Essec\Faculty\Model\Profile {#2206
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"slug" => "rombouts-jeroen"
"fullName" => "Jeroen ROMBOUTS"
"lastName" => "ROMBOUTS"
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]
"email" => "rombouts@essec.edu"
"status" => "ACTIF"
"campus" => "Campus de Cergy"
"departments" => []
"phone" => "+33 (0)1 34 43 30 49"
"sites" => []
"facNumber" => "2084"
"externalCvUrl" => "https://faculty.essec.edu/cv/rombouts-jeroen/pdf"
"googleScholarUrl" => "https://scholar.google.com/citations?user=XAKQzRgAAAAJ"
"facOrcId" => "https://orcid.org/0000-0003-4255-9227"
"career" => array:20 [
0 => Essec\Faculty\Model\CareerItem {#2220
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]
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]
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+"parent": Essec\Faculty\Model\Profile {#2206}
}
1 => Essec\Faculty\Model\CareerItem {#2221
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}
2 => Essec\Faculty\Model\CareerItem {#2222
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3 => Essec\Faculty\Model\CareerItem {#2223
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4 => Essec\Faculty\Model\CareerItem {#2224
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5 => Essec\Faculty\Model\CareerItem {#2225
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}
6 => Essec\Faculty\Model\CareerItem {#2226
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}
7 => Essec\Faculty\Model\CareerItem {#2227
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]
]
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+"parent": Essec\Faculty\Model\Profile {#2206}
}
8 => Essec\Faculty\Model\CareerItem {#2228
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]
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+"parent": Essec\Faculty\Model\Profile {#2206}
}
9 => Essec\Faculty\Model\CareerItem {#2229
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]
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}
10 => Essec\Faculty\Model\CareerItem {#2230
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11 => Essec\Faculty\Model\CareerItem {#2231
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}
12 => Essec\Faculty\Model\CareerItem {#2232
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13 => Essec\Faculty\Model\CareerItem {#2233
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}
14 => Essec\Faculty\Model\CareerItem {#2234
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15 => Essec\Faculty\Model\CareerItem {#2235
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16 => Essec\Faculty\Model\CareerItem {#2236
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17 => Essec\Faculty\Model\CareerItem {#2237
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}
18 => Essec\Faculty\Model\CareerItem {#2238
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}
19 => Essec\Faculty\Model\CareerItem {#2239
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0 => Essec\Faculty\Model\Diplome {#2208
#_index: null
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#_source: array:6 [
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}
1 => Essec\Faculty\Model\Diplome {#2210
#_index: null
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#_source: array:6 [
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}
2 => Essec\Faculty\Model\Diplome {#2207
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#_source: array:6 [
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}
3 => Essec\Faculty\Model\Diplome {#2211
#_index: null
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}
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"bio" => array:2 [
"fr" => """
<p>Ph.D. In Econometrics, CORE, Universite Catholique de Louvain. Currently CORE research associate and Elected Member of the International Statistical Institute. Joined ESSEC in 2013 and formerly professor at HEC Montreal (2004-2012).</p>\n
\n
<p>Professor Jeroen VK Rombouts joined ESSEC Business School in January 2013. He combines rich data sets with statistical tools to answer financial questions. His research outcomes are published in international peer reviewed journals. He teaches courses of Basic Statistics and Econometrics in the Master and PhD programs. He has been Visiting Scholar at several universities (University of Pittsburg, Tilburg University, Erasmus University Rotterdam, Aarhus University (CREATES) and CORE as a research associate among others). He is an expert consultant in financial econometrics and macro-economic forecasting. Prior to joining ESSEC Business School, Jeroen was Associate Professor at HEC Montreal (2004-2012)</p>
"""
"en" => "Ph.D. In Econometrics, CORE, Universite Catholique de Louvain. Currently CORE research associate and Elected Member of the International Statistical Institute. Joined ESSEC in 2013 and formerly professor at HEC Montreal (2004-2012). </p>Professor Jeroen VK Rombouts joined ESSEC Business School in January 2013. He combines rich data sets with statistical tools to answer financial questions. His research outcomes are published in international peer reviewed journals. He teaches courses of Basic Statistics and Econometrics in the Master and PhD programs. He has been Visiting Scholar at several universities (University of Pittsburg, Tilburg University, Erasmus University Rotterdam, Aarhus University (CREATES) and CORE as a research associate among others). He is an expert consultant in financial econometrics and macro-economic forecasting. Prior to joining ESSEC Business School, Jeroen was Associate Professor at HEC Montreal (2004-2012)"
