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Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders [article]

Elior Nehemya and Yael Mathov and Asaf Shabtai and Yuval Elovici
2021 arXiv   pre-print
Stock market traders utilize machine learning models to predict the market's behavior and execute an investment strategy accordingly.  ...  We show that when added to the input stream, our perturbation can fool the trading algorithms at future unseen data points, in both white-box and black-box settings.  ...  The authors contributed equally Accepted to ECML PKDD 2021 uploads/2021/07/sub_386.pdf  ... 
arXiv:2010.09246v2 fatcat:qj65qfj4anbplo3q2n4drste4y

Modeling systemic risks in financial markets [article]

Abhijnan Rej
2013 arXiv   pre-print
We survey systemic risks to financial markets and present a high-level description of an algorithm that measures systemic risk in terms of coupled networks.  ...  We will also discuss the rise of over-the-counter (OTC) markets in securities derivatives which operate outside the standard exchanges-based framework.  ...  A following investigation of the event by the SEC, the US securities market regulator blamed a large sell-order by Proctor and Gamble that caused a large number of automated traders to exit the market  ... 
arXiv:1311.3764v1 fatcat:vf4slidqbvcwnjgtjeeoerpwlm

Stock Market Analysis with Text Data: A Review [article]

Kamaladdin Fataliyev, Aneesh Chivukula, Mukesh Prasad, Wei Liu
2021 arXiv   pre-print
Then, we cover the analysis techniques and create a taxonomy of the main stock market forecast models.  ...  The aim of this study is to survey the main stock market analysis models, text representation techniques for financial market prediction, shortcomings of existing techniques, and propose promising directions  ...  [31] demonstrate the use of adversarial training in prediction of stock market movements assumed to be a classification problem with adversarial perturbations to simulate the stochasticity in stock  ... 
arXiv:2106.12985v2 fatcat:prfo5c6bmfd3piwpl5yevfycje

Market Making with Signals through Deep Reinforcement Learning

Bruno Gasperov, Zvonko Kostanjcar
2021 IEEE Access  
Experimental results on historical data demonstrate the superior reward-to-risk performance of the proposed framework over several standard market making benchmarks.  ...  INDEX TERMS Deep reinforcement learning, genetic algorithms, high-frequency trading, machine learning, market making, stochastic control.  ...  ACKNOWLEDGMENT The authors would like to thank the three anonymous reviewers for their suggestions and comments which proved helpful in enhancing the quality of the paper.  ... 
doi:10.1109/access.2021.3074782 fatcat:3jixvzrzbzekvbqgvbieilwje4

Stock market microstructure inference via multi-agent reinforcement learning [article]

J. Lussange, I. Lazarevich, S. Bourgeois-Gironde, S. Palminteri, B. Gutkin
2019 arXiv   pre-print
We calibrate the model to real market data from the London Stock Exchange over the years 2007 to 2018, and show that it can faithfully reproduce key market microstructure metrics, such as various price  ...  In order to address this, we designed a next-generation MAS stock market simulator, in which each agent learns to trade autonomously via model-free reinforcement learning.  ...  s work was supported by the Russian Science Foundation, grant nr. 18-11-00294.  ... 
arXiv:1909.07748v5 fatcat:qqk2jlgoo5fhhouxfubkhltmfq

Adversarial Attacks on Deep Algorithmic Trading Policies [article]

Yaser Faghan, Nancirose Piazza, Vahid Behzadan, Ali Fathi
2020 arXiv   pre-print
Furthermore, we demonstrate the effectiveness of the proposed attacks against benchmark and real-world DQN trading agents.  ...  Deep Reinforcement Learning (DRL) has become an appealing solution to algorithmic trading such as high frequency trading of stocks and cyptocurrencies.  ...  Basic Trading Environment In this scenario, our data is sourced from Russian stock market prices between the period of 2015-2016.  ... 
arXiv:2010.11388v1 fatcat:nqsqly7rojfqnajbhjqvbu4ila

Adversarial Attacks against Reinforcement Learning-based Portfolio Management Strategy

Yu-Ying Chen, Chiao-Ting Chen, Chuan-Yun Sang, Yao-Chun Yang, Szu-Hao Huang
2021 IEEE Access  
Enhanced EIIE was then applied to the adversarial agent for the agent to learn when and how much to attack (in the form of introducing perturbations).In our experiments, our proposed adversarial attack  ...  However, such methods result in complicated algorithmic trading models with several defects, especially when a DNN model is vulnerable to malicious adversarial samples.  ...  For the purpose of synthesizing robust adversarial example in the physical domain, we followed the Expectation Over Transformation (EOT) algorithm [12] to generate the adversarial examples that remain  ... 
doi:10.1109/access.2021.3068768 fatcat:wkvbhjffevfktk42jscwhu2c2i

