در محتوای پیش بینی تجزیه و تحلیل فنی

Notwithstanding its widespread use in financial markets and well-documented profitability, technical analysis is still perceived to carry useless information. This paper provides a possible explanation for this puzzle that goes beyond the standard self-fulfilling prophecy argument. If at least some of the asset price fundamentals are not currently observable, the oscillator model is able to infer regime shifts in the process of these variables through past asset prices. From this point of view, technical analysis can be interpreted as a cheap proxy for Bayesian learning.

The term “technical analysis” generally contains a large variety of trading techniques, which are based on past movements of the asset price and a few other related variables. The use of trading rules to detect patterns in the time series of asset prices dates back to the 1800s, when traders were clearly not able to develop a fundamental analysis on the basis of extensive financial information. Persistent shifts in supply and demand had to be detected in past price movements using simple to quite elaborate techniques. Many of these techniques are still applied by practitioners, as documented in Murphy (1999). However, the attitude of academia towards technical analysis is reserved at best, due to economists’ persuasion that financial markets are well described by the efficient-market hypothesis. Under these circumstances, it is obvious that trading rules not derived from a mathematically well-defined econometric or economic model, are bound not be very informative. Thus, the information content of trading signals concerning asset-price fundamentals is still viewed as largely worthless and often referred to as noise. This view was seriously challenged by a large body of empirical studies showing that, on the one hand, standard martingale models do not adequately describe short-run price movements (Lewis, 1995). On the other hand, the introduction of technical analysis into financial-market models seems to be justified by the results of micro survey data (Taylor & Allen, 1992), sustained ex post profits,1 and the overall ability of heterogeneous-agent models to explain the stylized facts of financial time series (Hommes, 2001 and Lux, 1998). Existing explanations of the presence of chartists are generally based on sequential trading and asymmetric information.2 If news is not commonly available on financial markets, uninformed traders may infer a signal from analyzing buy and sell decisions of informed traders or changes of the asset price itself. The resulting equilibrium can be described by models of herding behavior, as in Banerjee (1992) and Kirman (1993). Within such asymmetric information frameworks, technical trading might also be a suitable device for informed traders. Suppose that a trader receives what he believes to be private information, but that he cannot be sure if the information has already been incorporated into the asset price. Before changing positions, the trader applies technical analysis to check whether his information is indeed non-public (Treynor & Ferguson, 1985). As a general result of the models developed so far, it appears that the application of these techniques is rational from an individual trader's point of view, but leads to market inefficiencies such as misalignments and excess volatility. This is due to the self-fulfilling nature of technical trading, whether or not a given initial signal is useful to predict future asset prices. However, the existing literature has not yet explicitly addressed the question as to how technical analysis might infer information about the fundamental value of the asset price as well. To provide an information-revealing explanation of technical analysis, it is assumed that information on at least some of the asset-price fundamentals is available only with a considerable lag. We will argue that if the market price was indeed driven by a fundamental that is not yet observable, useful information about a possible regime shift in the stochastic process of this variable can be inferred by analyzing asset prices themselves.3 It is shown that within such a realistic informational set-up, the oscillator model, described as “Hold a long position when the difference between the short-term and the long-term average is positive, otherwise hold a short position” (Schulmeister, 1987), carries useful information for predicting future exchange-rate changes. The filter rules should be interpreted as a cheap proxy for Bayesian learning and cannot be deemed as irrational. Empirical support for this interpretation is provided by applying a Markov regime-switching model to various daily U.S.-dollar exchange rates. The rest of the paper is organized as follows. Section 2 outlines the informational set-up, using a standard learning model of foreign exchange introduced by Lewis (1989). The oscillator model is derived from rational sign prediction in Section 3. Section 4 reports on and discusses the estimation results and test statistics. The conclusions are summarized in Section 5.

This paper suggests a rationale for the application of technical analysis. In contrast to the assumptions of the standard asset-market approach, information about asset-price fundamentals is often only available with considerable lags. Within a more realistic informational environment, we find that the oscillator model based on moving averages is able to infer information about hidden fundamentals and can be interpreted as a cheap proxy of Bayesian learning. The logic of technical analysis presented in this paper suggests that its forecasting success will be state-dependent, because it predicts future exchange-rate changes sufficiently when regime shifts alter the time-series properties of unobservable fundamentals. However, the exchange rate is certainly not always driven by the dynamics of hidden fundamentals, implying that periods of technical-forecast dominance are followed by periods of standard fundamental analysis prevalence. To examine the empirical evidence of the filter rule, a Markov regime-switching model is applied to various daily U.S.-dollar spot exchange rates. Statistically significant parameter estimates provide support for the hypothesis that the oscillator model has proven useful for forecasting not only the direction but also the magnitude of future exchange-rate changes. Note that this is in contrast to the existing literature, which applies standard single-regime models to evaluate the forecasting performance of filter rules (Diebold & Nason, 1990). The parameter estimates appear to be robust with respect to the length of the short-run and the long-run moving average, which is in line with results of profitability studies. The empirical evidence from the Markov regime-switching approach indicates that technical analysis may have been used to learn about the new long-run fundamental value of the exchange rate. Since the information content of a trading signal is low when high exchange-rate volatility disturbs inference, technical analysis has consistently been assigned to the low-volatility regime. To sum up, technical trading appears to be a sensible learning device when the exchange rate is driven by hidden fundamentals and its volatility is sufficiently low.