]
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]
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"en" => "Econometrics - Decision Sciences - Probability Theory & Mathematical Statistics - Financial Markets & Institutions - Management - Marketing and Data Analytics"
]
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0 => Essec\Faculty\Model\ExtraActivity {#2205
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}
1 => Essec\Faculty\Model\ExtraActivity {#2209
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2 => Essec\Faculty\Model\ExtraActivity {#2212
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3 => Essec\Faculty\Model\ExtraActivity {#2213
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4 => Essec\Faculty\Model\ExtraActivity {#2214
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5 => Essec\Faculty\Model\ExtraActivity {#2215
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6 => Essec\Faculty\Model\ExtraActivity {#2216
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7 => Essec\Faculty\Model\ExtraActivity {#2217
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8 => Essec\Faculty\Model\ExtraActivity {#2218
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9 => Essec\Faculty\Model\ExtraActivity {#2219
#_index: null
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#_source: array:9 [
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"fr" => null
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"fr" => null
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0 => Essec\Faculty\Model\Contribution {#2241
#_index: "academ_contributions"
#_id: "2738"
#_source: array:18 [
"id" => "2738"
"slug" => "the-value-of-multivariate-model-sophistication-an-application-to-pricing-dow-jones-industrial-average-options"
"yearMonth" => "2014-01"
"year" => "2014"
"title" => "The Value of Multivariate Model Sophistication: An Application to Pricing Dow Jones Industrial Average Options"
"description" => "ROMBOUTS, J., STENTOFT, L. et VIOLANTE, F. (2014). The Value of Multivariate Model Sophistication: An Application to Pricing Dow Jones Industrial Average Options. <i>International Journal of Forecasting</i>, 30(1), pp. 78-98."
"authors" => array:3 [
0 => array:3 [
"name" => "ROMBOUTS Jeroen"
"bid" => "B00469813"
"slug" => "rombouts-jeroen"
]
1 => array:1 [
"name" => "STENTOFT L."
]
2 => array:1 [
"name" => "VIOLANTE Francesco"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-02-02 16:16:18"
"publicationUrl" => "https://doi.org/10.1016/j.ijforecast.2013.07.006"
"publicationInfo" => array:3 [
"pages" => "78-98"
"volume" => "30"
"number" => "1"
]
"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" => "We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their specification of the conditional variance, conditional correlation, innovation distribution, and estimation approach. All of the models belong to the dynamic conditional correlation class, which is particularly suitable because it allows consistent estimations of the risk neutral dynamics with a manageable amount of computational effort for relatively large scale problems. It turns out that increasing the sophistication in the marginal variance processes (i.e., nonlinearity, asymmetry and component structure) leads to important gains in pricing accuracy. Enriching the model with more complex existing correlation specifications does not improve the performance significantly. Estimating the standard dynamic conditional correlation model by composite likelihood, in order to take into account potential biases in the parameter estimates, generates only slightly better results. To enhance this poor performance of correlation models, we propose a new model that allows for correlation spillovers without too many parameters. This model performs about 60% better than the existing correlation models we consider. Relaxing a Gaussian innovation for a Laplace innovation assumption improves the pricing in a more minor way. In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances."
"en" => "We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their specification of the conditional variance, conditional correlation, innovation distribution, and estimation approach. All of the models belong to the dynamic conditional correlation class, which is particularly suitable because it allows consistent estimations of the risk neutral dynamics with a manageable amount of computational effort for relatively large scale problems. It turns out that increasing the sophistication in the marginal variance processes (i.e., nonlinearity, asymmetry and component structure) leads to important gains in pricing accuracy. Enriching the model with more complex existing correlation specifications does not improve the performance significantly. Estimating the standard dynamic conditional correlation model by composite likelihood, in order to take into account potential biases in the parameter estimates, generates only slightly better results. To enhance this poor performance of correlation models, we propose a new model that allows for correlation spillovers without too many parameters. This model performs about 60% better than the existing correlation models we consider. Relaxing a Gaussian innovation for a Laplace innovation assumption improves the pricing in a more minor way. In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances."
