arXiv - QuantFin - Computational Finance最新文献_第9页

Hedonic Models Incorporating ESG Factors for Time Series of Average Annual Home Prices 纳入 ESG 因素的年均房价时间序列的对数模型

arXiv - QuantFin - Computational Finance Pub Date : 2024-04-10 DOI: arxiv-2404.07132

Jason R. Bailey, W. Brent Lindquist, Svetlozar T. Rachev

{"title":"Hedonic Models Incorporating ESG Factors for Time Series of Average Annual Home Prices","authors":"Jason R. Bailey, W. Brent Lindquist, Svetlozar T. Rachev","doi":"arxiv-2404.07132","DOIUrl":"https://doi.org/arxiv-2404.07132","url":null,"abstract":"Using data from 2000 through 2022, we analyze the predictive capability of\u0000the annual numbers of new home constructions and four available environmental,\u0000social, and governance factors on the average annual price of homes sold in\u0000eight major U.S. cities. We contrast the predictive capability of a P-spline\u0000generalized additive model (GAM) against a strictly linear version of the\u0000commonly used generalized linear model (GLM). As the data for the annual price\u0000and predictor variables constitute non-stationary time series, to avoid\u0000spurious correlations in the analysis we transform each time series\u0000appropriately to produce stationary series for use in the GAM and GLM models.\u0000While arithmetic returns or first differences are adequate transformations for\u0000the predictor variables, for the average price response variable we utilize the\u0000series of innovations obtained from AR(q)-ARCH(1) fits. Based on the GAM\u0000results, we find that the influence of ESG factors varies markedly by city,\u0000reflecting geographic diversity. Notably, the presence of air conditioning\u0000emerges as a strong factor. Despite limitations on the length of available time\u0000series, this study represents a pivotal step toward integrating ESG\u0000considerations into predictive real estate models.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565598","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

Some variation of COBRA in sequential learning setup 顺序学习设置中 COBRA 的一些变化

arXiv - QuantFin - Computational Finance Pub Date : 2024-04-07 DOI: arxiv-2405.04539

Aryan Bhambu, Arabin Kumar Dey

引用次数: 0

The Life Care Annuity: enhancing product features and refining pricing methods 生命关怀年金：增强产品功能，完善定价方法

arXiv - QuantFin - Computational Finance Pub Date : 2024-04-03 DOI: arxiv-2404.02858

G. Apicella, A. Molent, M. Gaudenzi

引用次数: 0

Intelligent Optimization of Mine Environmental Damage Assessment and Repair Strategies Based on Deep Learning 基于深度学习的矿山环境损害评估与修复策略智能优化

arXiv - QuantFin - Computational Finance Pub Date : 2024-04-02 DOI: arxiv-2404.01624

Qishuo Cheng

{"title":"Intelligent Optimization of Mine Environmental Damage Assessment and Repair Strategies Based on Deep Learning","authors":"Qishuo Cheng","doi":"arxiv-2404.01624","DOIUrl":"https://doi.org/arxiv-2404.01624","url":null,"abstract":"In recent decades, financial quantification has emerged and matured rapidly.\u0000For financial institutions such as funds, investment institutions are\u0000increasingly dissatisfied with the situation of passively constructing\u0000investment portfolios with average market returns, and are paying more and more\u0000attention to active quantitative strategy investment portfolios. This requires\u0000the introduction of active stock investment fund management models. Currently,\u0000in my country's stock fund investment market, there are many active\u0000quantitative investment strategies, and the algorithms used vary widely, such\u0000as SVM, random forest, RNN recurrent memory network, etc. This article focuses\u0000on this trend, using the emerging LSTM-GRU gate-controlled long short-term\u0000memory network model in the field of financial stock investment as a basis to\u0000build a set of active investment stock strategies, and combining it with SVM,\u0000which has been widely used in the field of quantitative stock investment.\u0000Comparing models such as RNN, theoretically speaking, compared to SVM that\u0000simply relies on kernel functions for high-order mapping and classification of\u0000data, neural network algorithms such as RNN and LSTM-GRU have better principles\u0000and are more suitable for processing financial stock data. Then, through\u0000multiple By comparison, it was finally found that the LSTM- GRU gate-controlled\u0000long short-term memory network has a better accuracy. By selecting the LSTM-GRU\u0000algorithm to construct a trading strategy based on the Shanghai and Shenzhen\u0000300 Index constituent stocks, the parameters were adjusted and the neural layer\u0000connection was adjusted. Finally, It has significantly outperformed the\u0000benchmark index CSI 300 over the long term. The conclusion of this article is\u0000that the research results can provide certain quantitative strategy references\u0000for financial institutions to construct active stock investment portfolios.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566129","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

