In this section, we aim to provide brief detail about the forecasting power of technical analysis. Criticized by two key hypotheses in economics and finance, the Random Walk Hypothesis and the Efficient Market Hypothesis (EMH), the profitability of technical analysis has been an argument in the community of economists and financiers for ages.
The Random Walk Hypothesis states that stock market prices evolve on account of a random walk. Therefore, the prices of the stock market cannot be predicted, and this is consistent with the EMH that the market is efficient. According to these two hypotheses, they imply that there should not be any discernable and exploitable pattern in the financial data. On the other hand, this means that traders could not make any profit from technical trading rules generated by technical analysis, or it is impossible to beat the market.
Let begin to consider the three forms of the EMH: weak form, semi-strong form and strong form [31], and the detail of each one are as follows [11, 97].
• The weak form of market efficiency theorizes that a share price at any point in time reflects all the information contained in its price history, and it implies that the past pattern of price changes cannot be used to predict future price changes.
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• The semi-strong form of market efficiency states that a share price at any point in time reflects all readily available information which could affect the share’s price. It means that published information cannot be used to predict future price changes, but there is still other information not publicly available and is not fully reflected in the price. This implies that profitable trading, sometimes called insider trading, can be done by using information not yet known to the public.
• The strong form of market efficiency indicates that a share price at any point in time reflects all information available, including both public and private (non-public) information, and this implies that there is no information, published or not, which investors and traders can use to predict future price changes.
Since the Random Walk Hypothesis and the EMH emerged, there were a number of studies in the 1960s and 1970s that supported these hypotheses as follows: Alexander in 1964 [1], Fama in 1970 [31], Fama and Blume in 1970 [34], Jensen and Bennington in 1970 [50]. However, there were also a number of studies standing on the other side, rejecting these hypotheses. For instance, Pruitt and White in 1988 [82] developed the CRISMA trading system which showed positive returns over a 10-year period using transaction costs at 2%. Brock et al. in 1992 [13] successfully produced significant excess returns by investigating stock index trading on the S&P 500 using two test trading strategies, moving average and trading range break. Moreover, Bessembinder and Chan in 1995 [9], demonstrated that simple trading rules could be profitable as well, however, regardless of transaction costs. All these three works revealed that positive excess returns, compared with buy-and-hold, can be accomplished using technical trading rules. In terms of the effect on technical analysis caused by these hypotheses, there are different opinions about the profitability of technical analysis.
Many believe that technical analysis can be used to predict the stock price and make a profit, while some claim that it has predictive power but not enough to make any profit.
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In addition, in the academic and financial world, many academics and fundamentalists don’t agree with that and are convinced that it has no forecasting ability at all following EMH. On the other hand, technical analysts do not believe that the market is inefficient in the process of absorbing available information into security prices; they instead agree that prediction of market prices is difficult. As a result, there have been substantial arguments for a very long time among financial theorists; as can be seen, it is inconclusive whether technical analysis is profitable or not. Apart from those arguments, it is nevertheless widely applied in practice, and its apparent profitability can be seen from much evidence, some of which we discuss next.
Let us begin with the study of Taylor and Allen in [100] on behalf of the Bank of England; their study revealed that roughly 90% of financial institutions dealing in foreign exchange in London utilized information derived from technical analysis to some degree. Following the consideration of using technical analysis in stock markets of Brock, Lakonishok and LeBaron in [13], they found evidence that simple technical trading rules had predictive power and concluded that the findings of earlier studies that technical trading rules did not have such power were ‘premature’. Further, the studies of Sweeny in [99] and Levich and Thomas in [57] concluded that technical trading strategies may be profitable in the case of foreign-exchange markets [11].
1.3 Contribution
This thesis provides an empirical study of using genetic programming (GP) to evolve robust technical trading rules for monthly, weekly and daily trading with both single-objective and multi-single-objective methodologies and it also provides fundamental analysis on the technical trading rules generated from multiple experiments with both the single and multi-objective configurations. This first strand of work can be considered as within the interface of evolutionary computation and finance. In addition, the second strand of
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work explores an aspect of Grammatical Evolution (GE) through the development of strategies to reduce the number of invalid individuals resulting from incomplete mapping. It introduces new approach for grammatical evolution (GE) with a new suite of operators to bring about an improvement on the GE search process. From these two distinct strands of work, this thesis adopts a multi-disciplinary approach and therefore produces contributions spanning both the computer science (particularly in the field of evolutionary computation) and finance domains. In order to make a clear distinction between the contributions if the thesis to computer science and its contributions to finance, the specific contributions for each domain will be indentified separately as follows.
1.3.1 Contributions to Computer Science
1. Proper practice using training, test and validation sets (three data sets methodology) for model selection to choose the rules, and using varied data spits to test the robustness and sensitivity to the data of the rules. This proper practice, used in the work of Chapters 3 and 4, makes contributions across both computer science in the field of evolutionary computation (EC) and the finance domains.
2. General lessons related to the significant open issue of generalization, which indicate that the unpromising results of previous attempts to evolve profitable trading rules, were due in part to a methodology that led to poor generalization.
3. We demonstrate that a multi-objective methodology, which resists over-fitting by spreading functional complexity of the solutions throughout different expressions of each objective, can be use to increase the level of generalization, supported by convincing evidence from the results of multi-objective optimization in Chapter 4.
4. We provide additional evidence that making appropriate parameter decisions can lead to a successful GP application even in a dynamic problem environment, and
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this has been shown by using dynamic forms of mutation (4 different mutation operators) during a single GP run. This evidence is supported by the results of the experiments in Chapter 3 and Chapter 4.
5. We provide a comprehensive empirical study of genome mapping methods for grammatical evolution in Chapter 5, and also develop a new suite of operators that effectively allow us to vary a GE search between GE and GP style, by changing the rates of application of certain operators. This new suite of operators leads to a new GE approach that appears very effective, in comparison to standard GP and standard GE, when tested on a range of standard GE and GP test functions. This new approach also allows us to find GE configurations that are also effective in the trading context (unlike standard GE).
1.3.2 Contributions to Finance
1. A new and thorough evaluation of the capability of genetic programming to evolve profitable technical trading rules that can outperform a buy-and-hold strategy. In previous work using GP for trading, results have been often unpromising, but in Chapter 3 we replicate the more promising work of Becker and Seshadri, and we also build on that work in several ways and test the technique in several different trading environments. This enables us to identify, with more confidence than in the work of previous researchers, the conditions in which GP-evolved technical trading rules may be able to outperform the buy-and-hold strategy.
2. The development and evaluation of several multi-objective approaches to evolving technical trading rules with genetic programming. With comprehensive experiments in Chapter 4, we find a subset of configurations (mainly concerning the choice of objectives) that lead to robust and successful trading rules. We also
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find that, unlike the single-objective approach, the use of the multi-objective approach can lead to successful weekly trading.
3. The results of the experiments in Chapter 3 and Chapter 4 also provide additional evidence to support the forecasting ability of technical analysis working with evolutionary algorithms, regardless of Random Walk and Efficient Market Hypotheses.
4. By analysing the technical trading rules that arise from multiple experiments with both the single and multi-objective approaches (in sections 3.5 and 4.5), we are able to contribute some insights into trading strategies that are appropriate for different trading environments.