Quantitative Finance

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New submissions for Tue, 30 Apr 24

[1]  arXiv:2404.17713 [pdf, other]

Title: Revisiting the Resource Curse in the Age of Energy Transition: Cobalt Reserves and Conflict in Africa

Authors: Weihong Qi

Subjects: General Economics (econ.GN)

This study reevaluates the traditional understanding of the "political resource curse" by examining the unique impact of energy transition metals, specifically cobalt, on local-level conflicts in Africa. Contrary to previous studies that primarily focus on high-value minerals and their political outcomes resulted from substantial economic revenues, this study investigates cobalt's influence on local conflict. Despite its strategic importance, cobalt's limited commercial value presents a unique yet critical case for analysis. Different with the prevailing view that links mineral reserves with increased conflict, this research finds that regions rich in cobalt experience a reduction in conflict. This decrease is attributed to enhanced government security measures, which are implemented independently of the economic benefits derived from cobalt as a commodity. The study utilizes a combination of georeferenced data and a difference-in-difference design to analyze the causal relationship between cobalt deposits and regional conflict. The findings suggest that the presence of cobalt deposits leads to enhanced security interventions by governments, effectively reducing the likelihood of non-governmental actors taking control of these territories. This pattern offers a new perspective on the role of energy transition metals in shaping conflict and governance, highlighting the need to reassess theoretical frameworks related to the political implications of natural resources with the ongoing energy revolution.

[2]  arXiv:2404.18009 [pdf, other]

Title: Exit Spillovers of Foreign-invested Enterprises in Shenzhen's Electronics Manufacturing Industry

Authors: Hanqiao Zhang

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

Neighborhood characteristics have been broadly studied with different firm behaviors, e.g. birth, entry, expansion, and survival, except for firm exit. Using a novel dataset of foreign-invested enterprises operating in Shenzhen's electronics manufacturing industry from 2017 to 2021, I investigate the spillover effects of firm exits on other firms in the vicinity, from both the industry group and the industry class level. Significant neighborhood effects are identified for the industry group level, but not the industry class level.

[3]  arXiv:2404.18017 [pdf, ps, other]

Title: Application of Deep Learning for Factor Timing in Asset Management

Authors: Prabhu Prasad Panda, Maysam Khodayari Gharanchaei, Xilin Chen, Haoshu Lyu

Subjects: Portfolio Management (q-fin.PM); Machine Learning (cs.LG); Computational Finance (q-fin.CP)

The paper examines the performance of regression models (OLS linear regression, Ridge regression, Random Forest, and Fully-connected Neural Network) on the prediction of CMA (Conservative Minus Aggressive) factor premium and the performance of factor timing investment with them. Out-of-sample R-squared shows that more flexible models have better performance in explaining the variance in factor premium of the unseen period, and the back testing affirms that the factor timing based on more flexible models tends to over perform the ones with linear models. However, for flexible models like neural networks, the optimal weights based on their prediction tend to be unstable, which can lead to high transaction costs and market impacts. We verify that tilting down the rebalance frequency according to the historical optimal rebalancing scheme can help reduce the transaction costs.

[4]  arXiv:2404.18029 [pdf, other]

Title: Value-at-Risk- and Expectile-based Systemic Risk Measures and Second-order Asymptotics: With Applications to Diversification

Authors: Bingzhen Geng, Yang Liu, Yimiao Zhao

Comments: Keywords: Asymptotic approximation; Systemic risk; Expectile; Sarmanov distribution; Second-order regular variation; Diversification benefit

Subjects: Risk Management (q-fin.RM)

