Since the advent of generative artificial intelligence, every company and researcher has been rushing to develop their own generative models, whether commercial or not. Given the large number of users of these powerful new tools, there is currently no intrinsically verifiable way to explain from the ground up what happens when LLMs (large language models) learn. For example, those based on automatic speech recognition systems, which have to rely on huge and astronomical amounts of data collected from all over the web to produce fast and efficient results, In this article, we develop a backdoor attack called MarketBackFinal 2.0, based on acoustic data poisoning, MarketBackFinal 2.0 is mainly based on modern stock market models. In order to show the possible vulnerabilities of speech-based transformers that may rely on LLMs.
{"title":"Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization","authors":"Orson Mengara","doi":"arxiv-2407.14573","DOIUrl":"https://doi.org/arxiv-2407.14573","url":null,"abstract":"Since the advent of generative artificial intelligence, every company and\u0000researcher has been rushing to develop their own generative models, whether\u0000commercial or not. Given the large number of users of these powerful new tools,\u0000there is currently no intrinsically verifiable way to explain from the ground\u0000up what happens when LLMs (large language models) learn. For example, those\u0000based on automatic speech recognition systems, which have to rely on huge and\u0000astronomical amounts of data collected from all over the web to produce fast\u0000and efficient results, In this article, we develop a backdoor attack called\u0000MarketBackFinal 2.0, based on acoustic data poisoning, MarketBackFinal 2.0 is\u0000mainly based on modern stock market models. In order to show the possible\u0000vulnerabilities of speech-based transformers that may rely on LLMs.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141774364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the relationship between bank efficiency and stock market valuation using an unbalanced panel dataset of 42 listed banks in China from 2006 to 2023. We employ a non-radial and non-oriented slack based super-efficiency Data Envelopment Analysis (Super-SBM-UND-VRS based DEA) model, which treats Non-Performing Loans (NPLs) as an undesired output. Our results show that the relationship between super-efficiency and stock market valuation is stronger than that between Return on Asset (ROA) and stock market performance, as measured by Tobin's Q. Notably, the Super-SBM-UND-VRS model yields novel results compared to other efficiency methods, such as the Stochastic Frontier Analysis (SFA) approach and traditional DEA models. Furthermore, our results suggest that bank evaluations benefit from decreased ownership concentration, whereas interest rate liberalization has the opposite effect.
本研究使用 2006 年至 2023 年中国 42 家上市银行的非平衡面板数据集研究银行效率与股票市场估值之间的关系。我们采用了基于非径向和非定向松弛的超效率数据包络分析（Super-SBM-UND-VRS based DEA）模型，该模型将不良贷款（NPLs）视为非期望产出。我们的结果表明，超效率与股票市场估值之间的关系强于资产收益率（ROA）与托宾 Q 衡量的股票市场表现之间的关系。值得注意的是，与其他效率方法（如托氏前沿分析法（SFA）和传统 DEA 模型）相比，超 SBM-UND-VRS 模式产生了新颖的结果。
{"title":"Super-efficiency and Stock Market Valuation: Evidence from Listed Banks in China (2006 to 2023)","authors":"Yun Liao","doi":"arxiv-2407.14734","DOIUrl":"https://doi.org/arxiv-2407.14734","url":null,"abstract":"This study investigates the relationship between bank efficiency and stock\u0000market valuation using an unbalanced panel dataset of 42 listed banks in China\u0000from 2006 to 2023. We employ a non-radial and non-oriented slack based\u0000super-efficiency Data Envelopment Analysis (Super-SBM-UND-VRS based DEA) model,\u0000which treats Non-Performing Loans (NPLs) as an undesired output. Our results\u0000show that the relationship between super-efficiency and stock market valuation\u0000is stronger than that between Return on Asset (ROA) and stock market\u0000performance, as measured by Tobin's Q. Notably, the Super-SBM-UND-VRS model\u0000yields novel results compared to other efficiency methods, such as the\u0000Stochastic Frontier Analysis (SFA) approach and traditional DEA models.\u0000Furthermore, our results suggest that bank evaluations benefit from decreased\u0000ownership concentration, whereas interest rate liberalization has the opposite\u0000effect.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141774245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research examines whether sentiments conveyed in the State Bank of Pakistan's (SBP) communications impact financial market expectations and can act as a monetary policy tool. To achieve our goal, we first use sentiment analysis techniques to quantify the tone of SBP monetary policy documents and second, we use short time window, high frequency methodology to approximate the impact of tone on stock market returns. Our results show that positive (negative) change in the tone positively (negatively) impacts stock returns in Karachi Stock Exchange. Further extension shows that the communication of SBP still has a statistically significant impact on stock returns when controlling for different variables and monetary policy tool. Also, the communication of SBP does not have a long term constant effect on stock market.
