Iridium ‘Quant Lens’ Brings Artificial Intelligence to Valuation and Sentiment Analysis in Listed Companies

Iridium 'Quant Lens' Brings Artificial Intelligence to Valuation and Sentiment Analysis in Listed Companies

 Iridium Advisors, the investor relations consultancy, unveils two self-funded technologies that decode valuation drivers of listed companies and automate sentiment analysis in earnings calls. Based on algorithms that analyse massive volumes of publicly available data, these innovative tools are part of Iridium’s venture into artificial intelligence, data science and advanced analytics known as “Iridium Quant Lens”.

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Oliver Schutzmann, CEO of Iridium Advisors, said: “Iridium Quant Lens is one way we are evolving to support the corporate world to use artificial intelligence to address possible vulnerabilities and unlock potential for improved valuations. By providing organizations and leaders with this cutting-edge technology and pairing it with our knowledge and expertise of investor relations, we equip our clients with an unrivalled level of insights that they can use to close the gap between their business value and their market value.”

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Capital markets are a complex, dynamic and nuanced ecosystem that essentially seeks to synthesize a huge array of fundamental and alternative data to make intelligent investment decisions. Institutional investors and research analysts have been quick to embrace Machine Learning to augment their investment process and Natural Language Processing to identify hidden sentiment in earnings calls to confirm or discredit their investment views. Due to the limited availability of affordable tools, public companies across all sectors and geographies have been much slower to integrate these artificial intelligence concepts into their corporate decision-making and investor communications processes.

In a first foray to level the playing field, Iridium Quant Lens now provides Natural Language Processing and Machine Learning algorithms that not only help boards and management teams better understand how these technologies are applied by the investment community, but also to enable public companies to run their own algorithms that can quantify valuation drivers and anticipate market reactions to strategic decisions, financial results and management commentaries after earnings calls.

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