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Abstract
Stock price prediction is important in both financial and commercial domains, and using neural networks to forecast stock prices has been a topic of ongoing research and development. Traditional prediction models are often based on a single type of data and do not account for the interplay of many variables. This study covers a radial basis neural network modeling technique with multiview collaborative learning capabilities for incorporating the impacts of numerous elements into the prediction model. This research offers a multiview RBF neural network prediction model based on the classic RBF network by integrating a collaborative learning item with multiview learning capabilities (MV-RBF). MV-RBF can make full use of both the internal information provided by the correlation between each view and the distinct characteristics of each view to form independent sample information. By using two separate stock qualities as input feature information for trials, this study proves the viability of the multiview RBF neural network prediction model on a real data set.
Highlights
IntroductionPredicting the fluctuation trend of stock prices plays an extremely important role in asset pricing, investment decision-making, risk management, and market supervision
Predicting the fluctuation trend of stock prices plays an extremely important role in asset pricing, investment decision-making, risk management, and market supervision.rough the study of the autocorrelation function, power spectral density, and fluctuation range of stock prices, it is found that there are hidden long-term price linear trends and low-frequency periodic fluctuations in stock prices. is theoretically proves that stock prices that exhibit randomness and unpredictability at the micro level have overall certainty and predictability at the macro level [1]. erefore, the prediction of stock price trends through machine learning and other methods has become an important topic nowadays.e BP neural network is the most extensively used model for stock price prediction
White et al were the first to use the BP neural network to forecast stock prices [2], and numerous improved approaches based on the BP neural network followed [3]. e BP neural network, on the other hand, is readily stuck in local minimums, causing the model’s prediction impact to deteriorate
Summary
Predicting the fluctuation trend of stock prices plays an extremely important role in asset pricing, investment decision-making, risk management, and market supervision. RBF neural network is a useful basis for constructing multiview collaborative learning tasks, because it has strong multidimensional nonlinear mapping skills, generalization capabilities, and parallel information processing capabilities [14, 15]. In this paper’s experimental effort, we start with the question, “How can we enhance the accuracy of stock price prediction using existing approaches even more?” By reading the relevant literature in recent years, we have focused on the multiview direction, because the multiview thinking is a more feasible method in both cognition and theory. More complicated data gathering demonstrates that multiview approaches may be used to analyze stock data Another problem we encountered is what kind of algorithm can be used to capture the connection between different perspective data at the same time without destroying the independence of a single perspective data.
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