{"title":"Sentiment Analysis of State Bank of Pakistan's Monetary Policy Documents and its Impact on Stock Market","authors":"Aabid Karim, Heman Das Lohano","doi":"arxiv-2408.03328","DOIUrl":"https://doi.org/arxiv-2408.03328","url":null,"abstract":"This research examines whether sentiments conveyed in the State Bank of\u0000Pakistan's (SBP) communications impact financial market expectations and can\u0000act as a monetary policy tool. To achieve our goal, we first use sentiment\u0000analysis techniques to quantify the tone of SBP monetary policy documents and\u0000second, we use short time window, high frequency methodology to approximate the\u0000impact of tone on stock market returns. Our results show that positive\u0000(negative) change in the tone positively (negatively) impacts stock returns in\u0000Karachi Stock Exchange. Further extension shows that the communication of SBP\u0000still has a statistically significant impact on stock returns when controlling\u0000for different variables and monetary policy tool. Also, the communication of\u0000SBP does not have a long term constant effect on stock market.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, we present an alternative passive investment strategy. The passive investment philosophy comes from the Efficient Market Hypothesis (EMH), and its adoption is widespread. If EMH is true, one cannot outperform market by actively managing their portfolio for a long time. Also, it requires little to no intervention. People can buy an exchange-traded fund (ETF) with a long-term perspective. As the economy grows over time, one expects the ETF to grow. For example, in India, one can invest in NETF, which suppose to mimic the Nifty50 return. However, the weights of the Nifty 50 index are based on market capitalisation. These weights are not necessarily optimal for the investor. In this work, we present that volatility risk and extreme risk measures of the Nifty50 portfolio are uniformly larger than Markowitz's optimal portfolio. However, common people can't create an optimised portfolio. So we proposed an alternative passive investment strategy of an equal-weight portfolio. We show that if one pushes the maximum weight of the portfolio towards equal weight, the idiosyncratic risk of the portfolio would be minimal. The empirical evidence indicates that the risk profile of an equal-weight portfolio is similar to that of Markowitz's optimal portfolio. Hence instead of buying Nifty50 ETFs, one should equally invest in the stocks of Nifty50 to achieve a uniformly better risk profile than the Nifty 50 ETF portfolio. We also present an analysis of how portfolios perform to idiosyncratic events like the Russian invasion of Ukraine. We found that the equal weight portfolio has a uniformly lower risk than the Nifty 50 portfolio before and during the Russia-Ukraine war. All codes are available on GitHub (url{https://github.com/sourish-cmi/quant/tree/main/Chap_Risk_Anal_of_Passive_Portfolio}).
{"title":"Risk Analysis of Passive Portfolios","authors":"Sourish Das","doi":"arxiv-2407.08332","DOIUrl":"https://doi.org/arxiv-2407.08332","url":null,"abstract":"In this work, we present an alternative passive investment strategy. The\u0000passive investment philosophy comes from the Efficient Market Hypothesis (EMH),\u0000and its adoption is widespread. If EMH is true, one cannot outperform market by\u0000actively managing their portfolio for a long time. Also, it requires little to\u0000no intervention. People can buy an exchange-traded fund (ETF) with a long-term\u0000perspective. As the economy grows over time, one expects the ETF to grow. For\u0000example, in India, one can invest in NETF, which suppose to mimic the Nifty50\u0000return. However, the weights of the Nifty 50 index are based on market\u0000capitalisation. These weights are not necessarily optimal for the investor. In\u0000this work, we present that volatility risk and extreme risk measures of the\u0000Nifty50 portfolio are uniformly larger than Markowitz's optimal portfolio.\u0000However, common people can't create an optimised portfolio. So we proposed an\u0000alternative passive investment strategy of an equal-weight portfolio. We show\u0000that if one pushes the maximum weight of the portfolio towards equal weight,\u0000the idiosyncratic risk of the portfolio would be minimal. The empirical\u0000evidence indicates that the risk profile of an equal-weight portfolio is\u0000similar to that of Markowitz's optimal portfolio. Hence instead of buying\u0000Nifty50 ETFs, one should equally invest in the stocks of Nifty50 to achieve a\u0000uniformly better risk profile than the Nifty 50 ETF portfolio. We also present\u0000an analysis of how portfolios perform to idiosyncratic events like the Russian\u0000invasion of Ukraine. We found that the equal weight portfolio has a uniformly\u0000lower risk than the Nifty 50 portfolio before and during the Russia-Ukraine\u0000war. All codes are available on GitHub\u0000(url{https://github.com/sourish-cmi/quant/tree/main/Chap_Risk_Anal_of_Passive_Portfolio}).","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141612154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, a multiple split method is proposed that enables construction of multidimensional probabilistic forecasts of a selected set of variables. The method uses repeated resampling to estimate uncertainty of simultaneous multivariate predictions. This nonparametric approach links the gap between point and probabilistic predictions and can be combined with different point forecasting methods. The performance of the method is evaluated with data describing the German short-term electricity market. The results show that the proposed approach provides highly accurate predictions. The gains from multidimensional forecasting are the largest when functions of variables, such as price spread or residual load, are considered. Finally, the method is used to support a decision process of a moderate generation utility that produces electricity from wind energy and sells it on either a day-ahead or an intraday market. The company makes decisions under high uncertainty because it knows neither the future production level nor the prices. We show that joint forecasting of both market prices and fundamentals can be used to predict the distribution of a profit, and hence helps to design a strategy that balances a level of income and a trading risk.
