Stochastic methods use statistics to forecast financial values or signal approaching events. Regression models price data on past prices and volumes, with R-squared measuring the fit; genetic algorithms and neural networks are adaptive alternatives. Because no model foresees sudden shocks, they work best combined with news analysis.
Stochastic methods use mathematical — above all statistical — tools to forecast values or to signal that significant events are approaching.
Regression models
The most widely used statistical tool is regression, linear and non-linear, which models current prices from previous prices and volumes. These models come with a very reliable measure, R-squared, which shows how well the model predicts yesterday’s prices from earlier days’ information. There is, of course, no guarantee for tomorrow’s prices, especially when sudden economic changes cause shocks — which is why it is always good practice to pair regression models with natural-language news analysers. More sophisticated models also exist, capturing several layers of interaction to better reflect economic reality.
Genetic algorithms and neural networks
Other stochastic methods include genetic algorithms, which predict prices with a self-adapting strategy inspired by biology, and neural networks, which use a network of simple nodes that builds its own economic model of prices.
Common questions
What are stochastic methods in finance?
Methods that use mathematical, and above all statistical, tools to forecast values or to signal that significant events are approaching.
What does R-squared measure in a financial regression model?
How well the model explains recent prices — for example, how well it predicts yesterday’s prices from earlier days’ prices and volumes. It gives no guarantee about tomorrow’s prices.
Why combine statistical models with news analysis?
Because sudden economic changes cause shocks that models built on past prices cannot anticipate. Natural-language news analysis covers the information those models miss.
How are genetic algorithms and neural networks used to predict prices?
Genetic algorithms use a self-adapting strategy inspired by biology; neural networks use a network of simple nodes that builds its own economic model of prices.