The systemic risk measure plays a crucial role in analyzing individual losses conditioned on extreme system-wide disasters. In this paper, we provide a unified asymptotic treatment for systemic risk measures. First, we classify them into two families of Value-at-Risk- (VaR-) and expectile-based systemic risk measures. While VaR has been extensively studied, in the latter family, we propose two new systemic risk measures named the Individual Conditional Expectile (ICE) and the Systemic Individual Conditional Expectile (SICE), as alternatives to Marginal Expected Shortfall (MES) and Systemic Expected Shortfall (SES). Second, to characterize general mutually dependent and heavy-tailed risks, we adopt a modeling framework where the system, represented by a vector of random loss variables, follows a multivariate Sarmanov distribution with a common marginal exhibiting second-order regular variation. Third, we provide second-order asymptotic results for both families of systemic risk measures. This analytical framework offers a more accurate estimate compared to traditional first-order asymptotics. Through numerical and analytical examples, we demonstrate the superiority of second-order asymptotics in accurately assessing systemic risk. Further, we conduct a comprehensive comparison between VaR-based and expectile-based systemic risk measures. Expectile-based measures output higher risk evaluation than VaR-based ones, emphasizing the former's potential advantages in reporting extreme events and tail risk. As a financial application, we use the asymptotic treatment to discuss the diversification benefits associated with systemic risk measures. The expectile-based diversification benefits consistently deduce an underestimation and suggest a conservative approximation, while the VaR-based diversification benefits consistently deduce an overestimation and suggest behaving optimistically.

[5]  arXiv:2404.18183 [pdf, ps, other]

Title: Innovative Application of Artificial Intelligence Technology in Bank Credit Risk Management

Authors: Shuochen Bi, Wenqing Bao

Comments: 6 pages, 1 figure, 2 tables

Journal-ref: International Journal of Global Economics and Management ISSN: 3005-9690 (Print), ISSN: 3005-8090 (Online) | Volume 2, Number 3, Year 2024

Subjects: Risk Management (q-fin.RM); Artificial Intelligence (cs.AI)

With the rapid growth of technology, especially the widespread application of artificial intelligence (AI) technology, the risk management level of commercial banks is constantly reaching new heights. In the current wave of digitalization, AI has become a key driving force for the strategic transformation of financial institutions, especially the banking industry. For commercial banks, the stability and safety of asset quality are crucial, which directly relates to the long-term stable growth of the bank. Among them, credit risk management is particularly core because it involves the flow of a large amount of funds and the accuracy of credit decisions. Therefore, establishing a scientific and effective credit risk decision-making mechanism is of great strategic significance for commercial banks. In this context, the innovative application of AI technology has brought revolutionary changes to bank credit risk management. Through deep learning and big data analysis, AI can accurately evaluate the credit status of borrowers, timely identify potential risks, and provide banks with more accurate and comprehensive credit decision support. At the same time, AI can also achieve realtime monitoring and early warning, helping banks intervene before risks occur and reduce losses.

[6]  arXiv:2404.18184 [pdf, ps, other]

Title: Application and practice of AI technology in quantitative investment

Authors: Shuochen Bi, Wenqing Bao, Jue Xiao, Jiangshan Wang, Tingting Deng

Comments: 9 pages,2 figures

Journal-ref: Information Systems and Economics (2024) Clausius Scientific Press, Canada , ISSN 2523-6407 Vol. 5 Num. 2

Subjects: Portfolio Management (q-fin.PM)

With the continuous development of artificial intelligence technology, using machine learning technology to predict market trends may no longer be out of reach. In recent years, artificial intelligence has become a research hotspot in the academic circle,and it has been widely used in image recognition, natural language processing and other fields, and also has a huge impact on the field of quantitative investment. As an investment method to obtain stable returns through data analysis, model construction and program trading, quantitative investment is deeply loved by financial institutions and investors. At the same time, as an important application field of quantitative investment, the quantitative investment strategy based on artificial intelligence technology arises at the historic moment.How to apply artificial intelligence to quantitative investment, so as to better achieve profit and risk control, has also become the focus and difficulty of the research. From a global perspective, inflation in the US and the Federal Reserve are the concerns of investors, which to some extent affects the direction of global assets, including the Chinese stock market. This paper studies the application of AI technology, quantitative investment, and AI technology in quantitative investment, aiming to provide investors with auxiliary decision-making, reduce the difficulty of investment analysis, and help them to obtain higher returns.

