This paper tests two of the simplest and most popular trading rules—moving average and trading range break—by utilizing the Dow Jones Index from 1897 to 1986. Standard statistical analysis is extended through the use of bootstrap techniques. Overall, our results provide strong support for the technical strategies. The returns obtained from these strategies are not consistent with four popular null models: the random walk, the AR(1), the GARCH-M, and the Exponential GARCH. Buy signals consistently generate higher returns than sell signals, and further, the returns following buy signals are less volatile than returns following sell signals, and further, the returns following buy signals are less volatile than returns following sell signals. Moreover, returns following sell signals are negative, which is not easily explained by any of the currently existing equilibrium models.

Simple Technical Trading Rules and the Stochastic Properties of Stock Returns

In this article we use a single unifying framework to analyze the sources of profits to a wide spectrum of returnbased trading strategies implemented in the literature. We show that less than 50% of the 120 strategies implemented in the article yield statistically significant profits and, unconditionally, momentum and contrarian strategies are equally likely to be successful. However, when we condition on the return horizon (short, medium, or long) of the strategy, or the time period during which it is implemented, two patterns emerge. A momentum strategy is usually profitable at the medium (3to 12-month) horizon, while a contrarian strategy nets statistically significant profits at long horizons, but only during the 19261947 subperiod. More importantly, our results show that the cross-sectional variation in the mean returns of individual securities included in these strategies plays an important role in their profitability. The cross-sectional variation can potentially account for the profitability of momentum strategies and it is also responsible for atten-

An Anatomy of Trading Strategies

The purpose of this paper is to review the evidence on the profitability of technical analysis. The empirical literature is categorized into two groups, ‘early’ and ‘modern’ studies, according to the characteristics of testing procedures. Early studies indicate that technical trading strategies are profitable in foreign exchange markets and futures markets, but not in stock markets. Modern studies indicate that technical trading strategies consistently generate economic profits in a variety of speculative markets at least until the early 1990s. Among a total of 95 modern studies, 56 studies find positive results regarding technical trading strategies, 20 studies obtain negative results, and 19 studies indicate mixed results. Despite the positive evidence on the profitability of technical trading strategies, most empirical studies are subject to various problems in their testing procedures, e.g. data snooping, ex post selection of trading rules or search technologies, and difficulties in estimation of risk and transaction costs. Future research must address these deficiencies in testing in order to provide conclusive evidence on the profitability of technical trading strategies.

https://papers.ssrn.com/sol3/Delivery.cfm/joes_519.pdf?abstractid=1006275&mirid=2

What do we know about the profitability of technical analysis

Technical analysis, or the use ofpastprices to infer private information, has value in a model in which prices are not fully revealing and traders have rational conjectures about the relation between prices and signals. A two-period dynamic model of equilibrium is used to demonstrate that rational investors use historical prices in forming their demands and to illustrate the sensitivity of the value of technical analysis to changes in the values of the exogenous parameters.

On Technical Analysis

THE RANDOM WALK and martingale efficient market theories of security price behavior imply that stock market trading rules based solely on the past price series cannot earn profits greater than those generated by a simple buy-and-hold policy1. A vast amount of statistical testing of the behavior of security prices indicates very little evidence of any important dependencies in security price changes through time.2 Technical analysts or chartists, however, have insisted that this evidence does not imply their methods are invalid and have argued that the dependencies upon which their rules are based are much too subtle to be captured by simple statistical tests. In an effort to meet these criticisms Alexander (1961, 1964) and later Fama and Blume (1966) have examined the profitability of various "filter" trading rules based only on the past price series which purportedly capture the essential characteristics of many technical theories. These studies indicate the "filter" rules do not yield profits net of transactions costs which are higher than those earned by a simple buy-andhold strategy. Similarly, James (1968) and Van Horne and Parker (1967) have found that various trading rules based upon moving averages of past prices do not yield profits greater than those of a buy-and-hold policy.

https://www.jstor.org/stable/pdfplus/2325495.pdf

Random walks and technical theories: some additional evidence

(1967). The Random-Walk Theory: An Empirical Test. Financial Analysts Journal: Vol. 23, No. 6, pp. 87-92.

The Random-Walk Theory: An Empirical Test

Competitive Bidding in the Underwriting of Public Utility Securities

In recent years, there has been considerable interest in the random walk theory of stock price behavior. This theory, as applied to the stock market, implies that past stock-price movements cannot be used to predict future market prices in such a way as to “profit” from the predictions. By not “profiting,” we mean that a trader using the past history of stock prices cannot apply mechanical decision rules that result in a consistently better performance than a simple buy and hold strategy. If stock price movements were to become systematic so that a “profit” were possible, proponents of the random walk theory argue that a sufficient number of market participants would quickly recognize the recurring pattern and exploit it. In exploiting it, they would drive out the opportunity for “profit,” causing the price series to approximate a random walk.

George G. C. Parker

Papers

The Random-Walk Theory: An Empirical Test

Competitive Bidding in the Underwriting of Public Utility Securities

Stock Price Behavior and Trading