader.

2.3 Classification of traders

It will be very exciting and liberating to take control of financial in the future by trading to make profit. But this is not always easy to do, there are a lot of factors needed considered. For instance, holding period, market selection, stock selection and market timing are extremely important. These factors will influence if the result is gain or loss directly. But the one thing matters the most maybe is the trading style: How the traders’ selection and how they execute their trades. In the realistic market, two most common kinds of traders are discretionary trader and mechanical trader, or so called “system-generated trader”.

There are many discretionary traders out there who struggled with their method due to its natural arbitrariness and subjectivity which give them too much room with their emotion, and the emotion will drive them to disaster. On the contrary, other traders who use purely mechanical trading system also struggled with their own method because of its rigidity and sophistication.

Both these two kinds of traders have their own problem. There is a better option which is often ignored: probability based trading. With the development of spreadsheet applications and computerized data bases such as Excel, many pitfalls during the trading time can be avoid by traders, mean while, traders can enjoy the advantage of the both discretionary and mechanical methodologies. Next, a comparison among those kinds of traders will begin to show which method is the most profitable one.

(1).The discretionary trader

There are a lot of resources can be used by discretionary traders to make the decisions, such as fundamentals, technical analysis, trading express, moon phase and whatever they can think of, according to Tusha S. Chande’s textbook(2001). The trade decisions made by them are based on their own understandings of indicators and price patterns which can provide the sense of control, this is the major reason why they trade arbitrarily. There are no rules and disciplines during the trading, (Scott Andrews, 2011).

Despite of the feeling of control is attractive to the discretionary traders, there is little success example from them. There is more than ninety percent of this kind of traders who failed in the end

according to the survey. Many discretionary traders may think they failed just because the poor performance of money management. And while true, the poor money management just speeds up the fail, not the root causes of failure, (Scott Andrews, 2011).

The new traders who with the naive expectation, they may confused with capability and luck. Even worse, this will bring them a wealth illusion to make them lose more market and do not want to leave. All they want is regaining the money back.

Malcom Gladwell (2008) asserted in his book the “outliers”, the most common character of the elite in all fields is they must make the effort of more than 10000 hours practice. So they can be the best in their industry. Comparing with the trading industry, 10000 hours are equal to 7 years trading with no absence. However, more and more novices enter into this industry and with the false confident from their early success, easy money make them trade more aggressive and frequent than they should.

Another problem about the discretionary trader is that their memories may occupied by some well performed trades they have made lately. This unconscious bias may lead to over optimism and

make their trades more aggressive and beyond their capability,

In the real world, it is not easy for discretionary traders to get rich. With the changing market conditions from bullish to bearish, the results of traders also tend to change significantly from feast to famine. The traders’ accounts will often end up with huge losses or even worse, a marginal call by the broker.

There are many draw backs summarized by Scott Andrews (2011), the first one is hard to identify remarkable skills and random results. Secondly, charts understanding can be distorted by good result recently. Thirdly, emotions will get into proper trading execution. Finally, it will take many years to achieve success.

However, the elite trader belongs to “discretionary trader”. They could beat all the mechanical traders. Their biggest advantage is that they can change the key variables of each transaction, so they can change the scale of positions more wisely than the mechanical system traders. The discretionary traders can also change the relative importance of trading variables, so they can easily switch the models between the trending following model and the contrarian model, as well as the time frame switch. Those unusual remarkable profits are often from this strategy. And the large

amount of profitable trades is often the key to distinguish between good performance and excellent performance, (Tushar S. Chande, 2001).

But for the most of traders, to be a mechanical trader could maximize the possibility of success.

(2).The mechanical trader

Mechanical system traders’ goal is to select a time frame, identify trends state, and predict the future direction of the trend. Then the mechanical system trader must forecast the trend of the transaction, control loss or take profits. The trading rules must be specific, able to cover every aspect of the transaction. For example, the trading rules must specify how to calculate the number of contracts will have to deal, and the use of the admission order type. These rules must specify the location of the stop loss point of the initial money management. The trader must automatically implement the system and may not have any ambiguity.

Mechanical system traders are extremely objective, they use of a relatively small number of rules, and must remain non-emotional when loss or profit occurred. The most important feature of

mechanical system is that it must keep constant with rules. The system is usually to calculate the key variables in the same way, while ignoring the reaction of the market. Even if some of the indicators are based on the volatility of the market, all the rules of the system are fixed, and have the priority. So the mechanical system traders do not have the opportunity to modify the rules based on the fundamentals events, unable to effectively adjust scale of the position to match with the market. This can be both advantage and disadvantage. The main advantage of a mechanical trader is that they can trade more markets than the discretionary traders and they are also able to use of the diversified portfolio, (Scott Andrews, 2011).

To design different styles of trading systems, they only need a little judgment from traders. For example, we can have a clear and definite rule to increase the position size. This may include the fundamentals and technical analysis. As long as providing an explicit rule, in order to ensure the implementation of the consistency of the process.

