Essays in Expectation Formation and Asset Pricing

This dissertation examines the role of investors' belief formation in asset valuation. In the first chapter, I document that subjective bond risk premia implied by survey forecasts of future Treasury yields are acyclical at the one-year horizon. This is in stark contrast to large countercyclical variation in objective risk premia fitted from in-sample predictive regressions of future bond excess returns. This difference in risk premia implies a wedge between subjective and objective expectations of future short rates, which I show is predictable by trend and cycle components of macroeconomic forecasts. I show that these empirical findings can be explained with a learning model in which the agent filters latent trend and cycle components of fundamentals in real time, while an econometrician analyzing the data ex-post has full knowledge of the data-generating processes. The model also yields predictions, consistent with the data, on the joint behavior of the unconditional yield curve slope, the cyclicality of short-rate and macroeconomic expectation wedges, and the cyclicality of objective risk premia. My results suggest that equilibrium models of bond risk premia should target acyclical subjective risk premia and expectation formation, rather than ex-post in-sample fitted risk premia from predictive regressions. The second chapter (co-authored with Stefan Nagel) builds on recent evidence that lifetime experience shapes individuals’ macroeconomic expectations and it explores the asset-pricing implications of this evidence for the aggregate US stock market. We study an economy in which a representative agent learns---but with fading memory---about the constant underlying endowment growth rate. The agent downweighs observations in the distant past but is otherwise Bayesian in evaluating uncertainty. The model explains both standard asset pricing facts and investor expectations within a simple and tractable framework, in which subjective belief dynamics are constrained by survey data. In the model, fading memory implies perpetual learning and permanently high subjective uncertainty about long-run growth, but the subjective equity premium is virtually constant. In contrast, an econometrician who knows the true long-run growth will find a high and strongly countercyclical objective equity premium which is predictable. Consistent with this theory, we show empirically that experienced payout growth (an exponentially weighted average of past growth rates) is negatively related to future stock market excess returns, predicts survey expectation errors, and is positively related to aggregate analyst forecasts of long-run earnings growth. In the third chapter, I argue that econometricians find high returns from trading on the profitability anomaly because investors in real time failed to spot profitable firms that are difficult to analyze. I document that the Fama-French three-factor alphas of the profitability anomaly only exist among firms with high information frictions, proxied by young age, high forecast dispersion, high past return volatility, and/or high option-implied volatility. The results are robust to excluding micro-firms and using different measures of profitability. Short-sale constraints, liquidity, and financial distress do not fully account for the alphas. I show that the empirical pattern is consistent with a noisy rational expectations equilibrium model in which investors use profitability as a noisy signal to learn about future firm payoffs.PHDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155062/1/zhengyxu_1.pd

Analysis of the early response to chemotherapy in lung cancer using apparent diffusion coefficient single-slice histogram

Purpose: To evaluate the application of apparent diffusion coefficient (ADC) values derived from diffusion-weighted imaging (DWI) using single-slice histogram analysis to study the chemotherapy responses in lung cancer.Methods: A total of 22 chemotherapy patients with advanced lung cancer from the Nanjing Drum Tower Hospital (Nanjing, China) were included in the study. We obtained DWI before and during chemotherapy, performed single-slice histogram analysis of ADC values, and assessed responses after 3 months of chemotherapy. Differences in ADC histogram parameters were compared between the responder and non-responder groups.Results: After therapy, we classified 13 as responders and 9 patients as non-responders. The recorded peak ADC value (ADCpeak) and lowest ADC value (ADClowest) did not show any significant difference in baseline ADClowest and ADCpeak between responders and non-responders. After chemotherapy, 13 responders had significant increase in ADClowest and ADCpeak compared with pre-treatment values (p < 0.001). ADClowest significantly increased in 9 non-responders (p < 0.05), although ADCpeak did not significantly increase. ADCpeak changes were significantly larger in the responder group than in the nonresponder group (p = 0.024). ADClowest changes after treatment were larger in the responder group than in the non-responder group, though not significantly.Conclusion: ADC values derived from single-slice histogram analysis may provide a useful and clinically feasible method for monitoring early chemotherapy response in patients with lung cancer.Keywords: Lung cancer, Chemotherapy, Apparent diffusion coefficient values, Diffusion-weighted imaging, Single-slice histogram analysi

