Analyst users of the Exabel platform seek to augment their fundamental analysis perspectives with the supporting or challenging narrative contained in data. 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 capital markets research analysts. AI-boosted financial modelling and analysis has never been this 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.
Gaining an independent, data-driven view on a KPI compared to its benchmark, e.g. next quarter sales versus analyst consensus, can be a key advantage to better position around quarterly releases, supporting or challenging fundamentally developed theses. Exabel’s auto-modeling technology builds KPI prediction models trained on data pooled across comparable companies as ensembles of time series models optimized for short time series. The models are backtested and compared with analysts’ consensus estimates for accuracy, and profit analysis is performed on trading strategies based on the predictions.
The explosion in alternative data brings an exponentially growing variety and quantity of new data to market. These data undoubtedly hold the potential to fuel diverse alpha generating strategies across many universes. However, capturing the value in these data is restricted to those organisations with both the capability and capacity to both evaluate data value and then incorporate data-driven insight into implementable strategies. Exabel removes the need for a resource intensive quant and data science team approach to alternative data, providing a one-stop-shop for portfolio managers to seamlessly evaluate, ingest and model with alternative data.
Exabel’s price driver model type enables a portfolio manager to better understand and monitor price developments in the context of a limitless variety of factors, signals and/or fundamentals. Users can analyze 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 identified and connected with news and other events that may explain them.
I use Exabel to supplement my research theses with the independent story in the data. I value the additional dimension this gives to my position whether it supports or challenges my underlying assumptions.
Research Analyst at long-short London hedge fund
The insight and outcomes of all Exabel models can be built into dashboards and alerts to enable the Research Analyst to spot key anomalies, trends or opportunities across a wide watchlist universe and ahead of the curve. Analysts using Exabel recognise that often their time is their most scarce resource. They configure dashboards to focus their attention where it counts 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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