An enabling framework for automated extraction of signals from market information in real time

Abstract

This paper proposes a framework for a financial technology that characteristically embeds an automated parameter estimation scheme. Such a scheme ensures that market signals from the most recent observations are reflected readily in trading and decision making. This capitalises on filtering recursions that support a data-analytics scheme in the predictive domain using observed prices revealed in real time. The resulting technology facilitates and strengthens a futures trading environment that evolves dynamically and enhances market liquidity. In the implementation of our framework’s algorithms, we choose the fish futures market data in the context of providing the seafood market stability and sustainability. We develop a knowledge-based related mechanism, via a higher-order Markov chain and online filtering, that brings about price discovery and serves an important risk management tool for fish producers and buyers. Our results demonstrate the computational efficiency, flexibility in capturing price dynamics with complex features and adaptability to other financial markets of our proposed automation infrastructure.