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July 2025","Machine Learning & Black Swans",{"root":1738},{"type":167,"format":168,"indent":169,"version":4,"children":1739,"direction":28},[1740,1744],{"type":172,"format":168,"indent":169,"version":4,"children":1741,"direction":28,"textStyle":168,"textFormat":169},[1742],{"mode":175,"text":1743,"type":177,"style":168,"detail":169,"format":169,"version":4},"A column by Thierry Vessereau",{"type":172,"format":168,"indent":169,"version":4,"children":1745,"direction":28,"textStyle":168,"textFormat":169},[1746],{"mode":175,"text":1747,"type":177,"style":168,"detail":169,"format":169,"version":4},"Published in Allnews - July 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In finance, these events have the characteristic of being able to be exogenous: natural events, pandemic, war, but also of being able to be generated by the financial markets themselves in the form of speculative bubbles. Economic and financial markets have thus experienced several global events of this kind, for example the real-estate bubble of 2008, often classified in the category of Black Swans. The unpredictability of these bubbles rests on the difficulty of determining whether they really exist or not, of measuring their evolution and their capacity to burst, and above all their potential impact on the markets and the economy.",{"id":1761,"colorVariation":459,"blockName":28,"columns":1762,"blockType":339,"titleStart":1784,"titleEnd":1785},"6a3e769579839c6d1a2b12a6",[1763],{"id":1764,"content":1765},"6a3e76a579839c6d1a2b12a7",{"root":1766},{"type":167,"format":168,"indent":169,"version":4,"children":1767,"direction":28},[1768],{"type":172,"format":168,"indent":169,"version":4,"children":1769,"direction":28,"textStyle":168,"textFormat":169},[1770,1772,1774,1776,1778,1780,1782],{"mode":175,"text":1771,"type":177,"style":168,"detail":169,"format":169,"version":4},"The irruption of ChatGPT-4 in 2023 constituted a turning point in the understanding of the possibilities of AI and in the acceleration of the tools used until then. In market finance, the rapid development of new devices made it possible to generalise and extend algorithms that were, for some, already advanced (high-frequency trading, ",{"mode":175,"text":1773,"type":177,"style":168,"detail":169,"format":483,"version":4},"robot-advisory, web scraping",{"mode":175,"text":1775,"type":177,"style":168,"detail":169,"format":169,"version":4},"), while also multiplying the number of players active in the field. Whereas the emphasis was placed first on the massive processing of documents, synthesis and rewriting by ",{"mode":175,"text":1777,"type":177,"style":168,"detail":169,"format":483,"version":4},"Large Language Models",{"mode":175,"text":1779,"type":177,"style":168,"detail":169,"format":169,"version":4}," (LLM), intelligence is now increasingly used in investment itself, applying in particular the principles of ",{"mode":175,"text":1781,"type":177,"style":168,"detail":169,"format":483,"version":4},"machine learning",{"mode":175,"text":1783,"type":177,"style":168,"detail":169,"format":169,"version":4},".","Artificial","intelligence",{"id":1787,"colorVariation":344,"blockName":28,"columns":1788,"blockType":339,"titleStart":1810,"titleEnd":1331},"6a3e76b979839c6d1a2b12a8",[1789],{"id":1790,"content":1791},"6a3e76c679839c6d1a2b12a9",{"root":1792},{"type":167,"format":168,"indent":169,"version":4,"children":1793,"direction":28},[1794,1800,1802],{"type":172,"format":168,"indent":169,"version":4,"children":1795,"direction":28,"textStyle":168,"textFormat":483},[1796,1798],{"mode":175,"text":1797,"type":177,"style":168,"detail":169,"format":483,"version":4},"Machine-learning",{"mode":175,"text":1799,"type":177,"style":168,"detail":169,"format":169,"version":4}," algorithms are designed to learn on the basis of vast volumes of financial data and to identify trends and formulate predictions or decisions without human intervention or with limited human intervention. However, impressive as it may be, AI's capacity to predict or mitigate the impact of particular events is not assured. This is linked both to the by-definition unpredictable nature of the Black Swan, but also to the characteristics of learning. ",{"type":172,"format":168,"indent":169,"version":4,"children":1801,"direction":28,"textStyle":168,"textFormat":169},[],{"type":172,"format":168,"indent":169,"version":4,"children":1803,"direction":28,"textStyle":168,"textFormat":169},[1804,1806,1808],{"mode":175,"text":1805,"type":177,"style":168,"detail":169,"format":169,"version":4},"Indeed, the general objective of learning is to minimise entropy so that the predicted distribution is as close as possible to the observed distribution, thereby tending to treat separately or to ignore ",{"mode":175,"text":1807,"type":177,"style":168,"detail":169,"format":483,"version":4},"outliers",{"mode":175,"text":1809,"type":177,"style":168,"detail":169,"format":169,"version":4},", whether aberrant or difficult to explain. Learning is also dependent on the access to and length of historical data, on their availability according to the domains, but also on the hypothesis that past data, massively used, make it possible to determine trends having a certain predictive power. Finally, an important part of the fine-tuning is to avoid \"overfitting\", that