Louie Ren, Peter Ren and Yong Glasure
The purpose of this paper is to examine the profitability from various simple trading range break-out rules on the NASDAQ index.
Abstract
Purpose
The purpose of this paper is to examine the profitability from various simple trading range break-out rules on the NASDAQ index.
Design/methodology/approach
Runs test is used to test whether the returns from every other days on buy and sell days are random. If they are not random, then the Student T-test will not be applicable to test the predictive power for profitability from the simple trading range break-out rules on the NASDAQ index.
Findings
Empirical study in this paper shows that the returns on buy and sell days are not random via runs test. Therefore, the simple trading range break-out rules cannot lead to the conclusion that they have the predictive power for profitability from the T-test. Applying the simple trading range break-out rule to NASDAQ does not support or overturn the market efficiency hypothesis.
Research limitations/implications
The study is only based on the five simple trading range break-out rules from 9,311 daily closing prices on the NASDAQ over the period of February 5, 1971 to December 12, 2007. It can serve as a counter example for other studies about the predictive power of profitability from different trading rules.
Practical implications
Contrary to numerous previous research works, the study shows that the simple trading range break-out rules have no predictive power for profitability, and should not be used to test the market efficiency.
Originality/value
Based on the literature review, the study is one of the first empirical studies showing the returns on buy and sell days are not independent, and the authors cannot conclude that the trading range break-out rules have the predictive power for profitability on the NASDAQ index.
Details
Keywords
Louie Ren, Peter Ren and Yong Glasure
The purpose of this paper is to examine three different forms of returns based on the price difference, percentage change, and difference in logarithm price from moving average…
Abstract
Purpose
The purpose of this paper is to examine three different forms of returns based on the price difference, percentage change, and difference in logarithm price from moving average buy-sell trading rule. Statistical linear correlation, the means of returns from buy/sell days, and the flexibility of long-term moving periods are examined.
Design/methodology/approach
Traditional linear correlations, pairwise student t-test, and ϕ coefficient for two binary buy/sell decision variables are studied from the simple block bootstrap (convenience) sampling from S&P, Dow Jones, and NASDAQ price indices from January 29, 1985 to January 6, 2016.
Findings
The authors find that different forms of returns from MA(1-50) are strongly linearly correlated via 150 simple block bootstrap (convenience) samples from S&P, Dow Jones, and NASDAQ price indices from January 29, 1985 to January 6, 2016. In other words, the price differences, the percentage returns, and logarithmic returns are exchangeable for returns from S&P, Dow Jones, and NASDAQ. The authors refute the claims from Metghalchi et al.’s (2005, 2011) papers and Brock et al.’s (1992) paper. The authors conclude that the market is efficient and investors cannot gain benefits from moving average technical trading rule. Lastly, the authors find that the decisions from MA(1-50) and MA(1-200) are highly correlated; therefore, the length of periods used in long-period moving average is flexible.
Originality/value
It is one of the first studies about different forms of returns, their conclusions on the market efficiency, and the flexibility of long-term moving period for moving average buy/sell technical rules.