Our Mission
There are many widely used traditional techniques of stock forecasting each with its own limits and biases. Human-oriented approaches focus more on back-dated data while not being able to well define or relate how those back-dated financials or people actions will reflect on future performance or pricing. At its best the number of facts or signals that can be considered and correlated simultaneously in human-oriented approaches is quite limited and is subject to personal biases.
As AC Investment Research, our goal is to do fundamental research, bring forward a totally new, scientific technology and create frameworks for objective forecasting using machine learning and fundamentals of Game Theory.
Our Research
Game theory can be applied to the stock market in several ways. One way is to analyze the strategic decision-making of market participants, such as traders, investors, and companies. For example, game theory can be used to understand how traders might make decisions about when to buy or sell a particular stock, or how companies might make decisions about when to issue new stock or buy back existing stock.
Another way that game theory can be applied to the stock market is to analyze the overall market dynamics and how they might affect stock prices. For example, game theory can be used to understand how the actions of individual market participants might influence the overall market and how different market conditions, such as supply and demand, might affect stock prices.
Game theory and neural networks can be used together in a variety of ways. One way to use game theory with neural networks is to apply game-theoretic concepts and techniques to the design and analysis of neural networks.
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