is, operating by favouring the training data to the detriment of predictive capacity. This naturally leads to setting aside or minimising the particular events that do not fall within the average.","Machine Learning and Black Swans",{"id":1812,"colorVariation":900,"blockName":28,"columns":1813,"blockType":339,"titleStart":1829,"titleEnd":1331},"6a3e76ee79839c6d1a2b12aa",[1814],{"id":1815,"content":1816},"6a3e774079839c6d1a2b12ab",{"root":1817},{"type":167,"format":168,"indent":169,"version":4,"children":1818,"direction":28},[1819,1823,1825],{"type":172,"format":168,"indent":169,"version":4,"children":1820,"direction":28,"textStyle":168,"textFormat":169},[1821],{"mode":175,"text":1822,"type":177,"style":168,"detail":169,"format":169,"version":4},"The characteristics of learning, whether supervised or unsupervised, raise the question of the generation of a particular bubble and of the self-production of a Black Swan of a new kind through the multiplication of automated players whose diversity would be spurious. According to the principles established by Shannon, the number and multiplication of players does not mean that there is more information, nor that it is differentiated. This is illustrated by the paradox of the game invented by Shannon in 1948, which creates a discrete universal predictor profiting from the biases of the information provided (in this case an experimenter who generates numbers that are in reality not random). The facilitated access to advanced algorithms causes a multiplication of players, but these risk processing information in a similar manner. It is likely that these players, by making similar hypotheses and by using essentially the same data and the same algorithms, obtain similar results and reinforce the same predictions while limiting the possibilities of arbitrage. A parallel can be drawn with high-frequency trading, which made it possible to generate significant outperformance as long as the number of players was limited. ",{"type":172,"format":168,"indent":169,"version":4,"children":1824,"direction":28,"textStyle":168,"textFormat":169},[],{"type":172,"format":168,"indent":169,"version":4,"children":1826,"direction":28,"textStyle":168,"textFormat":169},[1827],{"mode":175,"text":1828,"type":177,"style":168,"detail":169,"format":169,"version":4},"One may then ask whether the use of learning models makes it possible to achieve greater market efficiency, by multiplying the players, or on the contrary increases the risk of inefficiency by reproducing artificially cloned players. The paradox would then be that the maturity of AI-driven investment ends up reproducing the biases held against index investments: excessive concentration, fear of bubble creation, herd behaviour. However, it does not a priori bear the same major disadvantages associated with these index funds: lack of outperformance, lack of flexibility and the impossibility of being able to profit from opportunities on the market.","Small and large swans",{"id":1831,"colorVariation":268,"blockName":28,"columns":1832,"blockType":339,"titleStart":1331,"titleEnd":1331},"6a3e776879839c6d1a2b12ac",[1833],{"id":1834,"content":1835},"6a3e776e79839c6d1a2b12ad",{"root":1836},{"type":167,"format":168,"indent":169,"version":4,"children":1837,"direction":28},[1838],{"type":172,"format":168,"indent":169,"version":4,"children":1839,"direction":28,"textStyle":168,"textFormat":4},[1840,1842],{"mode":175,"text":1841,"type":177,"style":168,"detail":169,"format":4,"version":4},"Thierry Vessereau ",{"mode":175,"text":1843,"type":177,"style":168,"detail":169,"format":169,"version":4},"joined BCGE Asset Management in July 2019 as an institutional manager on the passive and active funds of the Synchrony range. Previously, he worked from 2005 to 2019 at the bank Pictet & Cie as project and development manager, then as an investment specialist in the Indexation department of Pictet Asset Management. Thierry Vessereau holds a master's degree in computer science and a doctorate in economics.",{"id":1845,"alt":1846,"landscape":1847,"portrait":28,"folder":1700,"thumbnailUrl":1857,"updatedAt":1872,"createdAt":1873},127,"BCGE_TVE-publications",{"id":1848,"updatedAt":1849,"createdAt":1850,"url":1851,"thumbnailURL":28,"filename":1852,"mimeType":30,"filesize":1853,"width":1854,"height":1854,"focalX":33,"focalY":33,"sizes":1855},343,"2026-07-31T09:18:10.895Z","2026-07-31T09:18:10.738Z","https:\u002F\u002Fassets.synchrony.ch\u002FThierryVessereau_list-1.webp","ThierryVessereau_list-1.webp",55152,800,{"thumbnail":1856,"sm":1860,"md":1864,"lg":1867,"og":1868},{"url":1857,"width":37,"height":38,"mimeType":30,"filesize":1858,"filename":1859},"https:\u002F\u002Fassets.synchrony.ch\u002FThierryVessereau_list-1-300x200.webp",4632,"ThierryVessereau_list-1-300x200.webp",{"url":1861,"width":43,"height":43,"mimeType":30,"filesize":1862,"filename":1863},"https:\u002F\u002Fassets.synchrony.ch\u002FThierryVessereau_list-1-640x640.webp",27644,"ThierryVessereau_list-1-640x640.webp",{"url":1865,"width":1854,"height":1854,"mimeType":30,"filesize":1853,"filename":1866},"https:\u002F\u002Fassets.synchrony.ch\u002FThierryVessereau_list-1-800x800.webp","ThierryVessereau_list-1-800x800.webp",{"url":1865,"width":1854,"height":1854,"mimeType":30,"filesize":1853,"filename":1866},{"url":1869,"width":56,"height":57,"mimeType":30,"filesize":1870,"filename":1871},"https:\u002F\u002Fassets.synchrony.ch\u002FThierryVessereau_list-1-1200x630.webp",50798,"ThierryVessereau_list-1-1200x630.webp","2026-07-31T09:18:11.628Z","2026-07-09T15:50:31.325Z","A