Essec\Faculty\Model\Profile {#2220
#_index: "academ_cv"
#_id: "B00812211"
#_source: array:40 [
"bid" => "B00812211"
"academId" => 33196
"slug" => "somoza-luciano"
"fullName" => "Luciano SOMOZA"
"lastName" => "SOMOZA"
"firstName" => "Luciano"
"title" => array:2 [
"fr" => "Professeur assistant"
"en" => "Assistant Professor"
]
"email" => "luciano.somoza@essec.edu"
"status" => "ACTIF"
"campus" => "Campus de Cergy"
"departments" => []
"phone" => ""
"sites" => []
"facNumber" => 33196
"externalCvUrl" => "https://www.dropbox.com/scl/fi/x2agvphen8cgwzi358vnk/CV_Luciano_Somoza.pdf?rlkey=afosxuu8uptb3u8oszehve78h&dl=0"
"googleScholarUrl" => ""
"facOrcId" => "https://orcid.org/0009-0000-5559-4174"
"career" => array:1 [
0 => Essec\Faculty\Model\CareerItem {#2224
#_index: null
#_id: null
#_source: array:7 [
"startDate" => "2023-09-01"
"endDate" => null
"isInternalPosition" => true
"type" => array:2 [
"fr" => "Positions académiques principales"
"en" => "Full-time academic appointments"
]
"label" => array:2 [
"fr" => "Professeur assistant"
"en" => "Assistant Professor"
]
"institution" => array:2 [
"fr" => "ESSEC Business School"
"en" => "ESSEC Business School"
]
"country" => array:2 [
"fr" => "France"
"en" => "France"
]
]
+lang: "en"
+"parent": Essec\Faculty\Model\Profile {#2220}
}
]
"diplomes" => array:3 [
0 => Essec\Faculty\Model\Diplome {#2223
#_index: null
#_id: null
#_source: array:6 [
"diplome" => "DIPLOMA"
"type" => array:2 [
"fr" => "Diplômes"
"en" => "Diplomas"
]
"year" => "2014"
"label" => array:2 [
"en" => "Bachelor of Science, Finance"
"fr" => "Bachelor of Science, Finance"
]
"institution" => array:2 [
"fr" => "Università Bocconi"
"en" => "Università Bocconi"
]
"country" => array:2 [
"fr" => "Italie"
"en" => "Italy"
]
]
+lang: "en"
+"parent": Essec\Faculty\Model\Profile {#2220}
}
1 => Essec\Faculty\Model\Diplome {#2225
#_index: null
#_id: null
#_source: array:6 [
"diplome" => "DIPLOMA"
"type" => array:2 [
"fr" => "Diplômes"
"en" => "Diplomas"
]
"year" => "2015"
"label" => array:2 [
"en" => "Master of Science, Finance"
"fr" => "Master of Science, Finance"
]
"institution" => array:2 [
"fr" => "Simon Business School"
"en" => "Simon Business School"
]
"country" => array:2 [
"fr" => "États-Unis"
"en" => "United States of America"
]
]
+lang: "en"
+"parent": Essec\Faculty\Model\Profile {#2220}
}
2 => Essec\Faculty\Model\Diplome {#2222
#_index: null
#_id: null
#_source: array:6 [
"diplome" => "DIPLOMA"
"type" => array:2 [
"fr" => "Diplômes"
"en" => "Diplomas"
]
"year" => "2023"
"label" => array:2 [
"en" => "Doctor of Philosophy, Finance"
"fr" => "Doctor of Philosophy, Finance"
]
"institution" => array:2 [
"fr" => "HEC Lausanne"
"en" => "HEC Lausanne"
]
"country" => array:2 [
"fr" => "Suisse"
"en" => "Switzerland"
]
]
+lang: "en"
+"parent": Essec\Faculty\Model\Profile {#2220}
}
]
"bio" => array:2 [
"fr" => null
"en" => null
]
"department" => array:2 [
"fr" => "Finance"
"en" => "Finance"
]
"industrrySectors" => array:2 [
"fr" => null
"en" => null
]
"researchFields" => array:2 [
