Report
Causal AI in Capital Markets
Competing in capital markets requires trustworthy technology. However, correlation-based machine learning techniques lack reliability, flexibility and transparency. Systematic hedge funds, high-frequency traders, and market-makers trust the power of Causal AI in capital markets to augment their research pipelines, market analysis, and decision-making.
Top of mind for our customers operating in capital markets
- Extreme competition, with market participants crowding into trades
- Heightened macroeconomic uncertainty and volatility
- Flawed machine learning technology, generating strategies with very short half-lives
- Lack of transparency creating risk-management problems
Solutions
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 uncover the driving forces 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.
Resources on Causal AI for Capital Markets
Blog
Causal AI in financial markets: Causal drivers of FX Volume
2 min read
Report
Causal Portfolio Optimization: Modern Portfolio Theory 2.0
Systematic Trading
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.
High Frequency Trading & Market Making
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.
Data Search & Building New Products
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.
AI Capital Markets Can Trust
causaLens co-founders Darko Matovski and Maxim Sipos discuss why current AI fails and how Causal AI unlocks superior decision-making, in our Capital Markets webinar. Applied Data Science Director Andre Franca presents some causaLens success stories.
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