Various moving average convergence divergence trading strategies: a comparison

Hung, Nguyen Hoang

doi:10.21511/imfi.13(2-2).2016.11

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…MACD bersifat informatif karena memberikan dasar untuk pengambilan keputusan yang dapat mengarah pada investasi nilai dalam perdagangan ekuitas. Hung (2016) menyimpulkan bahwa strategi MACD memiliki kemampuan prediksi yang lebih efektif jika digunakan dalam lingkungan pasar modal yang memiliki cost tinggi.…”

Section: Abstrakunclassified

Analisis Teknikal Dengan Menggunakan Moving Average Convergence-Divergence Dan Relative Strength Index Pada Saham Perbankan

Suryanto

2021

JIKA

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ABSTRACT Stock investment is an investment that has a high risk. An investor needs to do an investment analysis before deciding to invest. Investment analysis can be carried out using both fundamental and technical approaches. Technical analysis is often an option because it is fast and easy to apply. This study aims to examine the level of differences in the use of technical analysis with the moving average convergence-divergence (MACD) method and the relative strength index (RSI) as a means of making stock investment decisions. The research method used in this research is the descriptive analysis method. This research was conducted on a group of banking stocks that are included in LQ45. The results showed that there was no difference between the price of the buy signal and the sell signal before and after using the MACD and RSI methods. The results also show that there is no difference between the buy signal and the sell signal between MACD and RSI. Therefore, it can be stated that for the same object and period, the MACD and RSI methods produce the same investment decisions (buy signal and sell signal). Keywords: technical analysis, MACD, RSI, buy signal, sell signal ABSTRAK Investasi saham merupakanjenis investasi yang memiliki resiko tinggi. Seorang investor perlu melakukan analisis investasi sebelum memutuskan untuk berinvestasi. Analisis investasi dapat dilakukan dengan menggunakan pendekatan fundamental dan teknikal. Analisis teknikal seringkali menjadi pilihan karena cepat dan mudah diterapkan. Penelitian ini bertujuan untuk menguji tingkat perbedaan penggunaan analisa teknikal dengan metode moving average convergence-divergence (MACD) dan relative strength index (RSI) sebagai alat pengambilan keputusan investasi saham. Metode penelitian yang digunakan dalam penelitian ini adalah metode analisis deskriptif. Penelitian ini dilakukan pada sekelompok saham perbankan yang termasuk dalam LQ45. Hasil penelitian menunjukkan bahwa tidak ada perbedaan harga antara sinyal beli dan sinyal jual sebelum dan sesudah menggunakan metode MACD maupun RSI. Hasil penelitian juga menunjukkan bahwa tidak ada perbedaan antara sinyal beli dan sinyal jual antara MACD dan RSI. Dengan demikian dapat dikatakan bahwa untuk objek dan periode yang sama, metode MACD dan RSI menghasilkan keputusan investasi yang sama (sinyal beli dan sinyal jual). Kata kunci: analisa teknikal, MACD, RSI, sinyal beli, sinyal jual

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Section: Abstrakunclassified

Analisis Teknikal Dengan Menggunakan Moving Average Convergence-Divergence Dan Relative Strength Index Pada Saham Perbankan

Suryanto

2021

JIKA

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“…(6) The best practice of enablers: From the study results, it was found that the best classifier is the decision tree C4.5 algorithm for the TEJ dataset, and the best model is Models D and E in terms of average accuracy. (7) Interpretation of two data-split methods: It was found that the cross-validation data approach makes more appropriate use of the TEJ dataset than the percentage-split data approach.…”

Section: Helpful Research Findings and Management Implicationsmentioning

confidence: 99%

“…MA has an average of closing prices of a specific number of time periods, such as a 7-period MA for seven days, which defines a flowing correlation between the time period and the money rate [5]. MACD uses exponential moving averages (EMAs) to calculate its value, equal to the value of the shorter time of EMAs and less than the value of the longer time of EMAs [7]. Furthermore, many varied and diverse perspectives for MA and MACD were studied for these purposes, such as EMA, simple moving average (SMA), or weighted moving average (WMA), and variety of MACD-1-4 [7].…”

