The Market Quality to Technical Analysis Performance: Intercountry Analysis

Hartono, Jogiyanto; Sulistiawan, Dedhy

doi:10.22146/gamaijb.5658

Cited by 10 publications

(13 citation statements)

References 6 publications

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“…The study [7] has identified the random forest classifier as the most accurate and consistent classifier where its ensemble was instrumental in achieving a higher level of accuracy. The study done by [4] has focused on analyzing the market quality's impact on the profitability of technical analysis with the special case of inter-country data. The study [4] confirmed that market quality which was measured as market capitalization was affecting the performance of technical analysis based on data of 50 companies selected across from 21 countries.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The study done by [4] has focused on analyzing the market quality's impact on the profitability of technical analysis with the special case of inter-country data. The study [4] confirmed that market quality which was measured as market capitalization was affecting the performance of technical analysis based on data of 50 companies selected across from 21 countries.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The main objective of this study is twofold; the first objective is to analyze if the features set created based on most used technical indicators are having any valuable contribution to predicting the stock market movement as compared to other financial news feeds, the second objective is to compare K-nearest neighbors (KNN), neural network (NN), and random forest algorithms for their predictive accuracy. Studies such as [3][4][5][6] have used technical indicators to build features set for training the model. Random forest classifier is applied by [3] for testing the forecasting ability of the algorithms, while [5] has used ML classifiers and classifiers based on deep learning to test the accuracy of predictive models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stock Prediction Using Machine Learning Algorithms with Special Reference to Technical Indicators

Gondaliya

Patel

Shah

2021

ICT With Intelligent Applications

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Machine learning algorithms have increasingly become chosen tools for stock price prediction. Using a variety of financial news as an input to compare various algorithms for accuracy level has been extensively studied. However, taking some of the prominent technical indicators as an input to test the algorithms' prediction accuracy for a stock price has remained less explored. This study focuses on using chosen seventeen technical indicators to compare selected algorithms to test the prediction accuracy for six Indian stocks as a sample. This study covers the critical time period of the outbreak of the Covid-19 pandemic and attempts to capture the impact on accuracy levels of algorithms. Three algorithms are tested, and among them random forest algorithm has demonstrated superior results. Based on these results, this study proposes a framework to create a platform for further application.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stock Prediction Using Machine Learning Algorithms with Special Reference to Technical Indicators

Gondaliya

Patel

Shah

2021

ICT With Intelligent Applications

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“…Developed markets and emerging markets have different characteristics. The study of Hartono and Sulistiawan (2014) presents the evidence that stock market in emerging countries, including Indonesia are less efficient compare to those in developed market, and hence, behavioural finance theory is more able to explain the behaviour of investors. By presenting evidence from emerging markets, this study provides comparative picture from different markets (Rahman and Mustafa, 2017).…”

Section: Introductionmentioning

confidence: 99%

Are Emotions Exacerbating the Recency Bias: An Experimental Study

Rudiawarni

Narsa

Tjahjadi

2020

IJTGM

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This research aims to examine whether emotions can affect the recency bias in judgement and decision making (JDM). This study finds that the effect of recency bias is so strong that emotion does not affect the recency bias experienced by participants when making decisions. Further analysis shows that humans tend to weigh negative information more heavily than positive information. This research contributes to behavioural finance research in which this research incorporates an element of emotion in the recency bias, where this is so far known, is still not widely researched, especially in emerging market.

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“…The first reason, technical analysis tends to produce better performance in developing market than that in developed market (Fifield et al 2005;McKenzie 2007;Hartono and Sulistiawan, 2014). The second, technical analysis is competing information to fundamental information, including accounting information (Franchis et al 2002;Flanegin and Rudd 2005;.…”

Section: Introductionmentioning

confidence: 99%