Extreme and Sustainable Graph Processing for Green Finance Investment and Trading

Abstract

In this paper we present a case addressing the drawbacks of financial market data, its limited volumes, history, and sometimes the incomplete and erroneous datasets with variable quality, limited availability, and price barriers. The case aims to enable fast, semi-automated creation of realistic and affordable synthetic (extreme) financial datasets, unlimited in size and accessibility, ready to be commercialized. Peracton Ltd. intends to apply the resulting extreme financial data multiverse for testing and improving artificial intelligence (AI)-enhanced financial algorithms (e.g., using machine learning) focused on green investment and trading. Using synthetic data for testing financial algorithms removes critical biases, such as prior knowledge, overfitting, and indirect contamination due to real-world data scarcity, and ensures data completeness at an affordable cost. The availability of extreme (volumes) of synthetic data will consolidate further financial algorithms and provide a statistically relevant sample size for advanced back-testing.