Proceedings of the First ACM International Conference on AI in Finance最新文献_第3页

An analysis of political turmoil effects on stock prices: a case study of US-China trade friction 政治动荡对股价的影响分析——以中美贸易摩擦为例

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422558

Y. Shirota, K. Yamaguchi, Akane Murakami, Michiya Morita

引用次数: 0

Recommending missing and suspicious links in multiplex financial networks 推荐多元化金融网络中缺失的和可疑的环节

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422538

R. E. Tillman, P. Reddy, M. Veloso

引用次数: 0

Index tracking with differentiable asset selection 可微分资产选择的指数跟踪

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422516

Yu Zheng, Yunpeng Li, Qiuhua Xu, Timothy M. Hospedales, Yongxin Yang

引用次数: 0

Power-law mixtures of bayesian forests for value added tax audit case selection 幂律混合贝叶斯森林在增值税审计案例选择中的应用

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422515

Christos Kleanthous, T. Christophides, S. Chatzis

{"title":"Power-law mixtures of bayesian forests for value added tax audit case selection","authors":"Christos Kleanthous, T. Christophides, S. Chatzis","doi":"10.1145/3383455.3422515","DOIUrl":"https://doi.org/10.1145/3383455.3422515","url":null,"abstract":"Tax authorities need to maximize the yield of the limited tax audits they afford to perform each year. Thus, they need to predict the likelihood of a candidate audit resulting in a satisfactory yield; this predictive process is usually referred to as audit case selection. Random Forests (RFs) constitute a standard method for Value Added Tax (VAT) audit case selection. Despite, though, their success, their predictive performance is still below the expectations of tax authorities, that need to timely detect cases of significant audit yield potential. This lackluster performance is mainly attributed to the fact that RFs cannot deal with data that entail non-stationary nature, multiple modalities, or discontinuities. These are common characteristics of real-world datasets; thus, the incapacity to properly address them is a major suspect for undermining their performance. This work addresses these issues by considering a generative non-parametric Bayesian model with power-law behavior, capable of generating distinct (Bayesian) RFs over the observations space of the modeled data. This way, our approach enables capturing an indefinite number of distinct classification patterns, while being able to effectively handle outliers. The latter advantage is of paramount importance for the effectiveness of the modeling procedure in cases where few large parts of the observations space can be modeled by few RF classifiers, yet there is a large number of small parts of the observations space that require distinct RFs to be properly modeled (power-law nature). We provide an efficient algorithm for model inference, based on the variational Bayesian framework, and prove its efficacy using real-world datasets.","PeriodicalId":447950,"journal":{"name":"Proceedings of the First ACM International Conference on AI in Finance","volume":"80 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120922923","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

Mixed membership recurrent neural networks for modeling customer purchases 混合隶属度递归神经网络用于客户购买建模

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422543

G. Fazelnia, Mark Ibrahim, C. Modarres, K. Wu, J. Paisley

{"title":"Mixed membership recurrent neural networks for modeling customer purchases","authors":"G. Fazelnia, Mark Ibrahim, C. Modarres, K. Wu, J. Paisley","doi":"10.1145/3383455.3422543","DOIUrl":"https://doi.org/10.1145/3383455.3422543","url":null,"abstract":"Models of sequential data such as the recurrent neural network (RNN) often implicitly treat a sequence of data as having a fixed time interval between observations. They also do not account for group-level effects when multiple sequences are observed generated from separate sources. A simple example is user purchasing behavior, where each user generates a unique sequence of purchases, and the time between purchases is variable. We propose a model for such sequential data based on the RNN that accounts for varying time intervals between observations in a sequence. We do this by learning a group-level \"base\" parameter to which each data-generating object can revert as more time passes before the next observation. This requires modeling assumptions about the data that we argue are typically satisfied by consumer purchasing behavior. Our approach is motivated by the mixed membership framework, with Latent Dirichlet Allocation being the canonical example, which we adapt to our dynamic setting. We demonstrate our approach on two consumer shopping datasets: The Instacart set of 3.4 million online grocery orders made by 206K customers, and a UK retail set consisting of over 500K orders.","PeriodicalId":447950,"journal":{"name":"Proceedings of the First ACM International Conference on AI in Finance","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124959154","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