[7]  arXiv:2404.18200 [pdf, other]

Title: Mean Field Game of High-Frequency Anticipatory Trading

Authors: Xue Cheng, Meng Wang, Ziyi Xu

Subjects: Mathematical Finance (q-fin.MF)

The interactions between a large population of high-frequency traders (HFTs) and a large trader (LT) who executes a certain amount of assets at discrete time points are studied. HFTs are faster in the sense that they trade continuously and predict the transactions of LT. A jump process is applied to model the transition of HFTs' attitudes towards inventories and the equilibrium is solved through the mean field game approach. When the crowd of HFTs is averse to running (ending) inventories, they first take then supply liquidity at each transaction of LT (throughout the whole execution period). Inventory-averse HFTs lower LT's costs if the market temporary impact is relatively large to the permanent one. What's more, the repeated liquidity consuming-supplying behavior of HFTs makes LT's optimal strategy close to uniform trading.

[8]  arXiv:2404.18445 [pdf, other]

Title: Strategic Behavior and AI Training Data

Authors: Christian Peukert, Florian Abeillon, Jérémie Haese, Franziska Kaiser, Alexander Staub

Subjects: General Economics (econ.GN); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)

Human-created works represent critical data inputs to artificial intelligence (AI). Strategic behavior can play a major role for AI training datasets, be it in limiting access to existing works or in deciding which types of new works to create or whether to create new works at all. We examine creators' behavioral change when their works become training data for AI. Specifically, we focus on contributors on Unsplash, a popular stock image platform with about 6 million high-quality photos and illustrations. In the summer of 2020, Unsplash launched an AI research program by releasing a dataset of 25,000 images for commercial use. We study contributors' reactions, comparing contributors whose works were included in this dataset to contributors whose works were not included. Our results suggest that treated contributors left the platform at a higher-than-usual rate and substantially slowed down the rate of new uploads. Professional and more successful photographers react stronger than amateurs and less successful photographers. We also show that affected users changed the variety and novelty of contributions to the platform, with long-run implications for the stock of works potentially available for AI training. Taken together, our findings highlight the trade-off between interests of rightsholders and promoting innovation at the technological frontier. We discuss implications for copyright and AI policy.

[9]  arXiv:2404.18467 [pdf, other]

Title: Dominance between combinations of infinite-mean Pareto random variables

Authors: Yuyu Chen, Taizhong Hu, Ruodu Wang, Zhenfeng Zou

Subjects: Portfolio Management (q-fin.PM); Theoretical Economics (econ.TH)

We study stochastic dominance between portfolios of independent and identically distributed (iid) extremely heavy-tailed (i.e., infinite-mean) Pareto random variables. With the notion of majorization order, we show that a more diversified portfolio of iid extremely heavy-tailed Pareto random variables is larger in the sense of first-order stochastic dominance. This result is further generalized for Pareto random variables caused by triggering events, random variables with tails being Pareto, bounded Pareto random variables, and positively dependent Pareto random variables. These results provide an important implication in investment: Diversification of extremely heavy-tailed Pareto profits uniformly increases investors' profitability, leading to a diversification benefit. Remarkably, different from the finite-mean setting, such a diversification benefit does not depend on the decision maker's risk aversion.

