By José Carlos Gonzáles Tanaka Prerequisites This blog is a hands-on tutorial that walks you through the math behind the ARIMA model and how to implement it as a backtesting strategy for stock trading. You’ll not only learn how to apply ARIMA models but also how to enhance your results with advanced concepts and references. … Read more
By José Carlos Gonzáles Tanaka The ARFIMA model is well suited for capturing long-range memory in financial time series. However, it’s not always the case the time series exhibits long memory in their autocorrelation. The ARTFIMA model comes to the rescue to capture not only the long memory but also its short one and the … Read more
In the context of autoregressive (AR) models, the coefficients represent the weights assigned to the lagged values of the time series to predict the current value. These coefficients capture the relationship between the current observation and its past values. The goal is to find the coefficients that best fit the historical data, allowing the model … Read more
By Terry Ashton, updated February 6, 2025 Prediction markets enable trading contracts on future events. Contract prices reflect the market’s belief in an event’s outcome. For instance, a candidate’s election contract price rises as their victory chances increase Traditionally, prediction markets have been centralized, controlled by a single entity. This centralization presents inherent risks: Manipulation: … Read more