Simulating and classifying behavior in adversarial environments based on action-state traces: an application to money laundering 基于行为状态痕迹的敌对环境中的行为模拟和分类:在洗钱中的应用

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422536

D. Borrajo, M. Veloso, Sameena Shah

{"title":"Simulating and classifying behavior in adversarial environments based on action-state traces: an application to money laundering","authors":"D. Borrajo, M. Veloso, Sameena Shah","doi":"10.1145/3383455.3422536","DOIUrl":"https://doi.org/10.1145/3383455.3422536","url":null,"abstract":"Many business applications involve adversarial relationships in which both sides adapt their strategies to optimize their opposing benefits. One of the key characteristics of these applications is the wide range of strategies that an adversary may choose as they adapt their strategy dynamically to sustain benefits and evade authorities. In this paper, we present a novel way of approaching these types of applications, in particular in the context of Anti-Money Laundering. We provide a mechanism through which diverse, realistic and new unobserved behavior may be generated to discover potential unobserved adversarial actions to enable organizations to preemptively mitigate these risks. In this regard, we make three main contributions. (a) Propose a novel behavior-based model as opposed to individual transactions-based models currently used by financial institutions. We introduce behavior traces as enriched relational representation to represent observed human behavior. (b) A modelling approach that observes these traces and is able to accurately infer the goals of actors by classifying the behavior into money laundering or standard behavior despite significant unobserved activity. And (c) a synthetic behavior simulator that can generate new previously unseen traces. The simulator incorporates a high level of flexibility in the behavioral parameters so that we can challenge the detection algorithm. Finally, we provide experimental results that show that the learning module (automated investigator) that has only partial observability can still successfully infer the type of behavior, and thus the simulated goals, followed by customers based on traces - a key aspiration for many applications today.","PeriodicalId":447950,"journal":{"name":"Proceedings of the First ACM International Conference on AI in Finance","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125479081","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: 11

Fast direct calibration of interest rate derivatives pricing models 快速直接校准利率衍生品定价模型

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422534

Luca Sabbioni, Marcello Restelli, Andrea Prampolini

{"title":"Fast direct calibration of interest rate derivatives pricing models","authors":"Luca Sabbioni, Marcello Restelli, Andrea Prampolini","doi":"10.1145/3383455.3422534","DOIUrl":"https://doi.org/10.1145/3383455.3422534","url":null,"abstract":"To price complex derivative instruments and to manage the associated financial risk, investment banks typically model the underlying asset price dynamics using parametric stochastic models. Model parameters are calibrated by fitting cross sections of option prices on the relevant risk factors. It is fundamental for a calibration method to be accurate and fast and, to this end, Deep Learning techniques have attracted increasing attention in recent years. In this paper, the aim is to propose a Neural Network based calibration of a pricing model, where learning is directly performed on market data by using a non-trivial loss function, which includes the financial model adopted. In particular, the model chosen is the two-additive factor Gaussian Interest Rates model in a multi-curve framework calibrated on at-the-money European swaptions. The main advantage lies in the independence from an external calibrator and in the calibration time, reduced from several seconds to milliseconds, achieved by offloading the computational-intensive tasks to an offline training process, while the online evaluation can be performed in a considerably shorter time. Finally, the efficiency of the proposed approach is tested in both a single-currency and a multi-currency framework.","PeriodicalId":447950,"journal":{"name":"Proceedings of the First ACM International Conference on AI in Finance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130980770","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

Predicting the behavior of dealers in over-the-counter corporate bond markets 预测场外公司债券市场上交易商的行为

