spb/wp17_uqo

1# Exhibit map — FIRC-0002 v2.1 (generated by scripts/r3k_20_tables_v2.py; do not edit)23Every table file in `paper/tables/` and every figure wrapper in `paper/figures_r3k/` is a self-contained float: `\input{tables/<name>}` or `\input{figures_r3k/<name>}` prints the float with its caption, label and notes. `paper/tables/appendix_exhibits.tex` (input from `main.tex` after the appendix) prints, behind `\ExhibitOnce` guards, every table not already placed and the appendix-bound figures. Placement = the Stage E decision (main text vs appendix).45| Label | File | Placement | Title | Sources |6|---|---|---|---|---|7| `tab:summ` | `paper/tables/tab_summary.tex` | main | Summary statistics of the estimation sample. | results/r3k_v2/t1_summary_stats.csv, panel_meta_v2.json, mktcap_diag.json, hygiene_summary.json |8| `tab:sortsboth` | `paper/tables/tab_sorts_both.tex` | main | Quintile sorts on AI language in the Russell~3000 universe and among S\&P~500 members. | results/r3k_v2/results.json (samples.*.sorts, vw_diag), results2.json (mht) |9| `tab:sorts` | `paper/tables/tab_sorts.tex` | appendix | Quintile sorts on every signal and weighting, Russell~3000 universe. | results/r3k_v2/results.json (sorts, vw_diag) |10| `tab:alphaboth` | `paper/tables/tab_alpha_both.tex` | main | Monthly long--short portfolios on AI language: mean return, Sharpe ratio and factor alphas. | results/r3k_v2/results.json (samples.*.ls), results2.json (mht.deflated_sharpe) |11| `tab:alpha` | `paper/tables/tab_alpha.tex` | appendix | Monthly long--short portfolios by signal and sub-period, Russell~3000 universe. | results/r3k_v2/results.json (ls_alpha) |12| `tab:panel` | `paper/tables/tab_panel.tex` | main | Two-way fixed-effects panel regressions of the forward return on AI language, Russell~3000. | results/r3k_v2/results.json (panel_fe) |13| `tab:fmboth` | `paper/tables/tab_fm_both.tex` | main | Fama--MacBeth regressions of the forward return on AI language, Russell~3000 and S\&P~500. | results/r3k_v2/results.json (samples.*.fm, fama_macbeth) |14| `tab:fm` | `paper/tables/tab_fm.tex` | appendix | Fama--MacBeth regressions, all specifications, Russell~3000 universe. | results/r3k_v2/results.json (fama_macbeth) |15| `tab:prepostboth` | `paper/tables/tab_prepost_both.tex` | main | AI-language spreads before and after the ChatGPT launch, Russell~3000 and S\&P~500. | results/r3k_v2/results.json (samples.*.sub, pooled, samples.sp500.pooled_post_w) |16| `tab:cohorts` | `paper/tables/tab_cohorts.tex` | main | Cohort-by-cohort spreads of the AI composite and tests of a 2022 break, Russell~3000. | results/r3k_v2/results2.json (cohorts*, placebo_breaks), results_surv.json (then_members_present) |17| `tab:groups` | `paper/tables/tab_groups.tex` | main | AI-composite spreads by size group and index membership, Russell~3000. | results/r3k_v2/results.json (groups, size_neutral, contrasts) |18| `tab:contrasts` | `paper/tables/tab_contrasts.tex` | main | Formal tests of the large-versus-small and member-versus-non-member post-launch differences. | results/r3k_v2/results.json (contrasts) |19| `tab:doublesort` | `paper/tables/tab_doublesort.tex` | appendix | AI language $\times$ size double sort, Russell~3000. | results/r3k_v2/results2.json (double_sort_*) |20| `tab:sectortech` | `paper/tables/tab_sector_tech.tex` | main | Within-sector estimates of the AI-language premium and technology-factor spanning, Russell~3000. | results/r3k_v2/results2.json (sector_controls, sector_neutral, residual_sort, monthly_ls) |21| `tab:placebo` | `paper/tables/tab_placebo.tex` | appendix | Placebo across eight semantic factors scored with the same pipeline, Russell~3000. | results/r3k_v2/results2.json (placebo, mht) |22| `tab:robust` | `paper/tables/tab_robust.tex` | main | Construction, horizon, benchmark, universe and survivorship checks on the composite spread, Russell~3000. | results/r3k_v2/results2.json (subperiod_spread, sector_neutral, residual_sort, mag7, alt_*, entrants, breaks, cluster_k), results_surv.json (conditional_spreads), results.json |23| `tab:filingtype` | `paper/tables/tab_filing_type.tex` | appendix | Form-specific AI composites, Russell~3000. | results/r3k_v2/results2.json (filing_type, mht.forms_fm_w) |24| `tab:event` | `paper/tables/tab_event.tex` | main | ChatGPT event study: cumulative abnormal returns by pre-launch AI tercile under the IWV market model, Russell~3000. | results/r3k_v2/results_event.json (by_benchmark.mm_2022, welch_reference, tercile_characteristics) |25| `tab:eventcda` | `paper/tables/tab_event_cda.tex` | main | Benchmark dependence of the one-year event-study gradient and placebo launch dates. | results/r3k_v2/results_event.json (by_benchmark, ff6_calendar_time, placebo_launches, placebo_summary, mht_event) |26| `tab:didspid` | `paper/tables/tab_did_spid.tex` | main | Difference-in-differences on pre-determined exposure and the SPID project signal, Russell~3000. | results/r3k_v2/results2.json (did, spid), t17_refit.json |27| `tab:spidbinary` | `paper/tables/tab_spid_binary.tex` | main | Binary contrasts on the SPID project signal with minimum detectable effects, Russell~3000. | results/r3k_v2/results2.json (spid.binary_contrasts, spid.tercile_nonzero_w), results.json (spid) |28| `tab:reversal` | `paper/tables/tab_reversal.tex` | appendix | Reversal tests and the cybersecurity-vocabulary check, Russell~3000. | results/r3k_v2/results2.json (cluster_k) |29| `tab:mht` | `paper/tables/tab_mht.tex` | main | Multiple-testing adjustments by family of tests. | results/r3k_v2/results2.json (mht, spec_curve), results_event.json (mht_event) |30| `tab:mhtfull` | `paper/tables/tab_mht_full_a.tex` | appendix | Member-level multiple-testing adjustments, part A: portfolio-sort and form families. | results/r3k_v2/results2.json (mht) |31| `tab:mhtfullb` | `paper/tables/tab_mht_full_b.tex` | appendix | Member-level multiple-testing adjustments, part B: robustness rows, placebo interactions and break years. | results/r3k_v2/results2.json (mht) |32| `tab:eventtime` | `paper/tables/tab_eventtime.tex` | appendix | Event-time profile of the premium on pre-determined AI exposure, Russell~3000. | results/r3k_v2/results2.json (did_eventtime*) |33| `tab:attrition` | `paper/tables/tab_attrition.tex` | main | Survivorship diagnostics on the current-membership universe, Russell~3000. | results/r3k_v2/results_surv.json (attrition, then_members_present, ew_vs_index, bounding), iwv_holdings_manifest.json |34| `tab:constructvalidity` | `paper/tables/tab_construct_validity.tex` | main | Construct validity of the semantic AI score: keyword baseline, stripped anchors, sector composition and encoder truncation. | results/r3k_v2/results_construct.json, results/n1/tokenizer_audit.json, results/n1/keyword_baseline.json |35| `tab:corr` | `paper/tables/tab_corr.tex` | appendix | Correlations of the within-year standardized variables. | results/r3k_v2/t1b_correlations.csv |36| `tab:topfirms` | `paper/tables/tab_topfirms.tex` | appendix | Firms with the highest average AI composite. | data/processed_r3k_v2/analysis_panel_v2.parquet |37| `tab:coverage` | `paper/tables/tab_coverage.tex` | appendix | Estimation sample by formation cohort: firms, size groups and text coverage. | results/r3k_v2/results2.json (coverage) |38| `tab:corpus` | `paper/tables/tab_corpus.tex` | main | Corpus composition by form. | data/raw_russell3000/filings, results/n1/corpus_counts.json |39| `tab:hygiene` | `paper/tables/tab_hygiene.tex` | appendix | Return hygiene and the effect of the data corrections on the headline statistics. | results/r3k_v2/hygiene_log.csv, hygiene_summary.json, results/r3k/*.json (HEAD 3cf7560), results/r3k_v2/results*.json |40| `tab:break` | `paper/tables/tab_break.tex` | appendix | Pooled test of a post-launch change in the