column by Thierry Vessereau -\nPublished in Allnews - 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",[],{"id":506,"layout":1933,"previewImage":2070,"title":1736,"excerpt":2078,"publishedDate":1875,"tags":2079,"slug":2080,"updatedAt":1880,"createdAt":1881},[1934,1969,1986,2016,2040,2058],{"id":1640,"colorVariation":238,"backgroundImage":1935,"foregroundImage":1950,"blockName":28,"blockType":255,"eyebrow":1958,"title":1736,"content":1959},{"id":839,"alt":1642,"landscape":1936,"portrait":1943,"folder":1700,"thumbnailUrl":1653,"updatedAt":1701,"createdAt":1702},{"id":1644,"updatedAt":1645,"createdAt":1646,"url":1647,"thumbnailURL":28,"filename":1648,"mimeType":30,"filesize":1649,"width":75,"height":1650,"focalX":33,"focalY":33,"sizes":1937},{"thumbnail":1938,"sm":1939,"md":1940,"lg":1941,"og":1942},{"url":1653,"width":37,"height":38,"mimeType":30,"filesize":1654,"filename":1655},{"url":1657,"width":43,"height":1658,"mimeType":30,"filesize":1659,"filename":1660},{"url":1662,"width":48,"height":1663,"mimeType":30,"filesize":1664,"filename":1665},{"url":1667,"width":75,"height":1650,"mimeType":30,"filesize":1649,"filename":1668},{"url":1670,"width":56,"height":57,"mimeType":30,"filesize":1671,"filename":1672},{"id":1674,"updatedAt":1675,"createdAt":1676,"url":1677,"thumbnailURL":28,"filename":1678,"mimeType":30,"filesize":1679,"width":1680,"height":1681,"focalX":33,"focalY":33,"sizes":1944},{"thumbnail":1945,"sm":1946,"md":1947,"lg":1948,"og":1949},{"url":1684,"width":37,"height":38,"mimeType":30,"filesize":1685,"filename":1686},{"url":1688,"width":43,"height":1689,"mimeType":30,"filesize":1690,"filename":1691},{"url":1693,"width":1680,"height":1681,"mimeType":30,"filesize":1679,"filename":1694},{"url":1693,"width":1680,"height":1681,"mimeType":30,"filesize":1679,"filename":1694},{"url":1697,"width":56,"height":57,"mimeType":30,"filesize":1698,"filename":1699},{"id":1704,"alt":1705,"landscape":1951,"portrait":28,"folder":1700,"thumbnailUrl":1715,"updatedAt":1733,"createdAt":1734},{"id":1707,"updatedAt":1708,"createdAt":1709,"url":1710,"thumbnailURL":28,"filename":1711,"mimeType":30,"filesize":1712,"width":75,"height":1650,"focalX":33,"focalY":33,"sizes":1952},{"thumbnail":1953,"sm":1954,"md":1955,"lg":1956,"og":1957},{"url":1715,"width":37,"height":38,"mimeType":30,"filesize":1716,"filename":1717},{"url":1719,"width":43,"height":1658,"mimeType":30,"filesize":1720,"filename":1721},{"url":1723,"width":48,"height":1663,"mimeType":30,"filesize":1724,"filename":1725},{"url":1727,"width":75,"height":1650,"mimeType":30,"filesize":1712,"filename":1728},{"url":1730,"width":56,"height":57,"mimeType":30,"filesize":1731,"filename":1732},"22 Juillet 2025",{"root":1960},{"type":167,"format":168,"indent":169,"version":4,"children":1961,"direction":28},[1962],{"type":172,"format":168,"indent":169,"version":4,"children":1963,"direction":28,"textStyle":168,"textFormat":169},[1964,1966,1967],{"mode":175,"text":1965,"type":177,"style":168,"detail":169,"format":169,"version":4},"Chronique de Thierry Vessereau ",{"type":1327,"version":4},{"mode":175,"text":1968,"type":177,"style":168,"detail":169,"format":169,"version":4},"Parue dans Allnews - Juillet 2025",{"id":1749,"colorVariation":755,"blockName":28,"columns":1970,"blockType":339,"titleStart":168,"titleEnd":168},[1971],{"id":1752,"content":1972},{"root":1973},{"type":167,"format":168,"indent":169,"version":4,"children":1974,"direction":28},[1975,1979,1982],{"type":172,"format":646,"indent":169,"version":4,"children":1976,"direction":28,"textStyle":168,"textFormat":169},[1977],{"mode":175,"text":1978,"type":177,"style":168,"detail":169,"format":169,"version":4},"Les cygnes noirs (Black Swans) ont été définis par Nassim Taleb en 2007, comme des événements qui se caractérisent par leur rareté et leur imprévisibilité, la gravité de leurs conséquences, et la tendance à les rationaliser a posteriori.",{"type":172,"format":646,"indent":169,"version":4,"children":1980,"direction":28,"textStyle":168,"textFormat":169},[1981],{"mode":175,"text":293,"type":177,"style":168,"detail":169,"format":169,"version":4},{"type":172,"format":646,"indent":169,"version":4,"children":1983,"direction":28,"textStyle":168,"textFormat":169},[1984],{"mode":175,"text":1985,"type":177,"style":168,"detail":169,"format":169,"version":4},"En finance, ces événements ont la caractéristique de pouvoir être exogènes : événements naturels, pandémie, guerre, mais aussi de pouvoir être générés par les marchés financiers eux-mêmes sous la forme de bulles spéculatives. Les marchés économiques et financiers ont ainsi connu plusieurs événements mondiaux de ce type, par exemple la bulle immobilière de 2008, souvent classifiés dans la catégorie des cygnes noirs. L’imprévisibilité de ces bulles repose sur le fait de déterminer si elles existent réellement ou non, de mesurer leur évolution et leur capacité à exploser, et surtout, leur impact potentiel sur les marchés et