California Lutheran University From Individual Decisions to Collective Outcomes

References

The 181 works cited in the review: 155 from the review’s corpus and 26 background references. Each entry links to where it is cited.

Verification labels follow Online Resource 1 and concern bibliographic identity. They do not independently verify the findings attributed to a source.

  1. Agliata, N., & Hasso, T. (2026). Generative AI as an Investment Advisor: Same Client, Different Advice. FinTech, 5, 54. https://doi.org/10.3390/fintech5020054

    d In the corpus (LLMF-0372) Bibliographic check: Verified Cited in §4.4

  2. Agrawal, K., Teo, V., Vazquez, J. J., et al. (2025). Evaluating LLM Agent Collusion in Double Auctions (arXiv:2507.01413). arXiv. https://arxiv.org/abs/2507.01413

    b In the corpus (LLMF-0109) Bibliographic check: Verified Cited in §6.2, §6.5

  3. Andrews, I., & Sarkar, S. (2026). Dutch Books for Language Models (arXiv:2609.02797). arXiv. https://arxiv.org/abs/2609.02797

    d In the corpus (LLMF-0296) Bibliographic check: Verified Cited in §4.5, §7.2

  4. Apte, M. (2026). Do LLM Trading Agents Herd? A Market Microstructure Stress Test. In 2026 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) (pp. 61–66). IEEE. https://doi.org/10.1109/cifer67845.2026.11692423

    a In the corpus (LLMF-0402) Bibliographic check: Verified Cited in §1

  5. Arksey, H., & O’Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8, 19–32. https://doi.org/10.1080/1364557032000119616

    Background reference Bibliographic check: not checked in the corpus audit Cited in §2.1

  6. Axtell, R. L., & Farmer, J. D. (2025). Agent-Based Modeling in Economics and Finance: Past, Present, and Future. Journal of Economic Literature, 63, 197–287. https://doi.org/10.1257/jel.20221319

    Background reference Bibliographic check: not checked in the corpus audit Cited in §3.1

  7. Bäckman, C. (2026). One advisor for the whole world? Cross-country evidence on financial advice from large language models (SAFE Working Paper No. 490). https://hdl.handle.net/10419/343061

    d In the corpus (LLMF-0425) Bibliographic check: Verified Cited in §4.4, §6.5, §9

  8. Bailey, D. H., & López de Prado, M. (2014). The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting, and Non-Normality. The Journal of Portfolio Management, 40, 94–107. https://doi.org/10.3905/jpm.2014.40.5.094

    Background reference Bibliographic check: not checked in the corpus audit Cited in §7.1

  9. Barton, T. J., Constantakis, C., Hauseman, P., et al. (2026a). Operating-Layer Controls for Onchain Language-Model Agents Under Real Capital (arXiv:2604.26091). arXiv. https://arxiv.org/abs/2604.26091

    c In the corpus (LLMF-0234) Bibliographic check: Verified Cited in §5

  10. Barton, T. J., Constantakis, C., Hauseman, P., et al. (2026b). What LLM Trading Agents Actually Do in Production: A Six-Month, Population-Scale Record from Two Fleets (arXiv:2609.05663). arXiv. https://arxiv.org/abs/2609.05663

    c In the corpus (LLMF-0300) Bibliographic check: Verified Cited in §1, §2.3, §5, §6.3, §6.5, §8, §9

  11. Basu, A., & Chakraborty, P. (2026). When Names Change Verdicts: Intervention Consistency Reveals Systematic Bias in LLM Decision-Making (arXiv:2603.18530). arXiv. https://arxiv.org/abs/2603.18530

    d In the corpus (LLMF-0428) Bibliographic check: Verified Cited in §4.4

  12. Batra, D., Hamill, C., Hartley, J., et al. (2025). A Review of LLM Agent Applications in Finance and Banking (SSRN Working Paper 5381584, version posted August 14, 2025). https://doi.org/10.2139/ssrn.5381584

    c In the corpus (LLMF-0535) Bibliographic check: Verified Cited in §1

  13. Begin, J., Gho, B., Muppavarapu, S., et al. (2026). Preference Optimization Drives Monoculture in LLM Prediction Markets (arXiv:2606.26583). arXiv. https://arxiv.org/abs/2606.26583

    b In the corpus (LLMF-0570) Bibliographic check: Verified Cited in §1, §6.5, §8

  14. Benhenda, M. (2026). Look-Ahead-Bench: a Standardized Benchmark of Look-ahead Bias in Point-in-Time LLMs for Finance (arXiv:2601.13770). arXiv. https://arxiv.org/abs/2601.13770

    e In the corpus (LLMF-0190) Bibliographic check: Verified Cited in §7.1, §9

  15. Bertomeu, J., Cheynel, E., Lunawat, R., & Milone, M. (2026). On humans and AI: A financial reporting dilemma (MPRA Paper No. 128775). https://mpra.ub.uni-muenchen.de/128775/

    d In the corpus (LLMF-0410) Bibliographic check: Verified Cited in §7.4

  16. Bhagwat, V., Cookson, J. A., Dim, C., & Niessner, M. (2025). The Market’s Mirror: Revealing Investor Disagreement with LLMs (SSRN Working Paper 5375473, draft of July 2025). https://doi.org/10.2139/ssrn.5375473

    a In the corpus (LLMF-0588) Bibliographic check: Verified Cited in §4.5

  17. Biancotti, C., Camassa, C., Coletta, A., et al. (2025). Chat Bankman-Fried: an Exploration of LLM Alignment in Finance (arXiv:2411.11853v3). arXiv. https://arxiv.org/abs/2411.11853v3

    d In the corpus (LLMF-0057) Bibliographic check: Verified Cited in §4.4

  18. Bikhchandani, S., & Sharma, S. (2001). Herd behavior in financial markets. IMF Staff Papers, 47, 279–310. https://www.imf.org/external/pubs/ft/staffp/2001/01/pdf/bikhchan.pdf

    Background reference Bibliographic check: not checked in the corpus audit Cited in §6.5

  19. Bini, P., Cong, L. W., Huang, X., & Jin, L. J. (2026). Behavioral Economics of AI: LLM Biases and Corrections (arXiv:2602.09362). arXiv. https://arxiv.org/abs/2602.09362

    a In the corpus (LLMF-0197) Bibliographic check: Verified Cited in §4.1, §4.2, §7.4

  20. Borjigin, Y., Hermann, A., Cyron, C., & Aydin, R. (2026). AnchorBench: A Multi-Pathway Benchmark for the Anchoring Effect in LLMs (arXiv:2608.14320). arXiv. https://arxiv.org/abs/2608.14320

    a In the corpus (LLMF-0283) Bibliographic check: Verified Cited in §4.3

  21. Bose, J. (2026). Recognition, Simulation, and Refusal: A Contamination-Aware Study of Classic Psychological Effects in LLM Agents (arXiv:2609.22090). arXiv. https://arxiv.org/abs/2609.22090

    e In the corpus (LLMF-0306) Bibliographic check: Verified Cited in §4.2, §7.1, §9

  22. Bowles, B., Duch, R. M., & Sorescu, S. M. (2026). Talking to Digital Twins: Selective Disclosure and Belief Measurement in Financial Social Media (arXiv:2608.01181). arXiv. https://arxiv.org/abs/2608.01181

    e In the corpus (LLMF-0276) Bibliographic check: Verified Cited in §7.2, §8, §9

  23. Brock, W. A., & Hommes, C. H. (1998). Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control, 22, 1235–1274. https://doi.org/10.1016/s0165-1889(98)00011-6

    Background reference Bibliographic check: not checked in the corpus audit Cited in §1, §3.1

  24. Buchanan, J., & Foster, J. (2026). The Innate Economic Preferences of Language Models (arXiv:2607.26288). arXiv. https://arxiv.org/abs/2607.26288

    a In the corpus (LLMF-0273) Bibliographic check: Verified with recorded discrepancy Cited in §4.1

