|
on Big Data |
| By: | Elliot Beck; Franziska Eckert; Linus Kühne; Helge Liebert; Rina Rosenblatt-Wisch |
| Abstract: | We develop a resource-efficient methodology for measuring economic outlook in news text that combines document embeddings with synthetic training data generated by large language models. Applied to 27 million news articles, the resulting indicator significantly improves GDP growth forecast accuracy and captures sentiment shifts weeks before official releases, proving particularly valuable during crises. The indicator outperforms both survey-based benchmarks and traditional dictionary methods and is interpretable, allowing identification of specific drivers of economic sentiment. Our approach addresses key institutional constraints: it performs sentiment classification locally, enabling analyses of proprietary news content without transmission to external services while requiring minimal computational resources compared to direct large language model classification. |
| Keywords: | Sentiment analysis, Economic outlook, Forecasting, Big data, Large language models, Natural language processing, Neural networks |
| JEL: | E66 C45 C55 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:snb:snbwpa:2026-04 |
| By: | Pietro Bini; Lin William Cong; Xing Huang; Lawrence J. Jin |
| Abstract: | Do generative AI models, particularly large language models (LLMs), exhibit systematic behavioral biases in economic and financial decisions? If so, how can these biases be mitigated? Drawing on the cognitive psychology and experimental economics literatures, we conduct the most comprehensive set of experiments to date$-$originally designed to document human biases$-$on prominent LLM families across model versions and scales. We document systematic patterns in LLM behavior. In preference-based tasks, responses become more human-like as models become more advanced or larger, while in belief-based tasks, advanced large-scale models frequently generate rational responses. Prompting LLMs to make rational decisions reduces biases. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.09362 |
| By: | Yijie Wang; Hao Gao; Campbell R. Harvey; Yan Liu; Xinyuan Tao |
| Abstract: | The standard approach to portfolio selection involves two stages: forecast the asset returns and then plug them into an optimizer. We argue that this separation is deeply problematic. The first stage treats cross-sectional prediction errors as equally important across all securities. However, given that final portfolios might differ given distinct risk preferences and investment restrictions, the standard approach fails to recognize that the investor is not just concerned with the average forecast error - but the precision of the forecasts for the specific assets that are most important for their portfolio. Hence, it is crucial to integrate the two stages. We propose a novel implementation utilizing machine learning tools that unifies the expected return generation process and the final optimized portfolio. Our empirical example provides convincing evidence that our end-to-end method outperforms the traditional two-stage approach. In our framework, each investor has their own, endogenously determined, efficient frontier that depends on risk preferences, investor-specific constraints, as well as exposure to market frictions. |
| JEL: | C45 C55 G11 G12 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34861 |
| By: | Hainaut, Donatien (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | This article introduces a novel in-processing method for integrating actuarial and equity fairness into neural networks used for actuarial valuation. We consider one primary network penalized during training to ensure balanced predictions (actuarial fairness) and independence from sensitive features (equity fairness). Global and local actuarial equilibrium is obtained by aligning the inter-quantile averages of predicted and observed responses. Meanwhile, a second auxiliary network penalizes the primary network for discriminatory predictions. The combined training algorithm eectively preserves predictive accuracy while mitigating discrimination. Numerical illustrations on real-world datasets demonstrate the method's ecacy in achieving fair and reliable insurance pricing models. |
| Keywords: | Neural network ; equity fairness ; actuarial fairness ; non-life pricing |
| Date: | 2025–05–12 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2025011 |
| By: | Stephan Ludwig; Peter J. Danaher; Xiaohao Yang; Yu-Ting Lin; Ehsan Abedin; Dhruv Grewal; Lan Du |
| Abstract: | Accurately measuring consumer emotions and evaluations from unstructured text remains a core challenge for marketing research and practice. This study introduces the Linguistic eXtractor (LX), a fine-tuned, large language model trained on consumer-authored text that also has been labeled with consumers' self-reported ratings of 16 consumption-related emotions and four evaluation constructs: trust, commitment, recommendation, and sentiment. LX consistently outperforms leading models, including GPT-4 Turbo, RoBERTa, and DeepSeek, achieving 81% macro-F1 accuracy on open-ended survey responses and greater than 95% accuracy on third-party-annotated Amazon and Yelp reviews. An application of LX to online retail data, using seemingly unrelated regression, affirms that review-expressed emotions predict product ratings, which in turn predict purchase behavior. Most emotional effects are mediated by product ratings, though some emotions, such as discontent and peacefulness, influence purchase directly, indicating that emotional tone provides meaningful signals beyond star ratings. To support its use, a no-code, cost-free, LX web application is available, enabling scalable analyses of consumer-authored text. In establishing a new methodological foundation for consumer perception measurement, this research demonstrates new methods for leveraging large language models to advance marketing research and practice, thereby achieving validated detection of marketing constructs from consumer data. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.15312 |
| By: | Marc Schmitt |
