{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T22:49:36Z","timestamp":1769640576008,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/S023151\/1, EP\/Y002113\/1"],"award-info":[{"award-number":["EP\/S023151\/1, EP\/Y002113\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,15]]},"DOI":"10.1145\/3768292.3770412","type":"proceedings-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:24:26Z","timestamp":1763105066000},"page":"431-439","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Factor-Driven Network Informed Restricted Vector Autoregression"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2168-8261","authenticated-orcid":false,"given":"Brendan","family":"Martin","sequence":"first","affiliation":[{"name":"Imperial College London, London, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8464-2152","authenticated-orcid":false,"given":"Mihai","family":"Cucuringu","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6407-9385","authenticated-orcid":false,"given":"Alessandra","family":"Luati","sequence":"additional","affiliation":[{"name":"Imperial College London, London, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4571-6681","authenticated-orcid":false,"given":"Francesco","family":"Sanna Passino","sequence":"additional","affiliation":[{"name":"Imperial College London, London, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Robert Adamek Stephan Smeekes and Ines Wilms. 2023. Lasso inference for high-dimensional time series. Journal of Econometrics 235 2 (2023) 1114\u20131143.","DOI":"10.1016\/j.jeconom.2022.08.008"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Alekh Agarwal Sahand Negahban and Martin\u00a0J Wainwright. 2012. Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions. The Annals of Statistics 40 2 (2012) 1171\u20131197.","DOI":"10.1214\/12-AOS1000"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Jushan Bai. 2003. Inferential theory for factor models of large dimensions. Econometrica 71 1 (2003) 135\u2013171.","DOI":"10.1111\/1468-0262.00392"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Jushan Bai and Serena Ng. 2002. Determining the number of factors in approximate factor models. Econometrica 70 1 (2002) 191\u2013221.","DOI":"10.1111\/1468-0262.00273"},{"key":"e_1_3_3_2_6_2","unstructured":"Matteo Barigozzi Giuseppe Cavaliere and Graziano Moramarco. 2025. Factor network autoregressions. Journal of Business & Economic Statistics (2025) 1\u201314."},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Matteo Barigozzi Haeran Cho and Dom Owens. 2024. FNETS: Factor-adjusted network estimation and forecasting for high-dimensional time series. Journal of Business & Economic Statistics 42 3 (2024) 890\u2013902.","DOI":"10.1080\/07350015.2023.2257270"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Matteo Barigozzi and Marc Hallin. 2024. The Dynamic the Static and the Weak: Factor Models and the Analysis of High-Dimensional Time Series. Journal of Time Series Analysis (2024).","DOI":"10.1111\/jtsa.12837"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"crossref","unstructured":"Sumanta Basu and George Michailidis. 2015. Regularized estimation in sparse high-dimensional time series models. The Annals of Statistics 43 4 (2015) 1535\u20131567.","DOI":"10.1214\/15-AOS1315"},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.5555\/17326"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"crossref","unstructured":"J Cape M Tang and CE Priebe. 2019. Signal-plus-noise matrix models: eigenvector deviations and fluctuations. Biometrika 106 1 (2019) 243\u2013250.","DOI":"10.1093\/biomet\/asy070"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Gary Chamberlain. 1983. Funds factors and diversification in arbitrage pricing models. Econometrica: Journal of the Econometric Society (1983) 1305\u20131323.","DOI":"10.2307\/1912276"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"crossref","unstructured":"Gary Chamberlain and Michael Rothschild. 1983. Arbitrage Factor Structure and Mean-Variance Analysis on Large Asset Markets. Econometrica: Journal of the Econometric Society (1983) 1281\u20131304.","DOI":"10.2307\/1912275"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"crossref","unstructured":"Elynn\u00a0Y Chen Jianqing Fan and Xuening Zhu. 2023. Community network auto-regression for high-dimensional time series. Journal of Econometrics 235 2 (2023) 1239\u20131256.","DOI":"10.1016\/j.jeconom.2022.10.005"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Yuxin Chen Yuejie Chi Jianqing Fan Cong Ma et\u00a0al. 2021. Spectral methods for data science: A statistical perspective. Foundations and Trends\u00ae in Machine Learning 14 5 (2021) 566\u2013806.","DOI":"10.1561\/2200000079"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Victor Chernozhukov Christian Hansen and Yuan Liao. 2017. A lava attack on the recovery of sums of dense and sparse signals. The Annals of Statistics 45 1 (2017) 39\u201376.","DOI":"10.1214\/16-AOS1434"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"crossref","unstructured":"David Donoho Matan Gavish and Elad Romanov. 