l’économie.",{"id":1761,"colorVariation":459,"blockName":28,"columns":1987,"blockType":339,"titleStart":2014,"titleEnd":2015},[1988],{"id":1764,"content":1989},{"root":1990},{"type":167,"format":168,"indent":169,"version":4,"children":1991,"direction":28},[1992],{"type":172,"format":168,"indent":169,"version":4,"children":1993,"direction":28,"textStyle":168,"textFormat":169},[1994,1996,1998,2000,2002,2004,2006,2008,2010,2011,2013],{"mode":175,"text":1995,"type":177,"style":168,"detail":169,"format":169,"version":4},"L’irruption de ChatGPT-4 en 2023 a constitué un point tournant dans la compréhension des possibilités de l’IA et dans l’accélération des outils utilisés jusqu’alors. En finance des marchés, le développement rapide de nouveaux dispositifs a permis de généraliser et d’étendre des algorithmes qui étaient, pour certains, déjà avancés (trading haute fréquence, ",{"mode":175,"text":1997,"type":177,"style":168,"detail":169,"format":483,"version":4},"robot advisory",{"mode":175,"text":1999,"type":177,"style":168,"detail":169,"format":169,"version":4},", ",{"mode":175,"text":2001,"type":177,"style":168,"detail":169,"format":483,"version":4},"web scraping",{"mode":175,"text":2003,"type":177,"style":168,"detail":169,"format":169,"version":4},"), en multipliant également le nombre d’acteurs actifs dans le domaine. Alors que l’accent a été porté dans un premier temps sur les traitements massifs de documents, la synthétisation et la réécriture par les ",{"mode":175,"text":2005,"type":177,"style":168,"detail":169,"format":483,"version":4},"Large Langage Models",{"mode":175,"text":2007,"type":177,"style":168,"detail":169,"format":169,"version":4}," (LLM), désormais l’intelligence est de plus en plus utilisée dans l’investissement même, en appliquant en particulier les principes du ",{"mode":175,"text":2009,"type":177,"style":168,"detail":169,"format":483,"version":4},"machine",{"mode":175,"text":293,"type":177,"style":168,"detail":169,"format":169,"version":4},{"mode":175,"text":2012,"type":177,"style":168,"detail":169,"format":483,"version":4},"learning",{"mode":175,"text":1783,"type":177,"style":168,"detail":169,"format":169,"version":4},"Intelligence","artificielle",{"id":1787,"colorVariation":344,"blockName":28,"columns":2017,"blockType":339,"titleStart":2039,"titleEnd":1331},[2018],{"id":1790,"content":2019},{"root":2020},{"type":167,"format":168,"indent":169,"version":4,"children":2021,"direction":28},[2022,2029,2032],{"type":172,"format":646,"indent":169,"version":4,"children":2023,"direction":28,"textStyle":168,"textFormat":169},[2024,2026,2027],{"mode":175,"text":2025,"type":177,"style":168,"detail":169,"format":169,"version":4},"Les algorithmes de ",{"mode":175,"text":1781,"type":177,"style":168,"detail":169,"format":483,"version":4},{"mode":175,"text":2028,"type":177,"style":168,"detail":169,"format":169,"version":4}," sont conçus pour apprendre sur la base de vastes volumes de données financières et pour identifier les tendances et formuler des prédictions ou des décisions sans intervention humaine ou avec une intervention humaine limitée. Cependant, aussi impressionnante soit-elle, la capacité de l’IA à prédire ou à atténuer l’impact des événements particuliers n’est pas assurée. Ceci est lié à la fois au caractère par définition imprévisible du cygne noir, mais également aux caractéristiques de l’apprentissage.",{"type":172,"format":646,"indent":169,"version":4,"children":2030,"direction":28,"textStyle":168,"textFormat":169},[2031],{"mode":175,"text":293,"type":177,"style":168,"detail":169,"format":169,"version":4},{"type":172,"format":646,"indent":169,"version":4,"children":2033,"direction":28,"textStyle":168,"textFormat":169},[2034,2036,2037],{"mode":175,"text":2035,"type":177,"style":168,"detail":169,"format":169,"version":4},"En effet, l’objectif général de l’apprentissage est de minimiser l’entropie pour que la distribution prédite soit la plus proche possible de la distribution observée, ayant ainsi tendance à traiter à part ou ignorer les ",{"mode":175,"text":1807,"type":177,"style":168,"detail":169,"format":483,"version":4},{"mode":175,"text":2038,"type":177,"style":168,"detail":169,"format":169,"version":4},", qu’ils soient aberrants ou difficiles à expliquer. L’apprentissage est aussi tributaire de l’accès et de la longueur des données historiques, de leur disponibilité selon les domaines, mais également de l’hypothèse que les données passées, massivement employées, permettent de déterminer des tendances ayant un certain pouvoir prédictif. Enfin, une part importante de la mise au point est d’éviter le « surapprentissage », c’est-à-dire de fonctionner en favorisant les données d’apprentissage au détriment de la capacité de prédiction. Ceci conduit naturellement à écarter ou minimiser les événements particuliers qui ne rentrent pas dans la moyenne.","Machine Learning et cygnes noirs",{"id":1812,"colorVariation":900,"blockName":28,"columns":2041,"blockType":339,"titleStart":2057,"titleEnd":1331},[2042],{"id":1815,"content":2043},{"root":2044},{"type":167,"format":168,"indent":169,"version":4,"children":2045,"direction":28},[2046,2050,2053],{"type":172,"format":646,"indent":169,"version":4,"children":2047,"direction":28,"textStyle":168,"textFormat":169},[2048],{"mode":175,"text":2049,"type":177,"style":168,"detail":169,"format":169,"version":4},"Les caractéristiques de l’apprentissage, qu’il soit supervisé ou non supervisé, posent la question de la génération d’une bulle particulière et de l’autoproduction d’un cygne noir d’un nouveau genre par la