It is no secret that machine learning (ML) and artificial intelligence (AI) techniques have been used extensively in stock market prediction. Many studies have shown that these techniques can be used to achieve significant prediction accuracy. In this article, we will explore some of the latest and most effective ways to use AI for stock market prediction.
One approach that has shown promise is using deep learning networks to predict stock prices. Deep learning is a subset of machine learning that uses algorithms that are inspired by the structure and function of the brain. Deep learning networks are particularly well-suited for prediction tasks because they can learn complex nonlinear relationships.
A recent study by Wang et al. used a deep learning network to predict stock prices from historical data. The network was trained on a dataset of historical stock prices and volume data. The results showed that the deep learning network was able to achieve significant prediction accuracy, outperforming other state-of-the-art machine learning methods.
Another approach that has been shown to be effective is using genetic algorithms. Genetic algorithms are a type of evolutionary algorithm that can be used to optimize arbitrary functions. In the context of stock market prediction, genetic algorithms can be used to find the best combination of input features
(e.g., stock price, volume, etc.) that leads to the highest prediction accuracy.
A study by Shayeghi et al. used genetic algorithms to find the best combination of input features for stock market prediction. The results showed that the approach was able to achieve significant prediction accuracy, outperforming other state-of-the-art machine learning methods.
In conclusion, AI techniques can be very effective for stock market prediction. The latest and most effective approaches include using deep learning networks and genetic algorithms. These techniques can be used to achieve significant prediction accuracy.
References:
https://www.nature.com/articles/s41598-019-49167-z
https://towardsdatascience.com/genetic-algorithm-for-feature-selection-in-machine-learning-11b7d55bca8e