scholarly journals Profitability of the Moving Averages Technical Trading Rules in an Emerging Stock Market: A Study of Stocks Listed in Pakistan Stock Exchange

2020 ◽  
Vol 12 (2) ◽  
pp. 165-176
Author(s):  
Muhammad Arif ◽  
Abdul Rauf Laghari ◽  
Avinash Advani
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Moving Average ◽  
Predictive Ability ◽  
Moving Averages ◽  
Technical Trading ◽  
Technical Trading Rules ◽  
Trading Rules ◽  
Future Studies ◽  
Portfolio Returns

This study examines the profitability of Moving Averages (MA) timing strategy over the buy and hold strategy for individual stocks listed at Pakistan Stock Exchange (PSX). We applied Han, Yang, and Zhou (2013), methodology to individual stock returns and found inconclusive evidence of MA timing strategy’s predictive ability to earn higher returns over buy and hold strategy. We also report market risk-adjusted returns to remove any market movement effects and apply alternative moving averages lag lengths to check the robustness of our results. We observe individual stock returns are noisier than portfolio returns and the simple technical trading rule of moving average lack the ability to predict individual stock returns. We propose the use of more complex trading rules in future studies to ascertain the profitability of technical trading rules in individual stocks.

scholarly journals Profitability of The Moving Averages Technical Trading Rules in an Emerging Stock Market-A Study of Individual Stocks Listed On Pakistan Stock Exchange

2018 ◽  
Vol 14 (2) ◽  
pp. 67-76
Author(s):  
Muhammad Arif ◽  
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Exchange ◽  
Moving Average ◽  
Market Development ◽  
Moving Averages ◽  
Technical Trading ◽  
Technical Trading Rules ◽  
Trading Rules ◽  
Portfolio Returns

This paper investigates the gainfulness of moving averages (MA) timing method over the purchase and hold procedure for single stocks deal in Pakistan Stock Exchange. We used (Han et al., 2013) approach of single stock returns and indeterminate evidence of MA timing methodology insightful ability to increase higher returns over the strategy of purchase and hold. In addition, we report market risk-adjusted returns to expel any market development impacts and apply elective moving averages lag lengths to check the robustness of our outcomes. We look at that individual stock returns are noisier than portfolio returns and the fundamental technical exchanging principle of moving average don't be able to anticipate single stock returns. We propose the utilization of more perplexing trading rules in future investigations to determine the gainfulness of technical trading rules in individual stocks.

Profitability of Trading Strategies Before and During the Greek Crisis: An Empirical Study.

2017 ◽  
Vol 11 (1) ◽  
pp. 1-26
Author(s):  
Efstathios Xanthopoulos ◽  
Konstantinos Aravossis ◽  
Spyros Papathanasiou
Keyword(s):  
Stock Exchange ◽  
Statistical Tests ◽  
Moving Average ◽  
Efficient Market Hypothesis ◽  
Trading Strategies ◽  
Technical Trading ◽  
Technical Trading Rules ◽  
Trading Rules ◽  
Greek Crisis ◽  
Athens Stock Exchange

This paper investigates the profitability of technical trading rules in the Athens Stock Exchange (ASE), utilizing the FTSE Large Capitalization index over the seven-year period 2005-2012, which was before and during the Greek crisis. The technical rules that will be explored are the simple moving average, the envelope (parallel bands) and the slope (regression). We compare technical trading strategies in the spirit of Brock, Lakonishok, and LeBaron (1992), employing traditional t-test and Bootstrap methodology under the Random Walk with drift, AR(1) and GARCH(1,1) models. We enrich our analysis via Fourier analysis technique (FFT) and more statistical tests. The results provide strong evidence on the profitability of the examined technical trading rules, even during recession period (2009-2012), and contradict the Efficient Market Hypothesis.

scholarly journals Technical Analysis on the Bitcoin Market: Trading Opportunities or Investors’ Pitfall?