  25. Bybee, L. (2023). Surveying Generative AI’s Economic Expectations (arXiv:2305.02823v2). arXiv. https://arxiv.org/abs/2305.02823v2

    a In the corpus (LLMF-0404) Bibliographic check: Verified Cited in §4.5, §8

  26. Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2020). Artificial Intelligence, Algorithmic Pricing, and Collusion. American Economic Review, 110, 3267–3297. https://doi.org/10.1257/aer.20190623

    Background reference Bibliographic check: not checked in the corpus audit Cited in §6.2

  27. Cartea, Á., Chang, P., Chen, N., & Zhong, M. (2026). AI Bubbles with Large Language Models (SSRN Working Paper 6426761). https://doi.org/10.2139/ssrn.6426761

    b In the corpus (LLMF-0371) Bibliographic check: Verified Cited in §6.3

  28. Chaboud, A. P., Chiquoine, B., Hjalmarsson, E., & Vega, C. (2014). Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market. The Journal of Finance, 69, 2045–2084. https://doi.org/10.1111/jofi.12186

    Background reference Bibliographic check: not checked in the corpus audit Cited in §6.5

  29. Chen, J., Yuan, S., Ye, R., Majumder, B. P., & Richardson, K. (2024). Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena (arXiv:2310.05746v4). arXiv. https://arxiv.org/abs/2310.05746v4

    c In the corpus (LLMF-0013) Bibliographic check: Verified Cited in §9

  30. Chen, S., Green, T. C., Gulen, H., & Zhou, D. (2024). What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts (arXiv:2409.11540). arXiv. https://arxiv.org/abs/2409.11540

    d In the corpus (LLMF-0041) Bibliographic check: Verified Cited in §4.2, §4.5

  31. Chen, Y., Liu, T. X., Shan, Y., & Zhong, S. (2023). The Emergence of Economic Rationality of GPT (arXiv:2305.12763v3). arXiv. https://arxiv.org/abs/2305.12763v3. Published version: Proceedings of the National Academy of Sciences 120(51):e2316205120 (2023). https://doi.org/10.1073/pnas.2316205120

    a In the corpus (LLMF-0005) Bibliographic check: Verified Cited in §1, §4.1

  32. Cheng, G., Jin, H., Zhang, W., et al. (2026). Learning to Conceal Risk: Controllable Multi-turn Red Teaming for LLMs in the Financial Domain (arXiv:2509.10546v2). arXiv. https://arxiv.org/abs/2509.10546v2. Published version: Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 41005–41020 (2026). https://doi.org/10.18653/v1/2026.acl-long.1903

    d In the corpus (LLMF-0422) Bibliographic check: Verified Cited in §4.4

  33. Chupilkin, M. (2026). Artificial Institutions: How Institutional Design Shapes LLM Simulations (arXiv:2608.04020). arXiv. https://arxiv.org/abs/2608.04020

    b In the corpus (LLMF-0569) Bibliographic check: Verified Cited in §1, §6.2, §6.5, §8, §9

  34. Cook, T. R., & Kazinnik, S. (2025). Social Group Bias in AI Finance (arXiv:2506.17490). arXiv. https://arxiv.org/abs/2506.17490

    d In the corpus (LLMF-0106) Bibliographic check: Verified Cited in §4.4

  35. Coqueret, G., Llull, J., Oswald, F., et al. (2026). Randomness in large language models: What researchers need to know (and report) (arXiv:2607.24372). arXiv. https://arxiv.org/abs/2607.24372

    e In the corpus (LLMF-0272) Bibliographic check: Verified Cited in §7.3, §8, §9

  36. Csaszar, F. A., Peterson, A., & Wilde, D. (2026). The Strategic Foresight of LLMs: Evidence from a Fully Prospective Venture Tournament (arXiv:2602.01684). arXiv. https://arxiv.org/abs/2602.01684

    d In the corpus (LLMF-0426) Bibliographic check: Verified Cited in §4.5

  37. Daníelsson, J., Macrae, R., & Uthemann, A. (2022). Artificial intelligence and systemic risk. Journal of Banking & Finance, 140, 106290. https://doi.org/10.1016/j.jbankfin.2021.106290

    Background reference Bibliographic check: not checked in the corpus audit Cited in §1, §6.5

  38. de Castro Soares, D., de Freitas Bittencourt, L. E. B., & Tessmann, M. S. (2026). Rationality and financial risk aversion in large language models: Evidence from a multi-model Holt–Laury experiment. Journal of Behavioral and Experimental Finance, 51, 101235. https://doi.org/10.1016/j.jbef.2026.101235

    a In the corpus (LLMF-0322) Bibliographic check: Verified Cited in §4.1, §7.4

  39. del Rio-Chanona, R. M., Pangallo, M., & Hommes, C. (2025). Can Generative AI agents behave like humans? Evidence from laboratory market experiments (arXiv:2505.07457). arXiv. https://arxiv.org/abs/2505.07457

    b In the corpus (LLMF-0094) Bibliographic check: Verified Cited in §6.1, §6.5, §7.1

  40. Dong, T., Kunievsky, N., & Evans, J. A. (2026). Measuring Behavior Portability in Large Language Models (arXiv:2606.22797). arXiv. https://arxiv.org/abs/2606.22797

    e In the corpus (LLMF-0262) Bibliographic check: Verified Cited in §7.2, §9

  41. Dong, Y., Wu, F., Zhang, K., et al. (2025). Large Language Model Agents in Finance: A Survey Bridging Research, Practice, and Real-World Deployment. In Findings of the Association for Computational Linguistics: EMNLP 2025 (pp. 17889–17907). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-emnlp.972

    c In the corpus (LLMF-0393) Bibliographic check: Verified Cited in §1

  42. Duan, Q., Li, C., Wang, C., et al. (2026). Can Agentic Trading Systems Pay for Their Own Intelligence? (arXiv:2607.10286v2). arXiv. https://arxiv.org/abs/2607.10286v2

    c In the corpus (LLMF-0267) Bibliographic check: Verified Cited in §2.3, §7.5

  43. Engel, C., Großmann, M. R. P., & Ockenfels, A. (2025). Integrating machine behavior into human subject experiments: a user-friendly toolkit and an application to framed prisoner’s dilemmas. Experimental Economics, 28, 1085–1100. https://doi.org/10.1017/eec.2025.10038

    e In the corpus (LLMF-0328) Bibliographic check: Verified Cited in §7.4, §9

  44. Erdem, O., & Ashok, R. P. (2025). Artificial Finance: How AI Thinks About Money (arXiv:2507.10933). arXiv. https://arxiv.org/abs/2507.10933. Published version: LLMs are not weird: Comparing AI and human financial decision-making. Journal of Behavioral and Experimental Economics 120:102505 (2026). https://doi.org/10.1016/j.socec.2025.102505

    a In the corpus (LLMF-0116) Bibliographic check: Verified Cited in §4.1

  45. Fedyk, A., Kakhbod, A., Li, P., & Malmendier, U. (2025). AI and perception biases in investments: An experimental study (Working paper, draft of June 2025). https://eml.berkeley.edu/~ulrike/Papers/AI_PerceptionBias_aug2025.pdf. Published version: Journal of Financial Economics 185:104350 (2026). https://doi.org/10.1016/j.jfineco.2026.104350

    d In the corpus (LLMF-0324) Bibliographic check: Verified Cited in §4.4

  46. Feng, Y., Choudhary, V., & Shrestha, Y. R. (2025). Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Making. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 7693–7706). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.emnlp-main.391

    a In the corpus (LLMF-0129) Bibliographic check: Verified Cited in §4.1, §6.5, §8, §9

  47. Feuerriegel, S., Barrie, C., Crockett, M. J., et al. (2026). A reporting checklist for large language models in behavioural science. Nature Human Behaviour, 10, 1182–1186. https://doi.org/10.1038/s41562-026-02492-7

    Background reference Bibliographic check: not checked in the corpus audit Cited in §9

