10 records from EconBiz based on author Name

1. Global gender gaps in the international migration of professionals on LinkedIn

Jacobs, Elizabeth; Perrotta, Daniela; Zhao, Xinyi; Anastasiadou, Athina; Zagheni, Emilio;
2024
Type: Graue Literatur; Non-commercial literature; Arbeitspapier; Working Paper;
Availability: Link

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2. Financial regulatory policy uncertainty : an informative predictor for financial industry stock returns

Zhang, Yaojie; Zhao, Xinyi; Zhang, Zhikai;
2025
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link

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3. Sub-national disparities in the global mobility of academic talent

Akbaritabar, Aliakbar; Dańko, Maciej J.; Zhao, Xinyi; Zagheni, Emilio;
2023
Type: Graue Literatur; Non-commercial literature; Arbeitspapier; Working Paper;
Availability: Link

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4. Estimating large-scale tree logit models

Jagabathula, Srikanth; Rusmevichientong, Paat; Venkataraman, Ashwin; Zhao, Xinyi;
2024
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link Link

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5. Unintended consequences of advances in matching technologies : information revelation and strategic participation on gig-economy platforms

Liu, Yi; Lou, Bowen; Zhao, Xinyi; Li, Xinxin;
2024
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link Link

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6. The role of higher moments in predicting China's oil futures volatility : evidence from machine learning models

Zhang, Hongwei; Zhao, Xinyi; Gao, Wang; Niu, Zibo;
2023
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link

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7. "Fulfilled by Amazon" : a strategic perspective of competition at the e-commerce platform

Lai, Guoming; Liu, Huihui; Xiao, Wenqiang; Zhao, Xinyi;
2022
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link Link
Citations: 22 (based on OpenCitations) Cited By

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8. The Role of Higher-Order Moments in Predicting China's Oil Futures Volatility : Evidence from Machine Learning Models

abstract

This paper expands the emerging literature on volatility forecasting for China’s oil market by exploring the predictive ability of higher-order moments (skewness, kurtosis, hyperskewness, and hyperkurtosis) based on high-frequency data. Our investigation is originally based on the heterogeneous autoregressive (HAR) framework, but considering the possible multicollinearity and nonlinearity, it is extended to various machine learning (ML) models and combination forecasting models. The results reveal that higher-order moments, including the two highest moments, always significantly improve predictive performance for the COVID-19 crisis. We further examine the interpretability of ML models and each factor’s contribution to the prediction, finding that odd and even moments contain short- and long-term prediction information, respectively. This paper also highlights the effectiveness of ML models for capturing trends in oil futures volatility with higher-order moments and the satisfactory performance of combination forecasting models. Finally, we investigate the predictability of asymmetric risk patterns and obtain identical results. Our study has important implications for financial risk management, asset pricing, and portfolio allocation

Zhang, Hongwei; Zhao, Xinyi; Gao, Wang; Niu, Zibo;
2022
Availability: Link Link

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9. The Coin of AI Has Two Sides : Matching Enhancement and Information Revelation Effects of AI on Gig-Economy Platforms

abstract

Artificial intelligence (AI) has been increasingly integrated into the process of matching between workers and employers requesting job tasks on a gig-economy platform. Unlike the conventional wisdom that adopting AI in the matching process always benefits the platform by assigning better-matched jobs (employers) to workers, we discover unintended but possible revenue-decreasing consequences for the AI-adopting platform. We build a stylized game theoretical model that considers gig workers’ strategic participation behavior. We find that while the matching enhancement effect of AI can increase the platform’s revenue by improving matching quality, AI-assigned jobs can also reveal information about the uncertain labor demand to workers and thus unfavorably change workers’ participation decisions, resulting in revenue loss for the platform. We extend our model to the cases where (1) the share of revenue between workers and platform is endogenous and (2) the workers compete for the job tasks, and find consistent results. Furthermore, we examine two approaches to mitigate the potential negative effect of AI-enabled matching for the platform and find that under certain conditions, the AI-adopting platform can be better off by revealing the labor demand or competition information directly to workers. Our results shed light on both the intended positive and unintended negative roles of utilizing AI to facilitate matching, and highlight the importance of thoughtful development, management, and application of AI in the gig economy

Liu, Yi; Zhao, Xinyi; Lou, Bowen; Li, Xinxin;
2021
Availability: Link Link
Citations: 1 (based on OpenCitations) Cited By

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10. Strategic Financing and Information Revelation Amid Market Competition

abstract

In practice, interest expense can account for a large proportion of firms' costs, while the interest rate is often influenced by a firm's market prospect. In the presence of information asymmetry, a firm may have an incentive to borrow a larger amount, thereby signaling a high prospect to the lenders. On the other hand, such market confidence, if publicly shown, may stimulate competitors to respond more aggressively, which may incentivize the firm to instead borrow a smaller amount. Motivated by such observations, we investigate the determinants of a firm's financing and information revelation strategy. First, under public financing where the borrowing information is openly accessible, we find that when the firm's internal capital level and the market competition intensity are both low, the firm over-finances, if the market prospect is high, so as to credibly reveal its information to lower the interest rate. On the contrary, when the firm's internal capital level and the competition intensity are both high, the firm under-finances, if the market prospect is low, to credibly signal its information to alleviate competition. In the remaining scenarios, these two opposing incentives are surprisingly neutralized -- the firm neither imitates nor signals -- and the first-best solution is attained. As such, rather counter-intuitively, we show that an increase of the internal capital level can sometimes even be harmful for the firm while benefiting the competitor, and a more competitive market may not always be detrimental. Second, we investigate when the firm may seek private financing so that the borrowing information is not publicly revealed. A classical signaling game arises between the firm and the lender, while the competitor relies on the prior information to make its response. We find that private financing emerges as an equilibrium outcome only when the firm's internal capital level is sufficiently high and the competition intensity is intermediate

Zhao, Xinyi; Lai, Guoming; Xiao, Wenqiang;
2020
Availability: Link Link
Citations: 2 (based on OpenCitations) Cited By

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