{"title":"Multiple split approach -- multidimensional probabilistic forecasting of electricity markets","authors":"Katarzyna Maciejowska, Weronika Nitka","doi":"arxiv-2407.07795","DOIUrl":"https://doi.org/arxiv-2407.07795","url":null,"abstract":"In this article, a multiple split method is proposed that enables\u0000construction of multidimensional probabilistic forecasts of a selected set of\u0000variables. The method uses repeated resampling to estimate uncertainty of\u0000simultaneous multivariate predictions. This nonparametric approach links the\u0000gap between point and probabilistic predictions and can be combined with\u0000different point forecasting methods. The performance of the method is evaluated\u0000with data describing the German short-term electricity market. The results show\u0000that the proposed approach provides highly accurate predictions. The gains from\u0000multidimensional forecasting are the largest when functions of variables, such\u0000as price spread or residual load, are considered. Finally, the method is used to support a decision process of a moderate\u0000generation utility that produces electricity from wind energy and sells it on\u0000either a day-ahead or an intraday market. The company makes decisions under\u0000high uncertainty because it knows neither the future production level nor the\u0000prices. We show that joint forecasting of both market prices and fundamentals\u0000can be used to predict the distribution of a profit, and hence helps to design\u0000a strategy that balances a level of income and a trading risk.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141586463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yu Cheng, Junjie Guo, Shiqing Long, You Wu, Mengfang Sun, Rong Zhang
The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection by synergistically combining the advantages of graph neural networks (gnn), convolutional neural networks (cnn) and long short-term memory (LSTM) networks. This convergence enables multifaceted analysis of complex transaction patterns, improving detection accuracy and resilience against complex fraudulent activities. A key novelty of this paper is the use of multilayer perceptrons (MLPS) to estimate node similarity, effectively filtering out neighborhood noise that can lead to false positives. This intelligent purification mechanism ensures that only the most relevant information is considered, thereby improving the model's understanding of the network structure. Feature weakening often plagues graph-based models due to the dilution of key signals. In order to further address the challenge of feature weakening, GNN-CL adopts reinforcement learning strategies. By dynamically adjusting the weights assigned to central nodes, it reinforces the importance of these influential entities to retain important clues of fraud even in less informative data. Experimental evaluations on Yelp datasets show that the results highlight the superior performance of GNN-CL compared to existing methods.