  48. Foltyn, R., & Olsson, J. (2026). The Worth of a “Wo”: Gender Bias in Financial Advice from LLMs (NHH Department of Economics Discussion Paper SAM 04/2026). https://nva.sikt.no/registration/019c9dd82846-c8bcfc07-b8da-430f-8a58-7c1eb56a4449

    d In the corpus (LLMF-0427) Bibliographic check: Verified Cited in §4.4

  49. Fu, W. (2025). The New Quant: A Survey of Large Language Models in Financial Prediction and Trading (arXiv:2510.05533). arXiv. https://arxiv.org/abs/2510.05533

    c In the corpus (LLMF-0144) Bibliographic check: Verified Cited in §1

  50. Galanis, S. (2026). Information Aggregation with AI Agents (arXiv:2604.20050v3). arXiv. https://arxiv.org/abs/2604.20050v3

    b In the corpus (LLMF-0228) Bibliographic check: Verified Cited in §6.2, §6.4, §6.5, §8

  51. Gamble, K. J., & Hummel, P. (2026). When Machines Think Fast: A Behavioral Economics Test of AI (SSRN Working Paper 6843005, version posted June 12, 2026). https://doi.org/10.2139/ssrn.6843005

    a In the corpus (LLMF-0587) Bibliographic check: Verified Cited in §1, §4.2, §4.5

  52. Gao, S., Wen, Y., Zhu, M., et al. (2024). Simulating Financial Market via Large Language Model based Agents (arXiv:2406.19966). arXiv. https://arxiv.org/abs/2406.19966

    b In the corpus (LLMF-0031) Bibliographic check: Verified Cited in §6.3, §8, §9

  53. Gao, T., Peng, L., Xu, Q., & Li, G. (2026). Heterogeneous-Agent Financial Market Simulation with Large Language Models. Procedia Computer Science, 287, 1016–1023. https://doi.org/10.1016/j.procs.2026.08.142

    b In the corpus (LLMF-0415) Bibliographic check: Verified Cited in §6.3, §8

  54. Gao, Y., Lee, D., Burtch, G., & Fazelpour, S. (2025). Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina (arXiv:2410.19599v3). arXiv. https://arxiv.org/abs/2410.19599v3. Published version: Take caution in using LLMs as human surrogates. Proceedings of the National Academy of Sciences 122(24):e2501660122 (2025). https://doi.org/10.1073/pnas.2501660122

    e In the corpus (LLMF-0049) Bibliographic check: Verified with recorded discrepancy Cited in §7.2, §7.3, §7.4, §7.5, §9

  55. Gao, Z., Jiang, W., & Yan, Y. (2026a). Debiasing LLMs by Fine-tuning (arXiv:2604.02921v2). arXiv. https://arxiv.org/abs/2604.02921v2

    e In the corpus (LLMF-0220) Bibliographic check: Verified Cited in §7.4

  56. Gao, Z., Jiang, W., & Yan, Y. (2026b). Detecting Lookahead Bias in LLM Forecasts (arXiv:2512.23847v2). arXiv. https://arxiv.org/abs/2512.23847v2

    e In the corpus (LLMF-0180) Bibliographic check: Verified Cited in §7.1

  57. Garcia, J. (2025). Homo Silicus is Hyper-Rational: Why LLM Agents Fail to Replicate Attention-Driven Trading (SSRN Working Paper 5901742, version revised February 16, 2026). https://doi.org/10.2139/ssrn.5901742

    a In the corpus (LLMF-0348) Bibliographic check: Verified Cited in §1, §4.2, §4.5

  58. Garcia, J. (2026a). Algorithmic Anchoring: How Prompt-Embedded Reference Points Bias LLM Financial Estimates (SSRN Working Paper 6366838, version posted March 9, 2026). https://doi.org/10.2139/ssrn.6366838

    d In the corpus (LLMF-0354) Bibliographic check: Verified Cited in §1, §4.3

  59. Garcia, J. (2026b). Are Aliases Neutral? Company-Name Form in LLM Equity Valuation (SSRN Working Paper 7532416). https://doi.org/10.2139/ssrn.7532416

    d In the corpus (LLMF-0527) Bibliographic check: Verified Cited in §7.1

  60. Garcia, J. (2026c). Which Number Is Sticky? What Survives Source Withdrawal in Sequential LLM Financial Analysis (SSRN Working Paper 7411618, version revised September 16, 2026). https://doi.org/10.2139/ssrn.7411618

    d In the corpus (LLMF-0430) Bibliographic check: Verified Cited in §4.3, §5

  61. Gençay, E. (2026). What survives honest evaluation? Leakage-safe, search-aware assessment of LLM-driven trading strategy discovery (arXiv:2608.27734). arXiv. https://arxiv.org/abs/2608.27734

    e In the corpus (LLMF-0291) Bibliographic check: Verified Cited in §7.1

  62. Gensler, G., & Bailey, L. (2020). Deep Learning and Financial Stability (SSRN Working Paper 3723132). https://doi.org/10.2139/ssrn.3723132

    Background reference Bibliographic check: not checked in the corpus audit Cited in §1, §6.5

  63. Gode, D. K., & Sunder, S. (1993). Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality. Journal of Political Economy, 101, 119–137. https://doi.org/10.1086/261868

    Background reference Bibliographic check: not checked in the corpus audit Cited in §1, §3.1, §6.2, §8

  64. Graham, J. R., Harvey, C. R., & Jha, M. (2026). CFOs Meet LLMs (arXiv:2606.13812v2). arXiv. https://arxiv.org/abs/2606.13812v2

    a In the corpus (LLMF-0257) Bibliographic check: Verified Cited in §4.5, §6.5, §7.2, §8, §9

  65. Greenwood, R., & Thesmar, D. (2011). Stock price fragility. Journal of Financial Economics, 102, 471–490. https://doi.org/10.1016/j.jfineco.2011.06.003

    Background reference Bibliographic check: not checked in the corpus audit Cited in §6.5

  66. Hansen, A. L., Horton, J. J., Kazinnik, S., et al. (2025). Simulating the Survey of Professional Forecasters (SSRN Working Paper 5066286, draft of February 2025). https://doi.org/10.2139/ssrn.5066286

    a In the corpus (LLMF-0590) Bibliographic check: Verified Cited in §4.5

  67. Hansen, A. L., & Lee, S. J. (2025). Financial Stability Implications of Generative AI: Taming the Animal Spirits (arXiv:2510.01451). arXiv. https://arxiv.org/abs/2510.01451. Also issued as Finance and Economics Discussion Series 2025-090, Board of Governors of the Federal Reserve System. https://doi.org/10.17016/feds.2025.090

    a In the corpus (LLMF-0139) Bibliographic check: Verified Cited in §1, §2.3, §4.2, §6.2, §6.4, §7.4, §9

  68. Hartley, J., Hamill, C. B., Seddon, D., et al. (2025). How Personality Traits Shape LLM Risk-Taking Behaviour. In Findings of the Association for Computational Linguistics: ACL 2025 (pp. 21068–21092). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-acl.1085

    a In the corpus (LLMF-0079) Bibliographic check: Verified Cited in §4.1

  69. Hashimoto, R., Takayanagi, T., Suzuki, M., & Izumi, K. (2025). Agent-Based Simulation of a Financial Market with Large Language Models (arXiv:2510.12189). arXiv. https://arxiv.org/abs/2510.12189. Published version: LLM agents reveal how human bias shapes path-dependent market dynamics. Journal of Computational Social Science 9(2):32 (2026). https://doi.org/10.1007/s42001-026-00465-4

    b In the corpus (LLMF-0150) Bibliographic check: Verified Cited in §1, §4.2, §4.5, §6.3