[10]  arXiv:2404.18499 [pdf, other]

Title: Quantitative Tools for Time Series Analysis in Natural Language Processing: A Practitioners Guide

Authors: W. Benedikt Schmal

Subjects: General Economics (econ.GN)

Natural language processing tools have become frequently used in social sciences such as economics, political science, and sociology. Many publications apply topic modeling to elicit latent topics in text corpora and their development over time. Here, most publications rely on visual inspections and draw inference on changes, structural breaks, and developments over time. We suggest using univariate time series econometrics to introduce more quantitative rigor that can strengthen the analyses. In particular, we discuss the econometric topics of non-stationarity as well as structural breaks. This paper serves as a comprehensive practitioners guide to provide researchers in the social and life sciences as well as the humanities with concise advice on how to implement econometric time series methods to thoroughly investigate topic prevalences over time. We provide coding advice for the statistical software R throughout the paper. The application of the discussed tools to a sample dataset completes the analysis.

[11]  arXiv:2404.18761 [pdf, other]

Title: A pure dual approach for hedging Bermudan options

Authors: Aurélien Alfonsi, Ahmed Kebaier, Jérôme Lelong

Subjects: Mathematical Finance (q-fin.MF); Probability (math.PR); Computational Finance (q-fin.CP)

This paper develops a new dual approach to compute the hedging portfolio of a Bermudan option and its initial value. It gives a "purely dual" algorithm following the spirit of Rogers (2010) in the sense that it only relies on the dual pricing formula. The key is to rewrite the dual formula as an excess reward representation and to combine it with a strict convexification technique. The hedging strategy is then obtained by using a Monte Carlo method, solving backward a sequence of least square problems. We show convergence results for our algorithm and test it on many different Bermudan options. Beyond giving directly the hedging portfolio, the strength of the algorithm is to assess both the relevance of including financial instruments in the hedging portfolio and the effect of the rebalancing frequency.

[12]  arXiv:2404.18822 [pdf, other]

Title: A Multi-Period Black-Litterman Model

Authors: Anas Abdelhakmi, Andrew Lim

Subjects: Portfolio Management (q-fin.PM)

The Black-Litterman model is a framework for incorporating forward-looking expert views in a portfolio optimization problem. Existing work focuses almost exclusively on single-period problems and assumes that the horizon of expert forecasts matches that of the investor. We consider a multi-period generalization where the horizon of expert views may differ from that of a dynamically-trading investor. By exploiting an underlying graphical structure relating the asset prices and views, we derive the conditional distribution of asset returns when the price process is geometric Brownian motion. We also show that it can be written in terms of a multi-dimensional Brownian bridge. The new price process is an affine factor model with the conditional log-price process playing the role of a vector of factors. We derive an explicit expression for the optimal dynamic investment policy and analyze the hedging demand associated with the new covariate. More generally, the paper shows that Bayesian graphical models are a natural framework for incorporating complex information structures in the Black-Litterman model.

Cross-lists for Tue, 30 Apr 24

[13]  arXiv:2404.17227 (cross-list from econ.GN) [pdf, other]

Title: Trust Dynamics and Market Behavior in Cryptocurrency: A Comparative Study of Centralized and Decentralized Exchanges

Authors: Xintong Wu, Wanling Deng, Yuotng Quan, Luyao Zhang

Subjects: General Economics (econ.GN); Computational Engineering, Finance, and Science (cs.CE); Cryptography and Security (cs.CR); Computers and Society (cs.CY); Risk Management (q-fin.RM)

In the evolving landscape of digital finance, the transition from centralized to decentralized trust mechanisms, primarily driven by blockchain technology, plays a critical role in shaping the cryptocurrency ecosystem. This paradigm shift raises questions about the traditional reliance on centralized trust and introduces a novel, decentralized trust framework built upon distributed networks. Our research delves into the consequences of this shift, particularly focusing on how incidents influence trust within cryptocurrency markets, thereby affecting trade behaviors in centralized (CEXs) and decentralized exchanges (DEXs). We conduct a comprehensive analysis of various events, assessing their effects on market dynamics, including token valuation and trading volumes in both CEXs and DEXs. Our findings highlight the pivotal role of trust in directing user preferences and the fluidity of trust transfer between centralized and decentralized platforms. Despite certain anomalies, the results largely align with our initial hypotheses, revealing the intricate nature of user trust in cryptocurrency markets. This study contributes significantly to interdisciplinary research, bridging distributed systems, behavioral finance, and Decentralized Finance (DeFi). It offers valuable insights for the distributed computing community, particularly in understanding and applying distributed trust mechanisms in digital economies, paving the way for future research that could further explore the socio-economic dimensions and leverage blockchain data in this dynamic domain.

