Stock market forecasting is an important task for investors and financial analysts. Over the years, various machine learning models have been developed to predict stock market prices. This article presents a comparative analysis of different machine learning models used for stock market forecasting.

One of the earliest models discussed is the Random Walk model, which was proposed by E.F. Fama in 1995. This model assumes that stock prices follow a random walk, making it difficult to predict future prices. However, more recent models have shown promising results in stock market forecasting.

Support Vector Machines (SVM) is one such model that has been widely used in financial time series forecasting. L. Cao and F. Tay (2003) proposed a SVM with adaptive parameters for stock market prediction. The SVM model aims to find the best hyperplane that separates different classes of data points, which can be used to predict stock market trends.

Another popular model discussed is the Long Short-Term Memory (LSTM) model, which is a type of recurrent neural network (RNN). The LSTM model was proposed by S. Hochreiter and J. Schmidhuber (1997) and has been successfully applied in various domains, including stock market forecasting. The LSTM model is capable of storing and remembering long-term dependencies in time series data, making it suitable for predicting stock market prices.

In recent years, variations of the LSTM model have been proposed to improve its performance. For example, bidirectional LSTM (BiLSTM) models have been developed to capture both past and future context information. M. Schuster and K.K. Paliwal (1997) introduced the concept of bidirectional recurrent neural networks, which have been applied in stock market forecasting.

Other models discussed in this article include the Extreme Learning Machine (ELM), Feature Weighted SVM (FWSVM), and Convolutional Neural Network (CNN) combined with LSTM. Each of these models has its own advantages and limitations in stock market forecasting.

In conclusion, machine learning models have shown great potential in stock market forecasting. However, the choice of model depends on the specific requirements and characteristics of the data. Researchers and practitioners continue to explore and develop new models to improve the accuracy and reliability of stock market predictions.

Sources:

– E.F. Fama, “Random walks in stock market prices,” Financ. Anal. J. 51(1995), 75–80
– L. Cao, F. Tay, “Support vector machine with adaptive parameters in financial time series forecasting,” IEEE Trans. Neural Netw. (2003)
– S. Hochreiter, J. Schmidhuber, “Long short-term memory,” Neural Comput 9(8),1735–1780 (1997)
– M. Schuster, K.K. Paliwal, “Bidirectional recurrent neural networks,” IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)
– And more…

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