{"title":"Advanced Financial Fraud Detection Using GNN-CL Model","authors":"Yu Cheng, Junjie Guo, Shiqing Long, You Wu, Mengfang Sun, Rong Zhang","doi":"arxiv-2407.06529","DOIUrl":"https://doi.org/arxiv-2407.06529","url":null,"abstract":"The innovative GNN-CL model proposed in this paper marks a breakthrough in\u0000the field of financial fraud detection by synergistically combining the\u0000advantages of graph neural networks (gnn), convolutional neural networks (cnn)\u0000and long short-term memory (LSTM) networks. This convergence enables\u0000multifaceted analysis of complex transaction patterns, improving detection\u0000accuracy and resilience against complex fraudulent activities. A key novelty of\u0000this paper is the use of multilayer perceptrons (MLPS) to estimate node\u0000similarity, effectively filtering out neighborhood noise that can lead to false\u0000positives. This intelligent purification mechanism ensures that only the most\u0000relevant information is considered, thereby improving the model's understanding\u0000of the network structure. Feature weakening often plagues graph-based models\u0000due to the dilution of key signals. In order to further address the challenge\u0000of feature weakening, GNN-CL adopts reinforcement learning strategies. By\u0000dynamically adjusting the weights assigned to central nodes, it reinforces the\u0000importance of these influential entities to retain important clues of fraud\u0000even in less informative data. Experimental evaluations on Yelp datasets show\u0000that the results highlight the superior performance of GNN-CL compared to\u0000existing methods.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141576491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this project, we propose to explore the Kalman filter's performance for estimating asset prices. We begin by introducing a stochastic mean-reverting processes, the Ornstein-Uhlenbeck (OU) model. After this we discuss the Kalman filter in detail, and its application with this model. After a demonstration of the Kalman filter on a simulated OU process and a discussion of maximum likelihood estimation (MLE) for estimating model parameters, we apply the Kalman filter with the OU process and trailing parameter estimation to real stock market data. We finish by proposing a simple day-trading algorithm using the Kalman filter with the OU process and backtest its performance using Apple's stock price. We then move to the Heston model, a combination of Geometric Brownian Motion and the OU process. Maximum likelihood estimation is commonly used for Heston model parameter estimation, which results in very complex forms. Here we propose an alternative but easier way of parameter estimation, called the method of moments (MOM). After the derivation of these estimators, we again apply this method to real stock data to assess its performance.
在本项目中，我们建议探索卡尔曼滤波器在估计资产价格方面的性能。我们首先介绍一个随机均值回复过程，即 Ornstein-Uhlenbeck (OU) 模型。之后，我们将详细讨论卡尔曼滤波器及其在该模型中的应用。在演示了卡尔曼滤波器在模拟 OU 过程中的应用，并讨论了用于估计模型参数的最大似然估计 (MLE)之后，我们将卡尔曼滤波器与 OU 过程和跟踪参数估计一起应用于真实股市数据。最后，我们提出了一种使用卡尔曼滤波和 OU 过程的简单日内交易算法，并使用苹果公司的股票价格对其性能进行了回溯测试。然后，我们转向赫斯顿模型，这是几何布朗运动和 OU 过程的结合。最大似然估计法通常用于赫斯顿模型参数估计，这会导致非常复杂的形式。在此，我们提出了另一种更简便的参数估计方法，即矩法（MOM）。在推导出这些估计方法后，我们再次将该方法应用于真实股票数据，以评估其性能。
{"title":"Stochastic Approaches to Asset Price Analysis","authors":"Michael Sekatchev, Zhengxiang Zhou","doi":"arxiv-2407.06745","DOIUrl":"https://doi.org/arxiv-2407.06745","url":null,"abstract":"In this project, we propose to explore the Kalman filter's performance for\u0000estimating asset prices. We begin by introducing a stochastic mean-reverting\u0000processes, the Ornstein-Uhlenbeck (OU) model. After this we discuss the Kalman\u0000filter in detail, and its application with this model. After a demonstration of\u0000the Kalman filter on a simulated OU process and a discussion of maximum\u0000likelihood estimation (MLE) for estimating model parameters, we apply the\u0000Kalman filter with the OU process and trailing parameter estimation to real\u0000stock market data. We finish by proposing a simple day-trading algorithm using\u0000the Kalman filter with the OU process and backtest its performance using\u0000Apple's stock price. We then move to the Heston model, a combination of\u0000Geometric Brownian Motion and the OU process. Maximum likelihood estimation is\u0000commonly used for Heston model parameter estimation, which results in very\u0000complex forms. Here we propose an alternative but easier way of parameter\u0000estimation, called the method of moments (MOM). After the derivation of these\u0000estimators, we again apply this method to real stock data to assess its\u0000performance.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141576489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a novel methodology for predicting international bilateral trade flows, emphasizing the growing importance of Preferential Trade Agreements (PTAs) in the global trade landscape. Acknowledging the limitations of traditional models like the Gravity Model of Trade, this study introduces a two-stage approach combining explainable machine learning and factorization models. The first stage employs SHAP Explainer for effective variable selection, identifying key provisions in PTAs, while the second stage utilizes Factorization Machine models to analyze the pairwise interaction effects of these provisions on trade flows. By analyzing comprehensive datasets, the paper demonstrates the efficacy of this approach. The findings not only enhance the predictive accuracy of trade flow models but also offer deeper insights into the complex dynamics of international trade, influenced by specific bilateral trade provisions.
