The next generation Investment Intelligence platform

Synthesize Data into Knowledge, and Knowledge into Action.

Make investment decision-making more timely, robust, and un-biased.

Aggregating and managing disparate sources of information, whether market data, news, independent research, or alternative data is a crucial prerequisite to enable effective investment decision making.

Sapiat helps gather, cleanse and manage your structured and unstructured data into a knowledge base to make your investment process more systematic.

The platform brings together both qualitative and quantitative information, displaying the implications for your portfolio in a set of dynamic and visually-compelling dashboards.

> Read more about our approach


…leveraging new Data Sources


The use of new, alternative data sources enriches the available information set beyond just market data.

Alternative data is all the rage. However, they tend to be expensive and difficult to apply in practice. Sapiat provides data products that are immediately consumable to both systematic and discretionary investors that leverage traditional market data, but enhanced with other data sources and machines learning techniques to extract more dynamic regime and risk information.

While these data can be used on their own to provide volatility forecasts and regime conditioning, they are also the raw ingredients into Sapiat’s more comprehensive investment intelligence framework.


… and advanced analytic frameworks for

Risk estimation,

Performance attribution, and

Scenario exploration…

Investors need to estimate the projected return and risk for their portfolios across a variety of scenarios. Risk engines are important in helping to understand how assets move together. Forward-looking simulations are essential to understand the stochastic impact of new information on your portfolio.

However, traditional numerical analysis such as factor models and Monte Carlo simulations are often limited by their linear approaches and strong assumptions — even though experience tells us that asset returns are non-stationary, noisy, and not IID.

Sapiat seeks to address these limitations by expanding both the data sets used as well as by expanding the approaches to understanding the sources of systematic and idiosyncratic risk in the use of new algorithms that are robust to sparse data and handle non-linearity while maintaining economic tractability.


enhanced by Machine Learning and High Performance Computing.


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.

> More about our ideas on the use of ML in Finance

latent nodes.png

Sapiat’s Edge

Sapiat’s tools and solutions are grounded in our philosophy that unbiased systematic decision-making is central to improving investment performance, and that it is necessary to consistently synthesize a broad array of sometimes inconsistent information sources, from market-derived covariance matrices to soft forecasts from independent research.

With over $100 trillion globally in investable assets managed by asset owners, managers, and other institutions, the stakes are high for getting investment decisions right.  Investors need more robust frameworks to explore the impact of uncertainty across different horizons, asset classes, and scenarios.  These tools should provide not just mind-numbing numbers in flat presentations— but rather dynamic, interactive, powerful visualizations of the environment and the impact of investment decisions.

Sapiat’s edge is in weaving together the new machine learning methodologies and data science to traditional and alternative data, coupled with an understanding of finance to build tools for investment and risk decisioning, and delivering the solutions built on a contemporary, high performance technology stack.

Traditional quantitative models rely on powerful statistical methods, but often rely on linear approaches applied almost exclusively to market returns.  Approaches such as factor modeling are an essential part of the tool kit, but can be fragile to nonlinearities, stress conditions, and long tails. 

Markets are not the only source of insight.  There are plenty of signals embedded in alternative data sources such as high frequency macroeconomic data, news, and in independent research.  However, noise is rampant in these sources also.  Information tend to be provided in heterogeneous formats (structured and unstructured), at different sampling frequencies, with different precision and horizons.  Synthesizing this information and converting it into actionable “investment intelligence” is at the heart of Sapiat’s mission. 

Sapiat believes that the availability of High Performance Computing, large and non-traditional datasets are key drivers in enabling a more systematic and holistic investment process to be applied in mainstream institutional investing.  The judicious use of machine inference and learning, coupled with traditional linear techniques (both core competencies of Sapiat) will provide efficient, robust, and more nuanced insights to allocation, portfolio construction, manager selection, and risk.


© 2019 Sapiat Pte. Ltd.