Improved Stock Price Prediction by Integrating Data Mining Algorithms and Technical Indicators: A Case Study on Dhaka Stock Exchange

Hasan, Syeda Shabnam; Rahman, R. Eki; Mannan, Noel; Khan, Haymontee; Moni, Jebun Nahar; Rahman, Rashedur M.

doi:10.1007/978-3-319-67074-4_28

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Stock price [23], [24], [25], [35], [36], [38], [45], [47], [51], [55], [53], [63], [65], [66], [69], [70], [72], [79], [80], [81], [83], [87], [89], [90], [91], [95] IMFs value [26], [31], [59], [60], [59], [60], [67], [74], [76], [86], [92] Technical indicator value [37], [71] Stock return [52], [82] Stock volatility [54], [50] Effect of external factors [50], [51], [78] Interval of time series [61] V. DECISION FUSION METHODS Admittedly, a better prediction can be obtained by fusing multiple forecasts of the base learners. However, the choice of the fusion method is also critical to the performance of the entire model.…”

Section: Forecasts Of Base Learnersmentioning

confidence: 99%

Decision Fusion for Stock Market Prediction: A Systematic Review

2022

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Section: Forecasts Of Base Learnersmentioning

confidence: 99%

Decision Fusion for Stock Market Prediction: A Systematic Review

2022

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“…The techniques utilize trend based classification, indicator selection, and stock market trading signal forecasting. Hasan et al [23] in their work utilized a number of ML techniques to predict the future stock prices. The ensemble is formed to combine the outcomes of different ML algorithms.…”

Section: Related Workmentioning

confidence: 99%

Stock Price Prediction using Technical Indicators: A Predictive Model using Optimal Deep Learning

2019

ijrte

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The stock market is highly volatile and complex in nature. Technical analysts often apply Technical Analysis (TA) on historical price data, which is an exhaustive task and might produce incorrect predictions. The machine learning coupled with fundamental and / or Technical Analysis also yields satisfactory results for stock market prediction. In this work an effort is made to predict the price and price trend of stocks by applying optimal Long Short Term Memory (O-LSTM) deep learning and adaptive Stock Technical Indicators (STIs). We also evaluated the model for taking buy-sell decision at the end of day. To optimize the deep learning task we utilized the concept of Correlation-Tensor built with appropriate STIs. The tensor with adaptive indicators is passed to the model for better and accurate prediction. The results are analyzed using popular metrics and compared with two benchmark ML classifiers and a recent classifier based on deep learning. The mean prediction accuracy achieved using proposed model is 59.25%, over number of stocks,which is much higher than benchmark approaches.

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“…At the end of the 20th century, Artificial Intelligence technology developed rapidly, and scholars began to use neural networks for stock prediction, pioneering this application [3]. Feedforward Neural Network (FNN) and Backpropagation (BP) were introduced and widely applied [4].…”

Section: Introductionmentioning

confidence: 99%

The Investigation of Stock Price Prediction Based on Machine Learning

Huang

2024

HSET

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As the global economy experiences unprecedented growth, the stock market is undergoing a period of expansion and prosperity. Driven by the allure of high risks and high returns, a significant number of individuals have committed themselves to stock trading. In the age of the Internet of Everything, the trajectory of the stock market is intertwined not only with the fortunes of individuals and businesses but also deeply influences the fate of nations and societies. However, stock market forecasting is quite a hard task owing to its volatile nature, where even seemingly unrelated factors can lead to dramatic changes. Therefore, the importance of constructing reliable stock prediction methods is self-evident. In recent years, scholars have mainly focused on machine learning and deep learning methods to complete this task. After literature review, this article summarizes and discussed several commonly used and effective methods, such as Linear Regression, Random Forest, Long Short-Term Memory (LSTM), etc. The advantages, disadvantages and applications of different methods are discussed, like the shortcoming of Linear Regression to handle non-linear models well and the relatively excellent ability of LSTM to predict long sequences. This article would be helpful for emerging scholars to have a grasp of the characteristics, advantages and scope of application of these methods, and facilitate the development of future research.

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Improved Stock Price Prediction by Integrating Data Mining Algorithms and Technical Indicators: A Case Study on Dhaka Stock Exchange

Cited by 5 publications

References 7 publications

Decision Fusion for Stock Market Prediction: A Systematic Review

Decision Fusion for Stock Market Prediction: A Systematic Review

Stock Price Prediction using Technical Indicators: A Predictive Model using Optimal Deep Learning

The Investigation of Stock Price Prediction Based on Machine Learning

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