Capital markets are changing due to technological innovation, event-driven shocks and broader macroeconomic forces. But current machine learning systems falsely assume that the future will be very similar to the past.
Causal AI finds the real drivers of capital markets, enabling industry players to discover market structures, unlock value and solve their biggest challenges.
Investors demand lower fees and greater explainability from discretionary managers, while systematic funds must constantly extract new signals from an ocean of marginally-useful data. Banks find their models frequently upended by regulations, geopolitics, or regime changes.
Artificial Intelligence and Machine Learning techniques can provide solutions to these and other problems. But current approaches fail to unlock the true potential of AI for Capital Markets:
causaLens is the only AI technology that can unlock true value in Capital Markets.
Unlock the hidden drivers within your business with causaLens. Standard techniques are overwhelmed by the many correlated data sources in a large organization; only Causal AI can cut through the noise and discover meaningful opportunities for efficiency.
Our causal discovery methods can identify the true causal drivers of exposure for a portfolio, rather than focusing on spurious correlations. Using causalNet, we can generate realistic scenarios that capture fundamental regime shifts during periods of market stress while also remaining intuitive and explicable.
Macroeconomic data can often tell a lot about what happened yesterday. causaLens allows you to provide causal explanations of tomorrow, merging different sources of data to create dynamic models of the world economy and uncovering the true structure behind global macroeconomic variables.
In today’s market, having the right data is a key to success. Evaluating the quality of data is nonetheless a challenging task. With causaLens this task is made easier with our proprietary dataset evaluator: benchmark the candidate data against public datasets and quantify the impact of the data on your KPIs, while avoiding the spurious correlations in all large datasets.
The novel Causal AI techniques available on the causaLens Platform have facilitated our joint effort of discovering valuable profitable trading strategies”
Causal AI plays an ever more important role in our investment analysis. It empowers our strategists and portfolio managers to generate alpha by identifying new causal relationships in economic, financial and alternative data, with sophisticated, adaptive and explainable models that don’t suffer from overfitting.”
Transparency and explainability of AI models requires an understanding of causality—an inherent advantage of the causaLens platform”
Causal AI enables us to identify significant and unexpected changes in key factors associated with the FX markets, enabling quick reactions to market conditions and enhancing investing strategies”
We use causaLens to autonomously discover alpha in our data. We would have not been able to discover these valuable causal signals without it.”
Causal AI examines entire dynamic systems to understand the true causal relationships that drive change – relationships that cannot be found with correlation-based techniques. These new angles can be employed in signals research, execution and risk management to create investment opportunities that persist and are robust to unexpected regime changes.
causaLens combines up-to-date academic methods with proprietary R&D from a leading causality research lab. The platform fits easily into existing pipelines, allowing systematic investors to move ahead of existing approaches by applying causal techniques across the business.
causaLens can be integrated directly into any pipeline to optimise high-frequency trading strategies, trade execution, and market making. Designed for high-throughput online time series prediction, our models continuously adapt in real time to discover the current causal drivers in the market and produce the most efficient execution.
causaLens empowers exchanges, data sourcing teams & data vendors to autonomously discover value in time-series data. Standard AI techniques are confused by large amounts of intercorrelated and mismatched data; only Causal AI allows users to extract valuable signals and build exciting data products that are easily monetized.
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