Construction of a Japanese Financial Benchmark for Large Language Models 为大型语言模型构建日语金融基准

arXiv - QuantFin - Computational Finance Pub Date : 2024-03-22 DOI: arxiv-2403.15062

Masanori Hirano

引用次数: 0

Enhancing Law Enforcement Training: A Gamified Approach to Detecting Terrorism Financing 加强执法培训：侦查资助恐怖主义行为的游戏化方法

arXiv - QuantFin - Computational Finance Pub Date : 2024-03-20 DOI: arxiv-2403.13625

Francesco Zola, Lander Segurola, Erin King, Martin Mullins, Raul Orduna

{"title":"Enhancing Law Enforcement Training: A Gamified Approach to Detecting Terrorism Financing","authors":"Francesco Zola, Lander Segurola, Erin King, Martin Mullins, Raul Orduna","doi":"arxiv-2403.13625","DOIUrl":"https://doi.org/arxiv-2403.13625","url":null,"abstract":"Tools for fighting cyber-criminal activities using new technologies are\u0000promoted and deployed every day. However, too often, they are unnecessarily\u0000complex and hard to use, requiring deep domain and technical knowledge. These\u0000characteristics often limit the engagement of law enforcement and end-users in\u0000these technologies that, despite their potential, remain misunderstood. For\u0000this reason, in this study, we describe our experience in combining learning\u0000and training methods and the potential benefits of gamification to enhance\u0000technology transfer and increase adult learning. In fact, in this case,\u0000participants are experienced practitioners in professions/industries that are\u0000exposed to terrorism financing (such as Law Enforcement Officers, Financial\u0000Investigation Officers, private investigators, etc.) We define training\u0000activities on different levels for increasing the exchange of information about\u0000new trends and criminal modus operandi among and within law enforcement\u0000agencies, intensifying cross-border cooperation and supporting efforts to\u0000combat and prevent terrorism funding activities. On the other hand, a game\u0000(hackathon) is designed to address realistic challenges related to the dark\u0000net, crypto assets, new payment systems and dark web marketplaces that could be\u0000used for terrorist activities. The entire methodology was evaluated using\u0000quizzes, contest results, and engagement metrics. In particular, training\u0000events show about 60% of participants complete the 11-week training course,\u0000while the Hackathon results, gathered in two pilot studies (Madrid and The\u0000Hague), show increasing expertise among the participants (progression in the\u0000achieved points on average). At the same time, more than 70% of participants\u0000positively evaluate the use of the gamification approach, and more than 85% of\u0000them consider the implemented Use Cases suitable for their investigations.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"159 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140204349","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

A path-dependent PDE solver based on signature kernels 基于签名核的路径依赖 PDE 求解器

arXiv - QuantFin - Computational Finance Pub Date : 2024-03-18 DOI: arxiv-2403.11738

Alexandre Pannier, Cristopher Salvi

引用次数: 0

The Democratization of Wealth Management: Hedged Mutual Fund Blockchain Protocol 财富管理的民主化：对冲共同基金区块链协议

arXiv - QuantFin - Computational Finance Pub Date : 2024-03-12 DOI: arxiv-2405.02302

Ravi Kashyap

{"title":"The Democratization of Wealth Management: Hedged Mutual Fund Blockchain Protocol","authors":"Ravi Kashyap","doi":"arxiv-2405.02302","DOIUrl":"https://doi.org/arxiv-2405.02302","url":null,"abstract":"We develop several innovations designed to bring the best practices of\u0000traditional investment funds to the blockchain landscape. Our innovations\u0000combine the superior mechanisms of mutual funds and hedge funds. Specifically,\u0000we illustrate how fund prices can be updated regularly like mutual funds and\u0000performance fees can be charged like hedge funds. We show how mutually hedged\u0000blockchain investment funds can operate with investor protection schemes - high\u0000water marks - and measures to offset trading slippage when redemptions happen.\u0000We provide detailed steps - including mathematical formulations and instructive\u0000pointers - to implement these ideas as blockchain smart contracts. We discuss\u0000how our designs overcome several blockchain bottlenecks and how we can make\u0000smart contracts smarter. We provide numerical illustrations of several\u0000scenarios related to the mechanisms we have tailored for blockchain\u0000implementation. The concepts we have developed for blockchain implementation can also be\u0000useful in traditional financial funds to calculate performance fees in a\u0000simplified manner. We highlight two main issues with the operation of mutual\u0000funds and hedge funds and show how blockchain technology can alleviate those\u0000concerns. The ideas developed here illustrate on one hand, how blockchain can\u0000solve many issues faced by the traditional world and on the other hand, how\u0000many innovations from traditional finance can benefit decentralized finance and\u0000speed its adoption. This becomes an example of symbiosis between decentralized\u0000and traditional finance - bringing these two realms closer and breaking down\u0000barriers between such artificial distinctions - wherein the future will be\u0000about providing better risk adjusted wealth appreciation opportunities to end\u0000customers through secure, reliable, accessible and transparent services -\u0000without getting too caught up about how such services are being rendered.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140882105","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

