Academia new trading systems and methods

Here are some new trading systems and methods that have been developed in academia:

  1. Machine Learning-based Trading Systems: Researchers have applied machine learning algorithms to develop trading systems that can learn from historical data and make predictions about future market movements. Examples include neural networks, decision trees, and random forests.
  2. Quantum Computing-based Trading Systems: With the advent of quantum computing, researchers have explored the potential of using quantum algorithms to develop trading systems that can process large amounts of data and make predictions about market movements.
  3. Deep Learning-based Trading Systems: Deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been applied to develop trading systems that can learn from large datasets and make predictions about market movements.
  4. Event Study-based Trading Systems: Event study analysis is a method used to analyze the impact of specific events on stock prices. Researchers have developed trading systems that use event study analysis to identify profitable trading opportunities.
  5. High-Frequency Trading Systems: High-frequency trading systems use advanced algorithms and high-speed computing to rapidly execute trades and take advantage of small price discrepancies in the market.
  6. Factor-Based Trading Systems: Factor-based trading systems use statistical models to identify and exploit specific market factors, such as value, momentum, and size, to generate trading signals.
  7. Network Analysis-based Trading Systems: Network analysis is a method used to analyze the relationships between different assets and identify patterns and structures in the market. Researchers have developed trading systems that use network analysis to identify profitable trading opportunities.
  8. Text Analysis-based Trading Systems: Text analysis is a method used to analyze large amounts of text data, such as news articles and social media posts, to identify patterns and sentiment that can be used to generate trading signals.
  9. Time Series Analysis-based Trading Systems: Time series analysis is a method used to analyze and forecast time series data, such as stock prices and trading volumes. Researchers have developed trading systems that use time series analysis to identify profitable trading opportunities.
  10. Hybrid Trading Systems: Hybrid trading systems combine multiple trading strategies and methods to generate trading signals. Examples include combining machine learning algorithms with technical analysis or combining fundamental analysis with quantitative models.

Some notable research papers and articles on these topics include:

These are just a few examples of the many research papers and articles on new trading systems and methods that have been developed in academia.