Stock market forecast based on wavelet neural network optimized by Cuckoo search

Zhi, Hua; Zhang, Jinghuan; Xue, Zheng; Zhang, Yan

doi:10.1109/icsess.2017.8342977

Cited by 2 publications

(2 citation statements)

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“… Zhi et al (2017) developed a network to foresee stock market activities and predict stock prices by optimizing the basic WNN parameters. To improve the initial parameters, this work uses a brand-new meta-heuristic technique called Cuckoo Search.…”

Section: Methodsmentioning

confidence: 99%

A systematic literature survey on recent trends in stock market prediction

Balasubramanian,

P.,

Badarudeen

et al. 2024

PeerJ Computer Science

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Prediction of the stock market is a challenging and time-consuming process. In recent times, various research analysts and organizations have used different tools and techniques to analyze and predict stock price movements. During the early days, investors mainly depend on technical indicators and fundamental parameters for short-term and long-term predictions, whereas nowadays many researchers started adopting artificial intelligence-based methodologies to predict stock price movements. In this article, an exhaustive literature study has been carried out to understand multiple techniques employed for prediction in the field of the financial market. As part of this study, more than hundreds of research articles focused on global indices and stock prices were collected and analyzed from multiple sources. Further, this study helps the researchers and investors to make a collective decision and choose the appropriate model for better profit and investment based on local and global market conditions.

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Section: Methodsmentioning

confidence: 99%

A systematic literature survey on recent trends in stock market prediction

Balasubramanian,

P.,

Badarudeen

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…In economic forecasting, people usually regard the prediction object as a dependent variable and the factors related to the prediction object as independent variables. By collecting sufficient independent variable data, the regression formula is obtained by correlation analysis and regression analysis and uses the regression equation to predict [2]. In regression prediction, the prediction object y is a random variable, and the common variable X is called an independent variable [3].…”

Section: Linear Regressionmentioning

confidence: 99%

Exploring the application of machine learning and python model lstm in predicting Tesla stock price

Qu¹,

Xiang²,

Xu³

2022

BCPBM

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Machine learning involves many fields such as probability theory, statistics, and algorithm complexity theory. Using existing algorithms and models to make judgments or predictions without clear instructions, relying on existing data, patterns, and reasoning. It is a subset of Artificial Intelligence that includes statistical techniques and knowledge to deal with a dataset with vast amounts of data. It can be applied to our daily life, such as investment decisions of stocks, options futures and other derivatives. In this article, the author will predict price performance by using a just-in-time LSTM model. The goal of the mission is to use past historical price data to make accurate predictions about Tesla's stock price over the future. Due to the characteristics of Tesla's case of high-volatility stocks, this problem is extremely challenging and has become a popular research topic in stock price forecasting. From a machine learning perspective, this is a good, critical but challenging prediction problem. That's because Tesla is already a leader in electric vehicle (EV) production. The linear regression model and LSTM (long and short-term model) model are respectively used to forecast Tesla's stock price. The RMSE value is used to evaluate the accuracy of the two models and the results are obtained: the accuracy of the LSTM model prediction is much higher than that of a linear regression model.

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Stock market forecast based on wavelet neural network optimized by Cuckoo search

Cited by 2 publications

References 1 publication

A systematic literature survey on recent trends in stock market prediction

A systematic literature survey on recent trends in stock market prediction

Exploring the application of machine learning and python model lstm in predicting Tesla stock price

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