RETRACTED: Extreme learning machine for stock price prediction

Zhang, Fangzhao

doi:10.1177/0020720920984675

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…“The design of an ANN [ Artificial Neural Network (ANN) ] is more of an art than a science” Zhang, Patuwo and Hu [ 31 ], and, in the case of the ELM-trained SFNN, it is mainly the number of hidden nodes in the single hidden layer that needs to be chosen. Some works in this area focus on developing theoretical bounds on the minimum and maximum number of hidden nodes required (for example, LeCun et al [ 18 ] while others develop complex algorithms for supporting this decision using the dataset at hand Xu and Chen [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Empirical validation of ELM trained neural networks for financial modelling

Novykov

Bilson

Gepp

et al. 2022

Neural Comput & Applic

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The purpose of this work is to compare predictive performance of neural networks trained using the relatively novel technique of training single hidden layer feedforward neural networks (SFNN), called Extreme Learning Machine (ELM), with commonly used backpropagation-trained recurrent neural networks (RNN) as applied to the task of financial market prediction. Evaluated on a set of large capitalisation stocks on the Australian market, specifically the components of the ASX20, ELM-trained SFNNs showed superior performance over RNNs for individual stock price prediction. While this conclusion of efficacy holds generally, long short-term memory (LSTM) RNNs were found to outperform for a small subset of stocks. Subsequent analysis identified several areas of performance deviations which we highlight as potentially fruitful areas for further research and performance improvement.

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Section: Methodsmentioning

confidence: 99%

Empirical validation of ELM trained neural networks for financial modelling

Novykov

Bilson

Gepp

et al. 2022

Neural Comput & Applic

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“…Machine learning models are extensively applied in research on price forecasting, providing valuable information and guiding decision‐making processes. Zhang 15 applied the extreme learning machine (ELM) model to predict stock prices and achieved good results. Jabeur et al 16 used the extreme gradient boosting (XGBoost) model to predict gold price trends and interpreted its feature importance using SHapley Additive exPlanations (SHAP).…”

Section: Introductionmentioning

confidence: 99%

Forecasting carbon price using signal processing technology and extreme gradient boosting optimized by the whale optimization algorithm

Duan,

Zhang,

Wang

et al. 2024

Energy Science & Engineering

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Predicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices.

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“…The research background of stock data analysis and price prediction can be traced back to the 1960s and 1970s [4]. At that time, with the development of computer technology, people began to try to use computers to predict stock prices [5].…”

Section: Introductionmentioning

confidence: 99%

Data Visualization and Machine Learning Trend Analysis Forecasting Based on Roblox Stock Data

Wang,

Zhang

2024

AEMPS

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The stock market, as a highly complex, unstable and dynamically changing system, is affected by a wide range of factors, such as political, economic, social and technological, in terms of price changes. Therefore, stock price prediction is a challenging problem, but also a problem of great importance. In order to provide a more accurate and reliable method for stock closing price prediction, this paper aims to reduce investment risk and improve investment returns by comparing the prediction effects of different regression models for investors to make investment strategies and decisions. In this paper, Open, High, Low, and Adj Close are used as input parameters, Close is used as the target parameter, the final closing price of the stock is determined from the various stock data, the training set, validation set, and test set are divided in accordance with 6:2:2, and the predictive effect of the model is evaluated using the parameters of MSE, RMSE, MAE, MAPE, and R2. After the comparison of multiple machine learning algorithms, XGBoost regression model and Random Forest Regression model perform the best in the prediction of stock closing price, and their MSEs are 0.061 and 0.072 respectively; followed by LightGBM regression model and decision tree regression model, the MSEs of the two models are 0.113 and 0.299 respectively, but the prediction results of BP neural network regression are poor. By comparing the prediction results of different regression models, it can provide investors with a more accurate and reliable method to predict the closing price of stocks, help investors better formulate investment strategies and decisions, reduce investment risks and improve investment returns. This is of great significance to both individual and institutional investors.

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RETRACTED: Extreme learning machine for stock price prediction

Cited by 8 publications

References 17 publications

Empirical validation of ELM trained neural networks for financial modelling

Empirical validation of ELM trained neural networks for financial modelling

Forecasting carbon price using signal processing technology and extreme gradient boosting optimized by the whale optimization algorithm

Data Visualization and Machine Learning Trend Analysis Forecasting Based on Roblox Stock Data

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