Time: October 13th, 2025 (Monday), 2.00-4.00 pm (Beijing Time)
Location: R910, SIMIS
Zooming Meeting ID: 871 4946 4784 (Passcode: SIMIS)
Talk One: Disentangling the Quantified Risk and Uncertainty Measures
Speaker: Chenghu Ma (马成虎), Fudan University.
Abstract: In this paper, we study whether it is possible to measure Knightian uncertainty and disentangle it from the conventional risk measure in the location-scale family of normal distributions. First, we establish and prove the impossibility theorems that no quantitative uncertainty measure would be mutually agreeable among investors with general risk-and-uncertainty-averse preferences. Then, we show it is possible to partially rank the uncertainty of the location-scale family of normal distributions by the first, second, fourth, and sixth moments. Lastly, we propose two ways to assess the stock uncertainty, the ranges of location and scale of the family of normal distributions computed from the 1st, 2nd, 4th and 6th moments, and a composite uncertainty measure proposed by Izhakian (2020), but with necessary and critical modification. We assess the uncertainty of stock market indices, S&P 500 in the U.S and CSI 300 in China and show that the uncertainty and risk measures are indeed distinct.
Speaker Bio: 马成虎，加拿大多伦多大学经济学博士，复旦大学管理学院财务金融系教授。先后在山东大学数学系、加拿大McGill大学经济系、英国Essex大学财务金融管理系、厦门大学王亚南经济研究院、复旦大学管理学院任教;日本京都大学经济研究所访问教授、新加坡国立大学经济系资深访问学者。研究涉及资产定价理论、博弈论、决策论、风险测量与管理等多个相关领域。主持国家自然科学基金、加拿大SSHRC和FCAR、英国ESRC等国家级研究课题5项，出版《金融经济学原理》、《高级资产定价理论》及“AdvancedAsset Pricing Theory”等专著三部。发表学术论文40余篇，研究成果具有国际影响力。
Talk Two: Evidence from Truck-Level Geolocation Data
Speaker: Guojun Wang (王国俊), Shanghai Normal University
Abstract: This paper investigates whether firm-level freight activity captures corporate fundamentals and predicts stock returns. Using smartphone geolocation data of truck drivers from 2019 to 2024, we construct a novel freight growth index (FGI) to quantify firms’ freight activity in the Chinese stock market. We find that firms’ freight growth is strongly associated with current operating performance and predicts future stock returns. A long-short portfolio sorted on FGI generates significant risk-adjusted monthly returns ranging from 57 to 72 basis points. Further evidence suggests that freight growth forecasts earnings announcement returns and is more predictive for firms with low information transparency. Moreover, freight growth provides incremental information in predicting stock returns beyond analyst forecasts. Our findings highlight that firm-level freight activity contains novel insights into firm fundamentals and stock pricing.
Speaker Bio: 王国俊，上海师范大学商学院副教授，加州大学戴维斯分校经济学博士，CFA持证人。目前主要研究领域包括资产定价、对冲基金、行为金融、金融科技等多个方面。已在Review of Finance、Journal of Banking and Finance、Financial Analysts Journal等金融学顶尖期刊上发表学术论文多篇，并主持国家自然科学基金项目一项。研究成果荣获Review of Finance的Editor’s Choice (主编精选)、CFA协会颁发的Graham and Dodd 大奖以及欧洲IQAM 最佳论文奖。王国俊教授在加盟上海师范大学之前，先后任职于同济大学经济与管理学院以及多家国家主权财富基金与对冲基金，对量化投资与资产配置有着丰富的理论与实践经验。
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