smq/directoryAn evolving quant resource archive

Browse / Research papers

Quant finance research papers

9383 research papers selected for learning quantitative finance. Highlighted picks come first. Showing 1501–1550; page 31 of 188.

  1. Calibration Bets on the Past: Post-Training Quantization for Financial Time-Series Forecasting

    Junyi Ye, Ivy Gateri Wanjiku · 2026 · advanced · machine learning

    financial machine learning · machine learning · neural networks · time series · volatility forecasting

  2. Calibration of local volatility using the local and implied instantaneous variance

    Gabriel Turinici · 2009 · advanced · options

    model calibration · numerical methods · option pricing

  3. Calibration of stochastic volatility models via second order approximation: the Heston model case

    Elisa Alòs, Rafael De Santiago, Josep Vives · 2012 · advanced · options

    model calibration · option pricing

  4. Calibration of the rating transition model for high and low default portfolios

    Jian He, Asma Khedher, Peter Spreij · 2024 · advanced · risk management

    credit risk · model calibration · numerical methods · statistical inference

  5. Calibration-Induced Degeneracy in LLM Financial Forecasting: An Audit-Trailed Case Study on Next-Day Market Risk

    Arin Mohanty · 2026 · advanced · machine learning

    financial machine learning · large language models · statistical inference · volatility forecasting

  6. Call Option Price using Pearson Diffusion Processes

    Tapan Kar, Suprio Bhar, Barun Sarkar · 2025 · advanced · options

    derivatives · option pricing · stochastic differential equations · stochastic processes

  7. Callable Bonds and Hedging

    Levent Güntay, N. R. Prabhala, Haluk Unal · intermediate · fixed income

    corporate finance · fixed income · interest rates

  8. Can a financial transaction tax prevent stock price booms?

    Adam, Klaus, Marcet, Albert, Merkel, Sebastian · 2015 · advanced · other

    asset pricing · economic policy · financial economics

  9. Can a GPT4-Powered AI Agent Be a Good Enough Performance Attribution Analyst?

    Bruno de Melo, Jamiel Sheikh · 2024 · intermediate · portfolio management

    large language models · performance attribution · portfolio management

  10. Can a Lucas model with habit generate realistic conditional volatility in exchange rate returns?

    Jingyi Liu · 2008 · advanced · time series

    asset pricing · foreign exchange

  11. Can a Machine Correct Option Pricing Models?

    Caio Almeida, Jianqing Fan, Gustavo Freire · 2022 · intermediate · options

    machine learning · neural networks · option pricing · volatility surface

  12. Can a stochastic cusp catastrophe model explain housing market crashes?

    Wang, J. · 2015 · advanced · time series

    econometrics · housing markets · statistical inference · stochastic processes

  13. Can AI Detect Wash Trading? Evidence from NFTs

    Brett Hemenway Falk, Gerry Tsoukalas, Niuniu Zhang · 2023 · advanced · machine learning

    cryptocurrencies · machine learning · statistics

  14. Can an unsupervised clustering algorithm reproduce a categorization system?

    Nathalia Castellanos, Dhruv Desai, Sebastian Frank · 2024 · intermediate · machine learning

    algorithms · data science · machine learning

  15. Can Artificial Intelligence Trade the Stock Market?

    Jędrzej Maskiewicz, Paweł Sakowski · 2025 · intermediate · machine learning

    algorithmic trading · deep learning · machine learning · reinforcement learning

  16. Can Blindfolded LLMs Still Trade? An Anonymization-First Framework for Portfolio Optimization

    Joohyoung Jeon, Hongchul Lee · 2026 · advanced · machine learning

    algorithmic trading · backtesting · financial machine learning · large language models · portfolio optimization · reinforcement learning

  17. Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps?

    Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana · 2024 · intermediate · machine learning

    equity markets · large language models · natural language processing

  18. Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models

    Alejandro Lopez-Lira, Yuehua Tang · 2023 · advanced · machine learning

    asset pricing · large language models · natural language processing

  19. Can ChatGPT Overcome Behavioral Biases in the Financial Sector? Classify-and-Rethink: Multi-Step Zero-Shot Reasoning in the Gold Investment

    Shuoling Liu, Gaoguo Jia, Yuhang Jiang · 2024 · intermediate · machine learning

    behavioral finance · large language models

  20. Can community-based microfinance groups match savers with borrowers? Evidence from rural Malawi

    Rachel Cassidy, Marcel Fafchamps · 2015 · intermediate · other

    financial economics · regression · statistical inference

  21. Can Exchange Rates Forecast Commodity Prices?

    Yu-chin Chen, Kenneth Rogoff, Barbara Rossi · 2010 · advanced · time series

    commodities · econometrics · foreign exchange · time series

  22. Can Exposure to Tail Risk Explain Size, Book-to-Market, and Idiosyncratic Volatility Anomalies?

    Sofiane Aboura, Eser Arisoy · 2017 · advanced · statistics

    asset pricing · tail risk

  23. Can GANs Learn the Stylized Facts of Financial Time Series?

    Sohyeon Kwon, Yongjae Lee · 2024 · advanced · machine learning

    deep learning · machine learning · neural networks · time series

  24. Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams

    Ethan Callanan, Amarachi Mbakwe, Antony Papadimitriou · 2023 · intermediate · machine learning

