Machine Learning in the Investment Industry
Predicting how the stock market will perform is a very challenging undertaking. There are many factors that affect daily price changes such as macroeconomic events, investor sentiment, earning reports, and irrational behavior. All of these elements combine to influence the volatility and unpredictable nature of the markets. Traditionally, fundamental and technical analysis have been the main strategy to generate stock market predictions. Machine learning is an alternative solution that eliminates human emotional biases, and can process considerably more data at one time.
What is Machine Learning?
Machine Learning (ML) is the application of data and algorithms to help machines mimic the way that humans learn and make decisions. It is a computer driven approach aimed at generating structure or predictions from data without being explicitly programmed to do so. ML is a subset of Artificial Intelligence (AI) which describes the intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans.
How Does Machine Learning Work?
The objective of machine learning is to extract knowledge from large amounts of data. ML aims to teach computers how to accomplish tasks where no fully satisfactory algorithm is available. It is often used on complex problems that would be hard for a human to solve. ML can be simplified into four steps:
The Decision Process: The goal of an ML algorithm is to make a prediction or classification. In this step, the algorithm is given some input data and outputs an estimate about a pattern in the data. The inputted data is called the “training data,” and the correct classifications are already known.
The Error Function: Once an estimate has been made, the error function evaluates the accuracy of the prediction. This is done by comparing the estimated model value with the known value of the training data.
The Model Optimization Process: Depending on the discrepancy between the estimated value and the known value, weights are adjusted within the model to tune the prediction accuracy. This process is repeated until the margin of error reaches a threshold value.
The Testing Process: The ML model is given new input data to test the model's prediction ability.
Machine Learning and the Investment Industry
ML algorithms have a broad range of uses within the investment industry including: price prediction, understanding clients better, investment advice, security selection, determining optimal portfolio weights and trade execution. At Koi, we use advanced mathematical models to develop state of the art ML algorithms to deliver results to our clients.
