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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
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publications
Fast Conformal Prediction using Conditional Interquantile Intervals
Published in Proceedings of the AAAI Conference on Artificial Intelligence (2026), 2010
We introduce Conformal Interquantile Regression (CIR), a conformal regression method that efficiently constructs near-minimal prediction intervals with guaranteed coverage. CIR leverages black-box machine learning models to estimate outcome distributions through interquantile ranges, transforming these estimates into compact prediction intervals while achieving approximate conditional coverage. We further propose CIR+ (Conditional Interquantile Regression with More Comparison), which enhances CIR by incorporating a width-based selection rule for interquantile intervals. This refinement yields narrower prediction intervals while maintaining comparable coverage, though at the cost of slightly increased computational time. Both methods address key limitations of existing distributional conformal prediction approaches: they handle skewed distributions more effectively than Conformalized Quantile Regression, and they achieve substantially higher computational efficiency than Conformal Histogram Regression by eliminating the need for histogram construction. Extensive experiments on synthetic and real-world datasets demonstrate that our methods optimally balance predictive accuracy and computational efficiency compared to existing approaches.
Recommend citation: Naixin Guo, Rui Luo, Zhixin Zhou. (2026). "Fast Conformal Prediction using Conditional Interquantile Intervals ." Proceedings of the AAAI Conference on Artificial Intelligence.
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Can News Predict Firm Bankruptcy?
Published in Journal of Financial Markets, 2025
We examine whether real-time business news predicts firm bankruptcy. Using full-text daily articles from the Dow Jones Newswires database, we generate firm-level predictors with ChatGPT and benchmark against FinBERT and dictionary-based models. ChatGPT-based variables outperform alternatives, with sentiment scores showing predictive power across horizons. Full-text news significantly enhance predictive accuracy over headlines. News-based measures add explanatory power beyond financial variables. Finally, we show that news captures timely information on macroeconomic conditions relevant to bankruptcy prediction, such as VIX, real GDP growth, and recession probability.
Recommend citation: Siyu Bie, Guanhao Feng, Naixin Guo, Jingyu He. (2025). "Can News Predict Firm Bankruptcy? ." Journal of Financial Markets .
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researchs
Can News Predict Firm Bankruptcy?
with Siyu Bie , Guanhao Feng , Jingyu He
Published in Journal of Financial Markets, 2025
We examine whether real-time business news predicts firm bankruptcy. Using full-text daily articles from the Dow Jones Newswires database, we generate firm-level predictors with ChatGPT and benchmark against FinBERT and dictionary-based models. ChatGPT-based variables outperform alternatives, with sentiment scores showing predictive power across horizons. Full-text news significantly enhance predictive accuracy over headlines. News-based measures add explanatory power beyond financial variables. Finally, we show that news captures timely information on macroeconomic conditions relevant to bankruptcy prediction, such as VIX, real GDP growth, and recession probability.
Spectral Group Lasso for Selecting Factors Hidden in Plain Sight
with Arash A. Amini , Zhixin Zhou , Guanhao Feng
One News, Two Markets: LLM-Derived Sentiment and Trading Volume
with Siyu Bie , Guanhao Feng , Jingyu He
Fast Conformal Prediction using Conditional Interquantile Intervals
with Rui Luo , Zhixin Zhou
To appear in Proceedings of the AAAI Conference on Artificial Intelligence (2026)
We introduce Conformal Interquantile Regression (CIR), a conformal regression method that efficiently constructs near-minimal prediction intervals with guaranteed coverage. CIR leverages black-box machine learning models to estimate outcome distributions through interquantile ranges, transforming these estimates into compact prediction intervals while achieving approximate conditional coverage. We further propose CIR+ (Conditional Interquantile Regression with More Comparison), which enhances CIR by incorporating a width-based selection rule for interquantile intervals. This refinement yields narrower prediction intervals while maintaining comparable coverage, though at the cost of slightly increased computational time. Both methods address key limitations of existing distributional conformal prediction approaches: they handle skewed distributions more effectively than Conformalized Quantile Regression, and they achieve substantially higher computational efficiency than Conformal Histogram Regression by eliminating the need for histogram construction. Extensive experiments on synthetic and real-world datasets demonstrate that our methods optimally balance predictive accuracy and computational efficiency compared to existing approaches.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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