Quantitative Finance

New submissions

• New submissions
• Cross-lists
• Replacements

New submissions for Wed, 1 May 24

[1]  arXiv:2404.18979 [pdf, other]

Title: Analysis of Proximity Informed User Behavior in a Global Online Social Network

Authors: Nils Breitmar, Matthew C. Harding, Hanqiao Zhang

Subjects: General Economics (econ.GN); Applications (stat.AP)

Despite the earlier claim of "Death of Distance", recent studies revealed that geographical proximity still greatly influences link formation in online social networks. However, it is unclear how physical distances are intertwined with users' online behaviors in a virtual world. We study the role of spatial dependence on a global online social network with a dyadic Logit model. Results show country-specific patterns for distance effect on probabilities to build connections. Effects are stronger when the possibility for two people to meet in person exists. Relative to weak ties, dependence on proximity is looser for strong social ties.

[2]  arXiv:2404.18980 [pdf, other]

Title: The Impact of COVID-19 on Co-authorship and Economics Scholars' Productivity

Authors: Hanqiao Zhang, Joy D. Xiuyao Yang

Subjects: General Economics (econ.GN); Physics and Society (physics.soc-ph); Applications (stat.AP)

The COVID-19 pandemic has disrupted traditional academic collaboration patterns, prompting a unique opportunity to analyze the influence of peer effects and coauthorship dynamics on research output. Using a novel dataset, this paper endeavors to make a first cut at investigating the role of peer effects on the productivity of economics scholars, measured by the number of publications, in both pre-pandemic and pandemic times. Results show that peer effect is significant for the pre-pandemic time but not for the pandemic time. The findings contribute to our understanding of how research collaboration influences knowledge production and may help guide policies aimed at fostering collaboration and enhancing research productivity in the academic community.

[3]  arXiv:2404.19324 [pdf, ps, other]

Title: The Effect of Data Types' on the Performance of Machine Learning Algorithms for Financial Prediction

Authors: Hulusi Mehmet Tanrikulu, Hakan Pabuccu

Comments: 33 Pages, 5 Figures

Subjects: Computational Finance (q-fin.CP)

Forecasting cryptocurrencies as a financial issue is crucial as it provides investors with possible financial benefits. A small improvement in forecasting performance can lead to increased profitability; therefore, obtaining a realistic forecast is very important for investors. Successful forecasting provides traders with effective buy-or-hold strategies, allowing them to make more profits. The most important thing in this process is to produce accurate forecasts suitable for real-life applications. Bitcoin, frequently mentioned recently due to its volatility and chaotic behavior, has begun to pay great attention and has become an investment tool, especially during and after the COVID-19 pandemic. This study provided a comprehensive methodology, including constructing continuous and trend data using one and seven years periods of data as inputs and applying machine learning (ML) algorithms to forecast Bitcoin price movement. A binarization procedure was applied using continuous data to construct the trend data representing each input feature trend. Following the related literature, the input features are determined as technical indicators, google trends, and the number of tweets. Random forest (RF), K-Nearest neighbor (KNN), Extreme Gradient Boosting (XGBoost-XGB), Support vector machine (SVM) Naive Bayes (NB), Artificial Neural Networks (ANN), and Long-Short-Term Memory (LSTM) networks were applied on the selected features for prediction purposes. This work investigates two main research questions: i. How does the sample size affect the prediction performance of ML algorithms? ii. How does the data type affect the prediction performance of ML algorithms? Accuracy and area under the ROC curve (AUC) values were used to compare the model performance. A t-test was performed to test the statistical significance of the prediction results.

[4]  arXiv:2404.19590 [pdf, other]

Title: Internal migration after a uniform minimum wage introduction

Authors: Alexander Moog

Subjects: General Economics (econ.GN)

Internal migration is an essential aspect to study labor mobility. I exploit the German statutory minimum wage introduction in 2015 to estimate its push and pull effects on internal migration using a 2% sample of administrative data. In a conditional fixed effects Poisson difference-in-differences framework with a continuous treatment, I find that the minimum wage introduction leads to an increase in the out-migration of low-skilled workers with migrant background by 25% with an increasing tendency over time from districts where a high share of workers are subject to the minimum wage (high-bite districts). In contrast the migration decision of native-born low-skilled workers is not affected by the policy. However, both native-born low-skilled workers and those with a migrant background do relocate across establishments, leaving high-bite districts as their workplace. In addition, I find an increase for unemployed individuals with a migrant background in out-migrating from high-bite districts. These results emphasize the importance of considering the effects on geographical labor mobility when implementing and analyzing policies that affect the determinants of internal migration.

[5]  arXiv:2404.19699 [pdf, ps, other]

Title: Generative AI Usage and Academic Performance

Authors: Janik Ole Wecks, Johannes Voshaar, Benedikt Jost Plate, Jochen Zimmermann

Comments: This version: May 2024

Subjects: General Economics (econ.GN)

This study evaluates the impact of students' usage of generative artificial intelligence (GenAI) tools such as ChatGPT on their academic performance. We analyze student essays using GenAI detection systems to identify GenAI users among the cohort. Employing multivariate regression analysis, we find that students using GenAI tools score on average 6.71 (out of 100) points lower than non-users. While GenAI tools may offer benefits for learning and engagement, the way students actually use it correlates with diminished academic outcomes. Exploring the underlying mechanism, additional analyses show that the effect is particularly detrimental to students with high learning potential, suggesting an effect whereby GenAI tool usage hinders learning. Our findings provide important empirical evidence for the ongoing debate on the integration of GenAI in higher education and underscores the necessity for educators, institutions, and policymakers to carefully consider its implications for student performance.

