Risk models and Simulations
Risk models are important to understand how assets move together, and forward-looking simulations are essential to understand the stochastic impact of new information on your portfolio.
Quantitative models are becoming more sophisticated with the integration of alternative data and machine learning. We seek to build out the next generation of models that provide more insights by leveraging high performance computing and the exploitation of new sources of data.
Investment Scenario Exploration
Investors need to synthesize and infer return and risk, for their portfolios across a variety of scenarios. Traditional numerical analysis such as factor models and Monte Carlo simulations only account for market-derived forecasts.
We seek to combine this market risk perspective with other sources of data to enrich the perspectives available to explore the potential outcomes of investment decisions.
Machine Learning, HPC and New Data Sources
The availability of more data, contemporary high performance computing, and the judicious use of machine learning and inference allow for us to go beyond the use of traditional quantitative techniques. However, these new methodologies need to be combined with deep domain knowledge to extract useful economic and financial insights . Sapiat applies its unique experience in Markets and expertise in Machine Learning in understanding the patterns and structures that are most relevant for institutional investment processes and risk management.