"fr" => "Finance d'entreprise - Entrepreneuriat - monnaie numérique - FinTech - Banque - Politique monétaire"
"en" => "Corporate Finance - Entrepreneurship - Digital Money - FinTech - Banking - Monetary Policy"
]
"teachingFields" => array:2 [
"fr" => "Investissements et évaluation des actifs - Finance d'entreprise"
"en" => "Investments & Asset Pricing - Corporate Finance"
]
"distinctions" => []
"teaching" => array:2 [
0 => Essec\Faculty\Model\TeachingItem {#2226
#_index: null
#_id: null
#_source: array:7 [
"startDate" => 2023
"endDate" => 2024
"program" => null
"label" => array:2 [
"fr" => "GBBA Finance I"
"en" => "GBBA Finance I"
]
"type" => array:2 [
"fr" => "Finance"
"en" => "Finance"
]
"institution" => array:2 [
"fr" => "ESSEC Business School"
"en" => "ESSEC Business School"
]
"country" => array:2 [
"fr" => "France"
"en" => "France"
]
]
+lang: "en"
}
1 => Essec\Faculty\Model\TeachingItem {#2219
#_index: null
#_id: null
#_source: array:7 [
"startDate" => 2023
"endDate" => 2024
"program" => "Grande Ecole - Master in Management"
"label" => array:2 [
"fr" => "Corporate Financial Management"
"en" => "Corporate Financial Management"
]
"type" => array:2 [
"fr" => "Finance"
"en" => "Finance"
]
"institution" => array:2 [
"fr" => "ESSEC Business School"
"en" => "ESSEC Business School"
]
"country" => array:2 [
"fr" => "France"
"en" => "France"
]
]
+lang: "en"
}
]
"otherActivities" => []
"theses" => []
"sitePerso" => "www.lucianosomoza.com"
"indexedAt" => "2026-06-04T11:23:00.000Z"
"contributions" => array:5 [
0 => Essec\Faculty\Model\Contribution {#2228
#_index: "academ_contributions"
#_id: "14670"
#_source: array:18 [
"id" => 14670
"slug" => "14670-central-bank-digital-currency-and-quantitative-easing"
"yearMonth" => "2023-08"
"year" => 2023
"title" => "Central Bank Digital Currency and Quantitative Easing"
"description" => "SOMOZA, L., FRASCHINI, M. et TERRACCIANO, T. (2023). Central Bank Digital Currency and Quantitative Easing. Dans: 2023 European Economic Association Meeting 2023. Barcelona."
"authors" => array:3 [
0 => array:3 [
"name" => "SOMOZA Luciano"
"bid" => "B00812211"
"slug" => "somoza-luciano"
]
1 => array:1 [
"name" => "FRASCHINI Martina "
]
2 => array:1 [
"name" => "TERRACCIANO Tammaro "
]
]
"ouvrage" => "2023 European Economic Association Meeting 2023"
"keywords" => []
"updatedAt" => "2024-01-31 01:00:38"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => ""
"en" => ""
]
"authors_fields" => array:2 [
"fr" => "Finance"
"en" => "Finance"
]
"indexedAt" => "2026-06-04T11:23:33.000Z"
]
+lang: "en"
+"_score": 7.682597
+"parent": null
}
1 => Essec\Faculty\Model\Contribution {#2230
#_index: "academ_contributions"
#_id: "15697"
#_source: array:18 [
"id" => 15697
"slug" => "15697-the-end-of-the-crypto-diversification-myth"
"yearMonth" => "2024-11"
"year" => 2024
"title" => "The End of the Crypto-Diversification Myth"
"description" => "DIDISHEIM, A., FRASCHINI, M. et SOMOZA, L. (2024). The End of the Crypto-Diversification Myth. Dans: 9th Emerging Scholars in Banking and Finance Conference 2024. London."
