A novel predictive analytics model for forecasting short-term trends in equity assets prices

Abstract

This paper introduces a new predictive analytics model for forecasting stock price trends in financial assets traded on major stock exchanges worldwide and the Colombian Stock Exchange. The model is built on a probability space definition that consists of a measurable space derived from filtration. In this paper, the filtration is used to index two distinct $\sigma$-algebras: one from the probability space generated by the Autoregressive Integrated Moving Average model (ARIMA) applied to the price of the asset and another from a probability space created by a random walk with parameters for step size and probability terms, reflecting the asset’s historical behavior. However, in other applications, different probability distribution functions can be utilized. We propose a hypothesis about the trend and assess it using the assets mentioned above.