Neural networks have shown promise for advancing investing and trading strategies. As an advanced machine learning technique, neural networks can uncover complex patterns in financial data that lead to better predictions. This article provides an overview of how neural networks are being used in finance based on publicly available information on GitHub, academic papers and online forums like Reddit.
GitHub repositories demonstrate neural network investing code
There are various GitHub repositories that share code examples of using neural networks for stock price forecasting, algorithmic trading systems, portfolio optimization and more. These repositories show neural network architectures like LSTMs and CNNs applied to financial use cases. The shared code provides a starting point for investors and developers to build their own neural network investing strategies.
Academic papers detail neural network investing techniques
Leading AI conferences and journals have published many papers on using neural networks for investing over the years. These papers test different network architectures and data representations. They also compare neural networks to traditional quant models. Overall, academic research shows neural networks can enhance strategies but have challenges like interpretability that need to be addressed.
Online forums feature discussions about real world usage
Active online communities like the AI and investing subreddits feature many discussions around practically applying neural networks to markets. Forum members share their experiments and results using neural networks in live trading. The discussions highlight important considerations around managing risk, monitoring model performance and combining neural networks with other techniques.
From shared code repos to academic studies to hands-on forum talks, publicly available neural network investing resources demonstrate the potential as well as the nuances of applying this advanced technique. Investors and developers have an opportunity to build on current progress to realize the promise of neural networks in finance.