  70. He, S., Lv, L., Manela, A., & Wu, J. (2025). Instruction Tuning Chronologically Consistent Language Models (arXiv:2510.11677v2). arXiv. https://arxiv.org/abs/2510.11677v2

    e In the corpus (LLMF-0554) Bibliographic check: Verified Cited in §7.1

  71. Heemeijer, P., Hommes, C., Sonnemans, J., & Tuinstra, J. (2009). Price stability and volatility in markets with positive and negative expectations feedback: An experimental investigation. Journal of Economic Dynamics and Control, 33, 1052–1072. https://doi.org/10.1016/j.jedc.2008.09.009

    Background reference Bibliographic check: not checked in the corpus audit Cited in §3.1, §6.1, §6.5

  72. Henning, T., Ojha, S. M., Spoon, R., et al. (2025). LLM Agents Do Not Replicate Human Market Traders: Evidence From Experimental Finance (arXiv:2502.15800v3). arXiv. https://arxiv.org/abs/2502.15800v3

    b In the corpus (LLMF-0074) Bibliographic check: Verified Cited in §1, §6.1, §6.4, §6.5, §7.1, §7.5, §8, §9

  73. Hintze, A., Bisht, C., Schossau, J., & Hertwig, R. (2025). Nonlinear transformation of probabilities by large language models. Computers in Human Behavior: Artificial Humans, 6, 100227. https://doi.org/10.1016/j.chbah.2025.100227

    a In the corpus (LLMF-0551) Bibliographic check: Verified Cited in §4.1, §7.4

  74. Hirano, M. (2025). Building LLM-Based Artificial Market Simulations: Can LLMs Function as Agents in Multi-agent Simulations for Finance? In PRIMA 2025: Principles and Practice of Multi-Agent Systems (pp. 56–71). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-13562-9_5

    b In the corpus (LLMF-0347) Bibliographic check: Verified Cited in §6.3

  75. Hommes, C. (2011). The heterogeneous expectations hypothesis: Some evidence from the lab. Journal of Economic Dynamics and Control, 35, 1–24. https://doi.org/10.1016/j.jedc.2010.10.003

    Background reference Bibliographic check: not checked in the corpus audit Cited in §1, §3.1

  76. Hommes, C., Sonnemans, J., Tuinstra, J., & van de Velden, H. (2008). Expectations and bubbles in asset pricing experiments. Journal of Economic Behavior & Organization, 67, 116–133. https://doi.org/10.1016/j.jebo.2007.06.006

    Background reference Bibliographic check: not checked in the corpus audit Cited in §3.1, §6.1, §6.5

  77. Horton, J. J., Filippas, A., & Manning, B. S. (2026). Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus? (arXiv:2301.07543v2). arXiv. https://arxiv.org/abs/2301.07543v2. Published version (extended abstract): Proceedings of the 25th ACM Conference on Economics and Computation, pp 614–615 (2024). https://doi.org/10.1145/3670865.3673513

    a In the corpus (LLMF-0001) Bibliographic check: Verified with recorded discrepancy Cited in §1, §4.5

  78. Hu, X., & Zhao, J. (2026). Fin-Bias: Comprehensive Evaluation for LLM Decision-Making under human bias in Finance Domain (arXiv:2605.09106). arXiv. https://arxiv.org/abs/2605.09106. Published version: Findings of the Association for Computational Linguistics: ACL 2026, pp 5678–5694 (2026). https://doi.org/10.18653/v1/2026.findings-acl.279

    d In the corpus (LLMF-0240) Bibliographic check: Verified Cited in §4.3

  79. Huang, C., Chen, C., Lin, C., et al. (2026a). Probing Outcome-Level Resemblance and Mechanism-Level Alignment in LLM Risk Decisions: Evidence from the St. Petersburg Game (arXiv:2606.04978). arXiv. https://arxiv.org/abs/2606.04978

    a In the corpus (LLMF-0254) Bibliographic check: Verified Cited in §4.1

  80. Huang, Y., Bie, B., Na, Z., et al. (2026b). Understanding the Anchoring Effect of LLM with Synthetic Data: Existence, Mechanism, and Potential Mitigations (arXiv:2505.15392v2). arXiv. https://arxiv.org/abs/2505.15392v2

    a In the corpus (LLMF-0100) Bibliographic check: Verified Cited in §4.3

  81. Itzhak, I., Stanovsky, G., Rosenfeld, N., & Belinkov, Y. (2024). Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias (arXiv:2308.00225v2). arXiv. https://arxiv.org/abs/2308.00225v2. Published version: Transactions of the Association for Computational Linguistics 12:771–785 (2024). https://doi.org/10.1162/tacl_a_00673

    a In the corpus (LLMF-0539) Bibliographic check: Verified Cited in §4.1, §7.4

  82. Jadhav, A., & Mirza, V. (2025). Large Language Models in equity markets: applications, techniques, and insights. Frontiers in Artificial Intelligence, 8, 1608365. https://doi.org/10.3389/frai.2025.1608365

    c In the corpus (LLMF-0374) Bibliographic check: Verified Cited in §1

  83. Jia, J., & Yuan, Z. (2024). An Experimental Study of Competitive Market Behavior Through LLMs (arXiv:2409.08357v2). arXiv. https://arxiv.org/abs/2409.08357v2

    b In the corpus (LLMF-0040) Bibliographic check: Verified Cited in §6.1, §7.1

  84. Jia, J., Yuan, Z., Pan, J., et al. (2024). Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context (arXiv:2406.05972v2). arXiv. https://arxiv.org/abs/2406.05972v2. Published version: Advances in Neural Information Processing Systems 37, pp 113360–113382 (2024). https://doi.org/10.52202/079017-3601

    a In the corpus (LLMF-0029) Bibliographic check: Verified Cited in §4.1

  85. Jo, N., Garg, N., & Raghavan, M. (2026). The Subjectivity of Monoculture (arXiv:2602.24086v1). arXiv. https://arxiv.org/abs/2602.24086v1

    Background reference Bibliographic check: not checked in the corpus audit Cited in §6.5, §9

  86. Kazinnik, S. (2026). Bank Run, Interrupted: Modeling Deposit Withdrawals with Generative AI. Management Science. https://doi.org/10.1287/mnsc.2024.06807

    b In the corpus (LLMF-0403) Bibliographic check: Verified Cited in §6.3

  87. Khandani, A. E., & Lo, A. W. (2011). What happened to the quants in August 2007? Evidence from factors and transactions data. Journal of Financial Markets, 14, 1–46. https://doi.org/10.1016/j.finmar.2010.07.005

    Background reference Bibliographic check: not checked in the corpus audit Cited in §6.5

  88. Khatchadourian, R., & Franco, R. (2025). LLM Output Drift: Cross-Provider Validation & Mitigation for Financial Workflows (arXiv:2511.07585). arXiv. https://arxiv.org/abs/2511.07585

    e In the corpus (LLMF-0164) Bibliographic check: Verified Cited in §7.3

  89. Khoukhi, A., & Slimani, I. (2026). From Agent-Based Models to LLM-Based Financial Agents: A PRISMA-Based Systematic Review, Multidimensional Taxonomy, and Future Research Agenda (SSRN Working Paper 7493756). https://doi.org/10.2139/ssrn.7493756

    b In the corpus (LLMF-0358) Bibliographic check: Verified Cited in §1, §10

  90. Kim, A., Muhn, M., & Nikolaev, V. (2024). Financial Statement Analysis with Large Language Models (arXiv:2407.17866v2). arXiv. https://arxiv.org/abs/2407.17866v2. Withdrawn by the authors in arXiv version 3 (February 20, 2025); the text of version 2 was read.

    c In the corpus (LLMF-0035) Bibliographic check: Verified with recorded discrepancy Cited in §7.3

  91. Kim, E. M., Garg, A., Peng, K., & Garg, N. (2025). Correlated Errors in Large Language Models. In Proceedings of the 42nd International Conference on Machine Learning (pp. 30038–30066). PMLR. https://proceedings.mlr.press/v267/kim25e.html

    Background reference Bibliographic check: not checked in the corpus audit Cited in §6.5