Like the discretionary traders, mechanical traders also have a lot of disadvantages: First of all, it is difficult to create a profitable trading

system if there is no extensive market experience. Secondly, a profitable trading system needs an outstanding programming skills or an expensive outsourcing of these efforts. Thirdly, difficult to create a unique system compared with other million systems in the real world. What is more, over optimized system may be underperformed due to the unrealistic expectation set. Finally, when the market tends to volatile, the stop-loss will occurs more frequently, (Scott Andrews, 2011).

In addition, the discretionary decision is not completely eliminated. Programming a complete trading system also requires some subjective judgment, including: selection criteria, buy signal, sell signal, as well as stop loss and position size.

Most of the traders allow the system to have some small amount of discretionary decisions, such as not acting on a signal when volatility spikes. As same as discretionary traders, the mechanical system traders will also focus on some technical information, but will weigh the information before to make its decisions.

This article is focus on the mechanical system traders, so the probability based trader will not be mentioned. On the next stage,

two most popular trading indicators will be introduced.

2.4 Simple price-related indicators

Moving average is the most widely used rule among the traders. This rule is cataloged into price related indicators and calculated or formed from prices, such as patterns, trend line, and other algorithmic indicators.

There are many trading rules derive from moving average, and the most popular one is about two moving average. The buy signal can be acted when a short period moving average crosses above a long period average. This indicates that the trend will begin to increase. On the contrary, the sell signal can be acted when a short period moving average crosses below a long period average. And the buy signals must with an upward slope of the moving average, so called “golden cross”. When the moving average with a downward slope, the sell signal executed, and this is called “dead cross”. The moving average strategy is to smooth out the volatility of prices, (Brock et al. 1992).

Brock et al. (1992) made the used of Dow Jones Industrial index

(DJIA) from 1897 to 1986 to look into the predictability of the moving average. There are four common null models applied by them with bootstrap methodology: these models respectively are, a random walk with a drift, autoregressive process of order one (AR1), generalized autoregressive conditional heteroskedasticity in-mean model (GARCH-M), and Exponential GARCH (EGARCH) which all gained different returns from the Dow Jones Industrial Average index. And their results prove that the technical analysis was working with the index. But they did not take transaction fees into consideration, this may also influence the result in the end with the same methodology. Hudson et al, (1996) and Bessembinder & Chan, (1995) repeated the Brock et al.’s work in different market which were in the UK and six Asian stock exchange. In general, they basically agreed with the predictive ability of moving average trading strategy that Brock has found. However, after they calculated the transaction fees, the excess return was wiped out by the cost of transaction. Hommes (2006) and LeBaron (2006) also did some research on moving average, they divided technical and fundamental analyst into two teams, and they were all rational people. Both teams utilized the different trading system about the moving average. Finally the target was to test the profitability of two teams.

Trend following rule is another popular rule among the traders, also classified into price related indicator. Trend following is one of the most general acceptable rules in technical analysis, and most profitable one, because it represents the principle that the price will go to the least resistance path. Hence, the primary task of the trader is to track the direction of the trend until it ends. Once finding the trend, the position must keep consistent with the direction of the trend, Link. M (2006). Traders usually use the price series to find the continuous or reverse patterns as trading signals, and ignore the external information, (Famer, 2000; Fang & Xu, 2003). And Dempster and Jones (2002) classified the price patterns in the market, and found out the connection between the patterns and profitability. Then they built a trading system based on their research, but after they test the data, the results were mixed. Michael Covel (2006) interviewed some top trend following traders and pointed out the trend following strategy were currently efficient. This strategy can still profit in the market in United States.

The stock market is always changing, a single strategy does not make the money completely safe, and sometimes when there is no obvious trend in the market. Hence, switching the strategy is totally

necessary, the following will introduce another indicator, as a complementary to the price related indicator.

2.5 Momentum and Multi-variable indicators

Many studies about the profitability of technical analysis were established recent years. Nowadays, more and more indicators have been created in order to make more money from the financial market, many research have shown it is feasible.

For example, a momentum trading method uses the physics classical dynamics that if the stock price change in a certain period, then it might represents the speed and trade volume is the inertial mass, (Wang & Pandey, 2004). In the global equity markets, Chan et al. (2000) conduct different momentum strategies to test the profitability. And the result shows that momentum trading strategy actually can profit in the stock market statistically and economically, especially for short holding periods, and more over, profit are consistent with the same practice. Nonsynchronous trading method cannot completely explicate that after the profit continue to increase with the increasing trading volume. When applying in the Dow Jones industrial index for ten years from 1992 to 2002, (Wang &

Pandey, 2004) that the momentum trading method also indicates the relatively high return, and after this, the price trend was always can be explained by some delayed reaction to fundamental information, (Scott et al. 2003).

In the book of Link .M(2006), He find that the oscillating indicator is an indispensable trading tool, because the oscillating indicators often pass on some trading information, including the trend strength, trend direction, and the potential trend reversal. Oscillating indicators also helps to manage the timing of buy and sell. Hence, if this method is used properly, it can improve the profitability during the trading. Especially when the market shows a interval trend, this indicator could judge the bottom and the resistance. Even the market shows a strong trend, this method could also work together with the trend following method to make the trading more accurate. There are a lot of momentum indicators used in the market, for example: Relative strength index (RSI), True strength index (TSI), and Williams %R (%R).

