arXiv - QuantFin - Statistical Finance最新文献_第4页

Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization 交易魔鬼决赛：通过股市和贝叶斯优化进行后门攻击

arXiv - QuantFin - Statistical Finance Pub Date : 2024-07-21 DOI: arxiv-2407.14573

Orson Mengara

引用次数: 0

Super-efficiency and Stock Market Valuation: Evidence from Listed Banks in China (2006 to 2023) 超效率与股市估值：中国上市银行的证据（2006 年至 2023 年）

arXiv - QuantFin - Statistical Finance Pub Date : 2024-07-20 DOI: arxiv-2407.14734

Yun Liao

引用次数: 0

Sentiment Analysis of State Bank of Pakistan's Monetary Policy Documents and its Impact on Stock Market 巴基斯坦国家银行货币政策文件的情绪分析及其对股市的影响

arXiv - QuantFin - Statistical Finance Pub Date : 2024-07-19 DOI: arxiv-2408.03328

Aabid Karim, Heman Das Lohano

引用次数: 0

Risk Analysis of Passive Portfolios 被动投资组合的风险分析

arXiv - QuantFin - Statistical Finance Pub Date : 2024-07-11 DOI: arxiv-2407.08332

Sourish Das

{"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}

引用次数: 0

Multiple split approach -- multidimensional probabilistic forecasting of electricity markets 多重分割法--电力市场的多维概率预测

arXiv - QuantFin - Statistical Finance Pub Date : 2024-07-10 DOI: arxiv-2407.07795

Katarzyna Maciejowska, Weronika Nitka

{"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}

引用次数: 0

Advanced Financial Fraud Detection Using GNN-CL Model 利用 GNN-CL 模型进行高级金融欺诈检测

arXiv - QuantFin - Statistical Finance Pub Date : 2024-07-09 DOI: arxiv-2407.06529

Yu Cheng, Junjie Guo, Shiqing Long, You Wu, Mengfang Sun, Rong Zhang

{"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}

引用次数: 0

Stochastic Approaches to Asset Price Analysis 资产价格分析的随机方法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-07-09 DOI: arxiv-2407.06745

Michael Sekatchev, Zhengxiang Zhou

{"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}

引用次数: 0

International Trade Flow Prediction with Bilateral Trade Provisions 利用双边贸易条款预测国际贸易流量

arXiv - QuantFin - Statistical Finance Pub Date : 2024-06-23 DOI: arxiv-2407.13698

Zijie Pan, Stepan Gordeev, Jiahui Zhao, Ziyi Meng, Caiwen Ding, Sandro Steinbach, Dongjin Song

引用次数: 0

Role of fee choice in revenue generation of AMMs: A quantitative study 收费选择在 AMM 创收中的作用：定量研究

arXiv - QuantFin - Statistical Finance Pub Date : 2024-06-18 DOI: arxiv-2406.12417

Abe Alexander, Jesse Moestaredjo, Mart Heuvelmans, Lars Fritz

引用次数: 0

Modeling a Financial System with Memory via Fractional Calculus and Fractional Brownian Motion 通过分数微积分和分数布朗运动模拟有记忆的金融系统

arXiv - QuantFin - Statistical Finance Pub Date : 2024-06-12 DOI: arxiv-2406.19408

Patrick Geraghty

引用次数: 0