2023. ScreeNOT: Exact MSE-optimal singular value thresholding in correlated noise. The Annals of Statistics 51 1 (2023) 122\u2013148.","DOI":"10.1214\/22-AOS2232"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Eugene\u00a0F Fama and Kenneth\u00a0R French. 1992. The cross-section of expected stock returns. the Journal of Finance 47 2 (1992) 427\u2013465.","DOI":"10.1111\/j.1540-6261.1992.tb04398.x"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"crossref","unstructured":"Jianqing Fan Yuan Ke and Kaizheng Wang. 2020. Factor-adjusted regularized model selection. Journal of econometrics 216 1 (2020) 71\u201385.","DOI":"10.1016\/j.jeconom.2020.01.006"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"crossref","unstructured":"Jianqing Fan Ricardo\u00a0P Masini and Marcelo\u00a0C Medeiros. 2023. Bridging factor and sparse models. The Annals of Statistics 51 4 (2023) 1692\u20131717.","DOI":"10.1214\/23-AOS2304"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Mario Forni Domenico Giannone Marco Lippi and Lucrezia Reichlin. 2009. Opening the black box: Structural factor models with large cross sections. Econometric Theory 25 5 (2009) 1319\u20131347.","DOI":"10.1017\/S026646660809052X"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Mario Forni Marc Hallin Marco Lippi and Lucrezia Reichlin. 2000. The generalized dynamic-factor model: Identification and estimation. Review of Economics and statistics 82 4 (2000) 540\u2013554.","DOI":"10.1162\/003465300559037"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Mario Forni and Marco Lippi. 2001. The generalized dynamic factor model: representation theory. Econometric theory 17 6 (2001) 1113\u20131141.","DOI":"10.1017\/S0266466601176048"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Ian Gallagher Andrew Jones Anna Bertiger Carey\u00a0E Priebe and Patrick Rubin-Delanchy. 2024. Spectral embedding of weighted graphs. J. Amer. Statist. Assoc. 119 547 (2024) 1923\u20131932.","DOI":"10.1080\/01621459.2023.2225239"},{"key":"e_1_3_3_2_25_2","unstructured":"John Geweke. 1977. The dynamic factor analysis of economic time series. Latent variables in socio-economic models (1977)."},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Domenico Giannone Michele Lenza and Giorgio\u00a0E Primiceri. 2021. Economic predictions with big data: The illusion of sparsity. Econometrica 89 5 (2021) 2409\u20132437.","DOI":"10.3982\/ECTA17842"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"crossref","unstructured":"Clive\u00a0WJ Granger and Michael\u00a0J Morris. 1976. Time series modelling and interpretation. Journal of the Royal Statistical Society Series A: Statistics in Society 139 2 (1976) 246\u2013257.","DOI":"10.2307\/2345178"},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"crossref","unstructured":"Shihao Gu Bryan Kelly and Dacheng Xiu. 2020. Empirical asset pricing via machine learning. The Review of Financial Studies 33 5 (2020) 2223\u20132273.","DOI":"10.1093\/rfs\/hhaa009"},{"key":"e_1_3_3_2_29_2","unstructured":"Fang Han Huanran Lu and Han Liu. 2015. A direct estimation of high dimensional stationary vector autoregressions. Journal of Machine Learning Research (2015)."},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"crossref","unstructured":"Peter\u00a0D Hoff Adrian\u00a0E Raftery and Mark\u00a0S Handcock. 2002. Latent space approaches to social network analysis. J. Amer. Statist. Assoc. 97 460 (2002) 1090\u20131098.","DOI":"10.1198\/016214502388618906"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"crossref","unstructured":"Marina Knight Kathryn Leeming Guy Nason and Matthew Nunes. 2020. Generalized network autoregressive processes and the GNAR package. Journal of Statistical Software 96 (2020) 1\u201336.","DOI":"10.18637\/jss.v096.i05"},{"key":"e_1_3_3_2_32_2","unstructured":"Marina\u00a0Iuliana Knight MA Nunes and GP Nason. 2016. Modelling detrending and decorrelation of network time series. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1603.03221 (2016)."},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"crossref","unstructured":"Jonas Krampe and Luca Margaritella. 2025. Factor Models With Sparse Vector Autoregressive Idiosyncratic Components. Oxford Bulletin of Economics and Statistics (2025).","DOI":"10.1111\/obes.12664"},{"key":"e_1_3_3_2_34_2","doi-asserted-by":"crossref","unstructured":"Laurent Laloux Pierre Cizeau Marc Potters and Jean-Philippe Bouchaud. 2000. Random matrix theory and financial correlations. International Journal of Theoretical and Applied Finance 3 03 (2000) 391\u2013397.","DOI":"10.1142\/S0219024900000255"},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"crossref","unstructured":"Clement Lee and Darren\u00a0J Wilkinson. 2019. A review of stochastic block models and extensions for graph clustering. Applied Network Science 4 1 (2019) 1\u201350.","DOI":"10.1007\/s41109-019-0232-2"},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"crossref","unstructured":"Jing Lei and Alessandro Rinaldo. 