"authors" => array:3 [
0 => array:3 [
"name" => "SOMOZA Luciano"
"bid" => "B00812211"
"slug" => "somoza-luciano"
]
1 => array:1 [
"name" => "DIDISHEIM Antoine"
]
2 => array:1 [
"name" => "FRASCHINI Martina"
]
]
"ouvrage" => "9th Emerging Scholars in Banking and Finance Conference 2024"
"keywords" => []
"updatedAt" => "2025-06-03 09:51:35"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => ""
"en" => ""
]
"authors_fields" => array:2 [
"fr" => "Finance"
"en" => "Finance"
]
"indexedAt" => "2026-06-04T11:23:33.000Z"
]
+lang: "en"
+"_score": 7.682597
+"parent": null
}
2 => Essec\Faculty\Model\Contribution {#2232
#_index: "academ_contributions"
#_id: "15719"
#_source: array:18 [
"id" => 15719
"slug" => "15719-cbdc-and-banks-threat-or-opportunity"
"yearMonth" => "2024-08"
"year" => 2024
"title" => "CBDC and Banks: Threat or Opportunity?"
"description" => "FRASCHINI, M., SOMOZA, L. et TERRACCIANO, T. (2024). CBDC and Banks: Threat or Opportunity? Dans: 2024 European Economic Association Annual Meeting. Rotterdam."
"authors" => array:3 [
0 => array:3 [
"name" => "SOMOZA Luciano"
"bid" => "B00812211"
"slug" => "somoza-luciano"
]
1 => array:1 [
"name" => "FRASCHINI Martina"
]
2 => array:1 [
"name" => "TERRACCIANO Tammaro"
]
]
"ouvrage" => "2024 European Economic Association Annual Meeting"
"keywords" => []
"updatedAt" => "2025-06-03 09:55:31"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => ""
"en" => ""
]
"authors_fields" => array:2 [
"fr" => "Finance"
"en" => "Finance"
]
"indexedAt" => "2026-06-04T11:23:33.000Z"
]
+lang: "en"
+"_score": 7.682597
+"parent": null
}
3 => Essec\Faculty\Model\Contribution {#2229
#_index: "academ_contributions"
#_id: "16148"
#_source: array:18 [
"id" => 16148
"slug" => "16148-ais-predictable-memory-in-financial-analysis"
"yearMonth" => "2025-10"
"year" => 2025
"title" => "AI’s predictable memory in financial analysis"
"description" => "DIDISHEIM, A., FRASCHINI, M. et SOMOZA, L. (2025). AI’s predictable memory in financial analysis. <i>Economics Letters</i>, 256, pp. 112602."
"authors" => array:3 [
0 => array:3 [
"name" => "SOMOZA Luciano"
"bid" => "B00812211"
"slug" => "somoza-luciano"
]
1 => array:1 [
"name" => "Didisheim Antoine"
]
2 => array:1 [
"name" => "Fraschini Martina"
]
]
"ouvrage" => ""
"keywords" => array:4 [
0 => "AI"
1 => "LLM"
2 => "Look-ahead bias"
3 => "Back-testing"
]
"updatedAt" => "2026-01-13 18:30:27"
"publicationUrl" => "https://doi.org/10.1016/j.econlet.2025.112602"
"publicationInfo" => array:3 [
"pages" => "112602"
"volume" => "256"
"number" => ""
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Look-ahead bias in Large Language Models (LLMs) arises when information that would not have been available at the time of prediction is included in the training data and inflates prediction performance. This paper proposes a practical methodology to quantify look-ahead bias in financial applications. By prompting LLMs to retrieve historical stock returns without context, we construct a proxy to estimate memorization-driven predictability. We show that the bias varies predictably with data frequency, model size, and aggregation level: smaller models and finer data granularity exhibit negligible bias. Our results help researchers navigate the trade-off between statistical power and bias in LLMs."
"en" => "Look-ahead bias in Large Language Models (LLMs) arises when information that would not have been available at the time of prediction is included in the training data and inflates prediction performance. This paper proposes a practical methodology to quantify look-ahead bias in financial applications. By prompting LLMs to retrieve historical stock returns without context, we construct a proxy to estimate memorization-driven predictability. We show that the bias varies predictably with data frequency, model size, and aggregation level: smaller models and finer data granularity exhibit negligible bias. Our results help researchers navigate the trade-off between statistical power and bias in LLMs."
