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Text as Treatment from Financial Reports: Effects of Natural Hazard Risk on Firm Profitability Using Causal Machine Learning
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s11142-008-9083-2openalex-snapshot
Financial reporting complexity and investor underreaction to 10-K information
inferredcites →https://doi.org/10.1016/j.ejor.2018.10.024openalex-snapshot
Deep learning models for bankruptcy prediction using textual disclosures
inferredcites →https://doi.org/10.1016/j.eswa.2014.06.009openalex-snapshot
Text mining for market prediction: A systematic review
inferredcites →https://doi.org/10.1016/j.irfa.2023.102745openalex-snapshot
When ESG talks: ESG tone of 10-K reports and its significance to stock markets
inferredcites →https://doi.org/10.1016/j.jacceco.2017.07.002openalex-snapshot
The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation
inferredcites →https://doi.org/10.1016/j.procs.2018.10.229openalex-snapshot
Comprehensive identification of operational risk factors based on textual risk disclosures
inferredcites →https://doi.org/10.1017/pan.2019.11openalex-snapshot
Why Propensity Scores Should Not Be Used for Matching
inferredcites →https://doi.org/10.1023/a:1027351630866openalex-snapshot
Got Information? Investor Response to Form 10-K and Form 10-Q EDGAR Filings
inferredcites →https://doi.org/10.1073/pnas.1804597116openalex-snapshot
Metalearners for estimating heterogeneous treatment effects using machine learning
inferredcites →https://doi.org/10.1080/01621459.2017.1319839openalex-snapshot
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
inferredcites →https://doi.org/10.1093/biomet/70.1.41openalex-snapshot
The central role of the propensity score in observational studies for causal effects
inferredcites →https://doi.org/10.1108/dta-05-2022-0215openalex-snapshot
Do SEC filings indicate any trends? Evidence from the sentiment distribution of forms 10-K and 10-Q with FinBERT
inferredcites →https://doi.org/10.1108/jaar-07-2020-0137openalex-snapshot
Does CFO accounting expertise matter to corporate social responsibility disclosure in 10-Ks?
inferredcites →https://doi.org/10.1109/ssci.2017.8280945openalex-snapshot
Comparative text analytics via topic modeling in banking
inferredcites →https://doi.org/10.1111/1911-3838.12150openalex-snapshot
Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10‐K Filings
inferredcites →https://doi.org/10.1111/ectj.12097openalex-snapshot
Double/debiased machine learning for treatment and structural parameters
inferredcites →https://doi.org/10.1111/j.1475-679x.2010.00382.xopenalex-snapshot
The Information Content of Forward‐Looking Statements in Corporate Filings—A Naïve Bayesian Machine Learning Approach
inferredcites →https://doi.org/10.1111/j.1475-679x.2012.00472.xopenalex-snapshot
A Measure of Competition Based on 10‐K Filings
inferredcites →https://doi.org/10.1126/sciadv.abg2652openalex-snapshot
How to make causal inferences using texts
inferredcites →https://doi.org/10.1145/2019618.2019624openalex-snapshot
A Multi-Label Text Classification Algorithm for Labeling Risk Factors in SEC Form 10-K
inferredcites →https://doi.org/10.1145/3220547.3220555openalex-snapshot
Analysis of year-over-year changes in Risk Factors Disclosure in 10-K filings
inferredcites →https://doi.org/10.1177/00027162221083877openalex-snapshot
Changing Americans’ Attitudes about Immigration: Using Moral Framing to Bolster Factual Arguments
inferredcites →https://doi.org/10.1177/00943061231214609oopenalex-snapshot
Text as Data: A New Framework for Machine Learning and the Social Sciences
inferredcites →https://doi.org/10.1214/17-sts612openalex-snapshot
Estimation of Causal Effects with Multiple Treatments: A Review and New Ideas
inferredcites →https://doi.org/10.1257/aer.p20171000openalex-snapshot
Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs
inferredcites →https://doi.org/10.1287/mnsc.2020.3682openalex-snapshot
A Text-Based Analysis of Corporate Innovation
inferredcites →https://doi.org/10.1287/mnsc.2022.01468openalex-snapshot
How Much Can Machines Learn Finance from Chinese Text Data?
inferredcites →https://doi.org/10.1353/obs.2019.0001openalex-snapshot
Estimating Treatment Effects with Causal Forests: An Application
inferredcites →https://doi.org/10.2139/ssrn.3389884openalex-snapshot
Predicting Returns with Text Data
inferredcites →https://doi.org/10.2139/ssrn.4670320openalex-snapshot
Text as treatment from Form 10-K: Causal effects of perceived natural hazards on firm profitability
inferredcites →https://doi.org/10.2307/1912705openalex-snapshot
Root-N-Consistent Semiparametric Regression
inferredcites →https://doi.org/10.3386/w23564openalex-snapshot
Double/Debiased Machine Learning for Treatment and Structural Parameters
inferredcites →https://doi.org/10.48550/arxiv.2108.13518openalex-snapshot
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
inferredcites →https://doi.org/10.5860/choice.38-1494aopenalex-snapshot
Causality: models, reasoning, and inference
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