Is Technical Analysis Profitable Even for an Amateur Investor? Evidence from the Greek Stock Market (2002-12)

2014 ◽  
Author(s):  
Evangelos Vasileiou
Keyword(s):  
Stock Market ◽  
Technical Analysis

scholarly journals EARNINGS ANNOUNCEMENTS AND COMPETING INFORMATION: THE INDONESIAN EVIDENCE

2015 ◽  
Vol 29 (1) ◽  
Author(s):  
Dedhy Sulistiawan ◽  
Jogiyanto Hartono ◽  
Eduardus Tandelilin ◽  
Supriyadi Supriyadi
Keyword(s):  
Stock Market ◽  
Empirical Evidence ◽  
Earnings Announcements ◽  
Negative Relationship ◽  
Technical Analysis ◽  
Earnings Announcement ◽  
Price Reaction ◽  
Price Reactions ◽  
The Relationship ◽  
The U.S

The main purpose of this study is to provide empirical evidence of the relationship betweeninvestors’ responses to two events, which are, (1) earnings anouncements, and (2) technicalanalysis signals, as competing information. This study is motivated by Francis, et al. (2002),whose study used stock analyst’s recommendations as competing information in the U.S stockmarket. To extend that idea, this study uses technical analysis signals as competing informationin the Indonesian stock market. Using Indonesian data from 2007-2012, this study shows thatthere are price reactions on the day of a technical analysis signal’s release, which is prior toearnings announcements. It means that investors react to the emergence of competinginformation. Reactions on earnings announcements also produce a negative relationship withthe reaction to a technical analysis signal before an earnings announcement. This study givesevidence about the importance of technical analysis as competing information to earningsannouncements.Keywords: competing information, earnings announcements, technical analysis, price reaction

scholarly journals Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator

2020 ◽  
Vol 17 (4) ◽  
pp. 44-60
Author(s):  
Alberto Antonio Agudelo Aguirre ◽  
Ricardo Alfredo Rojas Medina ◽  
Néstor Darío Duque Méndez
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms ◽  
Stock Market ◽  
Moving Average ◽  
Asset Price ◽  
Technical Analysis ◽  
Investment Strategies ◽  
Historical Data Analysis ◽  
Financial Asset Returns ◽  
Technical Indicator

The implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pricing of investment strategies into stock markets and investigate its advantages over its peers Buy & Hold and traditional technical analysis. The Genetic Algorithms strategy applied to the MACD was carried out in two different validation periods and sought to optimize the parameters that generate the buy-sell signals. The performance between the machine learning-based approach, technical analysis with the MACD and B&H was compared. The results suggest that it is possible to find optimal values of the technical indicator parameters that result in a higher return on investment through Genetic Algorithms, beating the traditional technical analysis and B&H by around 4%. This study offers a new insight for practitioners, traders, and finance researchers to take advantage of Genetic Algorithms for trading rules application in forecasting financial asset returns under a more efficient and robust methodology based on historical data analysis.

scholarly journals Use of Technical Analysis Tools in Nepalese Stock Market

2018 ◽  
Vol 11 (1) ◽  
pp. 55-64
Author(s):  
Rashesh Vaidya
Keyword(s):  
Stock Market ◽  
Investment Decisions ◽  
Technical Analysis ◽  
Trade Volume ◽  
Market Participants ◽  
Support Level ◽  
Analysis Tools

This paper has attempted to find the interest of Nepalese investors, brokers and depository participants on the use the technical tools for the analysis of the stock market. The use of technical analysis in context to Nepal shows that the participants in the Nepalese stock market are highly interested on the use of new Hi-Lo price while making their investment decisions. Another interest was seen for trade volume indicators. The Nepalese stock market participants are not seen interested in using the resistance and support level followed by the pattern i.e. candlestick charts while analyzing the stock market trend.

Is Technical Analysis Profitable even for an Amateur Investor?

2015 ◽  
pp. 255-269
Author(s):  
Vasileiou Evangelos
Keyword(s):  
Stock Market ◽  
Transaction Costs ◽  
Behavioral Finance ◽  
Investment Decisions ◽  
Technical Analysis ◽  
Moving Averages ◽  
Trading Rules ◽  
Trading Decisions ◽  
Transaction Fees

The purpose of this chapter is to examine if even the simplest trading rules could take advantage of the market's inefficiency and lead to profitable trading decisions. For this reason, this study examined the profitability of the simplest trading rules, using only the simple moving averages (SMA) rules that even an amateur investor could apply. In order to examine the specific issue a data sample from the Greek stock market during the period 2002-12 was used. The results suggest that even if one takes into account the most expensive transaction fees, the trading rules signal profitable investment decisions; therefore, even an amateur trader and/or investor who does not have a significant amount of money to invest (which may lead to reduced transaction costs) could take advantage of the market's inefficiency. Behavioral finance theories may provide some useful and alternative explanations regarding some of the reasons that contribute to the Greek stock market's inefficient environment.

Data Snooping on Technical Analysis: Evidence from the Taiwan Stock Market

2011 ◽  
Vol 14 (02) ◽  
pp. 195-212 ◽  
Author(s):  
Cheng-Wei Chen ◽  
Chin-Sheng Huang ◽  
Hung-Wei Lai
Keyword(s):  
Stock Market ◽  
Transaction Costs ◽  
Market Efficiency ◽  
Technical Analysis ◽  
The Other ◽  
Trading Rule ◽  
Data Snooping ◽  
Empirical Results

The main purpose of this paper is to investigate the validity and predictability of technical analysis in the Taiwan stock market. Bootstrapped tests of White (2000) and of Hansen (2005) are employed to ascertain whether there exists a superior trading rule among two broadly used sets of technical analysis. One coming from Brock et al. (1992) and the other from Sullivan et al. (1999). Moreover, this study brings together powerful bootstrapped tests along with two institutional adjustments to ascertain the efficacy of technical analysis: (1) non-synchronous trading and (2) transaction costs. The empirical results indicate that this triad-data snooping, non-synchronous trading and transaction costs, has a great impact on the performance of technical analysis. In fact, the Taiwan stock market stands for market efficiency, and economical profits cannot be rendered from technical analysis in this market.

A systematic review of fundamental and technical analysis of stock market predictions

2019 ◽  
Vol 53 (4) ◽  
pp. 3007-3057 ◽  
Author(s):  
Isaac Kofi Nti ◽  
Adebayo Felix Adekoya ◽  
Benjamin Asubam Weyori
Keyword(s):  
Systematic Review ◽  
Stock Market ◽  
Technical Analysis
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