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[Shaw] "A moving average will always lag stock price movement. The movement of a stock below an uptrending moving average is considered to be a sign of impending weakness. More important is that as the moving average itself flattens out and begins to trend down, it often will confirm that a shift in the basic trend in the stock has occurred".
Simple Technical Trading Rules and the Stochastic Properties of Stock Returns, BROCK, LAKONISHOK and LeBARON, 1992
"This paper tests two of the simplest and most popular trading rules - moving average and trading range break. Overall our results provide strong support for the technical strategies that we explored".
Data-snooping, Technical Trading Rule Performance, and the Bootstrap, Sullivan, Timmermann and White, 1997
"In this paper we utilize White's Reality Check bootstrap methodology (White (1997)) to evaluate simple technical trading rules while quantifying the data-snooping bias and fully adjusting for its effect in the context of the full universe from which the trading rules were drawn. Hence, for the first time, the paper presents a means of calculating a comprehensive test of performance across all trading rules. In particular, we consider the study of Brock, Lakonishok, and LeBaron (1992), expand their universe of 26 trading rules, apply the rules to 100 years of daily data on the Dow Jones Industrial Average, and determine the effects of data-snooping. During the sample period inspected by Brock, Lakonishok and LeBaron, we find that the best technical trading rule is capable of generating superior performance even after accounting for datasnooping. However, we also find that the best technical trading rule does not provide superior performance when used to trade in the subsequent 10-year post-sample period. We also perform a similar analysis, applying technical trading rules to the Standard and Poor's 500 futures contract. Here, too, we find no evidence that the best technical rule outperforms, once account is taken of data-snooping effects".
The moving averages demystified, Vandewallea, Ausloosa and Boverouxb , 1999
"A common method in technical analysis is the construction of moving averages along time series of stock prices. We show that they present a practical interest for physicists, and raise new questions on fundamental ground".
Other evidences of the predictive power of technical analysis: the moving averages rules on European indexes, DETRY and GREGOIRE
"Many authors discovered that simple forms of technical analysis possessed significant forecast power on various market indexes. We show that these results can be replicated on formally selected European indexes, which almost completely eliminates any influence from data-snooping. Implications of these results in terms of market efficiency are also discussed".
A Moving Average Comparison of the Tel-Aviv 25 and S & P 500 Stock Indices, Shachmurove, BenZion, Klein andYagil, 2001
"Random Walk and Efficient Market Hypotheses are central ideas in explaining financial market efficiencies. The assumption that market behavior embodies and reflects relevant information has a great impact on securities prices. Any change in the relevant information causes price adjustment. In contrast, technical analysts argue that prices gradually adjust to new information. Thus, historical analysis is useful in diagnosing the repeated pattern behaviors leading to active investment strategies that generate better- than-market returns. The purpose of this study is to examine the efficacy of using technical trading rules in the emerging market of Israel, through the analysis of the Tel-Aviv 25 Index (TA25) and to compare its weak-form market efficiency to the performance of the S&P 500".
Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of "Technical Analysis, NEFTCI, 1991
"This article attempts a formal study of technical analysis, which is a class of informal prediction rules, often preferred to Wiener-Kolmogorov prediction theory by participants of financial markets. Yet Wiener-Kolmogorov prediction theory provides optimal linear forecasts. This article investigates two issues that may explain this contradiction. First, the article attempts to devise formal algorithms to represent various forms of technical analysis in order to see if these rules are well defined. Second, the article discusses under which conditions (if any) technical analysis might capture those properties of stock prices left unexploited by linear models of Wiener-Kolmogorov theory".
Technical analysis in the Madrid stock exchange, Rodriguez, Rivero and Felix, 1999
"In this paper we assess whether some simple forms of technical analysis can predict stock price movements in the Madrid Stock Exchange. Our results provide strong support for profitability of these technical trading rules".
Application of simple technical trading rules to Swiss stock prices: Is it profitable?, Isakov and Hollist
"It is found that the most profitable rule is a double moving average with averages computed on one and five days".
Statistical Evidence on a New Method of Trading the Financial Markets,
"The concept - well known to practitioners - of moving average is recalled, and the one of adaptive moving average summarized. Then a new algorithm is introduced, and it is shown that statistical confidence limits are in favor of the thesis that such a method is able to make constant profits on financial markets, specifically on future markets, where commissions are not important. This results are an obvious challenge to the efficient market hypothesis, if the necessity of another challenge should be felt".
The predictability of asset returns: an approach combining technical analysis and time series forecasts, FANG, YUE and XU, 2002
"We investigate predictability of asset returns by developing an approach that combines technical analysis and conventional time series forecasts. While exploiting predictable components as functions of past prices or returns, technical trading rules and time series forecasts capture different aspects of market predictability: the former tends to identify periods to be in the market when returns are positive and the latter is capable of identifying periods to be out when returns are negative. Applied to daily Dow Jones Averages over the first 100 years, the combined strategies outperform both technical trading rules and time series forecasts. We focus on the widely used double cross-over trading strategies in which two moving averages are calculated and trading signals are generated when the two moving averages intersect. These trading rules are typical trend-following methods and serve frequently as the basis for more sophisticated schemes".
Unsystematic Futures Profits with Technical Trading Rules: A Case for Flexibility, BALSARA, NAUZER, CARLSON and RAO, 1966
"The dual moving average crossover rule, commonly used by technical traders, is employed to generate signals for entering into and exiting out of a trade. Moving averages of historic daily settlement prices are calculated. The lengths of the two moving averages are unequal, so as to allow for a crossover between the shorter and longer moving averages. The shorter the time period over which the moving average is calculated, the more responsive it is to price fluctuations. Therefore, when the shorter of the dual moving averages crosses above the longer moving average, this signifies an uptrend in prices, generating a buy signal at the crossover point. Similarly, when the shorter moving average crosses below the longer-term moving average, we have a downtrend in prices, and the crossover signals selling the commodity in question.
The conclusions arrived at in this paper support the findings of Stevenson and Bear (1970) to the extent that mechanical trading rules can be profitable at times. However, it would be naive to believe that a given rule will perform consistently well across different commodities and time periods. This is due to the fact that although price trends do exist, these trends do not recur with a regular periodicity. Consequently, the paper recommends the use of flexible-parameter trading rules which adapt to changes in market conditions, instead of expecting the market to operate within the specifications of an unalterable set of rules".