  92. Kleinberg, J., & Raghavan, M. (2021). Algorithmic monoculture and social welfare. Proceedings of the National Academy of Sciences, 118, e2018340118. https://doi.org/10.1073/pnas.2018340118

    Background reference Bibliographic check: not checked in the corpus audit Cited in §3.1, §6.5, §9

  93. Knipper, R. A., Knipper, C. S., Zhang, K., et al. (2025). The Bias is in the Details: An Assessment of Cognitive Bias in LLMs (arXiv:2509.22856). arXiv. https://arxiv.org/abs/2509.22856

    a In the corpus (LLMF-0137) Bibliographic check: Verified Cited in §4.2

  94. Kong, Y., Lee, H., Hwang, Y., et al. (2026). Evaluating LLMs in Finance Requires Explicit Bias Consideration (arXiv:2602.14233). arXiv. https://arxiv.org/abs/2602.14233

    e In the corpus (LLMF-0199) Bibliographic check: Verified Cited in §7.5, §9

  95. Kong, Y., Nie, Y., Dong, X., et al. (2024). Large Language Models for Financial and Investment Management: Applications and Benchmarks. The Journal of Portfolio Management, 51, 162–210. https://doi.org/10.3905/jpm.2024.1.645

    c In the corpus (LLMF-0340) Bibliographic check: Verified Cited in §1

  96. Koviazin, A., Mudarisov, T., Polyachenko, E., & State, R. (2026). Reproducibility in the TradingAgents Framework. In Proceedings of the 2026 International Conference on Artificial Intelligence and Fintech (pp. 365–370). ACM. https://doi.org/10.1145/3800973.3801029

    e In the corpus (LLMF-0443) Bibliographic check: Verified Cited in §7.3

  97. Lakonishok, J., Shleifer, A., & Vishny, R. W. (1992). The impact of institutional trading on stock prices. Journal of Financial Economics, 32, 23–43. https://doi.org/10.1016/0304-405x(92)90023-q

    Background reference Bibliographic check: not checked in the corpus audit Cited in §6.5, §9

  98. LeBaron, B. (2006). Agent-based computational finance. In L. Tesfatsion & K. L. Judd (Eds.), Handbook of Computational Economics (Vol. 2, pp. 1187–1233). Elsevier. https://doi.org/10.1016/s1574-0021(05)02024-1

    Background reference Bibliographic check: not checked in the corpus audit Cited in §3.1

  99. Lee, H., Park, S., Lee, S., et al. (2026). When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis (arXiv:2606.29251v3). arXiv. https://arxiv.org/abs/2606.29251v3

    d In the corpus (LLMF-0429) Bibliographic check: Verified Cited in §4.3, §5

  100. Lee, H., Seo, J., Park, S., et al. (2025). Your AI, Not Your View: The Bias of LLMs in Investment Analysis (arXiv:2507.20957v4). arXiv. https://arxiv.org/abs/2507.20957v4. Published version: Proceedings of the 6th ACM International Conference on AI in Finance, pp 150–158 (2025). https://doi.org/10.1145/3768292.3770375

    d In the corpus (LLMF-0120) Bibliographic check: Verified Cited in §4.4

  101. Lehner, S., & Lopez-Lira, A. (2026). ChatGPT as a Time Capsule: The Limits of Price Discovery (arXiv:2604.21433). arXiv. https://arxiv.org/abs/2604.21433

    e In the corpus (LLMF-0230) Bibliographic check: Verified Cited in §7.1

  102. Li, E., Shen, M., Tu, Z., & Zhou, D. (2025a). The Promise and Peril of Generative AI: Evidence from GPT as Sell-Side Analysts (arXiv:2412.01069v2). arXiv. https://arxiv.org/abs/2412.01069v2

    d In the corpus (LLMF-0060) Bibliographic check: Verified Cited in §4.5

  103. Li, T., & Yu, L. (2025). ESG Beliefs of Large Language Models: Evidence and Impact (arXiv:2601.00836). arXiv. https://arxiv.org/abs/2601.00836

    d In the corpus (LLMF-0183) Bibliographic check: Verified Cited in §4.4, §6.5, §9

  104. Li, W. W., Kim, H., Cucuringu, M., & Ma, T. (2026a). Can LLM-based Financial Investing Strategies Outperform the Market in Long Run? (arXiv:2505.07078v6). arXiv. https://arxiv.org/abs/2505.07078v6. Published version: Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, pp 2711–2722 (2026). https://doi.org/10.1145/3770854.3785702

    c In the corpus (LLMF-0093) Bibliographic check: Verified Cited in §7.5

  105. Li, X., Zeng, Y., Xing, X., et al. (2025b). Profit Mirage: Revisiting Information Leakage in LLM-based Financial Agents (arXiv:2510.07920). arXiv. https://arxiv.org/abs/2510.07920

    e In the corpus (LLMF-0146) Bibliographic check: Verified Cited in §7.1, §7.5

  106. Li, Z., Su, Y., White, M., et al. (2026b). Your Agent Says Yes: Interpreting Adversarial Market Behavior Beyond Individual Transactions (arXiv:2609.07675v2). arXiv. https://arxiv.org/abs/2609.07675v2

    b In the corpus (LLMF-0418) Bibliographic check: Verified Cited in §7.1, §7.5, §9

  107. Lim, Y. (2025). Is Artificial Intelligence (AI) Risk-Averse? [Paper presented at the American Finance Association Annual Meeting, January 4, 2025]. https://afajof.org/management/viewp.php?n=86324

    a In the corpus (LLMF-0589) Bibliographic check: Verified Cited in §4.5

  108. Lin, S. (2025). Real Bubbles, Synthetic Traders: AI-Agent-Based Simulations of the Speculative Market (Bachelor’s thesis, Harvard College). https://dash.harvard.edu/handle/1/42719538

    b In the corpus (LLMF-0399) Bibliographic check: Verified Cited in §8, §9

  109. Liu, J., Song, B., Dixit, V., et al. (2026a). Can large language models capture human risk preferences? A cross-cultural study. Communications in Transportation Research, 6, 9640025. https://doi.org/10.26599/commtr.2026.9640025

    a In the corpus (LLMF-0108) Bibliographic check: Verified with recorded discrepancy Cited in §4.1

  110. Liu, K., & Chen, L. (2026). OOM-RL: Out-of-Money Reinforcement Learning Market-Driven Alignment for LLM-Based Multi-Agent Systems (arXiv:2604.11477). arXiv. https://arxiv.org/abs/2604.11477

    c In the corpus (LLMF-0512) Bibliographic check: Verified Cited in §5

  111. Liu, M., & Liu, Z. (2026). Reading Is Not Using: Retrieval, Judgment, and the Design of AI Financial Research Workflows (arXiv:2608.24842). arXiv. https://arxiv.org/abs/2608.24842

    d In the corpus (LLMF-0290) Bibliographic check: Verified Cited in §4.3

  112. Liu, Q., Yao, L., Wang, Z., et al. (2026b). Large language model-based multi-agent systems for financial markets simulation: a survey. Science China Information Sciences, 69, 171201. https://doi.org/10.1007/s11432-026-4986-x

    b In the corpus (LLMF-0416) Bibliographic check: Verified Cited in §1

  113. Lopez-Lira, A. (2025). Can Large Language Models Trade? Testing Financial Theories with LLM Agents in Market Simulations (arXiv:2504.10789). arXiv. https://arxiv.org/abs/2504.10789

    b In the corpus (LLMF-0090) Bibliographic check: Verified Cited in §1, §6.3, §9

  114. Lopez-Lira, A., Tang, Y., & Zhu, M. (2025). The Memorization Problem: Can We Trust LLMs’ Economic Forecasts? (arXiv:2504.14765v2). arXiv. https://arxiv.org/abs/2504.14765v2