]
"authors_fields" => array:2 [
"fr" => "Finance"
"en" => "Finance"
]
"indexedAt" => "2024-09-19T15:21:49.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 7.3354225
+"parent": null
}
1 => Essec\Faculty\Model\Contribution {#2243
#_index: "academ_contributions"
#_id: "2124"
#_source: array:18 [
"id" => "2124"
"slug" => "on-loss-functions-and-ranking-forecasting-performances-of-multivariate-volatility-models"
"yearMonth" => "2013-03"
"year" => "2013"
"title" => "On Loss Functions and Ranking Forecasting Performances of Multivariate Volatility Models"
"description" => "LAURENT, G., ROMBOUTS, J. et VIOLANTE, F. (2013). On Loss Functions and Ranking Forecasting Performances of Multivariate Volatility Models. <i>Journal of Econometrics</i>, 173(1), pp. 1-10."
"authors" => array:3 [
0 => array:3 [
"name" => "LAURENT Gilles"
"bid" => "B00770447"
"slug" => "laurent-gilles"
]
1 => array:3 [
"name" => "ROMBOUTS Jeroen"
"bid" => "B00469813"
"slug" => "rombouts-jeroen"
]
2 => array:1 [
"name" => "VIOLANTE Francesco"
]
]
"ouvrage" => ""
"keywords" => array:5 [
0 => "Volatility"
1 => "Multivariate GARCH"
2 => "Matrix norm"
3 => "Loss function"
4 => "Model confidence set"
]
"updatedAt" => "2021-02-02 16:16:18"
"publicationUrl" => "https://doi.org/10.1016/j.jeconom.2012.08.004"
"publicationInfo" => array:3 [
"pages" => "1-10"
"volume" => "173"
"number" => "1"
]
"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" => "The ranking of multivariate volatility models is inherently problematic because when the unobservable volatility is substituted by a proxy, the ordering implied by a loss function may be biased with respect to the intended one. We point out that the size of the distortion is strictly tied to the level of the accuracy of the volatility proxy. We propose a generalized necessary and sufficient functional form for a class of non-metric distance measures of the Bregman type which ensure consistency of the ordering when the target is observed with noise. An application to three foreign exchange rates is provided."
"en" => "The ranking of multivariate volatility models is inherently problematic because when the unobservable volatility is substituted by a proxy, the ordering implied by a loss function may be biased with respect to the intended one. We point out that the size of the distortion is strictly tied to the level of the accuracy of the volatility proxy. We propose a generalized necessary and sufficient functional form for a class of non-metric distance measures of the Bregman type which ensure consistency of the ordering when the target is observed with noise. An application to three foreign exchange rates is provided."
]
"authors_fields" => array:2 [
"fr" => "Finance"
"en" => "Finance"
]
"indexedAt" => "2024-09-19T15:21:49.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 7.3354225
+"parent": null
}
2 => Essec\Faculty\Model\Contribution {#2245
#_index: "academ_contributions"
#_id: "10810"
#_source: array:18 [
"id" => "10810"
"slug" => "dynamics-of-variance-risk-premia-a-new-model-for-disentangling-the-price-of-risk"
"yearMonth" => "2020-08"
"year" => "2020"
"title" => "Dynamics of variance risk premia: A new model for disentangling the price of risk"
"description" => "ROMBOUTS, J., VIOLANTE, F. et STENTOFT, L. (2020). Dynamics of variance risk premia: A new model for disentangling the price of risk. <i>Journal of Econometrics</i>, 217(2), pp. 312-334."