multiplication d’acteurs automatisés dont la diversité serait factice. Selon les principes établis par Shannon, le nombre et la multiplication d’acteurs ne veut pas dire qu’il y a plus d’information, ni que celles-ci soient différentiées. Ceci est illustré par le paradoxe du jeu inventé par Shannon en 1948, qui créé un prédicteur universel discret profitant des biais de l’information fournie (en l’occurrence un expérimentateur qui génère des nombres qui ne sont en réalité pas aléatoires). L’accès facilité à des algorithmes avancés provoque une multiplication des acteurs, mais ceux-ci risquent de traiter les informations de manière similaire. Il est probable que ces acteurs, en faisant des hypothèses semblables et en utilisant essentiellement les mêmes données et les mêmes algorithmes, obtiennent des résultats similaires et renforcent les mêmes prédictions en limitant les possibilités d’arbitrage. Un parallèle, peut être fait avec le trading haute fréquence qui permettait de dégager une surperformance significative tant que le nombre d’acteurs était limité.",{"type":172,"format":646,"indent":169,"version":4,"children":2051,"direction":28,"textStyle":168,"textFormat":169},[2052],{"mode":175,"text":293,"type":177,"style":168,"detail":169,"format":169,"version":4},{"type":172,"format":646,"indent":169,"version":4,"children":2054,"direction":28,"textStyle":168,"textFormat":169},[2055],{"mode":175,"text":2056,"type":177,"style":168,"detail":169,"format":169,"version":4},"On peut alors se poser la question de savoir si l’utilisation de modèles d’apprentissage permet d’obtenir une meilleure efficience des marchés, en multipliant les acteurs, ou au contraire augmente le risque d’inefficience en reproduisant des acteurs clonés artificiellement. Le paradoxe serait alors que la maturité de l’investissement par IA finisse par reproduire les biais reprochés aux investissements indiciels : concentration excessive, crainte de création de bulle, comportements grégaires. Cependant, elle ne supporte a priori pas les mêmes inconvénients majeurs qui sont associés à ces fonds indexés : manque de surperformance, manque de flexibilité et impossibilité de pouvoir profiter d’opportunités sur le marché.","Petits et gros cygnes",{"id":1831,"colorVariation":268,"blockName":28,"columns":2059,"blockType":339,"titleStart":168,"titleEnd":168},[2060],{"id":1834,"content":2061},{"root":2062},{"type":167,"format":168,"indent":169,"version":4,"children":2063,"direction":28},[2064],{"type":172,"format":168,"indent":169,"version":4,"children":2065,"direction":28,"textStyle":168,"textFormat":4},[2066,2068],{"mode":175,"text":2067,"type":177,"style":168,"detail":169,"format":4,"version":4},"Thierry Vessereau",{"mode":175,"text":2069,"type":177,"style":168,"detail":169,"format":169,"version":4}," a rejoint BCGE Asset Management en juillet 2019 en tant que gérant institutionnel sur les fonds passifs et actifs de la gamme Synchrony. Auparavant, il a travaillé de 2005 à 2019 à la banque Pictet & Cie comme chef de projet et de développement, puis comme spécialiste d’investissement du service Indexation de Pictet Asset Management. 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In finanza, questi eventi hanno la caratteristica di poter essere esogeni: eventi naturali, pandemia, guerra, ma anche di poter essere generati dai mercati finanziari stessi sotto forma di bolle speculative. I mercati economici e finanziari hanno così conosciuto diversi eventi mondiali di questo tipo, ad esempio la bolla immobiliare del 2008, spesso classificati nella categoria dei cigni neri. L'imprevedibilità di queste bolle si fonda sul determinare se esse esistano realmente o meno, sul misurarne l'evoluzione e la capacità di esplodere e, soprattutto, il loro impatto potenziale sui mercati e sull'economia.",{"id":1761,"colorVariation":459,"blockName":28,"columns":2181,"blockType":339,"titleStart":2199,"titleEnd":2200},[2182],{"id":1764,"content":2183},{"root":2184},{"type":167,"format":168,"indent":169,"version":4,"children":2185,"direction":28},[2186],{"type":172,"format":168,"indent":169,"version":4,"children":2187,"direction":28,"textStyle":168,"textFormat":169},[2188,2190,2191,2193,2195,2197,2198],{"mode":175,"text":2189,"type":177,"style":168,"detail":169,"format":169,"version":4},"L'irruzione di ChatGPT-4 nel 2023 ha costituito un punto di svolta nella comprensione delle possibilità dell'IA e nell'accelerazione degli strumenti utilizzati fino ad allora. Nella finanza di mercato, il rapido sviluppo di nuovi dispositivi ha permesso di generalizzare ed estendere algoritmi che erano, per alcuni, già avanzati (trading ad alta frequenza, ",{"mode":175,"text":1773,"type":177,"style":168,"detail":169,"format":483,"version":4},{"mode":175,"text":2192,"type":177,"style":168,"detail":169,"format":169,"version":4},"), moltiplicando anche il numero di attori attivi nel settore. Mentre l'accento è stato posto in un primo momento sui trattamenti massivi di documenti, sulla sintesi e sulla riscrittura da parte dei ",{"mode":175,"text":2194,"type":177,"style":168,"detail":169,"format":483,"version":4},"Large Language Models ",{"mode":175,"text":2196,"type":177,"style":168,"detail":169,"format":169,"version":4},"(LLM), oggi l'intelligenza è sempre più utilizzata nell'investimento stesso, applicando in particolare i principi del ",{"mode":175,"text":1781,"type":177,"style":168,"detail":169,"format":483,"version":4},{"mode":175,"text":1783,"type":177,"style":168,"detail":169,"format":169,"version":4},"Intelligenza","artificiale",{"id":1787,"colorVariation":344,"blockName":28,"columns":2202,"blockType":339,"titleStart":2217,"titleEnd":1331},[2203],{"id":1790,"content":2204},{"root":2205},{"type":167,"format":168,"indent":169,"version":4,"children":2206,"direction":28},[2207,2211,2213],{"type":172,"format":168,"indent":169,"version":4,"children":2208,"direction":28,"textStyle":168,"textFormat":169},[2209],{"mode":175,"text":2210,"type":177,"style":168,"detail":169,"format":169,"version":4},"Gli algoritmi di machine learning sono concepiti per apprendere sulla base di vasti volumi di dati finanziari e per individuare le tendenze e formulare previsioni o decisioni senza intervento umano o con un intervento umano limitato. Tuttavia, per quanto impressionante possa essere, la capacità dell'IA di prevedere o attenuare l'impatto degli eventi particolari non è garantita. Ciò è legato sia al carattere per definizione imprevedibile del cigno nero, sia alle caratteristiche dell'apprendimento. ",{"type":172,"format":168,"indent":169,"version":4,"children":2212,"direction":28,"textStyle":168,"textFormat":169},[],{"type":172,"format":168,"indent":169,"version":4,"children":2214,"direction":28,"textStyle":168,"textFormat":169},[2215],{"mode":175,"text":2216,"type":177,"style":168,"detail":169,"format":169,"version":4},"Infatti, l'obiettivo generale dell'apprendimento è minimizzare l'entropia affinché la distribuzione prevista sia il più possibile vicina alla distribuzione osservata, tendendo così a trattare a parte o a ignorare gli outlier, siano essi aberranti o difficili da spiegare. L'apprendimento è anche tributario dell'accesso e della lunghezza dei dati storici, della loro disponibilità a seconda dei domini, ma anche dell'ipotesi che i dati passati, massivamente impiegati, permettano di determinare tendenze dotate di un certo potere predittivo. Infine, una parte importante della messa a punto consiste nell'evitare l'«overfitting», ossia il funzionare favorendo i dati di apprendimento a scapito della capacità di previsione. Ciò porta naturalmente a scartare o minimizzare gli eventi particolari che non rientrano nella media.","Machine Learning e cigni neri",{"id":1812,"colorVariation":900,"blockName":28,"columns":2219,"blockType":339,"titleStart":2234,"titleEnd":1331},[2220],{"id":1815,"content":2221},{"root":2222},{"type":167,"format":168,"indent":169,"version":4,"children":2223,"direction":28},[2224,2228,2230],{"type":172,"format":168,"indent":169,"version":4,"children":2225,"direction":28,"textStyle":168,"textFormat":169},[2226],{"mode":175,"text":2227,"type":177,"style":168,"detail":169,"format":169,"version":4},"Le caratteristiche dell'apprendimento, che sia supervisionato o non supervisionato, pongono la questione della generazione di una bolla particolare e dell'autoproduzione di un cigno nero di nuovo genere attraverso la moltiplicazione di attori automatizzati la cui diversità sarebbe fittizia. Secondo i principi stabiliti da Shannon, il numero e la moltiplicazione degli attori non significa che vi sia più informazione, né che essa sia differenziata. Ciò è illustrato dal paradosso del gioco inventato da Shannon nel 1948, che crea un predittore universale discreto che trae profitto dai bias dell'informazione fornita (nella fattispecie uno sperimentatore che genera numeri che in realtà non sono casuali). L'accesso facilitato ad algoritmi avanzati provoca una moltiplicazione degli attori, ma questi rischiano di trattare le informazioni in modo simile. È probabile che questi attori, formulando ipotesi simili e utilizzando essenzialmente gli stessi dati e gli stessi algoritmi, ottengano risultati simili e rafforzino le stesse previsioni limitando le possibilità di arbitraggio. Un parallelo può essere tracciato con il trading ad alta frequenza, che permetteva di generare una sovraperformance significativa finché il numero di attori era limitato. ",{"type":172,"format":168,"indent":169,"version":4,"children":2229,"direction":28,"textStyle":168,"textFormat":169},[],{"type":172,"format":168,"indent":169,"version":4,"children":2231,"direction":28,"textStyle":168,"textFormat":169},[2232],{"mode":175,"text":2233,"type":177,"style":168,"detail":169,"format":169,"version":4},"Ci si può allora chiedere se l'utilizzo di modelli di apprendimento permetta di ottenere una migliore efficienza dei mercati, moltiplicando gli attori, o al contrario aumenti il rischio di inefficienza riproducendo attori clonati artificialmente. Il paradosso sarebbe allora che la maturità dell'investimento tramite IA finisca per riprodurre i bias rimproverati agli investimenti indicizzati: concentrazione eccessiva, timore di creazione di bolle, comportamenti gregari. Tuttavia, essa non presenta a priori gli stessi svantaggi maggiori associati a questi fondi indicizzati: mancanza di sovraperformance, mancanza di flessibilità e impossibilità di poter approfittare delle opportunità sul mercato.","Piccoli e grandi cigni",{"id":1831,"colorVariation":268,"blockName":28,"columns":2236,"blockType":339,"titleStart":1331,"titleEnd":1331},[2237],{"id":1834,"content":2238},{"root":2239},{"type":167,"format":168,"indent":169,"version":4,"children":2240,"direction":28},[2241],{"type":172,"format":168,"indent":169,"version":4,"children":2242,"direction":28,"textStyle":168,"textFormat":4},[2243,2244],{"mode":175,"text":2067,"type":177,"style":168,"detail":169,"format":4,"version":4},{"mode":175,"text":2245,"type":177,"style":168,"detail":169,"format":169,"version":4}," è entrato in BCGE Asset Management nel luglio 2019 come gestore istituzionale sui fondi passivi e attivi della gamma Synchrony. 