2015. Consistency of spectral clustering in stochastic block models. The Annals of Statistics 43 1 (2015) 215\u2013237.","DOI":"10.1214\/14-AOS1274"},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"crossref","unstructured":"Helmut L\u00fctkepohl. 1984. Linear transformations of vector ARMA processes. Journal of Econometrics 26 3 (1984) 283\u2013293.","DOI":"10.1016\/0304-4076(84)90023-X"},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-27752-1"},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"crossref","unstructured":"Vladimir\u00a0A Mar\u010denko and Leonid\u00a0Andreevich Pastur. 1967. Distribution of eigenvalues for some sets of random matrices. Mathematics of the USSR-Sbornik 1 4 (1967) 457.","DOI":"10.1070\/SM1967v001n04ABEH001994"},{"key":"e_1_3_3_2_40_2","unstructured":"Brendan Martin Francesco Sanna\u00a0Passino Mihai Cucuringu and Alessandra Luati. 2024. NIRVAR: Network Informed Restricted Vector Autoregression. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2407.13314 (2024)."},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"crossref","unstructured":"Michael\u00a0W McCracken and Serena Ng. 2016. FRED-MD: A monthly database for macroeconomic research. Journal of Business & Economic Statistics 34 4 (2016) 574\u2013589.","DOI":"10.1080\/07350015.2015.1086655"},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"crossref","unstructured":"Ke Miao Peter\u00a0CB Phillips and Liangjun Su. 2023. High-dimensional VARs with common factors. Journal of Econometrics 233 1 (2023) 155\u2013183.","DOI":"10.1016\/j.jeconom.2022.02.002"},{"key":"e_1_3_3_2_43_2","doi-asserted-by":"crossref","unstructured":"Mark\u00a0EJ Newman. 2003. Mixing patterns in networks. Physical review E 67 2 (2003) 026126.","DOI":"10.1103\/PhysRevE.67.026126"},{"key":"e_1_3_3_2_44_2","unstructured":"William\u00a0B Nicholson Ines Wilms Jacob Bien and David\u00a0S Matteson. 2020. High dimensional forecasting via interpretable vector autoregression. Journal of Machine Learning Research 21 166 (2020) 1\u201352."},{"key":"e_1_3_3_2_45_2","unstructured":"Thomas\u00a0J Sargent Christopher\u00a0A Sims et\u00a0al. 1977. Business cycle modeling without pretending to have too much a priori economic theory. New methods in business cycle research 1 (1977) 145\u2013168."},{"key":"e_1_3_3_2_46_2","doi-asserted-by":"crossref","unstructured":"James\u00a0H Stock and Mark\u00a0W Watson. 2002. Forecasting using principal components from a large number of predictors. J. Amer. Statist. Assoc. 97 460 (2002) 1167\u20131179.","DOI":"10.1198\/016214502388618960"},{"key":"e_1_3_3_2_47_2","doi-asserted-by":"crossref","unstructured":"James\u00a0H Stock and Mark\u00a0W Watson. 2002. Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics 20 2 (2002) 147\u2013162.","DOI":"10.1198\/073500102317351921"},{"key":"e_1_3_3_2_48_2","doi-asserted-by":"crossref","unstructured":"Robert Tibshirani. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology 58 1 (1996) 267\u2013288.","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"e_1_3_3_2_49_2","volume-title":"Multivariate time series analysis: with R and financial applications","author":"Tsay Ruey\u00a0S","year":"2013","unstructured":"Ruey\u00a0S Tsay. 2013. Multivariate time series analysis: with R and financial applications. John Wiley & Sons."},{"key":"e_1_3_3_2_50_2","volume-title":"Pairs Trading: quantitative methods and analysis","author":"Vidyamurthy Ganapathy","year":"2004","unstructured":"Ganapathy Vidyamurthy. 2004. Pairs Trading: quantitative methods and analysis. John Wiley & Sons."},{"key":"e_1_3_3_2_51_2","doi-asserted-by":"crossref","unstructured":"Hang Yin Abolfazl Safikhani and George Michailidis. 2023. A general modeling framework for network autoregressive processes. Technometrics 65 4 (2023) 579\u2013589.","DOI":"10.1080\/00401706.2023.2203184"},{"key":"e_1_3_3_2_52_2","doi-asserted-by":"crossref","unstructured":"Mu Zhu and Ali Ghodsi. 2006. Automatic dimensionality selection from the scree plot via the use of profile likelihood. Computational Statistics & Data Analysis 51 2 (2006) 918\u2013930.","DOI":"10.1016\/j.csda.2005.09.010"},{"key":"e_1_3_3_2_53_2","doi-asserted-by":"crossref","unstructured":"X Zhu R Pan G Li Y Liu and H Wang. 2017. Network vector autoregression. The Annals of Statistics 45 3 (2017) 1096\u20131123.","DOI":"10.1214\/16-AOS1476"}],"event":{"name":"ICAIF '25: 6th ACM International Conference on AI in Finance","location":"Singapore Singapore","acronym":"ICAIF '25"},"container-title":["Proceedings of the 6th ACM International Conference on AI in Finance"],"original-title":[],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:28:31Z","timestamp":1763105311000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3768292.3770412"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"references-count":52,"alternative-id":["10.1145\/3768292.3770412","10.1145\/3768292"],"URL":"https:\/\/doi.org\/10.1145\/3768292.3770412","relation":{},"subject":[],"published":{"date-parts":[[2025,11,14]]},"assertion":[{"value":"2025-11-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}