Stock Prediction using Machine Learning a Review Paper

Pahwa, Nirbhey Singh; Khalfay, Neeha; Soni, Vidhi; Vora, Deepali

doi:10.5120/ijca2017913453

Cited by 22 publications

(5 citation statements)

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“…To predict stock prices, many researchers have developed methods, but only a few have developed a trading strategy. Some of the reviews of the developed methods are published [1,2]. For example, Shah et al classified stock prediction methods into four categories: statistical methods, pattern recognition, machine learning, and sentiment analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Trading Saudi Stock Market Shares using Multivariate Recurrent Neural Network with a Long Short-term Memory Layer

Alturki¹,

Aldughaiyem²

2020

IJACSA

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This study tests the Saudi stock market weak form using the weak form of an efficient market hypothesis and proposes a recurrent neural network (RNN) to produce a trading signal. To predict the next-day trading signal of several shares in the Saudi stock market, we designed the RNN with a long shortterm memory architecture. The network input comprises several time series features that contribute to the classification process. The proposed RNN output is fed to a trading agent that buys or sells shares based on the share current value, current available balance, and the current number of shares owned. To evaluate the proposed neural network, we used the historical oil price data of Brent crude oil in combination with other stock features (e.g., previous day ( opening and closing price of the evaluated share). The results indicate that oil price variations affect the Saudi stock market. Furthermore, with 55% accuracy, the proposed RNN model produces the next-day trading signal. For the same period, the proposed RNN trading method achieves an investment gain of 23%, whereas the buy-and-hold method obtained 1.2%.

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Section: Literature Reviewmentioning

confidence: 99%

Trading Saudi Stock Market Shares using Multivariate Recurrent Neural Network with a Long Short-term Memory Layer

Alturki¹,

Aldughaiyem²

2020

IJACSA

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“…This particularly holds true for statistical, pattern recognition, machine learning, and sentiment analysis approaches, alongside some hybrid techniques. There is no unequivocal voice among scholars and practitioners about the best approach, since some authors advocate statistical (see Islam and Nguyen 2020) whereas other advocate for machine learning approaches (see Singh et al 2017, Nõu et al 2023.…”

Section: Athens Journal Of Technology and Engineeringmentioning

confidence: 99%

Untitled

2024

AJTE

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ATINER is an independent and non-profit Association with a Mission to become a forum where Academics and Researchers from all over the world can meet in Athens, exchange ideas on their research and discuss future developments in their disciplines, as well as engage with professionals from other fields. Athens was chosen because of its long history of academic gatherings, which go back thousands of years to Plato's Academy and Aristotle's Lyceum. Both these historic places are within walking distance from ATINER's downtown offices. Since antiquity, Athens was an open city. In the words of Pericles, Athens"…is open to the world, we never expel a foreigner from learning or seeing". ("Pericles' Funeral Oration", in Thucydides, The History of the Peloponnesian War). It is ATINER's mission to revive the glory of Ancient Athens by inviting the World Academic Community to the city, to learn from each other in an environment of freedom and respect for other people's opinions and beliefs. After all, the free expression of one's opinion formed the basis for the development of democracy, and Athens was its cradle. As it turned out, the Golden Age of Athens was in fact, the Golden Age of the Western Civilization. Education and (Re)searching for the 'truth' are the pillars of any free (democratic) society. This is the reason why Education and Research are the two core words in ATINER's name.

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“…For example, Chung et al (10) showed that GRU outperformed LSTM in many tasks, and Chen L. (11) found that GRU was better at many tasks than LSTM, except for language modeling. Shewalar et al (12) found that LSTM was better at the voice recognition test than GRU, but they admit that GRU is faster. Faster optimization is one of the advantages of GRU over LSTM because it has fewer parameters.…”

Section: Gated Recurrent Unitmentioning

confidence: 99%

Prediction of Forex Prices on USD/NGN Using Deep Learning (LSTM and GRU) Techniques

Olanrewaju,

Luka,

Echobu

2023

J. Inf. Syst. Informatics

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The goal of the project is to develop a model to forecast the Foreign Exchange (FOREX) prices of United State Dollar to Nigerian Naira (USD/NGN), utilizing two machine learning algorithms, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). These were chosen for this study because they have been found to be effective in previous studies that have been examined. The principles of machine learning and its applications, as well as the many machine learning techniques and algorithms will be covered in this study. Additionally, various extraction methods that will be used in the study will be presented. Data from the Investing.com dataset would be retrieved for this study's purpose and divided into training and test sets. Using the two machine learning techniques previously mentioned, the model would be trained and tested. Then, to measure the model's performance in terms of accuracy and precision, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error would be utilized. The results obtained showed that, GRU performed better than LSTM with a 0.950 Test R2 score and an adjusted R2 score of 0.122. The RMSE is way lower than LSTMs at 0.105 and MAE is even lower at 0.950.

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Stock Prediction using Machine Learning a Review Paper

Cited by 22 publications

References 0 publications

Trading Saudi Stock Market Shares using Multivariate Recurrent Neural Network with a Long Short-term Memory Layer

Trading Saudi Stock Market Shares using Multivariate Recurrent Neural Network with a Long Short-term Memory Layer

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Prediction of Forex Prices on USD/NGN Using Deep Learning (LSTM and GRU) Techniques

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