[14]  arXiv:2404.17915 (cross-list from econ.TH) [pdf, ps, other]

Title: Bertrand oligopoly in insurance markets with Value at Risk Constraints

Authors: Kolos Csaba Ágoston, Veronika Varga

Comments: 47 pages

Subjects: Theoretical Economics (econ.TH); Risk Management (q-fin.RM)

Since 2016 the operation of insurance companies in the European Union is regulated by the Solvency II directive. According to the EU directive the capital requirement should be calculated as a 99.5\% of Value at Risk. In this study, we examine the impact of this capital requirement constraint on equilibrium premiums and capitals. We discuss the case of the oligopoly insurance market using Bertrand's model, assuming profit maximizing insurance companies facing Value at Risk constraints. First we analyze companies' decision on premium level. The companies strategic behavior can result positive as well as negative expected profit for companies. The desired situation where competition eliminate positive profit and lead the market to zero-profit state is rare. Later we examine ex post and ax ante capital adjustments. Capital adjustment does not rule out market anomalies, although somehow changes them. Possibility of capital adjustment can lead the market to a situation where all of the companies suffer loss. Allowing capital adjustment results monopolistic premium level or market failure with positive probabilities.

[15]  arXiv:2404.18148 (cross-list from cs.GT) [pdf, other]

Title: Decentralized Peer Review in Open Science: A Mechanism Proposal

Authors: Andreas Finke, Thomas Hensel

Comments: 14 pages, 1 figure

Subjects: Computer Science and Game Theory (cs.GT); Computers and Society (cs.CY); General Economics (econ.GN)

Peer review is a laborious, yet essential, part of academic publishing with crucial impact on the scientific endeavor. The current lack of incentives and transparency harms the credibility of this process. Researchers are neither rewarded for superior nor penalized for bad reviews. Additionally, confidential reports cause a loss of insights and make the review process vulnerable to scientific misconduct. We propose a community-owned and -governed system that 1) remunerates reviewers for their efforts, 2) publishes the (anonymized) reports for scrutiny by the community, 3) tracks reputation of reviewers and 4) provides digital certificates. Automated by transparent smart-contract blockchain technology, the system aims to increase quality and speed of peer review while lowering the chance and impact of erroneous judgements.

[16]  arXiv:2404.18470 (cross-list from cs.CE) [pdf, other]

Title: ECC Analyzer: Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction

Authors: Yupeng Cao, Zhi Chen, Qingyun Pei, Prashant Kumar, K.P. Subbalakshmi, Papa Momar Ndiaye

Comments: 15 pages, 3 figures, 5 tables

Subjects: Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Risk Management (q-fin.RM); Trading and Market Microstructure (q-fin.TR)

In the realm of financial analytics, leveraging unstructured data, such as earnings conference calls (ECCs), to forecast stock performance is a critical challenge that has attracted both academics and investors. While previous studies have used deep learning-based models to obtain a general view of ECCs, they often fail to capture detailed, complex information. Our study introduces a novel framework: \textbf{ECC Analyzer}, combining Large Language Models (LLMs) and multi-modal techniques to extract richer, more predictive insights. The model begins by summarizing the transcript's structure and analyzing the speakers' mode and confidence level by detecting variations in tone and pitch for audio. This analysis helps investors form an overview perception of the ECCs. Moreover, this model uses the Retrieval-Augmented Generation (RAG) based methods to meticulously extract the focuses that have a significant impact on stock performance from an expert's perspective, providing a more targeted analysis. The model goes a step further by enriching these extracted focuses with additional layers of analysis, such as sentiment and audio segment features. By integrating these insights, the ECC Analyzer performs multi-task predictions of stock performance, including volatility, value-at-risk (VaR), and return for different intervals. The results show that our model outperforms traditional analytic benchmarks, confirming the effectiveness of using advanced LLM techniques in financial analytics.