    large language models

  25. Can Higher-Order Risks Explain the Credit Spread Puzzle?

    Cédric Okou, Olfa Maalaoui Chun, Georges Dionne · 2016 · advanced · fixed income

    credit risk · fixed income · liquidity

  26. Can Investors Benefit from Hedge Fund Strategies? Utility-Based, Out-of-Sample Evidence

    Massimo Guidolin, Alexei Orlov · 2018 · advanced · portfolio management

    hedge funds · portfolio management · portfolio optimization · regression

  27. Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection

    Georgios Fatouros, Konstantinos Metaxas, John Soldatos · 2024 · advanced · machine learning

    large language models · natural language processing

  28. Can Large Language Models Execute Parent Orders?

    Zane Shen, Xinli Xu, Guangyi Zhang · 2026 · advanced · quantitative trading

    algorithmic trading · large language models

  29. Can Large Language Models Improve Venture Capital Exit Timing After IPO?

    Mohammadhossien Rashidi · 2025 · intermediate · machine learning

    large language models

  30. Can Large Language Models Trade? Testing Financial Theories with LLM Agents in Market Simulations

    Alejandro Lopez-Lira · 2025 · advanced · quantitative trading

    equity markets · large language models

  31. Can Leading Indicators Assess Country Vulnerability? Evidence from the 2008-09 Global Financial Crisis

    Frankel, Jeffrey A., Saravelos, George · 2012 · intermediate · risk management

    risk management

  32. Can Limited Liability Increase Stability for Banks: A Dynamic Portfolio Approach

    Deb Narayan Barik, Siddhartha P. Chakrabarty · 2025 · advanced · risk management

    optimization · portfolio management · portfolio optimization · risk management · stochastic processes

  33. Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?

    Weixian Waylon Li, Hyeonjun Kim, Mihai Cucuringu · 2025 · advanced · machine learning

    backtesting · financial machine learning · large language models

  34. Can Machine Learning Algorithms Outperform Traditional Models for Option Pricing?

    Georgy Milyushkov · 2025 · intermediate · options

    derivatives · machine learning · neural networks · option pricing

  35. Can machine learning help to select portfolios of mutual funds?

    Victor DeMiguel, Javier Gil-Bazo, Francisco J. Nogales · 2021 · advanced · portfolio management

    financial machine learning · machine learning · mutual funds · portfolio management

  36. Can Machines "Learn" Finance?

    Ronen Israel · 2020 · intermediate · machine learning

    financial machine learning · machine learning

  37. Can maker-taker fees prevent algorithmic cooperation in market making?

    Bingyan Han · 2022 · advanced · quantitative trading

    market making · reinforcement learning · transaction costs

  38. Can market volumes reveal traders' rationality and a new risk premium?

    Francesca Mariani, Maria Cristina Recchioni, Tai-Ho Wang · 2024 · advanced · quantitative trading

    optimization

  39. Can Monetary Policies Inflate a Stock Market Bubble? A Regime Switching Model of Periodically Collapsing Bubbles

    Monia Magnani · 2024 · advanced · time series

    asset pricing · econometrics · economic policy · equity markets · time series

  40. Can Nash inform capital requirements? Allocating systemic risk measures

    Çağın Ararat, Zachary Feinstein · 2025 · advanced · risk management

    systemic risk

  41. Can No-Arbitrage SDF Models with Regime Shifts Explain the Correlations Between Commodity, Stock, and Bond Returns?

    Marta Giampietro, Massimo Guidolin, Manuela Pedio · 2015 · advanced · statistics

    asset pricing · commodities

  42. Can Oil Prices Forecast Exchange Rates?

    Domenico Ferraro, Ken Rogoff, Barbara Rossi · 2011 · intermediate · time series

    econometrics · foreign exchange · time series

  43. Can rare events explain the equity premium puzzle?

    Julliard, Christian, Ghosh, Anisha · 2008 · advanced · statistics

    asset pricing · capm · equity markets · probability

  44. Can Reinforcement Learning Efficiently Discover Price Manipulation?

    Ioanna-Yvonni Tsaknaki, Andrea Macrì, Fabrizio Lillo · 2026 · advanced · quantitative trading

    market microstructure · reinforcement learning

  45. Can the stock market anticipate future operating performance? Evidence from equity rights issues

    Kabir, R., Roosenboom, P.G.J. · 2002 · intermediate · other

    equity markets · statistical inference

  46. Can We Forecast the Implied Volatility Surface Dynamics of Equity Options? Predictability and Economic Value Tests

    Alejandro Bernales, Massimo Guidolin · 2012 · advanced · options

    volatility forecasting · volatility surface · volatility trading

  47. Can we imitate the principal investor's behavior to learn option price?

    Xin Jin · 2021 · advanced · options

    bayesian statistics · machine learning · neural networks · option pricing · options hedging · reinforcement learning

  48. Can we infer microscopic financial information from the long memory in market-order flow?: a quantitative test of the Lillo-Mike-Farmer model

    Yuki Sato, Kiyoshi Kanazawa · 2023 · advanced · quantitative trading

    market microstructure · statistics · time series

  49. Can We Reliably Predict the Fed's Next Move? A Multi-Modal Approach to U.S. Monetary Policy Forecasting

    Fiona Xiao Jingyi, Lili Liu · 2025 · advanced · machine learning

    financial machine learning · interest rates · machine learning · natural language processing

  50. Can You Delete a Year of Market Data? Machine Unlearning Against Exact Retraining Oracles

    Junyi Ye · 2026 · advanced · machine learning

    deep learning · financial machine learning · machine learning · neural networks · time series