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An artificial market model for the forex market
As financial markets have transitioned toward electronic trading, there has been a corresponding increase in the number of algorithmic strategies and degree of transaction frequency. This move to high-frequency trading at the millisecond level, propelled by algorithmic strategies, has brought to the forefront short-term market reactions, like market impact, which were previously negligible in low-frequency trading scenarios. Such evolution necessitates a new framework for analyzing and developing algorithmic strategies in these rapidly evolving markets. Employing artificial markets stands out as a solution to this problem. This study aims to construct an artificial foreign exchange market referencing market microstructure theory, without relying on the assumption of information or technical traders. Furthermore, it endeavors to validate the model by replicating stylized facts, such as fat tails, which exhibit a higher degree of kurtosis in the return distribution than that predicted by normal distribution models. The validated artificial market model will be used to simulate market dynamics and algorithm strategies; its generated rates could also be applied to pricing and risk management for currency options and other foreign exchange derivatives. Moreover, this work explores the importance of order flow and the underlying factors of stylized facts within the artificial market model.
The risk effects of corporate digitalization: exacerbate or mitigate?
This study elaborates on the risk effects of corporate digital transformation (CDT). Using the ratio of added value of digital assets to total intangible assets as a measure of CDT, this study overall reveals an inverse relationship between CDT and revenue volatility, even after employing a range of technical techniques to address potential endogeneity. Heterogeneity analysis highlights that the firms with small size, high capital intensity, and high agency costs benefit more from CDT. It also reveals that advancing information infrastructure, intellectual property protection, and digital taxation enhances the effectiveness of CDT. Mechanism analysis uncovers that CDT not only enhances financial advantages such as bolstering core business and mitigating non-business risks but also fosters non-financial advantages like improving corporate governance and ESG performance. Further inquiries into the side effects of CDT and the dynamics of revenue volatility indicate that CDT might compromise cash flow availability. Excessive digital investments exacerbate operating risks. Importantly, the reduction in operating risk associated with CDT does not sacrifice the potential for enhanced company performance; rather, it appears to augment the value of real options.
Power price stability and the insurance value of renewable technologies
To understand if renewables stabilize or destabilize electricity prices, we simulate European power markets as projected by the National Energy and Climate Plans for 2030 but replicating the historical variability in electricity demand, the prices of fossil fuels and weather. We propose a β-sensitivity metric, defined as the projected increase in the average annual price of electricity when the price of natural gas increases by 1 euro. We show that annual power prices spikes would be more moderate because the β-sensitivity would fall from 1.4 euros to 1 euro. Deployment of solar photovoltaic and wind technologies exceeding 30% of the 2030 target would lower it further, below 0.5 euros. Our framework shows that this stabilization of prices would produce social welfare gains, that is, we find an insurance value of renewables. Because market mechanisms do not internalize this value, we argue that it should be explicitly considered in energy policy decisions.
Professional demand analysis for teaching Chinese to speakers of other languages: a text mining approach on internet recruitment platforms
The rapid development of international education in China highlights the growing importance of employment analysis in Teaching Chinese to Speakers of Other Languages (TCSOL). This study explores the enterprise demands for TCSOL professionals using text mining techniques to analyze recruitment data collected from four major platforms: Boss Zhipin, Zhaopin.com, 51job.com, and Liepin.com. Combining descriptive statistics, LDA topic modeling, BERT-BiLSTM-CRF-based named entity recognition, and co-occurrence network analysis were used. Results show that there is a high demand for TCSOL professionals, especially for small-scale enterprises located in first-tier cities such as Beijing, Shanghai, Guangzhou, and Shenzhen. Employers tend to favor candidates with at least a bachelor’s degree and 1–3 years of work experience. The topic model highlighted three central themes in job descriptions, emphasizing a shift toward a more diverse skill set. Named entity recognition identified essential attributes such as “communication ability”, “teaching experience”, “bachelor’s degree or above” and “responsibility” as core recruitment requirements. The co-occurrence network analysis revealed the importance of “teaching” and “priority” as core skill nodes. Time series analysis showed seasonal fluctuations in recruitment demand, peaking during spring recruitment and graduation periods. A hierarchical model of talent demand and development in TCSOL is proposed, integrating the perspectives of employers, job seekers, educators, and policymakers. This study provides valuable insights for aspiring TCSOL professionals, offering guidance to better align talent training with market needs and improve employment prospects.
Abstracts from the 2nd International Norges Teknisk-Naturvitenskapelige Universitet (NTNU) Symposium: Day 1—Immunotherapy and Hematology
E01 Mitochondrial manipulation improves the PD-1 blockade immunotherapy Kenji Chamoto1, Tasuku Honjo1 1Kyoto University, Kyoto, Japan From the Kyoto University, Japan, and the scientific group…
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