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422542

Yusen Lin, Jinming Xue, L. Raschid

引用次数: 1

Algorithms in future capital markets: a survey on AI, ML and associated algorithms in capital markets 未来资本市场中的算法:资本市场中AI、ML及相关算法的调查

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422539

A. Koshiyama, Nikan B. Firoozye, P. Treleaven

{"title":"Algorithms in future capital markets: a survey on AI, ML and associated algorithms in capital markets","authors":"A. Koshiyama, Nikan B. Firoozye, P. Treleaven","doi":"10.1145/3383455.3422539","DOIUrl":"https://doi.org/10.1145/3383455.3422539","url":null,"abstract":"This paper reviews Artificial Intelligence (AI), Machine Learning (ML) and associated algorithms in future Capital Markets. New AI algorithms are constantly emerging, with each 'strain' mimicking a new form of human learning, reasoning, knowledge, and decisionmaking. The current main disrupting forms of learning include Deep Learning, Adversarial Learning, Transfer and Meta Learning. Albeit these modes of learning have been in the AI/ML field more than a decade, they now are more applicable due to the availability of data, computing power and infrastructure. These forms of learning have produced new models (e.g., Long Short-Term Memory, Generative Adversarial Networks) and leverage important applications (e.g., Natural Language Processing, Adversarial Examples, Deep Fakes, etc.). These new models and applications will drive changes in future Capital Markets, so it is important to understand their computational strengths and weaknesses. Since ML algorithms effectively self-program and evolve dynamically, financial institutions and regulators are becoming increasingly concerned with ensuring there remains a modicum of human control, focusing on Algorithmic Interpretability/Explainability, Robustness and Legality. For example, the concern is that, in the future, an ecology of trading algorithms across different institutions may 'conspire' and become unintentionally fraudulent (cf. LIBOR) or subject to subversion through compromised datasets (e.g. Microsoft Tay). New and unique forms of systemic risks can emerge, potentially coming from excessive algorithmic complexity. The contribution of this paper is to review AI, ML and associated algorithms, their computational strengths and weaknesses, and discuss their future impact on the Capital Markets.","PeriodicalId":447950,"journal":{"name":"Proceedings of the First ACM International Conference on AI in Finance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126062602","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: 1

Differentially private secure multi-party computation for federated learning in financial applications 金融应用中联邦学习的差分私有安全多方计算

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-12 DOI: 10.1145/3383455.3422562

David Byrd, Antigoni Polychroniadou

{"title":"Differentially private secure multi-party computation for federated learning in financial applications","authors":"David Byrd, Antigoni Polychroniadou","doi":"10.1145/3383455.3422562","DOIUrl":"https://doi.org/10.1145/3383455.3422562","url":null,"abstract":"Federated Learning enables a population of clients, working with a trusted server, to collaboratively learn a shared machine learning model while keeping each client's data within its own local systems. This reduces the risk of exposing sensitive data, but it is still possible to reverse engineer information about a client's private data set from communicated model parameters. Most federated learning systems therefore use differential privacy to introduce noise to the parameters. This adds uncertainty to any attempt to reveal private client data, but also reduces the accuracy of the shared model, limiting the useful scale of privacy-preserving noise. A system can further reduce the coordinating server's ability to recover private client information, without additional accuracy loss, by also including secure multiparty computation. An approach combining both techniques is especially relevant to financial firms as it allows new possibilities for collaborative learning without exposing sensitive client data. This could produce more accurate models for important tasks like optimal trade execution, credit origination, or fraud detection. The key contributions of this paper are: We present a privacy-preserving federated learning protocol to a non-specialist audience, demonstrate it using logistic regression on a real-world credit card fraud data set, and evaluate it using an open-source simulation platform which we have adapted for the development of federated learning systems.","PeriodicalId":447950,"journal":{"name":"Proceedings of the First ACM International Conference on AI in Finance","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127278649","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: 60