{"title":"International Trade Flow Prediction with Bilateral Trade Provisions","authors":"Zijie Pan, Stepan Gordeev, Jiahui Zhao, Ziyi Meng, Caiwen Ding, Sandro Steinbach, Dongjin Song","doi":"arxiv-2407.13698","DOIUrl":"https://doi.org/arxiv-2407.13698","url":null,"abstract":"This paper presents a novel methodology for predicting international\u0000bilateral trade flows, emphasizing the growing importance of Preferential Trade\u0000Agreements (PTAs) in the global trade landscape. Acknowledging the limitations\u0000of traditional models like the Gravity Model of Trade, this study introduces a\u0000two-stage approach combining explainable machine learning and factorization\u0000models. The first stage employs SHAP Explainer for effective variable\u0000selection, identifying key provisions in PTAs, while the second stage utilizes\u0000Factorization Machine models to analyze the pairwise interaction effects of\u0000these provisions on trade flows. By analyzing comprehensive datasets, the paper\u0000demonstrates the efficacy of this approach. The findings not only enhance the\u0000predictive accuracy of trade flow models but also offer deeper insights into\u0000the complex dynamics of international trade, influenced by specific bilateral\u0000trade provisions.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abe Alexander, Jesse Moestaredjo, Mart Heuvelmans, Lars Fritz
In the ever evolving landscape of decentralized finance automated market makers (AMMs) play a key role: they provide a market place for trading assets in a decentralized manner. For so-called bluechip pairs, arbitrage activity provides a major part of the revenue generation of AMMs but also a major source of loss due to the so-called 'informed orderflow'. Finding ways to minimize those losses while still keeping uninformed trading activity alive is a major problem in the field. In this paper we will investigate the mechanics of said arbitrage and try to understand how AMMs can maximize the revenue creation or in other words minimize the losses. To that end, we model the dynamics of arbitrage activity for a concrete implementation of a pool and study its sensitivity to the choice of fee aiming to maximize the revenue for the AMM. We identify dynamical fees that mimic the directionality of the price due to asymmetric fee choices as a promising avenue to mitigate losses to toxic flow. This work is based on and extends a recent article by some of the authors.
{"title":"Role of fee choice in revenue generation of AMMs: A quantitative study","authors":"Abe Alexander, Jesse Moestaredjo, Mart Heuvelmans, Lars Fritz","doi":"arxiv-2406.12417","DOIUrl":"https://doi.org/arxiv-2406.12417","url":null,"abstract":"In the ever evolving landscape of decentralized finance automated market\u0000makers (AMMs) play a key role: they provide a market place for trading assets\u0000in a decentralized manner. For so-called bluechip pairs, arbitrage activity\u0000provides a major part of the revenue generation of AMMs but also a major source\u0000of loss due to the so-called 'informed orderflow'. Finding ways to minimize\u0000those losses while still keeping uninformed trading activity alive is a major\u0000problem in the field. In this paper we will investigate the mechanics of said\u0000arbitrage and try to understand how AMMs can maximize the revenue creation or\u0000in other words minimize the losses. To that end, we model the dynamics of\u0000arbitrage activity for a concrete implementation of a pool and study its\u0000sensitivity to the choice of fee aiming to maximize the revenue for the AMM. We\u0000identify dynamical fees that mimic the directionality of the price due to\u0000asymmetric fee choices as a promising avenue to mitigate losses to toxic flow.\u0000This work is based on and extends a recent article by some of the authors.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Financial markets have long since been modeled using stochastic methods such as Brownian motion, and more recently, rough volatility models have been built using fractional Brownian motion. This fractional aspect brings memory into the system. In this project, we describe and analyze a financial model based on the fractional Langevin equation with colored noise generated by fractional Brownian motion. Physics-based methods of analysis are used to examine the phase behavior and dispersion relations of the system upon varying input parameters. A type of anomalous marginal glass phase is potentially seen in some regions, which motivates further exploration of this model and expanded use of phase behavior and dispersion relation methods to analyze financial models.
{"title":"Modeling a Financial System with Memory via Fractional Calculus and Fractional Brownian Motion","authors":"Patrick Geraghty","doi":"arxiv-2406.19408","DOIUrl":"https://doi.org/arxiv-2406.19408","url":null,"abstract":"Financial markets have long since been modeled using stochastic methods such\u0000as Brownian motion, and more recently, rough volatility models have been built\u0000using fractional Brownian motion. This fractional aspect brings memory into the\u0000system. In this project, we describe and analyze a financial model based on the\u0000fractional Langevin equation with colored noise generated by fractional\u0000Brownian motion. Physics-based methods of analysis are used to examine the\u0000phase behavior and dispersion relations of the system upon varying input\u0000parameters. A type of anomalous marginal glass phase is potentially seen in\u0000some regions, which motivates further exploration of this model and expanded\u0000use of phase behavior and dispersion relation methods to analyze financial\u0000models.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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