"authors" => array:3 [
0 => array:3 [
"name" => "ROMBOUTS Jeroen"
"bid" => "B00469813"
"slug" => "rombouts-jeroen"
]
1 => array:1 [
"name" => "VIOLANTE Francesco"
]
2 => array:1 [
"name" => "STENTOFT Lars"
]
]
"ouvrage" => ""
"keywords" => array:3 [
0 => "Variance risk premium"
1 => "Return predictability"
2 => "Sentiment indicators"
]
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://doi.org/10.1016/j.jeconom.2019.12.006"
"publicationInfo" => array:3 [
"pages" => "312-334"
"volume" => "217"
"number" => "2"
]
"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" => "This paper formulates a new dynamic model for the variance risk premium based on a state space representation of a bivariate system for the observable ex-post realized variance and the ex-ante option implied variance expectation. A regime switching structure accommodates for periods of unusually high volatility, heterogeneous dynamics and changes in the dependence between the latent states. The model allows separating the continuous component of the variance risk premium from the impact of jumps on option implied variance expectations. Using options and high frequency returns for the S&P500 index, we explain what is generating return predictability by disentangling the part of the variance risk premium associated with normal sized price fluctuations from that associated with tail events. The latter component predicts to a significant extent, and asymmetrically with respect to their sign, future market return variations."
"en" => "This paper formulates a new dynamic model for the variance risk premium based on a state space representation of a bivariate system for the observable ex-post realized variance and the ex-ante option implied variance expectation. A regime switching structure accommodates for periods of unusually high volatility, heterogeneous dynamics and changes in the dependence between the latent states. The model allows separating the continuous component of the variance risk premium from the impact of jumps on option implied variance expectations. Using options and high frequency returns for the S&P500 index, we explain what is generating return predictability by disentangling the part of the variance risk premium associated with normal sized price fluctuations from that associated with tail events. The latter component predicts to a significant extent, and asymmetrically with respect to their sign, future market return variations."
]
"authors_fields" => array:2 [
"fr" => "Finance"
"en" => "Finance"
]
"indexedAt" => "2024-09-19T15:21:49.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 7.3354225
+"parent": null
}
3 => Essec\Faculty\Model\Contribution {#2242
#_index: "academ_contributions"
#_id: "10814"
#_source: array:18 [
"id" => "10814"
"slug" => "pricing-individual-stock-options-using-both-stock-and-market-index-information"
"yearMonth" => "2020-02"
"year" => "2020"
"title" => "Pricing Individual Stock Options Using both Stock and Market Index Information"
"description" => "ROMBOUTS, J., VIOLANTE, F. et STENTOFT, L. (2020). Pricing Individual Stock Options Using both Stock and Market Index Information. <i>Journal of Banking & Finance</i>, 111."
"authors" => array:3 [
0 => array:3 [
"name" => "ROMBOUTS Jeroen"
"bid" => "B00469813"
"slug" => "rombouts-jeroen"
]
1 => array:1 [
"name" => "VIOLANTE Francesco"
]
2 => array:1 [
"name" => "STENTOFT L."
]
]
"ouvrage" => ""
"keywords" => array:4 [
0 => "American option pricing"
1 => "Economic loss"
2 => "Forecasting"
3 => "Multivariate GARCH"
]
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://doi.org/10.1016/j.jbankfin.2019.1057277"
"publicationInfo" => array:3 [
"pages" => null
"volume" => "111"
"number" => null
]
"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" => "When it comes to individual stock option pricing, most applications consider a univariate framework. From a theoretical point of view this is unsatisfactory as we know that the expected return of any asset is closely related to the exposure to the market risk factors. To address this, we model the evolution of the individual stock returns together with the market index returns in a flexible bivariate model in line with theory. The model parameters are estimated using both historical returns and aggregated option data from the index and the individual stocks. We assess the model performance by pricing a large set of individual stock options on 26 major US stocks over a long time period including the global financial crisis. Our results show that the losses from using a univariate formulation amounts to 11% on average when compared to our preferred bivariate specification."