]
"authors_fields" => array:2 [
"fr" => "Finance"
"en" => "Finance"
]
"indexedAt" => "2026-06-04T11:23:33.000Z"
]
+lang: "en"
+"_score": 7.682597
+"_ignored": array:2 [
0 => "abstract.en.keyword"
1 => "abstract.fr.keyword"
]
+"parent": null
}
4 => Essec\Faculty\Model\Contribution {#2233
#_index: "academ_contributions"
#_id: "16469"
#_source: array:18 [
"id" => 16469
"slug" => "16469-is-ai-reasoning-useful-in-finance"
"yearMonth" => "2026-05"
"year" => 2026
"title" => "Is AI reasoning useful in finance?"
"description" => "DIDISHEIM, A., FRASCHINI, M., SOMOZA, L. et TIAN, H. (2026). Is AI reasoning useful in finance? <i>Finance Research Letters</i>, 97, pp. 109783."
"authors" => array:4 [
0 => array:3 [
"name" => "SOMOZA Luciano"
"bid" => "B00812211"
"slug" => "somoza-luciano"
]
1 => array:1 [
"name" => "Didisheim Antoine"
]
2 => array:1 [
"name" => "Fraschini Martina"
]
3 => array:1 [
"name" => "Tian Hanqing"
]
]
"ouvrage" => ""
"keywords" => array:3 [
0 => "AI"
1 => "Large Language Models"
2 => "Reasoning"
]
"updatedAt" => "2026-05-20 10:24:42"
"publicationUrl" => "https://doi.org/10.1016/j.frl.2026.109783"
"publicationInfo" => array:3 [
"pages" => "109783"
"volume" => "97"
"number" => ""
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Whether Large Language Models (LLMs) will result in a marginal productivity increase or a technological revolution largely depends on their ability to reason. LLMs with reasoning capabilities outperform vanilla ones on math and coding. However, it remains unclear whether such emergent abilities translate into improved economic insights. We evaluate state-of-the-art general-purpose reasoning-enhanced LLMs by OpenAI and DeepSeek on standard financial tasks: news sentiment and earnings direction prediction. Reasoning-enhanced models fail to demonstrate a significant advantage, while model size does. These findings indicate that improved reasoning does not necessarily translate into enhanced economic intuition, questioning their cost-effectiveness and practical utility in finance. Only finance-specific reasoning models yield a relatively modest increase in performance."
"en" => "Whether Large Language Models (LLMs) will result in a marginal productivity increase or a technological revolution largely depends on their ability to reason. LLMs with reasoning capabilities outperform vanilla ones on math and coding. However, it remains unclear whether such emergent abilities translate into improved economic insights. We evaluate state-of-the-art general-purpose reasoning-enhanced LLMs by OpenAI and DeepSeek on standard financial tasks: news sentiment and earnings direction prediction. Reasoning-enhanced models fail to demonstrate a significant advantage, while model size does. These findings indicate that improved reasoning does not necessarily translate into enhanced economic intuition, questioning their cost-effectiveness and practical utility in finance. Only finance-specific reasoning models yield a relatively modest increase in performance."
]
"authors_fields" => array:2 [
"fr" => "Finance"
"en" => "Finance"
]
"indexedAt" => "2026-06-04T11:23:33.000Z"
]
+lang: "en"
+"_score": 7.682597
+"_ignored": array:2 [
0 => "abstract.en.keyword"
1 => "abstract.fr.keyword"
]
+"parent": null
}
]
"avatar" => "https://faculty.essec.edu/wp-content/uploads/avatars/B00812211.jpg"
"contributionCounts" => 5
"personalLinks" => array:2 [
0 => "<a href="https://orcid.org/0009-0000-5559-4174" target="_blank">ORCID</a>"
1 => "<a href="www.lucianosomoza.com" target="_blank">Personal site</a>"
]
"docTitle" => "Luciano SOMOZA"
"docSubtitle" => "Assistant Professor"
"docDescription" => "Department: Finance<br>Campus de Cergy"
"docType" => "cv"
"docPreview" => "<img src="https://faculty.essec.edu/wp-content/uploads/avatars/B00812211.jpg"><span><span>Luciano SOMOZA</span><span>B00812211</span></span>"
"academ_cv_info" => ""
]
+lang: "en"
+"_score": 5.028257
+"parent": null
}
