STOCK MARKET TRANSACTION TIMING: AN EMPIRICAL EVALUATION (INVESTMENT, TRADING, TECHNICAL).

摘要

The major purpose of the study is to critically evaluate the application of several quantitative technical analysis techniques to the problem of stock market transaction timing. The study tests the null hypothesis that the "weak form" of the efficient market hypothesis, also known as the random walk hypothesis, is true. As an alternative hypothesis, we assume that it is not true and that past price behavior can be used to obtain returns superior to a buy-and-hold policy.;The study introduces the subject of stock market investment, discusses the efficient market hypothesis and technical analysis techniques, and presents an overview of the trading program used to conduct all of the empirical tests in the study. This is followed by a survey of selected quantitative technical trading strategies presently used by technical traders. The survey includes four classes of models: buy-and-hold, trend-following, character-of-market, and trend/no trend models.;The results of various empirical tests of transaction timing using technical trading models indicated that the major advantage of transaction timing lies in avoiding losses during bear market declines and participating in the early stages of bull market rallies. The study concludes that the three major issues discussed in the introduction have been answered positively and that evidence has been presented that does not support the random walk hypothesis. Strategies for different investment environments are recommended for the interested investor.;The study addresses the following issues: (1) Does empirical evidence show that the cyclical pattern of stock prices has predictive significance? (2) Can a practitioner-oriented computer program be designed to optimize the parameters of stock market trading strategies based on technical analysis and historical price data? (3) Can trading strategies that are optimized after-the-fact on historical price data be applied successfully to future price data to produce returns in excess of those obtainable with a buy-and-hold policy?