Calibration with Changing Checking Rules and Its Application to Short-Term Trading

TL;DR

This paper introduces a calibration-based trading strategy that adapts to changing market conditions using a generalized calibration concept, aiming to exploit market inefficiencies for profit.

Contribution

It extends calibration methods with changing checking rules and applies a modified randomized algorithm for improved short-term trading strategies.

Findings

Effective calibration with changing rules in financial markets

Potential for profit in inefficient markets using the proposed method

Enhanced forecasting accuracy through modified algorithms

Abstract

We provide a natural learning process in which a financial trader without a risk receives a gain in case when Stock Market is inefficient. In this process, the trader rationally choose his gambles using a prediction made by a randomized calibrated algorithm. Our strategy is based on Dawid's notion of calibration with more general changing checking rules and on some modification of Kakade and Foster's randomized algorithm for computing calibrated forecasts.