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FIRC0002-LITE-01/02/03/05/06/07/08/09/10/13 EDIT-14 (L11), ADVE-05 AI_C-04 EDIT-09 ECON-13 THEO-13 AI_C-13 ADVE-16 (L8), EDIT-04 DATA-03 (L6 inputs): DOI-verified bib additions, SPID sampling frame, IWV holdings manifest, FinData price check

references.bib: 19 entries appended (17 DOIs resolved via doi.org/Crossref incl. Eisfeldt et al. JF 2026, Hassan et al. QJE 2019,
Cao et al. RFS 2023, Hong-Lim-Stein JF 2000, Bloom et al. NBER w28999 'The Diffusion of New Technologies'; 2 self WP entries);
Xia et al. / Ofir not added (no identifier). SPID: 200 stratified mentions with source-text excerpts, no labels (AR-3 not granted).
IWV: 17 dated Wayback captures (2006-09, 2019-05..2025-08). FinData raw closes: 2,487/2,499 tickers, 98.9% within 2% of Yahoo-implied.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Nov 5, 2026) parent f3cae18 Browse files

12 changed files +3,876 −0

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modified paper/references.bib +193 −0
@@ -622,3 +622,196 @@
622 622 year = {2024},
623 623 doi = {10.3386/w32161}
624 624 }
625 +
626 +% ---- Stage D review additions (FIRC-0002, 2026-11-05): DOIs resolved through doi.org / Crossref by scripts/r3k_21_bib_crossref.py; cite from Stage E ----
627 +@techreport{basu2026aiexposure,
628 + author = {Basu, Sakshi and Thapa, Chandra and Neupane, Biwesh and Basu, Amlan},
629 + title = {{Firm-level AI Exposure: Measurement, Determinants, and Pricing Implications}},
630 + year = {2026},
631 + doi = {10.2139/ssrn.7575518},
632 + institution = {Elsevier BV},
633 + type = {Working paper},
634 + note = {Elsevier BV, DOI 10.2139/ssrn.7575518}
635 +}
636 +
637 +@techreport{borri2026aifactor,
638 + author = {Borri, Nicola and Liu, Yukun and Tsyvinski, Aleh},
639 + title = {{The Cross-Section of Stock Returns and AI Exposure}},
640 + year = {2026},
641 + doi = {10.48550/arXiv.2606.30583},
642 + institution = {arXiv},
643 + type = {Working paper},
644 + note = {arXiv, DOI 10.48550/arXiv.2606.30583}
645 +}
646 +
647 +@techreport{wulam2026genai,
648 + author = {Institute for Monetary and Financial Research, Hong Kong},
649 + title = {{Do Firms' Stock Prices Respond to AI-related Discussions? Evidence from Earnings Calls}},
650 + year = {2026},
651 + doi = {10.2139/ssrn.7482259},
652 + institution = {Elsevier BV},
653 + type = {Working paper},
654 + note = {Elsevier BV, DOI 10.2139/ssrn.7482259}
655 +}
656 +
657 +@article{eisfeldt2026generative,
658 + author = {EISFELDT, ANDREA L. and SCHUBERT, GREGOR and ZHANG, MIAO BEN and TASKA, BLEDI},
659 + title = {{Generative AI and Firm Values}},
660 + year = {2026},
661 + doi = {10.1111/jofi.70089},
662 + journal = {The Journal of Finance}
663 +}
664 +
665 +@techreport{song2026productexposure,
666 + author = {Song, Tianhao},
667 + title = {{What Firms Sell: Product Exposure to Generative AI *}},
668 + year = {2026},
669 + doi = {10.2139/ssrn.7527158},
670 + institution = {Elsevier BV},
671 + type = {Working paper},
672 + note = {Elsevier BV, DOI 10.2139/ssrn.7527158}
673 +}
674 +
675 +@techreport{erdogan2026aiwashing,
676 + author = {Erdogan, Sedat and Coskun Erdogan, Dilek and Li, Yanbing},
677 + title = {{SEC Regulatory Intervention and Artificial Intelligence (AI) Washing: Widening the Disclosure Gap Between Transparent and Opaque Firms}},
678 + year = {2026},
679 + doi = {10.2139/ssrn.6749213},
680 + institution = {Elsevier BV},
681 + type = {Working paper},
682 + note = {Elsevier BV, DOI 10.2139/ssrn.6749213}
683 +}
684 +
685 +@article{wang2026aiwashing,
686 + author = {Wang, Shan and Wang, Yanze and Gao, Yue and Lyu, Xinran},
