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The Perks and Perils of Machine Learning in Business and Economic Research
16 references·0 citing·50 second hop
The Perks and Perils of Machine Learning in Business and Economic Research (2024)The Perks and Perils of Machine Learning …Artificial intelligence and illusions of understanding in scientific research (2024)Artificial intelligence and i…Leakage and the reproducibility crisis in machine-learning-based science (2023)Leakage and the reproducibili…Reproducibility in machine learning for health research: Still a ways to go (2021)Reproducibility in machine le…Empirical Asset Pricing via Machine Learning (2020)Empirical Asset Pricing via M…Text as Data (2019)Text as DataLarge teams develop and small teams disrupt science and technology (2019)Large teams develop and small…Deep learning in business analytics and operations research: Models, applications and managerial implications (2019)Deep learning in business ana…State of the Art: Reproducibility in Artificial Intelligence (2018)State of the Art: Reproducibi…Prediction and explanation in social systems (2017)Prediction and explanation in…Evaluating replicability of laboratory experiments in economics (2016)Evaluating replicability of l…Team size matters: Collaboration and scientific impact since 1900 (2014)Team size matters: Collaborat…Big Data: New Tricks for Econometrics (2014)Big Data: New Tricks for Econ…False-Positive Psychology (2011)False-Positive PsychologyThe effect of scholar collaboration on impact and quality of academic papers (2010)The effect of scholar collabo…Detecting Management Fraud in Public Companies (2010)Detecting Management Fraud in…Scientific teams and institutional collaborations: Evidence from U.S. universities, 1981–1999 (2005)Scientific teams and institut…Little Science, Big Science (1963)What is research collaboration? (1997)The Increasing Dominance of Teams in Production of Knowledge (2007)Characterizing Social Media Metrics of Scholarly Papers: The Effect of Document Properties and Collaboration Patterns (2015)Exploratory Research (2020)The unequal impact of parenthood in academia (2021)Why Are There Still So Many Jobs? The History and Future of Workplace Automation (2015)Human Decisions and Machine Predictions (2017)American Economic Review (2019)Forecasting: theory and practice (2022)Biased evaluation and persistence in gambling. (1983)The case for motivated reasoning. (1990)Why Most Published Research Findings Are False (2005)Power failure: why small sample size undermines the reliability of neuroscience (2013)The New Statistics (2013)z-Tree: Zurich toolbox for ready-made economic experiments (2007)Estimating the reproducibility of psychological science (2015)A Unified Welfare Analysis of Government Policies* (2020)Financial education affects financial knowledge and downstream behaviors (2021)Replicability, Robustness, and Reproducibility in Psychological Science (2021)Twitter mood predicts the stock market (2011)When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks (2011)Measuring Economic Policy Uncertainty* (2016)Random forests for global sensitivity analysis: A selective review (2020)Climate Change Concerns and the Performance of Green vs. Brown Stocks (2022)Dissecting climate risks: Are they reflected in stock prices? (2023)Exploration and Exploitation in Organizational Learning (1991)Atypical Combinations and Scientific Impact (2013)Evolutionary dynamics of higher-order interactions in social networks (2021)Social physics (2022)Papers and patents are becoming less disruptive over time (2023)Moral Hazard in Teams (1982)Endogenous Technological Change (1989)Endogenous Technological Change (1990)Research collaboration at a distance: Changing spatial patterns of scientific collaboration within Europe (2010)Bias against novelty in science: A cautionary tale for users of bibliometric indicators (2017)Common risk factors in the returns on stocks and bonds (1993)Comparing Predictive Accuracy (1994)A five-factor asset pricing model (2014)Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis (2021)Machine learning advances for time series forecasting (2021)Machine learning in the Chinese stock market (2021)Integrating explanation and prediction in computational social science (2021)Is AI leading to a reproducibility crisis in science? (2023)REFORMS: Consensus-based Recommendations for Machine-learning-based Science (2024)Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective (1988)Situated Learning (1991)CRITICAL QUESTIONS FOR BIG DATA (2012)The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2024)Generative Artificial Intelligence (GenAI) in the research process – A survey of researchers’ practices and perceptions (2025)
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References

  • 2011False-Positive Psychology6,561 cited
  • 2020Empirical Asset Pricing via Machine Learning2,528 cited
  • 2014Big Data: New Tricks for Econometrics1,574 cited
  • 2016Evaluating replicability of laboratory experiments in economics1,314 cited
  • 2023Leakage and the reproducibility crisis in machine-learning-based science1,313 cited
  • 2019Text as Data1,260 cited
  • 2019Large teams develop and small teams disrupt science and technology1,040 cited
  • 2024Artificial intelligence and illusions of understanding in scientific research699 cited
  • 2005Scientific teams and institutional collaborations: Evidence from U.S. universities, 1981–1999550 cited
  • 2014Team size matters: Collaboration and scientific impact since 1900479 cited
  • 2017Prediction and explanation in social systems429 cited
  • 2019Deep learning in business analytics and operations research: Models, applications and managerial implications391 cited
  • 2010Detecting Management Fraud in Public Companies369 cited
  • 2018State of the Art: Reproducibility in Artificial Intelligence292 cited
  • 2010The effect of scholar collaboration on impact and quality of academic papers287 cited

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  • 2021Reproducibility in machine learning for health research: Still a ways to go270 cited
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