    e In the corpus (LLMF-0092) Bibliographic check: Verified Cited in §7.1

  115. Lou, J., & Sun, Y. (2024). Anchoring Bias in Large Language Models: An Experimental Study (arXiv:2412.06593v2). arXiv. https://arxiv.org/abs/2412.06593v2. Published version: Journal of Computational Social Science 9(1):11 (2026). https://doi.org/10.1007/s42001-025-00435-2

    a In the corpus (LLMF-0061) Bibliographic check: Verified Cited in §4.3

  116. Luo, Y., Jiang, Y., Xie, Q., et al. (2026). ReguSim: Evaluating LLM Agent Rule Grounding in Financial Compliance (arXiv:2608.19974). arXiv. https://arxiv.org/abs/2608.19974

    d In the corpus (LLMF-0285) Bibliographic check: Verified Cited in §5

  117. Ma, T., Du, J., Huang, W., et al. (2025). Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents (arXiv:2502.17967v2). arXiv. https://arxiv.org/abs/2502.17967v2. Published version: Findings of the Association for Computational Linguistics: EMNLP 2025, pp 5496–5514 (2025). https://doi.org/10.18653/v1/2025.findings-emnlp.294

    b In the corpus (LLMF-0076) Bibliographic check: Verified Cited in §7.1, §9

  118. Magar, J. (2026). Man v Machine: Technical System Architecture and Design Rationale (SSRN Working Paper 6823938). https://doi.org/10.2139/ssrn.6823938

    b In the corpus (LLMF-0595) Bibliographic check: Verified Cited in §9

  119. Maresca, C. (2026). Can Interest-Bearing Positions Solve the Long-Horizon Problem in Prediction Markets? (arXiv:2602.21091). arXiv. https://arxiv.org/abs/2602.21091

    b In the corpus (LLMF-0202) Bibliographic check: Verified Cited in §9

  120. Mei, Q., Xie, Y., Yuan, W., & Jackson, M. O. (2024). A Turing Test: Are AI Chatbots Behaviorally Similar to Humans? (arXiv:2312.00798v2). arXiv. https://arxiv.org/abs/2312.00798v2. Published version: A Turing test of whether AI chatbots are behaviorally similar to humans. Proceedings of the National Academy of Sciences 121(9):e2313925121 (2024). https://doi.org/10.1073/pnas.2313925121

    a In the corpus (LLMF-0017) Bibliographic check: Verified Cited in §4.1

  121. Menon, A., Saebo, M., Crosse, T., et al. (2026). Inherited Goal Drift: Contextual Pressure Can Undermine Agentic Goals (arXiv:2603.03258). arXiv. https://arxiv.org/abs/2603.03258

    d In the corpus (LLMF-0205) Bibliographic check: Verified Cited in §5

  122. Miyazaki, K., Kawahara, T., Roberts, S., & Zohren, S. (2026). Toward Expert Investment Teams: A Multi-Agent LLM System with Fine-Grained Trading Tasks (arXiv:2602.23330). arXiv. https://arxiv.org/abs/2602.23330. Published version: The Journal of Financial Data Science 8(3):168–202 (2026). https://doi.org/10.3905/jfds.2026.008

    c In the corpus (LLMF-0203) Bibliographic check: Verified Cited in §5

  123. Morla, T., Bellibatlu, R. R., Singh, M., & Kapoor, M. S. (2026). AgentFairBench: Do LLM Agents Discriminate When They Act? (arXiv:2606.16723v2). arXiv. https://arxiv.org/abs/2606.16723v2. Published version: IEEE Access (2026). https://doi.org/10.1109/access.2026.3736808

    d In the corpus (LLMF-0258) Bibliographic check: Verified Cited in §4.4

  124. Na, C., Ni, H., Szpruch, L., et al. (2026). Poisoning Agentic Alpha: Adversarial Vulnerabilities Across Roles and Architectures in Multi-Agent Trading Systems (arXiv:2608.24069). arXiv. https://arxiv.org/abs/2608.24069

    e In the corpus (LLMF-0289) Bibliographic check: Verified Cited in §5

  125. Nguyen, P., & Pham, T. (2026). Toward Reliable Evaluation of LLM-Based Financial Multi-Agent Systems: Taxonomy, Coordination Primacy, and Cost Awareness (arXiv:2603.27539). arXiv. https://arxiv.org/abs/2603.27539. Published version: Trends and Applications in Knowledge Discovery and Data Mining, pp 324–336 (2026). https://doi.org/10.1007/978-981-92-2014-4_26

    c In the corpus (LLMF-0219) Bibliographic check: Verified Cited in §1, §2.3, §5

  126. Ostrovsky, M. (2012). Information Aggregation in Dynamic Markets With Strategic Traders. Econometrica, 80, 2595–2647. https://doi.org/10.3982/ecta8479

    Background reference Bibliographic check: not checked in the corpus audit Cited in §1, §6.2, §6.5

  127. Ouyang, S., & Sui, P. (2026). Dissecting AI Trading: Behavioral Finance and Market Bubbles (arXiv:2604.18373). arXiv. https://arxiv.org/abs/2604.18373

    a In the corpus (LLMF-0226) Bibliographic check: Verified Cited in §1, §4.2, §4.5, §6.1, §6.4, §6.5

  128. Ouyang, S., Yun, H., & Zheng, X. (2025). AI as Decision-Maker: Ethics and Risk Preferences of LLMs (arXiv:2406.01168v3). arXiv. https://arxiv.org/abs/2406.01168v3

    a In the corpus (LLMF-0028) Bibliographic check: Verified with recorded discrepancy Cited in §4.1, §7.4

  129. Park, S., Park, S., Lee, H., et al. (2026). Your AI, On a Dial: Controlling Investment Bias in LLMs with a Single Neuron (arXiv:2608.22852). arXiv. https://arxiv.org/abs/2608.22852

    d In the corpus (LLMF-0287) Bibliographic check: Verified Cited in §4.4

  130. Payne, K. (2025). An analysis of AI Decision under Risk: Prospect theory emerges in Large Language Models (arXiv:2508.00902). arXiv. https://arxiv.org/abs/2508.00902

    a In the corpus (LLMF-0124) Bibliographic check: Verified Cited in §4.2

  131. Powdthavee, N. (2026). Large Language Models Outperform Humans in Fraud Detection and Resistance to Motivated Investor Pressure (arXiv:2604.20652). arXiv. https://arxiv.org/abs/2604.20652

    d In the corpus (LLMF-0229) Bibliographic check: Verified Cited in §4.4, §7.4

  132. Qian, H. (2026). Nous: An Attempt to Extract and Inject the Cognition Behind Prediction-Market Behavior (arXiv:2606.13038). arXiv. https://arxiv.org/abs/2606.13038

    b In the corpus (LLMF-0417) Bibliographic check: Verified Cited in §6.5

  133. Ross, J., & Lo, A. W. (2026). One Size Fits None: Heuristic Collapse in LLM Investment Advice (arXiv:2604.23837). arXiv. https://arxiv.org/abs/2604.23837

    d In the corpus (LLMF-0232) Bibliographic check: Verified Cited in §4.4

  134. Ross, J., So, E., De Simone, Z., et al. (2026). Why Better Models Can Create Riskier Systems: Evidence from LLM Agents in Financial Markets (arXiv:2609.04373). arXiv. https://arxiv.org/abs/2609.04373

    b In the corpus (LLMF-0298) Bibliographic check: Verified Cited in §1, §6.3, §6.5, §8, §9

  135. Roy, D., Misra, R., Singh, S. K., & Roy, A. (2026). MemGuard-Alpha: Limits of Membership Inference for Detecting and Filtering Memorization-Contaminated Signals in LLM-Based Financial Forecasting (arXiv:2603.26797v2). arXiv. https://arxiv.org/abs/2603.26797v2. The arXiv record lists two authors; the four listed here are those of the version read.