687 + title = {{Talk is cheap, green is hard: The effect of AI-washing on corporate energy transition}},
688 + year = {2026},
689 + doi = {10.1016/j.iref.2026.105756},
690 + journal = {International Review of Economics &amp; Finance},
691 + volume = {111},
692 + pages = {105756}
693 +}
694 +
695 +@techreport{celenymarechal2024cyber,
696 + author = {Celeny, Daniel and Maréchal, Loïc},
697 + title = {{Cyber risk and the cross-section of stock returns}},
698 + year = {2023},
699 + doi = {10.2139/ssrn.4587993},
700 + institution = {Elsevier BV},
701 + type = {Working paper},
702 + note = {Elsevier BV, DOI 10.2139/ssrn.4587993}
703 +}
704 +
705 +@techreport{kearney2025cyberchatgpt,
706 + author = {Kearney, Fearghal and Ma, Tren and Movaghari, Hadi and Sermpinis, Georgios},
707 + title = {{Cyber Risk, ChatGPT and Firm Value}},
708 + year = {2025},
709 + doi = {10.2139/ssrn.5068625},
710 + institution = {Elsevier BV},
711 + type = {Working paper},
712 + note = {Elsevier BV, DOI 10.2139/ssrn.5068625}
713 +}
714 +
715 +@article{hassan2019firmlevel,
716 + author = {Hassan, Tarek A and Hollander, Stephan and van Lent, Laurence and Tahoun, Ahmed},
717 + title = {{Firm-Level Political Risk: Measurement and Effects*}},
718 + year = {2019},
719 + doi = {10.1093/qje/qjz021},
720 + journal = {The Quarterly Journal of Economics},
721 + volume = {134},
722 + number = {4},
723 + pages = {2135-2202}
724 +}
725 +
726 +@techreport{davis2020exposures,
727 + author = {Davis, Steven and Hansen, Stephen and Seminario-Amez, Cristhian},
728 + title = {{Firm-Level Risk Exposures and Stock Returns in the Wake of COVID-19}},
729 + year = {2020},
730 + doi = {10.3386/w27867},
731 + institution = {National Bureau of Economic Research},
732 + type = {Working paper},
733 + note = {National Bureau of Economic Research, DOI 10.3386/w27867}
734 +}
735 +
736 +@techreport{kim2023llmrisks,
737 + author = {Kim, Alex G. and Muhn, Maximilian and Nikolaev, Valeri V.},
738 + title = {{From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI}},
739 + year = {2023},
740 + doi = {10.2139/ssrn.4593660},
741 + institution = {Elsevier BV},
742 + type = {Working paper},
743 + note = {Elsevier BV, DOI 10.2139/ssrn.4593660}
744 +}
745 +
746 +@article{cao2023machinelistening,
747 + author = {Cao, Sean and Jiang, Wei and Yang, Baozhong and Zhang, Alan L},
748 + title = {{How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI}},
749 + year = {2023},
750 + doi = {10.1093/rfs/hhad021},
751 + journal = {The Review of Financial Studies},
752 + volume = {36},
753 + number = {9},
754 + pages = {3603-3642}
755 +}
756 +
757 +@article{honglimstein2000,
758 + author = {Hong, Harrison and Lim, Terence and Stein, Jeremy C.},
759 + title = {{Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies}},
760 + year = {2000},
761 + doi = {10.1111/0022-1082.00206},
762 + journal = {The Journal of Finance},
763 + volume = {55},
764 + number = {1},
765 + pages = {265-295}
766 +}
767 +
768 +@article{yilmazkuday2026chatgpt,
769 + author = {Yilmazkuday, Hakan},
770 + title = {{ChatGPT and stock returns}},
771 + year = {2026},
772 + doi = {10.1007/s12197-026-09767-9},
773 + journal = {Journal of Economics and Finance},
774 + volume = {50},
775 + number = {1}
776 +}
777 +
778 +@techreport{uqowp0001,
779 + author = {Boucher, Simon-Pierre and Kammoun, Manel and Tandjambianda, Djerrycharli},
780 + title = {{From Corporate Disclosures to a Russell 3000 Strategic Project Database}},
781 + institution = {Universit\'e du Qu\'ebec en Outaouais, FIRC},
782 + type = {UQO/FIRC Working Paper},
783 + number = {0001},
784 + year = {2026},
785 + note = {Strategic Project Intelligence Database (SPID); EDGAR Research Database}
786 +}
787 +