Enhancing Price Prediction in Cryptocurrency Using Transformer Neural Network and Technical Indicators 利用变压器神经网络和技术指标加强加密货币的价格预测

arXiv - QuantFin - Computational Finance Pub Date : 2024-03-06 DOI: arxiv-2403.03606

Mohammad Ali Labbaf Khaniki, Mohammad Manthouri

{"title":"Enhancing Price Prediction in Cryptocurrency Using Transformer Neural Network and Technical Indicators","authors":"Mohammad Ali Labbaf Khaniki, Mohammad Manthouri","doi":"arxiv-2403.03606","DOIUrl":"https://doi.org/arxiv-2403.03606","url":null,"abstract":"This study presents an innovative approach for predicting cryptocurrency time\u0000series, specifically focusing on Bitcoin, Ethereum, and Litecoin. The\u0000methodology integrates the use of technical indicators, a Performer neural\u0000network, and BiLSTM (Bidirectional Long Short-Term Memory) to capture temporal\u0000dynamics and extract significant features from raw cryptocurrency data. The\u0000application of technical indicators, such facilitates the extraction of\u0000intricate patterns, momentum, volatility, and trends. The Performer neural\u0000network, employing Fast Attention Via positive Orthogonal Random features\u0000(FAVOR+), has demonstrated superior computational efficiency and scalability\u0000compared to the traditional Multi-head attention mechanism in Transformer\u0000models. Additionally, the integration of BiLSTM in the feedforward network\u0000enhances the model's capacity to capture temporal dynamics in the data,\u0000processing it in both forward and backward directions. This is particularly\u0000advantageous for time series data where past and future data points can\u0000influence the current state. The proposed method has been applied to the hourly\u0000and daily timeframes of the major cryptocurrencies and its performance has been\u0000benchmarked against other methods documented in the literature. The results\u0000underscore the potential of the proposed method to outperform existing models,\u0000marking a significant progression in the field of cryptocurrency price\u0000prediction.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140056532","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

Quasi-Monte Carlo for Efficient Fourier Pricing of Multi-Asset Options 多资产期权高效傅立叶定价的准蒙特卡洛方法

arXiv - QuantFin - Computational Finance Pub Date : 2024-03-05 DOI: arxiv-2403.02832

Christian Bayer, Chiheb Ben Hammouda, Antonis Papapantoleon, Michael Samet, Raúl Tempone

{"title":"Quasi-Monte Carlo for Efficient Fourier Pricing of Multi-Asset Options","authors":"Christian Bayer, Chiheb Ben Hammouda, Antonis Papapantoleon, Michael Samet, Raúl Tempone","doi":"arxiv-2403.02832","DOIUrl":"https://doi.org/arxiv-2403.02832","url":null,"abstract":"Efficiently pricing multi-asset options poses a significant challenge in\u0000quantitative finance. The Monte Carlo (MC) method remains the prevalent choice\u0000for pricing engines; however, its slow convergence rate impedes its practical\u0000application. Fourier methods leverage the knowledge of the characteristic\u0000function to accurately and rapidly value options with up to two assets.\u0000Nevertheless, they face hurdles in the high-dimensional settings due to the\u0000tensor product (TP) structure of commonly employed quadrature techniques. This\u0000work advocates using the randomized quasi-MC (RQMC) quadrature to improve the\u0000scalability of Fourier methods with high dimensions. The RQMC technique\u0000benefits from the smoothness of the integrand and alleviates the curse of\u0000dimensionality while providing practical error estimates. Nonetheless, the\u0000applicability of RQMC on the unbounded domain, $mathbb{R}^d$, requires a\u0000domain transformation to $[0,1]^d$, which may result in singularities of the\u0000transformed integrand at the corners of the hypercube, and deteriorate the rate\u0000of convergence of RQMC. To circumvent this difficulty, we design an efficient\u0000domain transformation procedure based on the derived boundary growth conditions\u0000of the integrand. This transformation preserves the sufficient regularity of\u0000the integrand and hence improves the rate of convergence of RQMC. To validate\u0000this analysis, we demonstrate the efficiency of employing RQMC with an\u0000appropriate transformation to evaluate options in the Fourier space for various\u0000pricing models, payoffs, and dimensions. Finally, we highlight the\u0000computational advantage of applying RQMC over MC or TP in the Fourier domain,\u0000and over MC in the physical domain for options with up to 15 assets.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140045473","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