"en" => "When it comes to individual stock option pricing, most applications consider a univariate framework. From a theoretical point of view this is unsatisfactory as we know that the expected return of any asset is closely related to the exposure to the market risk factors. To address this, we model the evolution of the individual stock returns together with the market index returns in a flexible bivariate model in line with theory. The model parameters are estimated using both historical returns and aggregated option data from the index and the individual stocks. We assess the model performance by pricing a large set of individual stock options on 26 major US stocks over a long time period including the global financial crisis. Our results show that the losses from using a univariate formulation amounts to 11% on average when compared to our preferred bivariate specification."
]
"authors_fields" => array:2 [
"fr" => "Finance"
"en" => "Finance"
]
"indexedAt" => "2024-09-19T15:21:49.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 7.3354225
+"parent": null
}
4 => Essec\Faculty\Model\Contribution {#2246
#_index: "academ_contributions"
#_id: "10836"
#_source: array:18 [
"id" => "10836"
"slug" => "variance-swap-payoffs-risk-premia-and-extreme-market-conditions"
"yearMonth" => "2020-01"
"year" => "2020"
"title" => "Variance swap payoffs, risk premia and extreme market conditions"
"description" => "ROMBOUTS, J., STENTOFT, L. et VIOLANTE, F. (2020). Variance swap payoffs, risk premia and extreme market conditions. <i>Econometrics and Statistics</i>, 13, pp. 106-124."
"authors" => array:3 [
0 => array:3 [
"name" => "ROMBOUTS Jeroen"
"bid" => "B00469813"
"slug" => "rombouts-jeroen"
]
1 => array:1 [
"name" => "STENTOFT L."
]
2 => array:1 [
"name" => "VIOLANTE Francesco"
]
]
"ouvrage" => ""
"keywords" => array:6 [
0 => "Variance risk premium"
1 => "Variance swaps"
2 => "Return predictability"
3 => "Factor model"
4 => "Kalman filter"
5 => "CAPM"
]
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://doi.org/10.1016/j.ecosta.2019.05.003"
"publicationInfo" => array:3 [
"pages" => "106-124"
"volume" => "13"
"number" => null
]
"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" => "The variance risk premium (VRP) is estimated directly from synthetic variance swap payoffs. Since variance swap payoffs are highly volatile, the VRP is extracted by using signal extraction techniques based on a state-space representation of the model in combination with a simple economic constraint. The proposed approach, only requiring option implied volatilities and daily returns for the underlying asset, provides measurement error free estimates of the part of the VRP related to normal market conditions, and allows constructing variables indicating agents’ expectations under extreme market conditions. The latter variables and the VRP generate different return predictability on the major US indices. A factor model is proposed to extract a market VRP which turns out to be priced when considering Fama and French portfolios."
"en" => "The variance risk premium (VRP) is estimated directly from synthetic variance swap payoffs. Since variance swap payoffs are highly volatile, the VRP is extracted by using signal extraction techniques based on a state-space representation of the model in combination with a simple economic constraint. The proposed approach, only requiring option implied volatilities and daily returns for the underlying asset, provides measurement error free estimates of the part of the VRP related to normal market conditions, and allows constructing variables indicating agents’ expectations under extreme market conditions. The latter variables and the VRP generate different return predictability on the major US indices. A factor model is proposed to extract a market VRP which turns out to be priced when considering Fama and French portfolios."
]
"authors_fields" => array:2 [
"fr" => "Finance"
"en" => "Finance"
]
"indexedAt" => "2024-09-19T15:21:49.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 7.3354225
+"parent": null
}
5 => Essec\Faculty\Model\Contribution {#2240
#_index: "academ_contributions"
#_id: "11354"
#_source: array:18 [
"id" => "11354"
"slug" => "on-the-forecasting-accuracy-of-multivariate-garch-models"
"yearMonth" => "2012-10"
"year" => "2012"
"title" => "On the forecasting accuracy of multivariate GARCH models"
"description" => "LAURENT, S., ROMBOUTS, J. et VIOLANTE, F. (2012). On the forecasting accuracy of multivariate GARCH models. <i>Journal of Applied Econometrics</i>, 27(6), pp. 934-955."