788 +@techreport{kammoun2026v11,
789 + author = {Kammoun, Manel and Tandjambianda, Djerrycharli and Boucher, Simon-Pierre},
790 + title = {{Does the Language of Artificial Intelligence Move Markets? A Semantic Analysis of S\&P 500 SEC Filings and the Cross-Section of Stock Returns, 2010--2026}},
791 + institution = {Universit\'e du Qu\'ebec en Outaouais, FIRC},
792 + type = {UQO Working Paper (version 1.1, S\&P 500)},
793 + number = {17},
794 + year = {2026},
795 + note = {Archived in docs/paper\_v1.1\_sp500\_20261026/ of the replication repository}
796 +}
797 +
798 +% ---- Stage D review additions (FIRC-0002, 2026-11-05): DOIs resolved through doi.org / Crossref by scripts/r3k_21_bib_crossref.py; cite from Stage E ----
799 +@techreport{hampole2025ai,
800 + author = {Hampole, Menaka and Papanikolaou, Dimitris and Schmidt, Lawrence and Seegmiller, Bryan},
801 + title = {{Artificial Intelligence and the Labor Market}},
802 + year = {2025},
803 + doi = {10.3386/w33509},
804 + institution = {National Bureau of Economic Research},
805 + type = {Working paper},
806 + note = {National Bureau of Economic Research, DOI 10.3386/w33509}
807 +}
808 +
809 +@techreport{bloom2021diffusion,
810 + author = {Kalyani, Aakash and Bloom, Nicholas and Carvalho, Marcela and Hassan, Tarek Alexander and Lerner, Josh and Tahoun, Ahmed},
811 + title = {{The Diffusion of New Technologies}},
812 + year = {2021},
813 + doi = {10.3386/w28999},
814 + institution = {National Bureau of Economic Research},
815 + type = {Working paper},
816 + note = {National Bureau of Economic Research, DOI 10.3386/w28999}
817 +}
added results/r3k_v2/bib_additions.json +252 −0
@@ -0,0 +1,252 @@
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": true
13 + },
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": true
24 + },
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": true
35 + },
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": true
46 + },
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": true
57 + },
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": true
68 + },
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 &amp; Finance",
75 + "n_authors": 4,
76 + "first_author": "Wang",
77 + "anchor": "LITE-03 AI washing (IREF 2026)",
78 + "title_matches_expectation": true
79 + },
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": true
90 + },
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": true
101 + },
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": true
112 + },
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": true
123 + },
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": true
134 + },
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": true
145 + },
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": true
156 + },
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": true
167 + },
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": true
178 + },
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": true
189 + }
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": 33.896973
203 + },
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": 33.87944
210 + },
211 + {
212 + "doi": "10.2139/ssrn.6298899",
213 + "title": "AI Washing: Strategic Disclosure and Backlash",
214 + "year": 2026,
215 + "container": "",
216 + "score": 33.102043
217 + }
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": 55.419296
231 + },
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": 31.04866
238 + },
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": 29.898045
245 + }
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 +}
\ No newline at end of file
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