{"id":"W4408057749","doi":"10.2139/ssrn.5159649","title":"The Effect of Cybersecurity Breaches on Analysts' Earnings Forecasts","year":2025,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"National Chengchi University","keywords":"Earnings; Business; Computer security; Data breach; Accounting; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001984816,0.0002096571,0.0002459231,0.0007982274,0.000617828,0.002401644,0.0003925968,0.0015396,0.008148043],"category_scores_gemma":[0.05357493,0.0001978806,0.0004683125,0.0006297553,0.0006868577,0.001739024,0.0009622134,0.003185512,0.0009397661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008668259,"about_ca_system_score_gemma":0.0007683142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01049916,"about_ca_topic_score_gemma":0.009701254,"domain_scores_codex":[0.9985908,0.0004607802,0.000127118,0.0001549237,0.0003127963,0.0003535567],"domain_scores_gemma":[0.9019336,0.06684071,0.02120242,0.002772558,0.004252453,0.0029983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006302221,0.00200806,0.9293805,0.00008629795,0.0003859768,0.0007338635,0.001181189,0.01590088,0.002745835,0.002622816,0.004155769,0.03449664],"study_design_scores_gemma":[0.00005685018,0.0009823224,0.9845368,0.0000504581,0.0001518084,0.0001302294,0.002028412,0.007467201,0.001759251,0.001788963,0.001007151,0.00004058527],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963177,0.0001190722,0.00006184005,0.0008561574,0.00003690002,0.000003603998,0.000146988,0.00001253742,0.00244508],"genre_scores_gemma":[0.9993418,0.00004619747,0.00001214672,0.00003652869,0.0000161638,0.000001049886,0.00007098806,0.000001942921,0.0004730745],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01049916,"threshold_uncertainty_score":0.02725792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003089062588399868,"score_gpt":0.2073923903530979,"score_spread":0.204303327764698,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}