spb/wp17_uqo

1% ============================================================================2% Appendix3% ============================================================================4\newpage5\begin{appendices}67\renewcommand{\thetable}{A\arabic{table}}8\renewcommand{\thefigure}{A\arabic{figure}}9\setcounter{table}{0}10\setcounter{figure}{0}1112\section{Variable Definitions}13\label{app:variables}1415\begin{table}[htbp]16  \centering17  \caption{Variable Definitions}18  \label{tab:app_variables}19  \small20  \begin{tabular}{@{}p{5.1cm}p{9.6cm}@{}}21    \toprule22    Variable & Definition \\23    \midrule24    AI\,Adopt (\texttt{ai\_adoption\_wmean}) & Average over the scored filings of firm $i$ dated in calendar year $Y$ of the section-weighted mean cosine similarity of the filing's text chunks to the AI-internal-adoption anchor, equations~\eqref{eq:cos}--\eqref{eq:annual}. \\[4pt]25    AI\,Infra (\texttt{ai\_infrastructure\_wmean}) & Same construction for the AI-compute-infrastructure anchor. \\[4pt]26    AI\,Risk (\texttt{ai\_disruption\_risk\_wmean}) & Same construction for the AI-product-disruption-risk anchor. \\[4pt]27    Simple mean, top-5\% tail & Filing-level unweighted mean of chunk scores, and mean of the chunks at or above the filing's 95th percentile, averaged over filings as above. \\[4pt]28    AI\,Compos & $\tfrac12[z(\text{AI\,Adopt})+z(\text{AI\,Infra})]$, with $z$ the within-formation-year cross-sectional standardization of equation~\eqref{eq:z}. \\[4pt]29    8-K-only composite & AI\,Compos rebuilt from the filing-level scores of Form~8-K filings only (\texttt{data/processed/filing\_type\_scores.parquet}). \\[4pt]30    Pre-determined exposure & Firm average of AI\,Compos over filing years 2019--2021, standardized across firms; binary treatment equals one above the median. \\[4pt]31    Forward return & Buy-and-hold simple return from July of $Y{+}1$ to June of $Y{+}2$, equation~\eqref{eq:fwd}, from adjusted closes (dividends reinvested), requiring at least six monthly returns. \\[4pt]32    Market-adjusted return & Forward return minus the contemporaneous SPY return. \\[4pt]33    Size & Natural logarithm of the market-cap proxy (June 2026 shares outstanding $\times$ price) at formation. \\[4pt]34    Momentum & Own return over the 12 months ending at formation, skipping the last month. \\[4pt]35    Volatility & Annualized standard deviation of monthly returns over the trailing 12 months. \\[4pt]36    Post & Indicator equal to one for formation (filing) years $\ge 2022$. \\[4pt]37    TECH & Equal-weighted monthly return of the Information Technology constituents of the panel minus the equal-weighted return of all constituents. \\[4pt]38    CAR & Cumulative market-model abnormal return, equations~\eqref{eq:mm}--\eqref{eq:car}; market model estimated 3~January--31~October 2022 on SPY, event window 1~November 2022--30~June 2024. \\39    \bottomrule40  \end{tabular}41\end{table}4243\section{Anchor Texts}44\label{app:anchors}4546Each concept is defined by the three paragraphs below (\texttt{src/factors/anchors.py} of the47EDGAR Research Database). The three paragraphs are embedded separately with48\texttt{all-MiniLM-L6-v2}, averaged, and normalized to a unit vector; chunk scores are cosine49similarities to that vector. The anchors were written by the authors in June 2026 and have not been50revised since; no alternative anchor set has been tested.5152\paragraph{AI adoption (operational weighting).} (1) ``The company internally uses artificial53intelligence, machine learning, generative AI, large language models, automation, predictive54analytics, intelligent agents, and data-driven systems to improve productivity, operations,55efficiency, decision-making, customer service, risk management, compliance, cybersecurity, supply56chain, software development, finance, human resources, and other business processes.'' (2) ``We57have implemented generative AI, large language models, and natural language processing58capabilities across our enterprise. Our AI strategy includes deploying intelligent automation,59computer vision, and predictive analytics systems to transform operations.'' (3) ``Investment in60artificial intelligence research and development, including partnerships with AI technology61providers, hiring of machine learning engineers, and deployment of AI infrastructure including GPU62computing clusters for internal business applications.''6364\paragraph{AI disruption risk (risk weighting).} (1) ``Artificial intelligence may reduce the65scarcity, defensibility, differentiation, pricing power, or competitive advantage of the company's66products or services by enabling rapid replication, automation, synthetic generation, low-cost67substitution, or commoditization of capabilities that were previously difficult, expensive, or68specialized.'' (2) ``The rapid advancement of AI technologies may render our current offerings less69competitive. New market entrants leveraging AI could fundamentally alter industry dynamics, reduce70barriers to entry, and erode our market position through AI-native products and services.''71(3) ``Risks associated with artificial intelligence include potential competitive displacement by72AI-powered alternatives, obsolescence of our existing products, regulatory uncertainty around AI73governance, algorithmic bias, intellectual property challenges, and emergence of AI-native74competitors.''7576\paragraph{AI infrastructure (operational weighting).