Adversarial Attacks on Probabilistic Autoregressive Forecasting Models [article]

Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin Vechev
2020 arXiv   pre-print
We demonstrate that our approach can successfully generate attacks with small input perturbations in two challenging tasks where robust decision making is crucial: stock market trading and prediction of  ...  We develop an effective generation of adversarial attacks on neural models that output a sequence of probability distributions rather than a sequence of single values.  ...  ., 2019) , robust inverse reinforcement learning on market data (Roa-Vicens et al., 2019) . Adversarial attacks against stock-market prediction algorithms was studied by (Feng et al., 2018) .  ... 
arXiv:2003.03778v1 fatcat:nibk7reftnab5ibqnulfv6mqqu

SoK: Decentralized Exchanges (DEX) with Automated Market Maker (AMM) Protocols [article]

Jiahua Xu, Krzysztof Paruch, Simon Cousaert, Yebo Feng
2022 arXiv   pre-print
Instead of matching the buy and sell sides, AMMs employ a peer-to-pool method and determine asset price algorithmically through a so-called conservation function.  ...  As an integral part of the decentralized finance (DeFi) ecosystem, decentralized exchanges (DEX) with automated market maker (AMM) protocols have gained massive traction with the recently revived interest  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of Ripple.  ... 
arXiv:2103.12732v5 fatcat:ix3mnwxts5ad5cwavuhrdrpncq

A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions [article]

Yong Xie, Dakuo Wang, Pin-Yu Chen, Jinjun Xiong, Sijia Liu, Sanmi Koyejo
2022 arXiv   pre-print
We address the task of adversarial generation by solving combinatorial optimization problems with semantics and budget constraints.  ...  Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation by simply concatenating a perturbed but semantically similar  ...  Gradient-based adversarial at-tacks against text transformers.  ... 
arXiv:2205.01094v3 fatcat:6vhgwmsdorhqzihlhsbafadglm

Novel Deep Reinforcement Algorithm With Adaptive Sampling Strategy for Continuous Portfolio Optimization

Szu-Hao Huang, Yu-Hsiang Miao, Yi-Ting Hsiao
2021 IEEE Access  
against highly stochastic price data.  ...  Thus, to maximize the generalizability of our portfolio management model, we introduced adversarial learning to introduce perturbations into our input stock price streams, making our model more robust  ... 
doi:10.1109/access.2021.3082186 fatcat:hkezsqehofcingcgg7pqivq6cu

Interpretability in Safety-Critical FinancialTrading Systems [article]

Gabriel Deza, Adelin Travers, Colin Rowat, Nicolas Papernot
2021 arXiv   pre-print
In an industry-standard trading pipeline, we perturb model inputs for eight S&P 500 stocks.  ...  We construct inputs -- whether in changes to sentiment or market variables -- that efficiently affect changes in the return distribution.  ...  We also thank the Vector Institute's sponsors.  ... 
arXiv:2109.15112v1 fatcat:4jf2zsaj3zhuflvvgrbdcmqwvu

Recent Advances in Reinforcement Learning in Finance [article]

Ben Hambly, Renyuan Xu, Huining Yang
2021 arXiv   pre-print
, market making, smart order routing, and robo-advising.  ...  Connections are made with neural networks to extend the framework to encompass deep RL algorithms.  ...  Some recent papers have focused on improving the robustness of market markers' strategies with respect to adversarial and volatile market conditions. [79] used perturbations by an opposing agentthe adversary  ... 
arXiv:2112.04553v1 fatcat:ay66scqcknhrlkvyvhlzonx4gy

Spoofing the Limit Order Book: A Strategic Agent-Based Analysis

Xintong Wang, Christopher Hoang, Yevgeniy Vorobeychik, Michael P. Wellman
2021 Games  
We evaluate the proposed approaches, taking into account potential strategic responses of agents, and characterize the conditions under which these approaches may deter manipulation and benefit market  ...  We present an agent-based model of manipulating prices in financial markets through spoofing: submitting spurious orders to mislead traders who learn from the order book.  ...  the Face of Market Manipulation" presented at the 1st ACM International Conference on AI in Finance [61] .  ... 
doi:10.3390/g12020046 fatcat:xi7c5g65yfbupaz6jtmpbzjc64

Machine learning concepts for correlated Big Data privacy

Sreemoyee Biswas, Nilay Khare, Pragati Agrawal, Priyank Jain
2021 Journal of Big Data  
The existing data privacy guarantees cannot assure the expected data privacy algorithms.  ...  Hence, by keeping the existence of data correlation into account, there is a dire need to reconsider the privacy algorithms.  ...  Authors' contributions All authors have equally contributed to the building of this survey paper. All authors read and approved the final manuscript.  ... 
doi:10.1186/s40537-021-00530-x fatcat:cnq6nkgv35gcpctu2u6ytt5g6y
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