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1{2 "resolved": [3 {4 "key": "basu2026aiexposure",5 "doi": "10.2139/ssrn.7575518",6 "title": "Firm-level AI Exposure: Measurement, Determinants, and Pricing Implications",7 "year": 2026,8 "container": "Elsevier BV",9 "n_authors": 4,10 "first_author": "Basu",11 "anchor": "LITE-01 competitor (SSRN 7575518)",12 "title_matches_expectation": true13 },14 {15 "key": "borri2026aifactor",16 "doi": "10.48550/arXiv.2606.30583",17 "title": "The Cross-Section of Stock Returns and AI Exposure",18 "year": 2026,19 "container": "arXiv",20 "n_authors": 3,21 "first_author": "Borri",22 "anchor": "LITE-08 (arXiv 2606.30583)",23 "title_matches_expectation": true24 },25 {26 "key": "wulam2026genai",27 "doi": "10.2139/ssrn.7482259",28 "title": "Do Firms' Stock Prices Respond to AI-related Discussions? Evidence from Earnings Calls",29 "year": 2026,30 "container": "Elsevier BV",31 "n_authors": 1,32 "first_author": "Institute for Monetary and Financial Research",33 "anchor": "LITE-08 (SSRN 7482259)",34 "title_matches_expectation": true35 },36 {37 "key": "eisfeldt2026generative",38 "doi": "10.1111/jofi.70089",39 "title": "Generative AI and Firm Values",40 "year": 2026,41 "container": "The Journal of Finance",42 "n_authors": 4,43 "first_author": "EISFELDT",44 "anchor": "LITE-02 / EDIT-14: JF 2026 version replacing eisfeldt2023generative",45 "title_matches_expectation": true46 },47 {48 "key": "song2026productexposure",49 "doi": "10.2139/ssrn.7527158",50 "title": "What Firms Sell: Product Exposure to Generative AI *",51 "year": 2026,52 "container": "Elsevier BV",53 "n_authors": 1,54 "first_author": "Song",55 "anchor": "LITE-02 (SSRN 7527158)",56 "title_matches_expectation": true57 },58 {59 "key": "erdogan2026aiwashing",60 "doi": "10.2139/ssrn.6749213",61 "title": "SEC Regulatory Intervention and Artificial Intelligence (AI) Washing: Widening the Disclosure Gap Between Transparent and Opaque Firms",62 "year": 2026,63 "container": "Elsevier BV",64 "n_authors": 3,65 "first_author": "Erdogan",66 "anchor": "LITE-03 AI washing (SSRN 6749213)",67 "title_matches_expectation": true68 },69 {70 "key": "wang2026aiwashing",71 "doi": "10.1016/j.iref.2026.105756",72 "title": "Talk is cheap, green is hard: The effect of AI-washing on corporate energy transition",73 "year": 2026,74 "container": "International Review of Economics & Finance",75 "n_authors": 4,76 "first_author": "Wang",77 "anchor": "LITE-03 AI washing (IREF 2026)",78 "title_matches_expectation": true79 },80 {81 "key": "celenymarechal2024cyber",82 "doi": "10.2139/ssrn.4587993",83 "title": "Cyber risk and the cross-section of stock returns",84 "year": 2023,85 "container": "Elsevier BV",86 "n_authors": 2,87 "first_author": "Celeny",88 "anchor": "LITE-05 / LITE-09 (SSRN 4587993)",89 "title_matches_expectation": true90 },91 {92 "key": "kearney2025cyberchatgpt",93 "doi": "10.2139/ssrn.5068625",94 "title": "Cyber Risk, ChatGPT and Firm Value",95 "year": 2025,96 "container": "Elsevier BV",97 "n_authors": 4,98 "first_author": "Kearney",99 "anchor": "LITE-05 (SSRN 5068625)",100 "title_matches_expectation": true101 },102 {103 "key": "hassan2019firmlevel",104 "doi": "10.1093/qje/qjz021",105 "title": "Firm-Level Political Risk: Measurement and Effects*",106 "year": 2019,107 "container": "The Quarterly Journal of Economics",108 "n_authors": 4,109 "first_author": "Hassan",110 "anchor": "LITE-09 / EDIT-14 (QJE 2019)",111 "title_matches_expectation": true112 },113 {114 "key": "davis2020exposures",115 "doi": "10.3386/w27867",116 "title": "Firm-Level Risk Exposures and Stock Returns in the Wake of COVID-19",117 "year": 2020,118 "container": "National Bureau of Economic Research",119 "n_authors": 3,120 "first_author": "Davis",121 "anchor": "LITE-09 (NBER w27867)",122 "title_matches_expectation": true123 },124 {125 "key": "kim2023llmrisks",126 "doi": "10.2139/ssrn.4593660",127 "title": "From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI",128 "year": 2023,129 "container": "Elsevier BV",130 "n_authors": 3,131 "first_author": "Kim",132 "anchor": "LITE-09 (SSRN 4593660)",133 "title_matches_expectation": true134 },135 {136 "key": "cao2023machinelistening",137 "doi": "10.1093/rfs/hhad021",138 "title": "How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI",139 "year": 2023,140 "container": "The Review of Financial Studies",141 "n_authors": 4,142 "first_author": "Cao",143 "anchor": "LITE-09 / EDIT-14 (RFS 2023)",144 "title_matches_expectation": true145 },146 {147 "key": "honglimstein2000",148 "doi": "10.1111/0022-1082.00206",149 "title": "Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies",150 "year": 2000,151 "container": "The Journal of Finance",152 "n_authors": 3,153 "first_author": "Hong",154 "anchor": "LITE-07 (JF 2000)",155 "title_matches_expectation": true156 },157 {158 "key": "yilmazkuday2026chatgpt",159 "doi": "10.1007/s12197-026-09767-9",160 "title": "ChatGPT and stock returns",161 "year": 2026,162 "container": "Journal of Economics and Finance",163 "n_authors": 1,164 "first_author": "Yilmazkuday",165 "anchor": "LITE-08 (J. Econ. Finance 2026)",166 "title_matches_expectation": true167 },168 {169 "key": "hampole2025ai",170 "doi": "10.3386/w33509",171 "title": "Artificial Intelligence and the Labor Market",172 "year": 2025,173 "container": "National Bureau of Economic Research",174 "n_authors": 4,175 "first_author": "Hampole",176 "anchor": "plan L11 (NBER w33509: Hampole, Papanikolaou, Schmidt, Seegmiller)",177 "title_matches_expectation": true178 },179 {180 "key": "bloom2021diffusion",181 "doi": "10.3386/w28999",182 "title": "The Diffusion of New Technologies",183 "year": 2021,184 "container": "National Bureau of Economic Research",185 "n_authors": 6,186 "first_author": "Kalyani",187 "anchor": "EDIT-14: Bloom et al. NBER 28999 = The Diffusion of New Technologies (Bloom, Hassan, Kalyani, Lerner, Tahoun), DOI verified",188 "title_matches_expectation": true189 }190 ],191 "not_added": [192 {193 "key": "xia2026aiwashingindex",194 "anchor": "LITE-03 Xia et al., Systems 2026 (no identifier given)",195 "reason": "no identifier given by the referee; Crossref candidates listed for the author to confirm (not added)",196 "candidates": [197 {198 "doi": "10.22541/au.177187613.38652471/v1",199 "title": "Cognitive Integrity Security: Securing Human-AI Interaction in Large Language Model Systems",200 "year": 2026,201 "container": "",202 "score": 33.896973203 },204 {205 "doi": "10.22541/au.177437389.95026495/v1",206 "title": "Cognitive Integrity Security: Securing Human-AI Interaction in Large Language Model Systems",207 "year": 2026,208 "container": "",209 "score": 33.87944210 },211 {212 "doi": "10.2139/ssrn.6298899",213 "title": "AI Washing: Strategic Disclosure and Backlash",214 "year": 2026,215 "container": "",216 "score": 33.102043217 }218 ]219 },220 {221 "key": "ofir2026aiwashinglaw",222 "anchor": "LITE-03 Ofir, ABLJ 2026 (no identifier given)",223 "reason": "no identifier given by the referee; Crossref candidates listed for the author to confirm (not added)",224 "candidates": [225 {226 "doi": "10.1111/ablj.70020",227 "title": "<scp>AI</scp>\n washing",228 "year": 2026,229 "container": "American Business Law Journal",230 "score": 55.419296231 },232 {233 "doi": "10.1111/ablj.70025",234 "title": "The law of\n <scp>AI</scp>\n knowledge distillation",235 "year": 2026,236 "container": "American Business Law Journal",237 "score": 31.04866238 },239 {240 "doi": "10.1111/ablj.70024",241 "title": "Copyright and\n <scp>AI</scp>\n : The building blocks",242 "year": 2026,243 "container": "American Business Law Journal",244 "score": 29.898045245 }246 ]247 }248 ],249 "when": "2026-11-05T15:12:25.813372",250 "n_entries_appended": 2,251 "note_eisfeldt": "eisfeldt2023generative is kept (cited in the text); eisfeldt2026generative is the JF version to substitute at Stage E"252}