Section: Introductionmentioning

confidence: 99%

“…MACD uses exponential moving averages (EMAs) to calculate its value, equal to the value of the shorter time of EMAs and less than the value of the longer time of EMAs [7]. Furthermore, many varied and diverse perspectives for MA and MACD were studied for these purposes, such as EMA, simple moving average (SMA), or weighted moving average (WMA), and variety of MACD-1-4 [7]. These are suitable for stock predictions, as shown in recent studies that demonstrated good performance and were involved in risk aversion.…”

Section: Introductionmentioning

confidence: 99%

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Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model

et al. 2020

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Applied human large-scale data are collected from heterogeneous science or industry databases for the purposes of achieving data utilization in complex application environments, such as in financial applications. This has posed great opportunities and challenges to all kinds of scientific data researchers. Thus, finding an intelligent hybrid model that solves financial application problems of the stock market is an important issue for financial analysts. In practice, classification applications that focus on the earnings per share (EPS) with financial ratios from an industry database often demonstrate that the data meet the abovementioned standards and have particularly high application value. This study proposes several advanced multicomponential discretization models, named Models A–E, where each model identifies and presents a positive/negative diagnosis based on the experiences of the latest financial statements from six different industries. The varied components of the model test performance measurements comparatively by using data-preprocessing, data-discretization, feature-selection, two data split methods, machine learning, rule-based decision tree knowledge, time-lag effects, different times of running experiments, and two different class types. The experimental dataset had 24 condition features and a decision feature EPS that was used to classify the data into two and three classes for comparison. Empirically, the analytical results of this study showed that three main determinants were identified: total asset growth rate, operating income per share, and times interest earned. The core components of the following techniques are as follows: data-discretization and feature-selection, with some noted classifiers that had significantly better accuracy. Total solution results demonstrated the following key points: (1) The highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning with a percentage-split method for two classes in one run; (2) the highest accuracy mean, 91.44%, occurred in Models D and E from the use of naïve Bayes learning for cross-validation and percentage-split methods for each class for 10 runs; (3) the highest average accuracy mean, 87.53%, occurred in Models D and E with a cross-validation method for each class; (4) the highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning-C4.5 with the percentage-split method and no time-lag for each class. This study concludes that its contribution is regarded as managerial implication and technical direction for practical finance in which a multicomponential discretization model has limited use and is rarely seen as applied by scientific industry data due to various restrictions.

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“…Capital gain relates to the return investors receive; the higher the capital gain value, the greater the stock return obtained (Kususma & Topowijono, 2020). To measure stocks, namely the relative strength index indicator (Alhilfi, 2019), moving average convergence divergence (Hung, 2016), Bollinger bands (Day et al, 2023), and stochastic oscillator.…”

Section: Introductionmentioning

confidence: 99%

Bibliometric Analysis of Determinants of Stock Price Returns in Indonesia

Elwisam,

Digdowiseiso

2023

ATESTASI

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Investment is activity aimed at making a profit. One manifestation of strategic investment management is through selecting the right investment instruments. Currently the instrument with the highest interest in Indonesia is stock. One indicator to measure investment success is stock price returns. Therefore, a study is needed to examine the determinants of stock price returns in Indonesia, so this research is to explain the literature statistics on the determinants of stock stock returns, explain the determinants of stock price returns through bibliometric clustering and density analysis in Indonesia. The data needed is in the form of scientific literature originating from Google Scholar. The results of the research show that (1) the total number of papers that examine the determinants of stock returns in Indonesia is 59 and there has been an increase in studies in the period 2021 to 2023. (2) there are five main topic clusters that are discussed in research, (3) The density model show that there are 19 keywords that appear most frequently, as for these determinants such as deposit interest, exchange rate, profit per share, company financial condition, structure capital, profitability, solvency, price earning ratio, and price to book value.

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Various moving average convergence divergence trading strategies: a comparison

Cited by 12 publications

References 8 publications

Analisis Teknikal Dengan Menggunakan Moving Average Convergence-Divergence Dan Relative Strength Index Pada Saham Perbankan

Analisis Teknikal Dengan Menggunakan Moving Average Convergence-Divergence Dan Relative Strength Index Pada Saham Perbankan

Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model

Bibliometric Analysis of Determinants of Stock Price Returns in Indonesia

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