Replacements for Tue, 30 Apr 24

[17]  arXiv:1911.12944 (replaced) [pdf, ps, other]

Title: Pricing and hedging short-maturity Asian options in local volatility models

Authors: Jaehyun Kim, Hyungbin Park, Jonghwa Park

Subjects: Mathematical Finance (q-fin.MF)

[18]  arXiv:2006.13850 (replaced) [pdf, other]

Title: Global Sensitivity and Domain-Selective Testing for Functional-Valued Responses: An Application to Climate Economy Models

Authors: Matteo Fontana, Massimo Tavoni, Simone Vantini

Subjects: Methodology (stat.ME); General Economics (econ.GN)

[19]  arXiv:2101.08559 (replaced) [pdf, ps, other]

Title: To VaR, or Not to VaR, That is the Question

Authors: Victor Olkhov

Comments: 11 pages

Subjects: General Economics (econ.GN); General Finance (q-fin.GN); Portfolio Management (q-fin.PM); Pricing of Securities (q-fin.PR); Risk Management (q-fin.RM)

[20]  arXiv:2306.00574 (replaced) [pdf, other]

Title: Life after (Soft) Default

Authors: Giacomo De Giorgi, Costanza Naguib

Subjects: General Economics (econ.GN)

[21]  arXiv:2306.08157 (replaced) [pdf, other]

Title: Causal Feature Engineering of Price Directions of Cryptocurrencies using Dynamic Bayesian Networks

Authors: Rasoul Amirzadeh, Asef Nazari, Dhananjay Thiruvady, Mong Shan Ee

Comments: 32 pages, 8 figures, 6 tables

Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Statistical Finance (q-fin.ST)

[22]  arXiv:2309.02072 (replaced) [pdf, other]

Title: Data Scaling Effect of Deep Learning in Financial Time Series Forecasting

Authors: Chen Liu, Minh-Ngoc Tran, Chao Wang, Richard Gerlach, Robert Kohn

Subjects: Econometrics (econ.EM); Artificial Intelligence (cs.AI); Computational Finance (q-fin.CP)

[23]  arXiv:2312.14437 (replaced) [pdf, other]

Title: Time-inconsistent mean field and n-agent games under relative performance criteria

Authors: Zongxia Liang, Keyu Zhang

Subjects: Mathematical Finance (q-fin.MF)

[24]  arXiv:2401.11345 (replaced) [pdf, ps, other]

Title: Fake Google restaurant reviews and the implications for consumers and restaurants

Authors: Shawn Berry

Comments: pp.1-158, 41 tables, 11 figures. Doctor of Business Administration Dissertation

Subjects: General Economics (econ.GN)

[25]  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)

[26]  arXiv:2403.18837 (replaced) [pdf, ps, other]

Title: Repetitive Dilemma Games in Distribution Information Using Interplay of Droop Quota: Meek's Method in Impact of Maximum Compensation and Minimum Cost Routes in Information Role of Marginal Contribution in Two-Sided Matching Markets

Authors: Yasuko Kawahata

Comments: Wallace's Law, Droop Quota, Meek's Method, Marginal Contribution, Two-Sided Matching Market, Repetitive Dilemma Game, Maximum Compensation Problem, Minimum Cost Pathways, Fake News, Fact-Checking, Information Market Equilibrium

Subjects: General Economics (econ.GN); Computer Science and Game Theory (cs.GT); Theoretical Economics (econ.TH); Physics and Society (physics.soc-ph)

• New submissions
• Cross-lists
• Replacements