We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models.

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股价预测的文献计量文献综述：从统计模型到深度学习方法

我们对股票价格预测中使用的几种方法进行了全面分析，包括统计、机器学习和深度学习模型。讨论这些模型的优点和局限性，以提供对股票价格预测的深入了解。传统的统计方法，例如自回归积分移动平均线及其变体，因其效率而受到认可，但它们在解决非线性问题和提供长期预测方面也存在一些局限性。机器学习方法，包括人工神经网络和随机森林等算法，因其在不依赖随机数据或经济理论的情况下掌握非线性信息的能力而受到称赞。此外，深度学习方法，例如卷积神经网络和循环神经网络，可以处理股票价格的复杂模式。此外，本研究进一步研究混合模型，结合各种方法来探索其优势并抵消个体弱点，从而提高预测准确性。通过对各种研究和方法的详细回顾，本研究阐明了股票价格预测的方向，并强调了进一步研究完善股票价格预测模型的潜在方法。