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",{"type":172,"format":168,"indent":169,"version":4,"children":2356,"direction":28,"textStyle":168,"textFormat":169},[],{"type":172,"format":168,"indent":169,"version":4,"children":2358,"direction":28,"textStyle":168,"textFormat":169},[2359],{"mode":175,"text":2360,"type":177,"style":168,"detail":169,"format":169,"version":4},"In der Finanzwelt haben diese Ereignisse die Eigenschaft, exogen sein zu können: Naturereignisse, Pandemie, Krieg, aber auch von den Finanzmärkten selbst in Form spekulativer Blasen erzeugt werden zu können. Die Wirtschafts- und Finanzmärkte haben so mehrere globale Ereignisse dieser Art erlebt, zum Beispiel die Immobilienblase von 2008, die oft in die Kategorie der Black Swans eingeordnet wird. Die Unvorhersehbarkeit dieser Blasen beruht darauf, zu bestimmen, ob sie tatsächlich existieren oder nicht, ihre Entwicklung und ihre Fähigkeit zu platzen zu messen und vor allem ihre potenziellen Auswirkungen auf die Märkte und die Wirtschaft.",{"id":1761,"colorVariation":459,"blockName":28,"columns":2362,"blockType":339,"titleStart":2383,"titleEnd":2384},[2363],{"id":1764,"content":2364},{"root":2365},{"type":167,"format":168,"indent":169,"version":4,"children":2366,"direction":28},[2367],{"type":172,"format":168,"indent":169,"version":4,"children":2368,"direction":28,"textStyle":168,"textFormat":169},[2369,2371,2373,2375,2377,2379,2381],{"mode":175,"text":2370,"type":177,"style":168,"detail":169,"format":169,"version":4},"Der Einbruch von ChatGPT-4 im Jahr 2023 stellte einen Wendepunkt im Verständnis der Möglichkeiten der KI und in der Beschleunigung der bis dahin eingesetzten Werkzeuge dar. In der Marktfinanzierung erlaubte die rasche Entwicklung neuer Vorrichtungen, Algorithmen zu verallgemeinern und auszuweiten, die zum Teil bereits fortgeschritten waren (Hochfrequenzhandel, ",{"mode":175,"text":2372,"type":177,"style":168,"detail":169,"format":483,"version":4},"Robo-Advisory, Web Scraping",{"mode":175,"text":2374,"type":177,"style":168,"detail":169,"format":169,"version":4},"), wobei sich auch die Zahl der im Bereich aktiven Akteure vervielfachte. Während der Schwerpunkt zunächst auf der massiven Verarbeitung von Dokumenten, der Synthese und dem Umschreiben durch",{"mode":175,"text":2376,"type":177,"style":168,"detail":169,"format":483,"version":4}," Large Language Models ",{"mode":175,"text":2378,"type":177,"style":168,"detail":169,"format":169,"version":4},"(LLM) lag, wird die Intelligenz heute zunehmend in der Anlage selbst eingesetzt, wobei insbesondere die Prinzipien des ",{"mode":175,"text":2380,"type":177,"style":168,"detail":169,"format":483,"version":4},"Machine Learning",{"mode":175,"text":2382,"type":177,"style":168,"detail":169,"format":169,"version":4}," angewandt werden.","Künstliche","Intelligenz",{"id":1787,"colorVariation":344,"blockName":28,"columns":2386,"blockType":339,"titleStart":2409,"titleEnd":1331},[2387],{"id":1790,"content":2388},{"root":2389},{"type":167,"format":168,"indent":169,"version":4,"children":2390,"direction":28},[2391,2399,2401],{"type":172,"format":168,"indent":169,"version":4,"children":2392,"direction":28,"textStyle":168,"textFormat":169},[2393,2395,2397],{"mode":175,"text":2394,"type":177,"style":168,"detail":169,"format":169,"version":4},"Die ",{"mode":175,"text":2396,"type":177,"style":168,"detail":169,"format":483,"version":4},"Machine-Learning-Algorithmen",{"mode":175,"text":2398,"type":177,"style":168,"detail":169,"format":169,"version":4}," sind darauf ausgelegt, auf der Basis riesiger Volumen von Finanzdaten zu lernen, Trends zu identifizieren und Vorhersagen oder Entscheide ohne menschliches Eingreifen oder mit begrenztem menschlichem Eingreifen zu formulieren. Doch so beeindruckend sie auch sein mag, die Fähigkeit der KI, die Auswirkungen besonderer Ereignisse vorherzusagen oder abzumildern, ist nicht gesichert. Dies hängt sowohl mit dem per Definition unvorhersehbaren Charakter des Black Swan zusammen, als auch mit den Merkmalen des Lernens. ",{"type":172,"format":168,"indent":169,"version":4,"children":2400,"direction":28,"textStyle":168,"textFormat":169},[],{"type":172,"format":168,"indent":169,"version":4,"children":2402,"direction":28,"textStyle":168,"textFormat":169},[2403,2405,2407],{"mode":175,"text":2404,"type":177,"style":168,"detail":169,"format":169,"version":4},"Denn das allgemeine Ziel des Lernens besteht darin, die Entropie zu minimieren, damit die vorhergesagte Verteilung der beobachteten Verteilung möglichst nahekommt, wodurch es dazu neigt, die ",{"mode":175,"text":2406,"type":177,"style":168,"detail":169,"format":483,"version":4},"Ausreisser (Outliers)",{"mode":175,"text":2408,"type":177,"style":168,"detail":169,"format":169,"version":4}," gesondert zu behandeln oder zu ignorieren, seien sie aberrant oder schwer zu erklären. Das Lernen ist zudem abhängig vom Zugang zu und der Länge