Yonghang Zhou

Yidong Chai, Yanguang Liu, Xuan Tian et al.

We study investor learning and information acquisition in private markets using a large dataset of expert network calls. We develop a sequential Large Language Model (LLM)-Bayesian framework that treats expert interactions as sequential signals and recovers time-varying beliefs about firm success and associated uncertainty from unstructured conversations, providing a measurement system for how qualitative information is aggregated into investment expectations. We show that expert network calls contain decision-relevant information: a single call increases subsequent investment probability by 6.9 to 9.0 percentage points, while positive sentiment raises deal likelihood by 3.9 to 4.1 percentage points. Informativeness varies across topics and environments: discussions of technology adoption and customer acquisition increase deal probability by up to 14.7 percentage points, particularly in high-uncertainty settings. Information is asymmetric across horizons, with positive signals predicting short-term investment decisions and negative signals more informative about long-run firm performance. Consistent with a belief-based mechanism, investment decisions respond to inferred beliefs rather than raw signals. A one standard deviation increase in success belief raises deal probability by approximately 11 percentage points, while reductions in uncertainty further increase investment likelihood. Our framework improves capital allocation, increasing portfolio returns by 15.26% and F1 by 6.69%, with gains concentrated in the upper tail. Attention and ablation analyses show that conversational cues are particularly informative for technologically complex startups, young firms, diverse founding teams, and firms with low public visibility, where information frictions are severe.

Yidong Chai, Yang Liu, Yonghang Zhou et al.

Large Language Models (LLMs) have demonstrated transformative potential in reshaping the world. As these models are pretrained on general corpora, they often require domain-specific fine-tuning to optimize performance in specialized business applications. Due to their massive scale, parameter-efficient fine-tuning (PEFT) methods are widely used to reduce training costs. Among them, hybrid PEFT methods that combine multiple PEFT techniques have achieved the best performance. However, existing hybrid PEFT methods face two main challenges when fine-tuning LLMs for specialized applications: (1) relying on point estimates, lacking the ability to quantify uncertainty for reliable decision-making, and (2) struggling to dynamically adapt to emerging data, lacking the ability to suit real-world situations. We propose Bayesian Hybrid Parameter-Efficient Fine-Tuning (BH-PEFT), a novel method that integrates Bayesian learning into hybrid PEFT. BH-PEFT combines Adapter, LoRA, and prefix-tuning to fine-tune feedforward and attention layers of the Transformer. By modeling learnable parameters as distributions, BH-PEFT enables uncertainty quantification. We further propose a Bayesian dynamic fine-tuning approach where the last posterior serves as the prior for the next round, enabling effective adaptation to new data. We evaluated BH-PEFT on business tasks such as sentiment analysis, news categorization, and commonsense reasoning. Results show that our method outperforms existing PEFT baselines, enables uncertainty quantification for more reliable decisions, and improves adaptability in dynamic scenarios. This work contributes to business analytics and data science by proposing a novel BH-PEFT method and dynamic fine-tuning approach that support uncertainty-aware and adaptive decision-making in real-world situations.