A Fuzzy Logic-Based Stock Market Trading Algorithm Using Bollinger Bands

Lauguico, Sandy; Concepcion, Ronnie; Alejandrino, Jonnel; Macasaet, Dailyne; Tobias, Rogelio Ruzcko; Dadios, Elmer P.

doi:10.1109/hnicem48295.2019.9072734

Cited by 27 publications

(13 citation statements)

References 13 publications

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“…To determine performance methods cannot be chosen due to different data sets and attributes; still, it is understood that MPL can provide an accuracy of 71.63% in detecting stock prices using this type of technical analysis [15]. Additionally, LSTM provides an accuracy performance of 70% in technical analysis and sentiment [23]. The method will produce good performance with suitable techniques and proper preprocessing for each data set.…”

Section: Discussionmentioning

confidence: 99%

“…Then the grouping will give the output of the grouping of stock data such as "buy," "sell," "hold" [7], [12], [23], or "up" and "down" [24]. Several algorithms found will classify in this study, namely LSTM [10], [24]- [28], Random Forest used in research [9], [13], [29], and also random forest ensemble in research [12].…”

Section: Associationmentioning

confidence: 99%

“…. Also using other algorithms such as fuzzy logic, MLP, FNN, and Decision Boosted Tree [2], [8], [23], [30]. Several studies have compared the best method for clustering, such as [31] comparing Naive Bayes and SVM, and found that Naive Bayes is better than others.…”

Section: Associationmentioning

confidence: 99%

“…[6], [15], [19], [27], [33], [34]. In addition, some use attributes in the form of technical data such as Moving Average, Bollinger Bands, Weighted Moving Average (WMA) and share other technical data attributes [7], [12], [23], [29], [35]- [39]. Then sentiment analysis is carried out by analyzing data or sentiment from an object in the form of news and social media to predict stock prices.…”

Section: The Most Type Of Analysis Used In Predicting Stocksmentioning

confidence: 99%

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Systematic Literature Review: Stock Price Prediction Using Machine Learning and Deep Learning

Dopi¹,

Hartanto²,

Fauziati³

2021

Advances in Economics, Business and Management Research

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This research was conducted using a literature review method to analyze various studies that will identify the type of analysis with the attributes used, the methods used, the methods most often used, and the methods that have the best performance. This study collected research from 2016 -July 2021, selected based on predetermined criteria and then collected 40 papers. This review found that there are four research topics, namely estimation, classification, clustering, and association. The findings of this study are four research topics, namely fundamentals, technicals, sentiment analysis, and even a combination of analyzes that use their respective attributes and datasets. There are thirty-one different methods found to be used in predicting stock prices. LSTM, MLP, RF, and SVM are the most widely used methods. In addition, MLP is a method that gives the best performance of 71.63% and LSTM of 70%. The use of combined machine learning methods with ensemble techniques, deep learning, and selection of input attributes in pre-processing is recommended for better accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Associationmentioning

confidence: 99%

Section: Associationmentioning

confidence: 99%

Section: The Most Type Of Analysis Used In Predicting Stocksmentioning

confidence: 99%

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Systematic Literature Review: Stock Price Prediction Using Machine Learning and Deep Learning

Dopi¹,

Hartanto²,

Fauziati³

2021

Advances in Economics, Business and Management Research

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“…In their study, they made comparisons using TAIEX and HIS stock market data. Lauguico et al proposed an algorithm that goes through a specific trading strategy using three fuzzy logic controllers (Lauguico et al, 2019). They tested and compared the system in two portfolios of the Nasdaq-100 and Eurostoxx market.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Decision Mechanism for Stock Market Trading

Çapkan¹,

Şenol²,

Ulu³

2021

European Journal of Science and Technology

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Investors utilize various methods to make buy/sell decisions depending on time-dependent stock market prices. In this study, a fuzzy decision mechanism that makes buy/sell decisions for stock market data is proposed. The proposed mechanism generates instant buy/sell decisions by evaluating three popular indicators which are the Moving Average Convergence/Divergence (MACD) Strategy, Chaikin Money Flow (CMF), and Stochastic Oscillator (SO). The fuzzy decision mechanism has three inputs and one output which are defined by using Gaussian membership functions. In the design of the decision mechanism, Mamdani inference method is used and the rule table is defined by nine rules. Therefore, the structure of the proposed fuzzy decision mechanism is simple and straightforward. The performance of the proposed fuzzy decision mechanism is compared with two classical decision mechanisms using MACD and CMF indicators separately. In the comparisons, the stock market data of Borsa Istanbul 100 Index (XU100), Dow Jones Industrial Average (^DJI), and S&P 500 (^GSPC) are used. The comparison results show that the proposed fuzzy decision mechanism provides significantly higher profit than the mechanisms using either MACD or CMF indicators for all stock market data.

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An Empirical Mode Decomposition Based Method to Synthesize Ensemble Multidimensional Gaussian Neuro-Fuzzy Models in Financial Forecasting

Vlasenko

Vynokurova

et al. 2020

Communications in Computer and Information Science

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A Fuzzy Logic-Based Stock Market Trading Algorithm Using Bollinger Bands

Cited by 27 publications

References 13 publications

Systematic Literature Review: Stock Price Prediction Using Machine Learning and Deep Learning

Systematic Literature Review: Stock Price Prediction Using Machine Learning and Deep Learning

Fuzzy Decision Mechanism for Stock Market Trading

An Empirical Mode Decomposition Based Method to Synthesize Ensemble Multidimensional Gaussian Neuro-Fuzzy Models in Financial Forecasting

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