| Abstract: | I construct a Market Stress Probability Index (MSPI) that estimates the probability of high stress in the U.S. equity market one month ahead using information from the cross-section of individual stocks. Using CRSP daily data, each month is summarized by a set of interpretable cross-sectional fragility signals and mapped into a forward-looking stress probability via an L1-regularized logistic regression in a real-time expanding-window design. Out of sample, MSPI tracks major stress episodes and improves discrimination and accuracy relative to a parsimonious benchmark based on lagged market return and realized volatility, delivering calibrated stress probabilities on an economically meaningful scale. Further, I illustrate how MSPI can be used as a probability-based measurement object in financial econometrics. The resulting index provides a transparent and easily updated measure of near-term equity-market stress risk. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.07066 |
| By: | Sara Caicedo-Silva (Universidad de los Andes) |
| Abstract: | This paper examines if and how economic ideas spread across political language. The publication of the TV Series and the book Free to Choose (FC) by Milton and Rosa Friedman in 1980 serves as a tool to understand how economic ideas are popularized and adopted by politicians. Using natural language models, I compute the semantic similarity between FC and the interventions in congressional records from 1975 to 1985 to assess the change in political debate speeches in the US. I find that Democratic legislators increasingly adopted the rhetorical framing of FC, reaching or even surpassing Republicans in the similarity of their speeches relative to FC. This convergence was especially strong in debates on macroeconomic policy and foreign trade. These results suggest that FC amplified existing liberal ideas and transformed them into a shared language of both advocacy and critique within Congress. |
| Keywords: | Political Discourse, Text-as-Data, Semantic Similarity, U.S. Congress |
| JEL: | B25 B41 D72 P16 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:col:000089:022309 |
| By: | Lauren Cohen; Yiwen Lu; Quoc H. Nguyen |
| Abstract: | We use frontier advancements in Artificial Intelligence and machine learning to extract and classify the part of key economic agents’ behaviors that are predictable from past behaviors. Even the agents themselves might view these as novel (innovative) decisions; however, we show in strong contrast that a large percentage of these actions and behaviors can be predicted—and thus mimicked—in the absence of these individuals. In particular, we show that 71% of mutual fund managers’ trade directions can be predicted in the absence of the agent making a single trade. For some managers, this increases to nearly all of their trades in a given quarter. Further, we find that manager behavior is more predictable and replicable for managers who have a longer history of trading and are in less competitive categories. The larger the ownership stake of the manager in the fund, the less predictable their behavior. Lastly, we show strong performance implications: less predictable managers strongly outperform their peers, while the most predictable managers significantly underperform. Even within each manager's portfolio, those stock positions that are more difficult to predict strongly outperform those that are easier to predict. Aggregating across the universe of fund managers each quarter, stocks whose position changes are least predictable additionally significantly outperform stocks whose position changes are most predictable across the universe. Our framework allows researchers to delineate and classify the portion of financial agents’ action sets which are predictable from those which are novel responses to stimuli -- open to being evaluated for value creation or destruction. |
| JEL: | C45 C53 C55 C82 G11 G23 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34849 |
| By: | Omri Feldman; Amar Venugopal; Jann Spiess; Amir Feder |
| Abstract: | Understanding the causal effects of text on downstream outcomes is a central task in many applications. Estimating such effects requires researchers to run controlled experiments that systematically vary textual features. While large language models (LLMs) hold promise for generating text, producing and evaluating controlled variation requires more careful attention. In this paper, we present an end-to-end pipeline for the generation and causal estimation of latent textual interventions. Our work first performs hypothesis generation and steering via sparse autoencoders (SAEs), followed by robust causal estimation. Our pipeline addresses both computational and statistical challenges in text-as-treatment experiments. We demonstrate that naive estimation of causal effects suffers from significant bias as text inherently conflates treatment and covariate information. We describe the estimation bias induced in this setting and propose a solution based on covariate residualization. Our empirical results show that our pipeline effectively induces variation in target features and mitigates estimation error, providing a robust foundation for causal effect estimation in text-as-treatment settings. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.15730 |
| By: | Thomas R. Cook; Sophia Kazinnik; Zach Modig; Nathan M. Palmer |
| Abstract: | Large language models (LLMs) are now used for economic reasoning, but their implicit "preferences" are poorly understood. We study these preferences by analyzing revealed choices in canonical allocation games and a sequential job-search environment. In dictator-style allocation games, most models favor equal splits, consistent with inequality aversion. Structural estimation of Fehr-Schmidt parameters suggests this aversion exceeds levels typically observed in human experiments. However, LLM preferences prove malleable. Interventions such as prompt framing (e.g., masking social context) and control vectors reliably shift models toward more payoff-maximizing behavior, while persona-based prompting has more limited impact. We then extend our analysis to a sequential decision-making environment based on the McCall job search model. Here, we recover implied discount factors from accept/reject behavior, but find that responses are less consistently rationalizable and preferences more fragile. Our findings highlight two core insights: (i) LLMs exhibit structured, latent preferences that often align with human behavioral norms, and (ii) these preferences can be steered, albeit more effectively in simple settings than in complex, dynamic ones. |
| Keywords: | Behavioral economics; Game theory; Search and matching models |
| JEL: | C63 C68 C61 D14 D83 D91 E20 E21 |
| Date: | 2026–01–30 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:102439 |