Another trading rule was developed based on neural network and genetic algorithm, and with the help of the powerful computing program, aiming to find the nonlinear relationships between profits

and technical indicators. This technology does not need to rely on any statistical constraints, such as data mining, and more over, it can test the infinite rules. From the investigation of Fyfe et al. (1999), he utilized a genetic program to test a quoted property investment company from 1980 to 1997 with a series of prices. They finally found that the rule generally performed better than the buy-and-hold rule. In spite of, another study by Neely, (2003) who did a research with genetic programming about to test the risk-adjusted, ex ante, optimal, trading rules for the S&P 500 Index from 1929 to 1995. The results have the different opinion on last one, showed that the rules did not significantly better performed than the buy-and-hold policy on a risk-adjusted basis, and point out that when using one trading rule, should be very carefully clarified according to the risk adjustment.

Some research seeks to study on multi-component indicators. CRISMA trading system, for instance, this system collects the moving average, relative strength, and cumulative volume to operate as a filter system for selecting stock targets in US equity market, (Goodacre et al. 1999). And this trading strategy works well in the US equity market, (Pruitt et al. 1992). But in the UK market, the result shows that it cannot earn abnormal returns after adjusted

risk and costs, (Goodacre et al. 1999). Fang & Xu, (2003) used a trading method mixing with technical analysis and time series to forecast the predictive ability of Dow Jones Averages over 100 years from different aspects over 100 years. In the long run, this rule is in the costly trading environment, and be able to capture the higher potential profit, even requires less transaction cost.

There are numerous technical indicators in the market, but knowing the indicators is just a part of trading, it is more importantly that using of these indicators to create a trading system, and then implement the system mechanically. After experiencing the ups and downs in the market, and do apply what learned from the market, face the loss or profit calmly, finally must recognize that the loss is just a part of successful trading and cannot avoid of. Therefore, a feasible mechanical trading system is necessary in order to survive in the market.

2.6 Mechanical trading system

To put it simply, the trading system is a tool used by traders to develop a set of rules for making trading decision. Setting trading rules may be very simple, and also can be very complicated, there may be more than 10 conditions to meet at the same time in order

to carry out the transaction. As a complete system, it requires not only to provide an entry signal, but also should have a exist signal. Put to use a purely mechanical trading signal, regardless of whether the signal provided by the computer, such transactions can be collectively referred as the systematic traders. In other words, as long as they see the signal, they will take action according to the signal without hesitation, (Link. M 2006)

The most important reason to use the trading system that it has a character called "statistical advantage". The meaning of this common terminology is that we can test the system, and the average trading profits, including all losses and profits, finally, the result is a positive number. The average trading profits should be large enough to make the system worth using, (Tushar S. Chande, 2001)

The reason why mechanical trading systems works is that they force traders to execute the trade may feel the fear and not to take the one they prefer. Sometimes they may force you to trade opposite to the crowd. The emotion of fear and greed can be wiped out by using mechanical trading system, (Art Collins, 2004)

Using mechanical trading system can maintain objectivity. And can withstand the news events, hot spots, tips, rumors, or the temptation of boredom. Maintaining objectivity helps to comply with the analysis of decision-making easily. Consistency is another important reason to use the mechanical trading system. Since only a few rules in the trading system is always used in the same way, so it can keep consistency of action in trading. In many cases, objectivity and consistency will come together, (Tushar S. Chande, 2001). Tao (1998) points out in his book, the completeness and objectivity of the mechanical trading system is to ensure that the reproducibility of the results. In theory, if any user in the exactly the same conditions, the operating results are identical too.

The mechanical trading system also has another key advantage: diversification, especially in cross-trading model, the diversified framework of across markets and across time. No one can predict when the market has a big fluctuated trend, but diversification is one way to increase in winning percentage, (Tushar S. Chande, 2001).

There are six major factors to build a profitable mechanical trading system summarized by Tushar S. Chande (2001):

1. The trading system must have a positive profit expect return.

2. The trading system must use less rules as possible to meet the compatibility of the different markets.

3. The trading system must have perfect parameter values in order to adapt multiple time periods.

4. If possible, the trading system must allow trading a number of contracts at same time.

5. The trading system must apply with risk control, money management and securities combination design.

6. The trading system must be fully mechanized.

Nowadays, technical analysis still plays a vital role in the financial market, but the suspect sound never ends. Especially in the academic field, they think the technical analysis is useless due to data snooping, market adapting, period effects, and arbitrary defining rules. But in fact, more and more people in the use of technical analysis and get a huge profit. And this situation cannot be attributed to good luck, it is totally based on the statistical sciences. It also makes more and more people start to research the extension of the technical analysis, which will lead to the new theory in the financial market. This paper will continue to test the usefulness of technical analysis in the Chinese stock market, in

reality, whether the two most popular different types of indicators will be profitable.

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