    e In the corpus (LLMF-0440) Bibliographic check: Verified Cited in §7.1, §7.3

  136. Roychoudhury, S. (2026). Do Language Models Round and “Manage” Earnings Forecasts? Round Numbers and Meet-or-Beat Beliefs in LLM EPS Forecasts (SSRN Working Paper 7143218). https://doi.org/10.2139/ssrn.7143218

    d In the corpus (LLMF-0390) Bibliographic check: Verified Cited in §4.3

  137. Ruano, C., & Rajan, S. (2026). Social Contagion and Bank Runs: An Agent-Based Model with LLM Depositors (arXiv:2602.15066). arXiv. https://arxiv.org/abs/2602.15066

    b In the corpus (LLMF-0350) Bibliographic check: Verified Cited in §6.3, §8

  138. Rumpf, M., Haliassos, M., Kosyakova, T., & Otter, T. (2026). From humans to algorithms: How financial advice differs across professionals, peers, and LLMs (IMFS Working Paper No. 243). https://hdl.handle.net/10419/343086

    d In the corpus (LLMF-0423) Bibliographic check: Verified Cited in §4.4, §7.4

  139. Safder, M. U., Gull, A., Elbadry, R., et al. (2026). FinPersona-Bench: A Benchmark for Longitudinal Psychometric Stability of Autonomous Financial Agents (arXiv:2606.31522v2). arXiv. https://arxiv.org/abs/2606.31522v2

    e In the corpus (LLMF-0265) Bibliographic check: Verified Cited in §7.2

  140. Saxena, M., Pangallo, M., Hommes, C., et al. (2026). Machine Spirits: Speculation and Adaptation of LLM Agents in Asset Markets (arXiv:2604.18602v2). arXiv. https://arxiv.org/abs/2604.18602v2

    b In the corpus (LLMF-0227) Bibliographic check: Verified Cited in §1, §4.1, §6.1, §6.4, §6.5, §7.1, §8, §9, §11

  141. Scheurer, J., Balesni, M., & Hobbhahn, M. (2024). Large Language Models can Strategically Deceive their Users when Put Under Pressure (arXiv:2311.07590v4). arXiv. https://arxiv.org/abs/2311.07590v4

    d In the corpus (LLMF-0014) Bibliographic check: Verified Cited in §4.4

  142. Shah, A., Ye, L., Jaskowski, S., et al. (2025a). Beyond the Reported Cutoff: Where Large Language Models Fall Short on Financial Knowledge (arXiv:2504.00042v2). arXiv. https://arxiv.org/abs/2504.00042v2

    e In the corpus (LLMF-0435) Bibliographic check: Verified Cited in §7.1

  143. Shah, A., Zhu, K., Jiang, Y., et al. (2025b). Learning from Synthetic Labs: Language Models as Auction Participants (arXiv:2507.09083v1). arXiv. https://arxiv.org/abs/2507.09083v1

    b In the corpus (LLMF-0114) Bibliographic check: Verified Cited in §1, §2.3, §6.2, §6.5, §7.1, §8, §9

  144. Shi, J., & Hollifield, B. (2025). Market-Dependent Communication in Multi-Agent Alpha Generation (arXiv:2511.13614). arXiv. https://arxiv.org/abs/2511.13614

    c In the corpus (LLMF-0169) Bibliographic check: Verified Cited in §5

  145. Shkolnikov, Y. P. (2026). From Deceptive Outputs to Deceptive Mechanisms: A Causal Framework for Language-Model Deception Research (arXiv:2609.04166v1). arXiv. https://arxiv.org/abs/2609.04166v1

    d In the corpus (LLMF-0603) Bibliographic check: Verified Cited in §4.4

  146. Sias, R. W. (2004). Institutional Herding. The Review of Financial Studies, 17, 165–206. https://doi.org/10.1093/rfs/hhg035

    Background reference Bibliographic check: not checked in the corpus audit Cited in §6.5, §9

  147. Smith, V. L. (1962). An Experimental Study of Competitive Market Behavior. Journal of Political Economy, 70, 111–137. https://doi.org/10.1086/258609

    Background reference Bibliographic check: not checked in the corpus audit Cited in §1, §6.1, §6.5

  148. Smith, V. L., Suchanek, G. L., & Williams, A. W. (1988). Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets. Econometrica, 56, 1119–1151. https://doi.org/10.2307/1911361

    Background reference Bibliographic check: not checked in the corpus audit Cited in §3.1, §6.1, §6.5

  149. So, E. (2026). The Profit Alignment Problem: How Profit Mandates Induce Alignment Failures in LLMs (arXiv:2609.07731). arXiv. https://arxiv.org/abs/2609.07731

    d In the corpus (LLMF-0301) Bibliographic check: Verified Cited in §4.4, §5

  150. Struski, P., Swistak, J., Okulska, I., & Biecek, P. (2026). Competitive Market Behavior of LLMs (arXiv:2609.02580). arXiv. https://arxiv.org/abs/2609.02580

    b In the corpus (LLMF-0295) Bibliographic check: Verified Cited in §6.1, §6.2, §6.5, §7.1

  151. Sua, Q. R., Dong, J., Thai, N. D., et al. (2026). Contagion on the Trading Floor: How Adversarial Signals Spread in Multi-Agent Trading Systems (arXiv:2609.19789). arXiv. https://arxiv.org/abs/2609.19789. Published version: Machine Learning and Knowledge Discovery in Databases. Research Track, pp 206–223 (2026). https://doi.org/10.1007/978-3-032-37673-2_12

    e In the corpus (LLMF-0305) Bibliographic check: Verified Cited in §5

  152. Sun, B., Min, R., Wang, Y., et al. (2026). Some Large Language Models Exhibit Consistent Risk Attitudes (arXiv:2607.16197). arXiv. https://arxiv.org/abs/2607.16197

    a In the corpus (LLMF-0542) Bibliographic check: Verified Cited in §4.1

  153. Tan, L., & Li, C. (2024). Dissecting Disposition Effect of Large Language Models in Financial Decisions (SSRN Working Paper 4818096). https://doi.org/10.2139/ssrn.4818096

    a In the corpus (LLMF-0406) Bibliographic check: Verified with recorded discrepancy Cited in §4.2, §4.5

  154. Tian, A., Ding, A., Wu, S., & Chan, A. (2026). FIDES: A Concordance Protocol for LLM-Generated Trading Strategies (arXiv:2608.23308). arXiv. https://arxiv.org/abs/2608.23308

    e In the corpus (LLMF-0288) Bibliographic check: Verified Cited in §7.5

  155. Tolety, S. (2025). Tacit Bidder-Side Collusion: Artificial Intelligence in Dynamic Auctions (arXiv:2511.21802). arXiv. https://arxiv.org/abs/2511.21802

    b In the corpus (LLMF-0171) Bibliographic check: Verified Cited in §6.2, §6.5

  156. Tricco, A. C., Lillie, E., Zarin, W., et al. (2018). PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Annals of Internal Medicine, 169, 467–473. https://doi.org/10.7326/m18-0850

    Background reference Bibliographic check: not checked in the corpus audit Cited in §2.1

  157. Tsuchihashi, T. (2023). How much do you bid? Answers from ChatGPT in first-price and second-price auctions. Journal of Digital Life, 3, 11. https://doi.org/10.51015/jdl.2023.3.11

    b In the corpus (LLMF-0543) Bibliographic check: Verified Cited in §6.2, §7.1, §9