Risks ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 44 ◽  
Author(s):  
Marina Resta ◽  
Paolo Pagnottoni ◽  
Maria Elena De Giuli
Keyword(s):  
Technical Analysis ◽  
Daily Basis ◽  
Moving Averages ◽  
Technical Trading ◽  
Technical Trading Rules ◽  
Trading Rules ◽  
Price Series ◽  
Daily Data

In this paper we aimed to examine the profitability of technical trading rules in the Bitcoin market by using trend-following and mean-reverting strategies. We applied our strategies on the Bitcoin price series sampled both at 5-min intervals and on a daily basis, during the period 1 January 2012 to 20 August 2019. Our findings suggest that, overall, trading on daily data is more profitable than going intraday. Furthermore, we concluded that the Buy and Hold strategy outperforms the examined alternatives on an intraday basis, while Simple Moving Averages yield the best performances when dealing with daily data.

scholarly journals Market Efficiency For S&P 500: 1954-2004

2011 ◽  
Vol 4 (7) ◽  
Author(s):  
Massoud Metghalchi ◽  
Xavier Garza-Gomez ◽  
Chien-Ping Chen ◽  
Stanley Monsef
Keyword(s):  
Market Efficiency ◽  
Predictive Power ◽  
Moving Average ◽  
Stock Index ◽  
Technical Trading ◽  
Technical Trading Rules ◽  
Trading Rules ◽  
Daily Data ◽  
Market Inefficiency ◽  
Price Patterns

This paper tests three moving average technical trading rules for the S&P 500 stock index. Using daily data from 1954 to 2004, our results indicate that moving average rules did indeed had predictive power and could discern recurring-price patterns for the period up to mid 1980s. However, since mid 1980s, technical trading rules do not work and could not discern recurring-price patterns. Our results are consistent with market inefficiency from 1954 to 1984 and market efficiency from 1984 to present.

The Profitability of Technical Trading Rules in the KOSPI200 Futures Market

2007 ◽  
Vol 15 (2) ◽  
pp. 85-119
Author(s):  
Cheol Ho Park
Keyword(s):  
Statistical Significance ◽  
Predictive Ability ◽  
Futures Market ◽  
Data Snooping ◽  
Technical Trading ◽  
Technical Trading Rules ◽  
Trading Rules ◽  
Net Return ◽  
Reality Check ◽  
Ratio Criterion

This article investigates the profitability of technical trading rules in the KOSPI200 futures market from 1997 through 2006 after accounting for transaction costs, risk. and data-snooping problems. To effectively mitigate data - snooping problems resulted from survivorship bias, we largely expand the full set of technical trading rules handled in the previous literature and measure statistical significance of technical trading performance using White’s (2000) Bootstrap Reality Check (BRC) methodology and Hansen’s (2005) Superior Predictive Ability (SPA) test that can take account of interdependency across individual technical trading rules. The results indicate that under the net return criterion the best trading rule generates the highest mean net return of about 32% per annum during the sample period but the trading return is statistically insignificant when the effect of data-snooping is considered. Similar results are found under the Sharpe ratio criterion. These findings suggest that substantial technical trading profits may be obtained due to chance rather than the Inherent predictability of technical trading rules.

scholarly journals Moving Average Trading Rules for NASDAQ Composite Index

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Chien-Ping Chen
Keyword(s):  
Transaction Costs ◽  
Predictive Power ◽  
Moving Average ◽  
Composite Index ◽  
Technical Trading ◽  
Technical Trading Rules ◽  
Trading Rules ◽  
Nasdaq Composite Index

This paper tests a few moving average technical trading rules for the NASDAQ Composite and Goldman Sack commodity indexes from 1972 to 2015. Our results indicate that moving average rules do exhibit strong predictive power for NADSAQ composite index but much weaker predictive power for GSCI. Can a trader use this predictive to beat the B&H strategy? We show that MA-100 days could most of the time make an abnormal profit in the case of NASDAQ composite index by considering both transaction costs and risk. 

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