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Technical analysis on foreign exchange: 1975-2004


The market for foreign exchange is the most heavily traded of all financial markets. Trading is undertaken by individuals and organizations in support of management of assets and liabilities in different currencies and trade of goods and services between national economies or those who profit from buying and selling currencies at different exchange rates (Hutcheson, 2000). The Bank for International Settlements (2002) tracks the global foreign exchange and derivatives market activity through its Triennial Central Bank Survey. The last survey (April 2001) reports that, average daily turnover was US$1,200 billion in traditional foreign exchange markets.

Besides, global daily turnover in foreign exchange and interest rate derivatives contracts, including what are considered to be “traditional” foreign exchange derivatives instruments, reached US$1.4 trillion. This is although the substantial decline in volume that the introduction of the euro represented, apparently due to the elimination of intra-EMS trading. The types of information used by market participants to form predictions of exchange rate movements and to trade consistently can be separated into two broad categories:

(a) fundamental information, such as inflation rates, GDP, unemployment rates, trade figures, etc.;

(b) technical information such as prices and quantities traded. The use of the former is known as fundamental analysis, and the latter technical analysis, upon which this research will focus.

Technical analysis claims that an investor can develops successful investment strategies using past information of prices and volumes (Murphy, 1986). This is an extremely controversial issue. Economists have traditionally been skeptical of the value of technical analysis, affirming the theory of efficient markets that holds that no strategy should allow investors and traders to make unusual returns except by taking excessive risk (Samuelson, 1965). According to Fama (1970) a market is efficient if “all prices reflect all relevant information”.

Specifically, in the weak form efficiency, no investor can earn excess returns by developing trading rules based on historical prices or return information. In others words, the information in past prices or returns is not useful or relevant in achieving excess returns. However, technical analysis is widely used in the financial community. Taylor and Allen (1992) report that more than 90% of surveyed foreign exchange dealers in London say that they use some form of technical analysis to inform their trading decisions and, at the shortest time horizons, 60% of respondents judged technical analysis to be at least as important as fundamentals when generating exchange rate predictions.

This evidence is supported by Hutcheson (2000) who studying the Australian foreign exchange market found that: “Fundamental analysis is regarded to be better at explaining long term exchange rate movements while technical analysis is better at explaining short term movements”. Additionally, Cheung and Chinn (2000) report findings from a survey of United States foreign exchange traders. Theirs results indicate that “technical trading best characterizes about 30% of traders, with this proportion rising from five years ago… (While) economic fundamentals are perceived to be more important at longer horizons…” These conclusions are in line with the work of Menkhoff (1997) and, Cheung, Chinn and Marsh (2000).

The previous issue moves the direction quickly to the next thing: Is technical analysis profitable? And specifically in this paper: Is technical analysis in the foreign exchange market profitable? First, being a controversial issue and although technical analysis is not new, it has traditionally been regarded with skepticism between academics. Since the publication of Fama and Blume (1966) most academics have agreed that the usefulness of these ad hoc forecasting techniques was probably close to zero. However, important research presents evidence that seems to favor technical analysis. Generally speaking, maybe the most publicized research is the study by Brock, Lakonishok, and LeBaron (1992). They analyzed moving averages and trading range breaks on the DJII from 1897 to 1985.

Contrary to previous tests, they found that both types of rules work quite well. Buy signals were followed by an average 12% return at an annual rate and sell signals were followed by a 7% loss at an annual rate. They say: “The previous conclusion that technical analysis is useless was premature”. Timmermann, Sullivan, and White (1999) revisited the paper attempting to determine the effect of data-snooping on the results. They also use data collected from the period following the original study in order to provide an out of sample test. They calculated a break even transaction cost level of 0.27% percent per trade for the best performing trading rule for the full period and found "that the results appear to be robust to data-snooping...

However, we also find that the superior performance of the best trading rule is not repeated in the out-ofsample experiment covering the period 1987-1996" and "there is scant evidence that technical trading rules were of any economic value during the period 1987-1996." Second, one obvious explanation of the apparent popularity of technical analysis could be that it is simply profitable. Hence much of the empirical work has been aimed at determining whether or not it is profitable.

For example: Dooley and Shafer (1976) obtained results in favor of filters. However, their results were not systematically positive. Sweeney (1986) analyzed the same type of filter strategy and found, once again, positive results with one third of them being statistically significant. Levich et Thomas (1993) studied the profitability of two simple trading rules as filter and moving average rules on the foreign exchange. Once transaction costs are taken into account (equal to 1.62% to 2.60% per annum for 65 operations), profits are however reduced but not enough for the profits to be close to zero. Neely, Weller and Dittmar (1997) using genetic programming techniques to find technical trading rules, found strong evidence of economically significant out-of-sample excess returns to those rules for each of six exchange rates, over the period 1981-1995.

Betas calculated for the returns according to various benchmark portfolios provide no evidence that the returns to these rules are compensation for bearing systematic risk. Empirical support for technical trading rules in foreign exchange markets can additionally be found in Kho (1996), Gencay (1999), LeBaron (1999), among others. The key finding of these papers is that following simple technical trading rules does lead to statistically significant excess returns. Of course, as a controversial issue this is, there is evidence in favor of market efficiency.

For example: Neely and Weller (2001) examine the out-of-sample performance of intraday technical trading strategies selected using two methodologies. When realistic transaction costs and trading hours are taken into account, they find no evidence of excess returns to the trading rules derived with either methodology. Thus, their results are consistent with market efficiency. They do, however, find that the trading rules discover some remarkably stable patterns in the data.

Prof. F. Rubio

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