scholarly journals Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data

2015 ◽  
Vol 42 (14) ◽  
pp. 5963-5975 ◽  
Author(s):  
Roberto Cervelló-Royo ◽  
Francisco Guijarro ◽  
Karolina Michniuk
Keyword(s):  
Pattern Recognition ◽  
Stock Market ◽  
Technical Analysis ◽  
Trading Rule ◽  
Rule Based ◽  
Intraday Data ◽  
Stock Market Trading

scholarly journals A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting

2017 ◽  
Vol 81 ◽  
pp. 177-192 ◽  
Author(s):  
Rubén Arévalo ◽  
Jorge García ◽  
Francisco Guijarro ◽  
Alfred Peris
Keyword(s):  
Pattern Recognition ◽  
Stock Market ◽  
Market Price ◽  
Price Forecasting ◽  
Trading Rule ◽  
Rule Based ◽  
Dynamic Trading

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.

scholarly journals Stock market trading rule discovery using technical charting heuristics

2002 ◽  
Vol 23 (2) ◽  
pp. 155-159 ◽  
Author(s):  
William Leigh ◽  
Naval Modani ◽  
Russell Purvis ◽  
Tom Roberts
Keyword(s):  
Stock Market ◽  
Rule Discovery ◽  
Trading Rule ◽  
Stock Market Trading

scholarly journals Moving Average Convergence-Divergence (MACD) Trading Rule: An Application in Nepalese Stock Market "NEPSE"

2020 ◽  
Vol 1 (6) ◽  
Author(s):  
Rashesh Vaidya
Keyword(s):  
Stock Market ◽  
Intrinsic Value ◽  
Moving Average ◽  
Technical Analysis ◽  
Future Trend ◽  
Trading Rule ◽  
Minimal Period ◽  
Current Pattern ◽  
Signal Line ◽  
Market Convergence

There are two types of analysis done for a stock market. One is fundamental analysis, where an investor looks at an intrinsic value of the stock, and another is technical analysis, where investors determine the future trend of the market looking at the current pattern or trend of the market. This paper is focused on one of the technical analysis tools, i.e., Moving Average Convergence-Divergence. It is a tool based on the three exponential moving average (9-12-26 EMA Rule). The MACD analysis, with the help of a single line, was helpful to find out the exact bullish and the bearish trend of the Nepse. A signal line is a benchmark to determine the stock market moving either to a bullish or bearish trend. It can help an investor, where the market is going in a direction. A market convergence, divergence, and crossover were better identified with the help of the MACD histogram. The paper found that the Nepse return was stable for a very minimal period from 1998-99 to 2019-20. The shift from the bullish to bearish or vice-verse were seen easily identified with the help of a MACD histogram. Finally, a better-combined knowledge of moving average and candlestick chart analysis will help an investor, to put a clear picture of a market trend with the help of MACD analysis.

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