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It is interesting to note that it is the simple patterns that appear to work better than more complex rules with many conditions. There may not be enough data to justify

building complex rules with more than 2 parameters, and JBnding such rules might be just progressively fitting noise in the market. The single largest Z-score recorded in the entire investigation was for the condition “Good Example = 4”, which is clearly a 1 parameter rule. This result that simple rules are more robust is consistent with the findings o f Oussaidène et. al.[OCPT97].

The fact that the out-sample results have positively skewed Z-scores implies that charting is not completely random and hence could have value. This is not a proof that charting works, just a demonstration that a chartist’s interpretation can be systematic, which in turn demonstrates that charts do present information that is useful. However, this does not constitute a proof o f the value o f charting because:

1. It is impossible to determine from the information available whether the trader has out-performed or under-performed the market. Charting will only be considered of value if it permits the traders to out-perform the market on a risk adjusted basis.

Otherwise, the trader could simply invest his capital in an efficient index tracking fund'^. However, debate exists about whether risk adjusting returns (a theoretical finance activity) is sensible for a trading scheme that should not w ork if theoretical finance is correct. This is covered in greater depth in Chapter 6.

2. It is impossible to say what extra information the trader uses in his everyday trading activity. Schwager[Schw84] asserts that methodologies such as charting, technical analysis and fundamental analysis do not have intrinsic value in themselves, but that they provide the trader with a firamework with which to think about the market. If the information content comes from elsewhere, then it is not impossible that charting is o f no value other than as being a mental framework. It is clear from these results however, that there is some intrinsic information content to the chart formations. If this was not the case the GA would not have been able to find as many rules as it did that had out-sample Z-scores significant at the 95% level, but it is not clear as to whether this would aUow a trader to outperform the market.

Many financial theorists claim that investing in portfolios that track stock market indices is, on average, the best an investor can do.

One criticism o f this work would be that this is only a single sample path, and it could all be coincidence from a random data set. This is unlikely as it can be seen that the

distributions are starting to appear in figure 4.4, and 17% of the rules are significant at the 95% level. If this was completely random, and given that the distributions are starting to appear, it would be expected that approximately 5% o f the rules would exist in this section of the Z-score distribution. It is interesting to note that the distribution is loosely normal, and so there is probably some random element at work.

Stationarity could be an issue - the time dependencies were removed from the trading history by probabilisticly dividing the records into the in-sample, validation and out- sample sets, and it is possible that the nature of markets have changed over this period in an intrinsic way. For example, the rates and means o f information dissemination have changed and new financial products have become available. Moreover, the chartist himself may have changed some opinions about charting over the 22 years that he has been developing the system he currently operates. It is also not inconceivable that the position sizes he enters now are different from those he used to place when he first began developing his system. The larger the position sizes, the greater their effect on the market and so shghtly different approaches need to be taken towards trading. The system found some patterns that provoked some interesting reactions from the trader. These reactions were usually one of two types:

1. “Hmmm, I never thought o f that, that’s very g o o d ...”

2. “This appears to w ork well, but the pattern doesn’t mean anything.”

It is interesting that some of the trader’s ‘home-grown’ patterns had very high, but negative Z-scores. This means that the trader had thought that he had found a pattern that worked, but in fact, it had a rehably lower success rate than simply behaving randomly.

4.6 Summary

The main points of this chapter were as follows:

• The problem was to capture expert knowledge about trading in financial markets without the need for knowledge elicitation exercises.

• The expert knowledge under study in this chapter was a charting methodology - a way o f trading based on the analysis of the movement o f the market price.

• The data available was a history o f past trades - the market conditions that led to the trade being entered and the trade outcome.

• This data was analysed to see if any of the patterns worked at statistically significant levels.

• A genetic algorithm rule induction engine was used to build multi-variate rules. • Simple rules fire more often and appear to work more reliably than more complex

rules.

• Information is definitely contained within charts, but it not clear that their analysis would enable the trader to out-perform the market.

Chapter 5:

The Continually Adaptive