  158. Vidler, A., & Walsh, T. (2025). Shifting Power: Leveraging LLMs to Simulate Human Aversion in ABMs of Bilateral Financial Exchanges, A bond market study (arXiv:2503.00320v2). arXiv. https://arxiv.org/abs/2503.00320v2. Published version: Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025), pp 2777–2779. https://doi.org/10.65109/ftjv9080

    b In the corpus (LLMF-0077) Bibliographic check: Verified Cited in §7.2

  159. Wang, C., Wang, C., Zeng, S., et al. (2025a). Advanced simulation paradigm of human behaviour unveils complex financial systemic projection (arXiv:2503.20787v2). arXiv. https://arxiv.org/abs/2503.20787v2

    b In the corpus (LLMF-0413) Bibliographic check: Verified Cited in §7.1

  160. Wang, H., Pan, Z., Zhang, H., et al. (2025b). InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior (arXiv:2507.06528). arXiv. https://arxiv.org/abs/2507.06528. Published version: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 10021–10052 (2025). https://doi.org/10.18653/v1/2025.acl-long.495

    e In the corpus (LLMF-0112) Bibliographic check: Verified Cited in §7.4

  161. Wang, R., Lin, Q., Liu, J., et al. (2026a). Rethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertainty (arXiv:2508.08992v4). arXiv. https://arxiv.org/abs/2508.08992v4

    a In the corpus (LLMF-0126) Bibliographic check: Verified Cited in §4.2

  162. Wang, S., Yao, Z., Zhang, S., et al. (2025c). When Experimental Economics Meets Large Language Models: Evidence-based Tactics (arXiv:2505.21371v2). arXiv. https://arxiv.org/abs/2505.21371v2

    e In the corpus (LLMF-0102) Bibliographic check: Verified Cited in §7.3, §7.5, §9

  163. Wang, Y., Dai, D., Ma, D., & Geng, K. (2026b). Macro Economists in the Machine: A Multi-Agent LLM Framework for Commodity-Related ETF Portfolio Construction (arXiv:2606.08283). arXiv. https://arxiv.org/abs/2606.08283

    c In the corpus (LLMF-0255) Bibliographic check: Verified Cited in §5

  164. Wang, Z., Liu, Y., Wang, T., & Liu, Z. (2025d). Developmental trajectories of decision making and affective dynamics in large language models (arXiv:2601.14268). arXiv. https://arxiv.org/abs/2601.14268

    a In the corpus (LLMF-0191) Bibliographic check: Verified Cited in §1, §4.1, §4.2, §7.3, §9

  165. Wu, K., Yang, B., Ying, Z., & Zhou, D. (2026). Anonymization and Information Loss (arXiv:2511.15364v2). arXiv. https://arxiv.org/abs/2511.15364v2

    e In the corpus (LLMF-0592) Bibliographic check: Verified Cited in §7.1

  166. Wu, W. (2026). Auditing Asset-Specific Preferences in Financial Large Language Models: Evidence from Bitcoin Representations and Portfolio Allocation (arXiv:2606.02528v3). arXiv. https://arxiv.org/abs/2606.02528v3

    d In the corpus (LLMF-0253) Bibliographic check: Verified Cited in §4.4, §6.5

  167. Xia, Y., You, P., Wang, T., et al. (2026). Agentic Trading: When LLM Agents Meet Financial Markets (arXiv:2605.19337). arXiv. https://arxiv.org/abs/2605.19337

    c In the corpus (LLMF-0245) Bibliographic check: Verified Cited in §1

  168. Yan, Y., Tang, R., Gao, Z., et al. (2026). DatedGPT: Preventing Lookahead Bias in Large Language Models with Time-Aware Pretraining (arXiv:2603.11838v2). arXiv. https://arxiv.org/abs/2603.11838v2

    e In the corpus (LLMF-0208) Bibliographic check: Verified Cited in §7.1, §9

  169. Yang, W., Liu, H., & Wang, Y. (2026a). From Algorithmic Trading to Generative AI: Artificial Intelligence, Systemic Risk, and the Transformation of Financial Markets. Journal of Economic Surveys. Advance online publication. https://doi.org/10.1111/joes.70167

    c In the corpus (LLMF-0601) Bibliographic check: Verified Cited in §1

  170. Yang, Y., Zhang, Y., Wu, M., et al. (2025). TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets (arXiv:2502.01506v5). arXiv. https://arxiv.org/abs/2502.01506v5. Published version: Advances in Neural Information Processing Systems 38, pp 70830–70880 (2025). https://doi.org/10.52202/085713-2132

    b In the corpus (LLMF-0069) Bibliographic check: Verified Cited in §1, §6.3, §6.4, §6.5, §7.1, §7.5, §8, §9

  171. Yang, Z., Li, R., Qiang, Q., et al. (2026b). FinVault: Benchmarking Financial Agent Safety in Execution-Grounded Environments (arXiv:2601.07853v1). arXiv. https://arxiv.org/abs/2601.07853v1. Withdrawn by the authors in arXiv version 2 (July 30, 2026); the text of version 1 was read.

    d In the corpus (LLMF-0186) Bibliographic check: Verified Cited in §7.3

  172. Yao, J., Zheng, Z., & Li, B. (2026). Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems (arXiv:2606.08285v2). arXiv. https://arxiv.org/abs/2606.08285v2. The text of version 2 is titled “Execution Realism and Reproducibility in LLM-Based Trading Systems: A Targeted Review and Audit.”

    e In the corpus (LLMF-0256) Bibliographic check: Verified Cited in §7.5

  173. Ye, Y., Han, J., Hu, A., et al. (2026). The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence (arXiv:2605.16895). arXiv. https://arxiv.org/abs/2605.16895

    e In the corpus (LLMF-0244) Bibliographic check: Verified Cited in §7.5, §9

  174. Yechiam, E., & Tarabeih, A. (2026). Mimicry without understanding: the origins of decision bias in large language models (arXiv:2608.12339). arXiv. https://arxiv.org/abs/2608.12339

    e In the corpus (LLMF-0282) Bibliographic check: Verified Cited in §7.4, §7.5

  175. Yee, B., & Koh, P. (2026). Calibrating Behavioral Parameters with Large Language Models (arXiv:2602.01022v3). arXiv. https://arxiv.org/abs/2602.01022v3. Version 2 (April 24, 2026) withdrew version 1 for revision; the text of version 3 was read, which is titled “Large Language Models as Calibrated Measurement Instruments for Behavioral Parameters.”

    e In the corpus (LLMF-0195) Bibliographic check: Verified Cited in §4.2, §4.5, §7.4, §7.5

  176. Yu, B. (2023). Benchmarking Large Language Model Volatility (arXiv:2311.15180). arXiv. https://arxiv.org/abs/2311.15180

    e In the corpus (LLMF-0016) Bibliographic check: Verified Cited in §7.3

  177. Zhang, C., Liu, X., Zhang, Z., et al. (2026a). When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments (arXiv:2407.18957v5). arXiv. https://arxiv.org/abs/2407.18957v5. Published version: When AI Meets Finance (StockAgent): A Benchmark for Simulating Large Language Model Behaviors in Controlled Trading Environments. ACM Transactions on Intelligent Systems and Technology (2026). https://doi.org/10.1145/3844605

    b In the corpus (LLMF-0036) Bibliographic check: Verified with recorded discrepancy Cited in §1, §6.3, §7.1, §9

  178. Zhang, J., Liu, G., Johansson, O., et al. (2026b). Prediction Arena: Benchmarking AI Models on Real-World Prediction Markets (arXiv:2604.07355). arXiv. https://arxiv.org/abs/2604.07355

    c In the corpus (LLMF-0222) Bibliographic check: Verified Cited in §5, §7.5

  179. Zhu, L., & Cai, M. (2026). Artificial Intelligence in Equity and Crypto Markets: Progress, Profitability Evidence, and the Limits of Automated Investing (arXiv:2609.04917). arXiv. https://arxiv.org/abs/2609.04917

    c In the corpus (LLMF-0299) Bibliographic check: Verified Cited in §1

  180. Zhu, T., Zhao, W., Sun, R., et al. (2026). From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Markets (arXiv:2605.28359). arXiv. https://arxiv.org/abs/2605.28359

    c In the corpus (LLMF-0250) Bibliographic check: Verified Cited in §7.5

  181. Zou, M., Chen, J., Luo, A., et al. (2026). FinEvo: From Isolated Backtests to Ecological Market Games for Multi-Agent Financial Strategy Evolution (arXiv:2602.00948). arXiv. https://arxiv.org/abs/2602.00948

    b In the corpus (LLMF-0194) Bibliographic check: Verified Cited in §7.1