CONTINUATION OF ORDER NUMBER 40020472

Continuation of order number 40020472

Continuation of order number 40020472

In very simple terms, technical analysis is a well-established method of forecasting future market movements by generating buy or sell signals based on specific information gained from previous prices. There are several schools of thought on technical analysis, amongst which, the 'chartists' argue that price patterns reflect market buying and selling activity, and market psychology.

The continuing prevalence and application of technical analysis within stock markets has come to be widely recognized, even amongst academic scholars,1 with the techniques for discovering any hidden patterns ranging from the very rudimentary analysis of moving averages, to the recognition of quite complex time-series patterns. However, the actual efficacy of such an approach remains something of a puzzle, particularly since the power of the statistical tests that are currently available for examining the efficacy of technical analysis is potentially diluted by a “data snooping” bias.

The subtleties in survivorship are demonstrated, quite convincingly, by White (2000) in a manner similar to that proposed by Jensen and Bennington (1970), where it was noted that: “given enough computer time, we are sure that we can find a mechanical trading rule which 'works' on a table of random numbers … provided of course that we are allowed to test the rule on the same table of numbers which we used to discover the rule”. In the present study, we set out to empirically test the efficacy of technical analysis within eight Asian equity markets, employing the two data snooping adjustment methods for non-synchronous trading and transaction costs proposed by White (2000) and Hansen (2005).

It was suggested, almost a decade ago, that the main reason for the obvious lack of research into the performance of technical analysis was essentially because the whole issue was too subjective and lacked any well-defined trading signals (Lo, MacKinlay, & Wang, 2000). Nowadays, although more study has been carried out into this particular area of research, we still find that the research community is unable to offer unambiguous conclusions with regard to the controversy surrounding the performance of technical analysis rules. It is, nevertheless, clear that a great deal of effort has now been spent on the study of many aspects of technical analysis.

In one of the earliest examples of such analysis, Treynor and Ferguson (1985) demonstrated that past prices, when combined with other valuable information, were able to produce abnormal profits. Netfci (1991) went on to demonstrate that simple trading rules with non-linear forecasting capacity were also capable of outperforming the classical Wiener-Kolmogorov linear model. Thereafter, Blume, Easley, and O'Hara (1994) developed a dynamic model to describe the ways in which trading volume could provide useful information for market participants.

In other empirical studies, sets of simple trading rules have been applied to the stock markets of the US (Brock, Lakonishok, & Lebaron, 1992) and the UK (Hudson, Dempsey, & Keasey, 1996), with the results of these studies reporting that technical analysis was indeed capable of providing significant economic information ...