} (1) ``Investment in AI computing77infrastructure including data centers, GPU and TPU clusters, cloud computing resources,78semiconductor chips, accelerator hardware, and high-performance computing systems dedicated to79artificial intelligence and machine learning workloads.'' (2) ``Capital expenditures for AI80infrastructure, including networking equipment for distributed AI training, specialized computing81platforms for large-scale model development, edge computing for AI inference, and data pipeline82infrastructure for machine learning operations.'' (3) ``Cloud infrastructure spending for AI83workloads, including partnerships with hyperscale cloud providers, deployment of AI-optimized84hardware, GPU supply chain management, and investment in next-generation computing architectures85for artificial intelligence applications.''8687\paragraph{Placebo concepts.} The ESG-commitment anchor describes sustainability reporting,88corporate social responsibility, diversity and inclusion, stakeholder engagement, adherence to89SASB, GRI, and TCFD frameworks, emission reductions, and board-level oversight of environmental and90social risk. The climate-risk anchor describes physical and transition risks, carbon pricing,91stranded assets, water scarcity, TCFD scenario analysis, and Scope~1--3 emissions targets. The92cybersecurity anchor describes data breaches, ransomware, GDPR and CCPA compliance, security93operations, zero-trust architecture, cyber insurance, and ``AI-powered threat detection.'' The94automation/labour anchor describes robotic process automation, workforce restructuring,95reskilling, human--machine collaboration, and ``the future of work transformation driven by AI and96robotics.'' The macro-risk anchor describes inflation, interest and exchange rates, recession,97geopolitical tensions, tariffs, commodity prices, and monetary and fiscal policy. The full texts98are in the source file cited above. Two of the placebo anchors (cybersecurity and automation/labour)99mention AI explicitly, which bears on the interpretation of Table~\ref{tab:placebo}.100101\section{Corpus Composition and the 84 Firms with Scored 10-Q Filings}102\label{app:corpus}103104The EDGAR Research Database downloaded \num{7655} Form~10-K, \num{22542} Form~10-Q, and105\num{103927} Form~8-K filings for the 500 constituents over January 2010--June 2026. At the time106the panel was built, the section parser had completed all 8-K filings and \num{3710} 10-Q filings107and had not started on the 10-K filings; the scoring engine processed the parsed filings only. The10810-Q filings that were parsed belong to the following 84 tickers, essentially those from SMCI to ZTS in alphabetical order plus a few others: BF.B, BNY, BRK.B, DIS, GWW, SMCI, SNPS, STE, STLD, SYF, SYK, SYY, TDG, TDY, TEL, TER, TFC, TGT, TJX, TKO, TMO, TMUS, TPL, TPR, TRGP, TRMB, TROW, TRV, TSCO, TSLA, TSN, TT, TTD, TTWO, TXN, TXT, TYL, UAL, UBER, UDR, UHS, ULTA, UNH, UNP, UPS, URI, USB, V, VEEV, VICI, VLO, VLTO, VMC, VRSK, VRSN, VRT, VRTX, VST, VTR, VTRS, VZ, WAB, WAT, WBD, WDAY, WDC, WEC, WELL, WFC, WM, WMB, WMT, WRB, WSM, WST, WTW, WY, WYNN, XEL, XYL, YUM, ZBH, ZBRA, ZTS.\footnote{The list is read from the committed \texttt{data/processed/filing\_type\_scores.parquet}, which109\texttt{scripts/10\_filing\_type\_scores.py} exports from the database's \texttt{filing\_scores} table.} The year variable of the panel is the110calendar year of the filing date (\texttt{EXTRACT(YEAR FROM filing\_date)} in the scoring engine),111not the fiscal year covered by the filing. Of the \num{7869} firm-years, 16.4\% contain at least one112scored 10-Q.113114\section{Data Sources and Reproducibility}115\label{app:repro}116117Semantic scores are from the EDGAR Research Database (UQO), which is not redistributed; only the118firm-year panel (\texttt{data/processed/merged\_panel.parquet}) and the filing-type aggregates119(\texttt{filing\_type\_scores.parquet}) enter the repository, together with the monthly and daily120prices, market capitalizations, factor series, and long--short series needed to reproduce every121table and figure without network access. Equity prices are from Yahoo Finance (downloaded 17~June1222026); the Fama--French five factors, the momentum factor, and the risk-free rate are from the123Kenneth R.\ French data library (through April 2026). The pipeline in \texttt{scripts/} runs in124numerical order: \texttt{01} downloads prices, \texttt{02} builds the look-ahead-free panel,125\texttt{03} runs the sorts, factor regressions, panel and Fama--MacBeth estimators and the break126interaction, \texttt{06} the placebo, double sorts, exclusions, alternative signals and horizons,127principal components, difference-in-differences, event-time profile, and forward volatility,128\texttt{07} the event study, \texttt{04} and \texttt{08} the figures, and \texttt{05} and129\texttt{09} the LaTeX fragments of the original tables. Scripts \texttt{10}--\texttt{12}, added130in the October 2026 revision, export the filing-type aggregates, run the re-analyses of131Tables~\ref{tab:corpus}, \ref{tab:postinf}, \ref{tab:reviewrobust}, and~\ref{tab:eventcda}, and132write their LaTeX fragments. All numerical results are serialized to133\texttt{results/tables/*.json}. The revision also corrected the clustering of the event-time134regression in \texttt{06\_extended.py} (by firm rather than by year); every other number produced by135scripts \texttt{03}, \texttt{06}, and \texttt{07} is unchanged from the June 2026 draft. The table136notes of the original fragments were edited by hand after generation and are documented in137\texttt{docs/CHANGELOG-REVISION.md}.138139\end{appendices}140