Synergetic planning method for energy stations, pipeline networks, and demand response

Rational planning for energy stations and their lower-level energy supply pipeline networks is vital for improving the economy of the regional integrated energy system. Many studies have been focused on synergetic planning for energy stations and pipeline networks, but few have been oriented from the perspective of synergetic planning for energy stations, pipeline networks, and demand response, which may result in a redundant configuration of the regional integrated energy system. This paper proposes a synergetic planning method for energy stations, pipeline networks, and demand response. Initially, the impact of demand response on the traditional synergetic planning for energy stations and pipeline networks is analyzed. Subsequently, a synergetic planning method for energy stations, pipeline networks, and demand response is proposed to determine the optimal locations of energy stations, the optimal equipment capacity of energy stations, the optimal demand response configuration, and the optimal layout of pipeline networks. Finally, case studies are conducted to verify the effectiveness of the proposed method. Compared with the traditional synergetic planning method for energy stations and pipeline networks without considering demand response, the proposed method can reduce the construction cost of energy stations by approximately 4.8% and pipeline networks by around 8.5%. Thus, the proposed method can be applied for planning energy stations and pipeline networks.</p

Synergetic planning method for energy stations, pipeline networks, and demand response

Rational planning for energy stations and their lower-level energy supply pipeline networks is vital for improving the economy of the regional integrated energy system. Many studies have been focused on synergetic planning for energy stations and pipeline networks, but few have been oriented from the perspective of synergetic planning for energy stations, pipeline networks, and demand response, which may result in a redundant configuration of the regional integrated energy system. This paper proposes a synergetic planning method for energy stations, pipeline networks, and demand response. Initially, the impact of demand response on the traditional synergetic planning for energy stations and pipeline networks is analyzed. Subsequently, a synergetic planning method for energy stations, pipeline networks, and demand response is proposed to determine the optimal locations of energy stations, the optimal equipment capacity of energy stations, the optimal demand response configuration, and the optimal layout of pipeline networks. Finally, case studies are conducted to verify the effectiveness of the proposed method. Compared with the traditional synergetic planning method for energy stations and pipeline networks without considering demand response, the proposed method can reduce the construction cost of energy stations by approximately 4.8% and pipeline networks by around 8.5%. Thus, the proposed method can be applied for planning energy stations and pipeline networks.</p

SJTU-AISPEECH System for VoxCeleb Speaker Recognition Challenge 2022

This report describes the SJTU-AISPEECH system for the Voxceleb Speaker Recognition Challenge 2022. For track1, we implemented two kinds of systems, the online system and the offline system. Different ResNet-based backbones and loss functions are explored. Our final fusion system achieved 3rd place in track1. For track3, we implemented statistic adaptation and jointly training based domain adaptation. In the jointly training based domain adaptation, we jointly trained the source and target domain dataset with different training objectives to do the domain adaptation. We explored two different training objectives for target domain data, self-supervised learning based angular proto-typical loss and semi-supervised learning based classification loss with estimated pseudo labels. Besides, we used the dynamic loss-gate and label correction (DLG-LC) strategy to improve the quality of pseudo labels when the target domain objective is a classification loss. Our final fusion system achieved 4th place (very close to 3rd place, relatively less than 1%) in track3.Comment: System description of VoxSRC 202

Build a SRE Challenge System: Lessons from VoxSRC 2022 and CNSRC 2022

Different speaker recognition challenges have been held to assess the speaker verification system in the wild and probe the performance limit. Voxceleb Speaker Recognition Challenge (VoxSRC), based on the voxceleb, is the most popular. Besides, another challenge called CN-Celeb Speaker Recognition Challenge (CNSRC) is also held this year, which is based on the Chinese celebrity multi-genre dataset CN-Celeb. This year, our team participated in both speaker verification closed tracks in CNSRC 2022 and VoxSRC 2022, and achieved the 1st place and 3rd place respectively. In most system reports, the authors usually only provide a description of their systems but lack an effective analysis of their methods. In this paper, we will outline how to build a strong speaker verification challenge system and give a detailed analysis of each method compared with some other popular technical means