Proceedings of the First ACM International Conference on AI in Finance最新文献

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Understanding distributional ambiguity via non-robust chance constraint 通过非鲁棒机会约束理解分布模糊性

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2019-06-03 DOI: 10.1145/3383455.3422522

Qi Wu, Shumin Ma, Cheuk Hang Leung, Wei Liu

{"title":"Understanding distributional ambiguity via non-robust chance constraint","authors":"Qi Wu, Shumin Ma, Cheuk Hang Leung, Wei Liu","doi":"10.1145/3383455.3422522","DOIUrl":"https://doi.org/10.1145/3383455.3422522","url":null,"abstract":"This paper provides a non-robust interpretation of the distributionally robust optimization (DRO) problem by relating the distributional uncertainties to the chance probabilities. Our analysis allows a decision-maker to interpret the size of the ambiguity set, which is often lack of business meaning, through the chance parameters constraining the objective function. We first show that, for general ϕ-divergences, a DRO problem is asymptotically equivalent to a class of mean-deviation problems. These mean-deviation problems are not subject to uncertain distributions, and the ambiguity radius in the original DRO problem now plays the role of controlling the risk preference of the decision-maker. We then demonstrate that a DRO problem can be cast as a chance-constrained optimization (CCO) problem when a boundedness constraint is added to the decision variables. Without the boundedness constraint, the CCO problem is shown to perform uniformly better than the DRO problem, irrespective of the radius of the ambiguity set, the choice of the divergence measure, or the tail heaviness of the center distribution. Thanks to our high-order expansion result, a notable feature of our analysis is that it applies to divergence measures that accommodate well heavy tail distributions such as the student t-distribution and the lognormal distribution, besides the widely-used Kullback-Leibler (KL) divergence, which requires the distribution of the objective function to be exponentially bounded. Using the portfolio selection problem as an example, our comprehensive testings on multivariate heavy-tail datasets, both synthetic and real-world, shows that this business-interpretation approach is indeed useful and insightful.","PeriodicalId":447950,"journal":{"name":"Proceedings of the First ACM International Conference on AI in Finance","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114712189","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

Learning-based trading strategies in the face of market manipulation 面对市场操纵时基于学习的交易策略

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2019-06-01 DOI: 10.1145/3383455.3422568

Xintong Wang, Christopher Hoang, M. Wellman

{"title":"Learning-based trading strategies in the face of market manipulation","authors":"Xintong Wang, Christopher Hoang, M. Wellman","doi":"10.1145/3383455.3422568","DOIUrl":"https://doi.org/10.1145/3383455.3422568","url":null,"abstract":"We study learning-based trading strategies in markets where prices can be manipulated through spoofing: the practice of submitting spurious orders to mislead traders who use market information. To reduce the vulnerability of learning traders to such manipulation, we propose two variations based on the standard heuristic belief learning (HBL) trading strategy, which learns transaction probabilities from market activities observed in an order book. The first variation selectively ignores orders at certain price levels, particularly where spoof orders are likely to be placed. The second considers the full order book, but adjusts its limit order price to correct for bias in decisions based on the learned heuristic beliefs. We employ agent-based simulation to evaluate these variations on two criteria: effectiveness in non-manipulated markets and robustness against manipulation. Background traders can adopt the (non-learning) zero intelligence strategies or HBL, in its basic form or the two variations. We conduct empirical game-theoretic analysis upon simulated payoffs to derive approximate strategic equilibria, and compare equilibrium outcomes across a variety of trading environments. Results show that agents can strategically make use of the option to block orders to improve robustness against spoofing, while retaining a comparable competitiveness in non-manipulated markets. Our second HBL variation exhibits a general improvement over standard HBL, in markets with and without manipulation. Further explorations suggest that traders can enjoy both improved profitability and robustness by combining the two proposed variations.","PeriodicalId":447950,"journal":{"name":"Proceedings of the First ACM International Conference on AI in Finance","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131268475","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: 4

Dynamic prediction length for time series with sequence to sequence network 序列到序列网络时间序列的动态预测长度

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2018-07-02 DOI: 10.1145/3383455.3422533

Mark Harmon, D. Klabjan

引用次数: 8

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