signal slope, by signal, Russell~3000. | results/r3k_v2/results.json (structural_break, subperiod_spread) |41| `fig:quintiles` | `paper/figures_r3k/fig1_quintiles.tex` (PDF `figures/r3k_v2/fig1_quintiles.pdf`) | appendix | Mean forward return by AI-composite quintile before and after the ChatGPT launch. | scripts/r3k_19_figures_v2.py |42| `fig:cumls` | `paper/figures_r3k/fig2_cumulative_ls.tex` (PDF `figures/r3k_v2/fig2_cumulative_ls.pdf`) | appendix | Cumulative return of the AI-composite long--short portfolio by size group. | scripts/r3k_19_figures_v2.py |43| `fig:cohorts` | `paper/figures_r3k/fig3_cohort_spreads.tex` (PDF `figures/r3k_v2/fig3_cohort_spreads.pdf`) | main | Annual AI-composite spreads by formation cohort, Russell~3000. | scripts/r3k_19_figures_v2.py |44| `fig:trend` | `paper/figures_r3k/fig4_trend.tex` (PDF `figures/r3k_v2/fig4_trend.pdf`) | main | The secular rise of AI language in SEC filings. | scripts/r3k_19_figures_v2.py |45| `fig:fmyear` | `paper/figures_r3k/fig5_fm_byyear.tex` (PDF `figures/r3k_v2/fig5_fm_byyear.pdf`) | main | Year-by-year Fama--MacBeth slopes on the AI composite, Russell~3000. | scripts/r3k_19_figures_v2.py |46| `fig:placebo` | `paper/figures_r3k/fig6_placebo.tex` (PDF `figures/r3k_v2/fig6_placebo.pdf`) | appendix | Placebo across eight semantic factors scored with the same pipeline. | scripts/r3k_19_figures_v2.py |47| `fig:event` | `paper/figures_r3k/fig7_event_car.tex` (PDF `figures/r3k_v2/fig7_event_car.pdf`) | main | Cumulative abnormal returns around the ChatGPT launch by pre-launch AI tercile. | scripts/r3k_19_figures_v2.py |48| `fig:double` | `paper/figures_r3k/fig8_double_sort.tex` (PDF `figures/r3k_v2/fig8_double_sort.pdf`) | appendix | AI language $\times$ size double sort before and after the launch, Russell~3000. | scripts/r3k_19_figures_v2.py |49| `fig:eventtime` | `paper/figures_r3k/fig9_eventtime.tex` (PDF `figures/r3k_v2/fig9_eventtime.pdf`) | main | Event-time profile of the premium on pre-determined AI exposure, Russell~3000. | scripts/r3k_19_figures_v2.py |50| `fig:filingtype` | `paper/figures_r3k/fig10_filing_type.tex` (PDF `figures/r3k_v2/fig10_filing_type.pdf`) | appendix | Pre- and post-launch spreads of the form-specific AI composites. | scripts/r3k_19_figures_v2.py |51| `fig:placebobreaks` | `paper/figures_r3k/fig11_placebo_breaks.tex` (PDF `figures/r3k_v2/fig11_placebo_breaks.pdf`) | main | Placebo break dates and contiguous-block comparison, AI composite, Russell~3000. | scripts/r3k_19_figures_v2.py |52| `fig:sizegroups` | `paper/figures_r3k/fig12_size_groups.tex` (PDF `figures/r3k_v2/fig12_size_groups.pdf`) | main | Pre- and post-launch AI-composite spreads by size group and index membership. | scripts/r3k_19_figures_v2.py |53| `fig:cohortsboth` | `paper/figures_r3k/fig13_cohorts_both.tex` (PDF `figures/r3k_v2/fig13_cohorts_both.pdf`) | main | Annual AI-composite spreads by cohort, Russell~3000 universe versus S\&P~500 members. | scripts/r3k_19_figures_v2.py |54| `fig:cumboth` | `paper/figures_r3k/fig14_cumulative_both.tex` (PDF `figures/r3k_v2/fig14_cumulative_both.pdf`) | main | Cumulative long--short return on the AI composite in the two universes. | scripts/r3k_19_figures_v2.py |55| `fig:speccurve` | `paper/figures_r3k/fig15_spec_curve.tex` (PDF `figures/r3k_v2/fig15_spec_curve.pdf`) | main | Specification curve of the AI-composite spread, Russell~3000. | scripts/r3k_19_figures_v2.py |56| `fig:placebolaunches` | `paper/figures_r3k/fig16_placebo_launches.tex` (PDF `figures/r3k_v2/fig16_placebo_launches.pdf`) | main | Placebo launch dates: the event-study design applied to 30 November of 2015--2021. | scripts/r3k_19_figures_v2.py |57| `fig:attrition` | `paper/figures_r3k/fig17_attrition.tex` (PDF `figures/r3k_v2/fig17_attrition.pdf`) | appendix | Attrition of IWV constituents by market-value tercile, from dated holdings captures. | scripts/r3k_19_figures_v2.py |58