"authors" => array:3 [
0 => array:3 [
"name" => "ROMBOUTS Jeroen"
"bid" => "B00469813"
"slug" => "rombouts-jeroen"
]
1 => array:1 [
"name" => "LAURENT Sebastien"
]
2 => array:1 [
"name" => "VIOLANTE Francesco"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2022-11-28 10:54:00"
"publicationUrl" => "https://doi.org/10.1002/jae.1248"
"publicationInfo" => array:3 [
"pages" => "934-955"
"volume" => "27"
"number" => "6"
]
"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" => "This paper addresses the question of the selection of multivariate generalized autoregressive conditional heteroskedastic (GARCH) models in terms of variance matrix forecasting accuracy, with a particular focus on relatively large‐scale problems."
"en" => "This paper addresses the question of the selection of multivariate generalized autoregressive conditional heteroskedastic (GARCH) models in terms of variance matrix forecasting accuracy, with a particular focus on relatively large‐scale problems."
]
"authors_fields" => array:2 [
"fr" => "Finance"
"en" => "Finance"
]
"indexedAt" => "2024-09-19T15:21:49.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 7.3354225
+"parent": null
}
6 => Essec\Faculty\Model\Contribution {#2244
#_index: "academ_contributions"
#_id: "14480"
#_source: array:18 [
"id" => "14480"
"slug" => "tuning-in-what-ai-and-user-generated-content-can-tell-us-about-consumers"
"yearMonth" => "2023-07"
"year" => "2023"
"title" => "Tuning In - What AI and User Generated Content Can Tell Us About Consumers"
"description" => "KÜBLER, R. et ROMBOUTS, J. (2023). Tuning In - What AI and User Generated Content Can Tell Us About Consumers. <i>ESSEC Knowledge</i>."
"authors" => array:2 [
0 => array:3 [
"name" => "KÜBLER Raoul"
"bid" => "B00806952"
"slug" => "kubler-raoul"
]
1 => array:3 [
"name" => "ROMBOUTS Jeroen"
"bid" => "B00469813"
"slug" => "rombouts-jeroen"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2023-10-17 11:13:41"
"publicationUrl" => "https://knowledge.essec.edu/en/innovation/tuning-ai-and-user-generated-content-consumers.html"
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Articles ou vidéos de vulgarisation"
"en" => "Press article, video or other popular media"
]
"support_type" => array:2 [
"fr" => "Presse"
"en" => "Press"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "We are living in the most data-rich period of humankind. We create and store more data on a daily basis than our ancestors did in the last 2000 years - combined. This data comes from various digital sources, where consumers leave intentionally (and unintentionally) valuable information for marketers in the form of user-generated content. The authors explain what this data can tell us about consumer behavior."
"en" => "We are living in the most data-rich period of humankind. We create and store more data on a daily basis than our ancestors did in the last 2000 years - combined. This data comes from various digital sources, where consumers leave intentionally (and unintentionally) valuable information for marketers in the form of user-generated content. The authors explain what this data can tell us about consumer behavior."
]
"authors_fields" => array:2 [
"fr" => "Marketing"
"en" => "Marketing"
]
"indexedAt" => "2024-09-19T15:21:49.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 7.3354225
+"parent": null
}
]
"avatar" => "https://faculty.essec.edu/wp-content/uploads/avatars/B00469813.jpg"
"contributionCounts" => 7
"personalLinks" => array:2 [
0 => "<a href="https://orcid.org/0000-0003-4255-9227" target="_blank">ORCID</a>"
1 => "<a href="https://scholar.google.com/citations?user=XAKQzRgAAAAJ" target="_blank">Google scholar</a>"
]
"docTitle" => "Jeroen ROMBOUTS"
"docSubtitle" => "Professeur"
"docDescription" => "Campus de Cergy"
"docType" => "cv"
"docPreview" => "<img src="https://faculty.essec.edu/wp-content/uploads/avatars/B00469813.jpg"><span><span>Jeroen ROMBOUTS</span><span>B00469813</span></span>"
"academ_cv_info" => ""
]
#_index: "academ_cv"
+lang: "fr"
+"_type": "_doc"
+"_score": 5.0369525
+"parent": null
}