der historischen Daten, von ihrer Verfügbarkeit je nach Bereich, aber auch von der Hypothese, dass die massiv verwendeten vergangenen Daten es erlauben, Trends mit einer gewissen prädiktiven Kraft zu bestimmen. Schliesslich besteht ein wichtiger Teil der Feinabstimmung darin, das «Overfitting» zu vermeiden, das heisst zu funktionieren, indem man die Trainingsdaten zulasten der Vorhersagefähigkeit begünstigt. Dies führt naturgemäss dazu, die besonderen Ereignisse, die nicht in den Durchschnitt fallen, auszuklammern oder zu minimieren.","Machine Learning und Black Swans",{"id":1812,"colorVariation":900,"blockName":28,"columns":2411,"blockType":339,"titleStart":2426,"titleEnd":1331},[2412],{"id":1815,"content":2413},{"root":2414},{"type":167,"format":168,"indent":169,"version":4,"children":2415,"direction":28},[2416,2420,2422],{"type":172,"format":168,"indent":169,"version":4,"children":2417,"direction":28,"textStyle":168,"textFormat":169},[2418],{"mode":175,"text":2419,"type":177,"style":168,"detail":169,"format":169,"version":4},"Die Merkmale des Lernens, ob überwacht oder unüberwacht, werfen die Frage nach der Erzeugung einer besonderen Blase und nach der Selbstproduktion eines Black Swan neuer Art durch die Vervielfachung automatisierter Akteure auf, deren Vielfalt vorgetäuscht wäre. Gemäss den von Shannon aufgestellten Prinzipien bedeutet die Zahl und Vervielfachung der Akteure nicht, dass es mehr Information gibt, noch dass diese differenziert wäre. Dies wird durch das Paradox des von Shannon 1948 erfundenen Spiels veranschaulicht, das einen diskreten universellen Prädiktor schafft, der aus den Verzerrungen der bereitgestellten Information Nutzen zieht (in diesem Fall ein Experimentator, der Zahlen erzeugt, die in Wirklichkeit nicht zufällig sind). Der erleichterte Zugang zu fortgeschrittenen Algorithmen bewirkt eine Vervielfachung der Akteure, doch diese riskieren, die Informationen auf ähnliche Weise zu verarbeiten. Es ist wahrscheinlich, dass diese Akteure, indem sie ähnliche Hypothesen aufstellen und im Wesentlichen die gleichen Daten und die gleichen Algorithmen verwenden, ähnliche Ergebnisse erzielen und die gleichen Vorhersagen verstärken, wodurch sie die Arbitragemöglichkeiten einschränken. Eine Parallele lässt sich zum Hochfrequenzhandel ziehen, der eine signifikante Outperformance erlaubte, solange die Zahl der Akteure begrenzt war. ",{"type":172,"format":168,"indent":169,"version":4,"children":2421,"direction":28,"textStyle":168,"textFormat":169},[],{"type":172,"format":168,"indent":169,"version":4,"children":2423,"direction":28,"textStyle":168,"textFormat":169},[2424],{"mode":175,"text":2425,"type":177,"style":168,"detail":169,"format":169,"version":4},"Man kann sich dann fragen, ob die Verwendung von Lernmodellen es erlaubt, eine bessere Effizienz der Märkte zu erzielen, indem man die Akteure vervielfacht, oder im Gegenteil das Risiko der Ineffizienz erhöht, indem man künstlich geklonte Akteure reproduziert. Das Paradox wäre dann, dass die Reife der KI-gestützten Anlage am Ende die Verzerrungen reproduziert, die den Indexanlagen vorgeworfen werden: übermässige Konzentration, Angst vor der Bildung einer Blase, Herdenverhalten. Sie trägt jedoch a priori nicht die gleichen wesentlichen Nachteile, die mit diesen Indexfonds verbunden sind: fehlende Outperformance, fehlende Flexibilität und die Unmöglichkeit, von Chancen am Markt zu profitieren.","Kleine und grosse Schwäne",{"id":1831,"colorVariation":268,"blockName":28,"columns":2428,"blockType":339,"titleStart":1331,"titleEnd":1331},[2429],{"id":1834,"content":2430},{"root":2431},{"type":167,"format":168,"indent":169,"version":4,"children":2432,"direction":28},[2433],{"type":172,"format":168,"indent":169,"version":4,"children":2434,"direction":28,"textStyle":168,"textFormat":4},[2435,2436],{"mode":175,"text":1841,"type":177,"style":168,"detail":169,"format":4,"version":4},{"mode":175,"text":2437,"type":177,"style":168,"detail":169,"format":169,"version":4},"stiess im Juli 2019 zur BCGE Asset Management als institutioneller Manager für die passiven und aktiven Fonds der Palette Synchrony. Zuvor arbeitete er von 2005 bis 2019 bei der Bank Pictet & Cie als Projekt- und Entwicklungsleiter, danach als Anlagespezialist des Bereichs Indexation der Pictet Asset Management. Thierry Vessereau ist Inhaber eines Masters in Informatikwissenschaften und Doktor der Wirtschaftswissenschaften.",{"id":1845,"alt":1846,"landscape":2439,"portrait":28,"folder":1700,"thumbnailUrl":1857,"updatedAt":1872,"createdAt":1873},{"id":1848,"updatedAt":1849,"createdAt":1850,"url":1851,"thumbnailURL":28,"filename":1852,"mimeType":30,"filesize":1853,"width":1854,"height":1854,"focalX":33,"focalY":33,"sizes":2440},{"thumbnail":2441,"sm":2442,"md":2443,"lg":2444,"og":2445},{"url":1857,"width":37,"height":38,"mimeType":30,"filesize":1858,"filename":1859},{"url":1861,"width":43,"height":43,"mimeType":30,"filesize":1862,"filename":1863},{"url":1865,"width":1854,"height":1854,"mimeType":30,"filesize":1853,"filename":1866},{"url":1865,"width":1854,"height":1854,"mimeType":30,"filesize":1853,"filename":1866},{"url":1869,"width":56,"height":57,"mimeType":30,"filesize":1870,"filename":1871},"Eine Kolumne von Thierry Vessereau -\nErschienen in Allnews - Juli 2025",{"id":4,"name":1700,"slug":1877,"updatedAt":1878,"createdAt":1878},1790840677663]