Asset managers face increased competition against a backdrop of highly uncertain market conditions. AI can create a decisive advantage. But correlation-based machine learning fails to inspire the trust that’s needed to influence critical decisions. Leading asset managers are leveraging Causal AI in asset management to gain a competitive edge.
Transparency and explainability of AI models requires an understanding of causality—an inherent advantage of the causaLens platform”
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.”
We use causaLens to autonomously discover alpha in our data. We would have not been able to discover these valuable causal signals without it.”
causaLens’ causality-based techniques & automation of quantitative workflows help us discover more orthogonal signals faster while discarding spurious correlations”
The causaLens platform has enabled us to discover additional value in our data. Their causal AI technology autonomously finds valuable signals in huge datasets and has helped us to understand relationships between our data and other datasets.”
With Causal AI enablement, asset managers can construct portfolios that adapt to shifting correlations between assets. Learn how Causal AI achieves 19% greater Sharpe compared to machine learning-based methods for allocation.
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.
Causal AI allows domain experts to include their own assumptions into the model, producing a new class of intelligence that learns from data, but within a contextualized view of the world. Discretionary managers can have the best of both worlds, where their expertise is augmented by state-of-the-art artificial intelligence, without relying on a black-box to get insights from data. These insights empower users to harness the full potential of their domain knowledge and become ‘quantamental’ managers.
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.
Private equity investors often work in low-data regimes in which standard machine learning techniques are prone to overfit, because they confuse correlation with causation. But data-driven approaches can produce outperformance when used properly. causaLens produces robust, explainable insights and actionable forecasts from limited data, and our causalNet technology allows domain experts to embed their knowledge directly into the AI.
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.