{"id":3696,"date":"2026-03-17T15:56:43","date_gmt":"2026-03-17T14:56:43","guid":{"rendered":"https:\/\/wp.unil.ch\/iaunil\/?p=3696"},"modified":"2026-03-24T11:28:28","modified_gmt":"2026-03-24T10:28:28","slug":"predicting-the-unpredictable-ai-to-understand-ceo-decisions","status":"publish","type":"post","link":"https:\/\/wp.unil.ch\/iaunil\/en\/predicting-the-unpredictable-ai-to-understand-ceo-decisions\/","title":{"rendered":"Predicting the unpredictable: Artificial Intelligence to understand CEO decisions"},"content":{"rendered":"\n

Original text published on <\/em>https:\/\/wp.unil.ch\/hecoutreach\/predicting-the-unpredictable-ai-to-understand-ceo-decisions\/<\/a><\/p>\n\n\n\n

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Researchers use artificial intelligence to analyze corporate leaders\u2019 decisions, revealing that they are driven not only by profit but also by personal motivations, paving the way for more transparent, data-driven governance.<\/p>\n<\/div>\n\n\n\n

We live in unpredictable times, whether this relates to the global economy and business, the climate or geopolitics. Anticipating the risks is important to humanity\u2019s future resilience. This involves making sense of increasingly complex information aided with new tools. In this series we look at the work of researchers as they strive to make better predictions.<\/em><\/p>\n\n\n\n

Corporate governance matters. It\u2019s the system by which businesses are held accountable, balancing the interests of shareholders, management, customers and communities. The challenge is that human behaviour can be unpredictable, because decisions by executives aren\u2019t always aligned to maximising profits, shareholder value or sustainability goals.<\/p>\n\n\n\n

\u201cThrough our research we try to understand what decision makers focus on. What is really exciting is that we can build a framework that tries to understand the objectives CEOs appear to be pursuing from their decisions. Before this study, it was difficult to observe directly what they were optimising,\u201d explains Boris Nikolov<\/a>, Professor of Finance at HEC Lausanne.<\/p>\n\n\n\n

The researchers used a large dataset of investment and financial policies from publicly traded U.S. firms over decades, including data on CEO compensation, as well as ESG or environmental, social and governance ratings and other information.1<\/sup>  <\/p>\n\n\n\n

They then designed a machine learning algorithm, which they trained on the dataset. The technique used is called inverse reinforcement learning. The aim is to deduce what goals executives are maximising when making investment and financial decisions. This is because the researchers knew the outcomes, since these are shown in the dataset.  <\/p>\n\n\n\n

\u201cWe can now ask \u2013 Is the decision making of corporate executives optimal? Is it efficient? Is it what we would like them to do? Are they making the right or wrong decisions? Are they biased?\u201d, details Professor Nikolov.<\/p>\n\n\n\n

The professor and his team found that top managers care a lot about profits. Importantly, the goals of managers and shareholders are imperfectly aligned. ESG does matter, however complying with environmental standards comes at a cost to executives. While social and governance aspects are viewed more positively \u2014 they\u2019re seen as benefits, not costs.<\/p>\n\n\n\n

\u201cWe found that managers like to overinvest in all aspects of the business, beyond what is optimal for maximising profits. This is because they like to build empires, even if this reduces shareholder value. This is due to the fact that CEO pay is linked to the size of the assets they\u2019re managing. It is therefore in their interest to accumulate them,\u201d says the Professor.<\/p>\n\n\n\n

While executives may hold analyst and press conferences, as well as investor meetings to talk about the future, it is their concrete actions that really matter, since they can be calibrated with this new tool.<\/p>\n\n\n\n

\u201cWe offer an analytical framework that can now help board members hold CEOs to account for their actions, as well as the words they say. The aim is to debias managerial decisions, offer better guidance, and put in place optimal policies,\u201d states Boris Nikolov.  <\/p>\n\n\n\n

He adds: \u201cUltimately, this machine learning tool can be used to improve decision-making and streamline compliance, as well as better analyse CEO behaviour and performance.\u201d<\/p>\n\n\n\n

This research could also shift corporate governance from a reactive, manual and check-box exercise to a proactive, real-time and data-driven function. New studies will look at how CEO actions affect other factors, including ESG and carbon emissions, to see if these strategies can be optimised as well. <\/p>\n\n\n\n

Reference:<\/strong><\/p>\n\n\n\n

1. AI in Corporate Governance: Can Machines Recover Corporate Purpose?<\/a>Swiss Finance Institute Research Paper No. 25-23, European Corporate Governance Institute \u2013 Finance Working Paper No. 1048\/2025, <\/em>B. Nikolov, N. Schuerhoff, S. Wagner, 16 Mar 2025.<\/em><\/strong><\/p>\n\n\n\n

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Professor of Finance at HEC Lausanne and the Swiss Finance Institute, Boris Nikolov specializes in corporate finance and governance, with research that notably explores the use of artificial intelligence to better understand corporate decision-making.<\/p>\n\n\n\n

Faculty of Business and Economics<\/strong><\/p>\n\n\n\n

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Profile<\/a><\/div>\n\n\n\n
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\"boris\"<\/figure>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Researchers use Artificial Intelligence to analyze leaders\u2019 decisions, revealing motivations beyond profit and paving the way for more transparent governance.<\/p>\n","protected":false},"author":1002648,"featured_media":3723,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","footnotes":""},"categories":[21],"tags":[],"class_list":{"0":"post-3696","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-research"},"_links":{"self":[{"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/posts\/3696","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/users\/1002648"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/comments?post=3696"}],"version-history":[{"count":4,"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/posts\/3696\/revisions"}],"predecessor-version":[{"id":3705,"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/posts\/3696\/revisions\/3705"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/media\/3723"}],"wp:attachment":[{"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/media?parent=3696"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/categories?post=3696"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/tags?post=3696"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}