Use Exabel to easily build dynamic universe screens, rank on smart signal combinations, and backtest hypotheses and strategies to reveal their alpha potential and factor attributions – all in an intuitive interface designed for and by portfolio managers. Exabel makes AI-boosted financial modelling easy, robust and accessible.
Identifying quantitatively backtestable, novel and long-living alpha-generating strategies can yield high value. This exploration is, however, a complex and time-consuming process where success has historically been built on large multi-disciplinary teams working on proprietary platforms. The Exabel platform hides much of the technical complexity away, allowing you to quickly build, simulate and robustly test your domain-specific investment hypotheses.
Using Exabel’s auto-modelling technology you can gain an independent, data-driven view on a KPI compared to its benchmark, for example next-quarter revenue compared to the analyst consensus. This can be a key advantage which you can leverage to improve a position around earnings releases, or you can use it to support or challenge fundamentally developed theses. Our modeller builds KPI prediction models by training time series models optimised for short time series, using data pooled from an ensemble of comparable companies. The models are backtested and compared with analysts’ consensus estimates for accuracy, and profit analyses are performed on trading strategies based on these predictions.
A large variety of data sources are becoming available thanks to the explosion in alternative data. These data undoubtedly hold the potential to fuel diverse alpha-generating strategies across many universes. However, in order to capture the value in these data, you need both the capability and the capacity to evaluate the data value and then incorporate the data-driven insights into implementable strategies. Traditionally this has required a resource-intensive quant and data science team approach, outside the purview of many actors on the financial market. Exabel removes this requirement, giving you a one-stop-shop where you can seamlessly evaluate, ingest and model with all data, whether from traditional or alternative sources.
Exabel offers price driver models which enable you to better understand and monitor price developments using a limitless variety of factors, signals and fundamentals. You can analyse share prices on the fly to measure their sensitivity to underlying factors and uncover insights about what is driving them. Anomalies in the share price movements are automatically identified and connected with news and other events that may explain them.
I use Exabel to first identify and then backtest alpha strategies by screening and ranking my investment universe based on multiple traditional and Alternative data sources. I can articulate and solve modelling problems in the Intelligent Modeller without needing a Quant team to support me. Exabel also allows me to monitor the market beyond my portfolio for specific trends or opportunities, freeing up my time and improving my risk-managed returns.
Portfolio Manager at European alternative strategies fund
The insights and outcomes of all Exabel models can be built into dashboards and alerts which enable you to spot key anomalies, trends or opportunities ahead of the curve. Given that time is your scarcest resource, you can configure dashboards to focus your attention where it counts the most.
Portfolio Managers often report spending significant time manually downloading, maintaining and transforming data, not to mention updating and re-running spreadsheets or other local models based on those data. The Exabel Intelligent Modeller includes a data ingestion, transformation and modelling pipeline, allowing you to focus your time and effort on interpreting the data and extracting useful signals from them. Data integrity and point-in-time features are provided out of the box.
Exabel is a state-of-the-art web application, which can be securely accessed from any computer with a web browser. You can be up and running in no time, without the need for any implementation resources.
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