Cross-lists for Wed, 1 May 24

[6]  arXiv:2404.19109 (cross-list from cs.LG) [pdf, other]

Title: The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset

Authors: Claudio Bellei, Muhua Xu, Ross Phillips, Tom Robinson, Mark Weber, Tim Kaler, Charles E. Leiserson, Arvind, Jie Chen

Subjects: Machine Learning (cs.LG); General Finance (q-fin.GN)

Subgraph representation learning is a technique for analyzing local structures (or shapes) within complex networks. Enabled by recent developments in scalable Graph Neural Networks (GNNs), this approach encodes relational information at a subgroup level (multiple connected nodes) rather than at a node level of abstraction. We posit that certain domain applications, such as anti-money laundering (AML), are inherently subgraph problems and mainstream graph techniques have been operating at a suboptimal level of abstraction. This is due in part to the scarcity of annotated datasets of real-world size and complexity, as well as the lack of software tools for managing subgraph GNN workflows at scale. To enable work in fundamental algorithms as well as domain applications in AML and beyond, we introduce Elliptic2, a large graph dataset containing 122K labeled subgraphs of Bitcoin clusters within a background graph consisting of 49M node clusters and 196M edge transactions. The dataset provides subgraphs known to be linked to illicit activity for learning the set of "shapes" that money laundering exhibits in cryptocurrency and accurately classifying new criminal activity. Along with the dataset we share our graph techniques, software tooling, promising early experimental results, and new domain insights already gleaned from this approach. Taken together, we find immediate practical value in this approach and the potential for a new standard in anti-money laundering and forensic analytics in cryptocurrencies and other financial networks.

[7]  arXiv:2404.19290 (cross-list from math.NA) [pdf, other]

Title: Efficient inverse $Z$-transform and Wiener-Hopf factorization

Authors: Svetlana Boyarchenko, Sergei Levendorskiĭ

Subjects: Numerical Analysis (math.NA); Computational Finance (q-fin.CP)

We suggest new closely related methods for numerical inversion of $Z$-transform and Wiener-Hopf factorization of functions on the unit circle, based on sinh-deformations of the contours of integration, corresponding changes of variables and the simplified trapezoid rule. As applications, we consider evaluation of high moments of probability distributions and construction of causal filters. Programs in Matlab running on a Mac with moderate characteristics achieves the precision E-14 in several dozen of microseconds and E-11 in several milliseconds, respectively.

[8]  arXiv:2404.19555 (cross-list from cs.CE) [pdf, other]

Title: Transforming Credit Guarantee Schemes with Distributed Ledger Technology

Authors: Sabrina Leo, Andrea Delle Foglie, Luca Barbaro, Edoardo Marangone, Ida Claudia Panetta, Claudio Di Ciccio

Subjects: Computational Engineering, Finance, and Science (cs.CE); General Economics (econ.GN)

Credit Guarantee Schemes (CGSs) are crucial in mitigating SMEs' financial constraints. However, they are renownedly affected by critical shortcomings, such as a lack of financial sustainability and operational efficiency. Distributed Ledger Technologies (DLTs) have shown significant revolutionary influence in several sectors, including finance and banking, thanks to the full operational traceability they bring alongside verifiable computation. Nevertheless, the potential synergy between DLTs and CGSs has not been thoroughly investigated yet. This paper proposes a comprehensive framework to utilise DLTs, particularly blockchain technologies, in CGS processes to improve operational efficiency and effectiveness. To this end, we compare key architectural characteristics considering access level, governance structure, and consensus method, to examine their fit with CGS processes. We believe this study can guide policymakers and stakeholders, thereby stimulating further innovation in this promising field.

Replacements for Wed, 1 May 24

[9]  arXiv:2403.03367 (replaced) [pdf, ps, other]

Title: am-AMM: An Auction-Managed Automated Market Maker

Authors: Austin Adams, Ciamac Moallemi, Sara Reynolds, Dan Robinson

Subjects: Trading and Market Microstructure (q-fin.TR); Computer Science and Game Theory (cs.GT); Optimization and Control (math.OC); Mathematical Finance (q-fin.MF)

[10]  arXiv:2403.09272 (replaced) [pdf, ps, other]

Title: Global Shipyard Capacities Limiting the Ramp-Up of Global Hydrogen Transport

Authors: Maximilian Stargardt (1,2), David Kress (1), Heidi Heinrichs (1), Jörn-Christian Meyer (3), Jochen Linßen (1), Grit Walther (3), Detlef Stolten (1,2) ((1) Forschungszentrum Jülich GmbH, Institute of Energy and Climate Research - Techno-economic Systems Analysis (IEK-3), Jülich, Germany (2) RWTH Aachen University, Chair of Fuel Cells, Faculty of Mechanical Engineering, Aachen, Germany (3) RWTH Aachen University, Chair of Operations Management, Schoolf of Business and Economics, Aachen, Germany)

Comments: Number of pages:26 + 4 pages Appendix; Number of figures: 10

Subjects: General Economics (econ.GN)

[11]  arXiv:2404.06489 (replaced) [pdf, other]

Title: Finding Stable Price Zones in European Electricity Markets: Aiming to Square the Circle?

Authors: Teodora Dobos, Martin Bichler, Johannes Knörr

Comments: 36 pages, 12 figures

Subjects: General Economics (econ.GN)

• New submissions
• Cross-lists
• Replacements