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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Sun, M"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" 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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11848">arXiv:2411.11848</a> <span> [<a href="https://arxiv.org/pdf/2411.11848">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Statistical Finance">q-fin.ST</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Robust Graph Neural Networks for Stability Analysis in Dynamic Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-fin?searchtype=author&query=Zhang%2C+X">Xin Zhang</a>, <a href="/search/q-fin?searchtype=author&query=Xu%2C+Z">Zhen Xu</a>, <a href="/search/q-fin?searchtype=author&query=Liu%2C+Y">Yue Liu</a>, <a href="/search/q-fin?searchtype=author&query=Sun%2C+M">Mengfang Sun</a>, <a href="/search/q-fin?searchtype=author&query=Zhou%2C+T">Tong Zhou</a>, <a href="/search/q-fin?searchtype=author&query=Sun%2C+W">Wenying Sun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.11848v1-abstract-short" style="display: inline;"> In the current context of accelerated globalization and digitalization, the complexity and uncertainty of financial markets are increasing, and the identification and prevention of economic risks have become a key link in maintaining the stability of the financial system. Traditional risk identification methods often have limitations because they are difficult to cope with the multi-level and dyna… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11848v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11848v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11848v1-abstract-full" style="display: none;"> In the current context of accelerated globalization and digitalization, the complexity and uncertainty of financial markets are increasing, and the identification and prevention of economic risks have become a key link in maintaining the stability of the financial system. Traditional risk identification methods often have limitations because they are difficult to cope with the multi-level and dynamically changing complex relationships in financial networks. With the rapid development of financial technology, graph neural network (GNN) technology, as an emerging deep learning method, has gradually shown great potential in the field of financial risk management. GNN can map transaction behaviors, financial institutions, individuals, and their interactive relationships in financial networks into graph structures, and effectively capture potential patterns and abnormal signals in financial data through embedded representation learning. Using this technology, financial institutions can extract valuable information from complex transaction networks, identify hidden dangers or abnormal behaviors that may cause systemic risks in a timely manner, optimize decision-making processes, and improve the accuracy of risk warnings. This paper explores the economic risk identification algorithm based on the GNN algorithm, aiming to provide financial institutions and regulators with more intelligent technical tools to help maintain the security and stability of the financial market. Improving the efficiency of economic risk identification through innovative technical means is expected to further enhance the risk resistance of the financial system and lay the foundation for building a robust global financial system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11848v1-abstract-full').style.display = 'none'; document.getElementById('2411.11848v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">It was accepted by the 3rd International Conference on Cloud Computing Big Data Application and Software Engineering</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.06529">arXiv:2407.06529</a> <span> [<a href="https://arxiv.org/pdf/2407.06529">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Statistical Finance">q-fin.ST</span> </div> </div> <p class="title is-5 mathjax"> Advanced Financial Fraud Detection Using GNN-CL Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-fin?searchtype=author&query=Cheng%2C+Y">Yu Cheng</a>, <a href="/search/q-fin?searchtype=author&query=Guo%2C+J">Junjie Guo</a>, <a href="/search/q-fin?searchtype=author&query=Long%2C+S">Shiqing Long</a>, <a href="/search/q-fin?searchtype=author&query=Wu%2C+Y">You Wu</a>, <a href="/search/q-fin?searchtype=author&query=Sun%2C+M">Mengfang Sun</a>, <a href="/search/q-fin?searchtype=author&query=Zhang%2C+R">Rong Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.06529v1-abstract-short" style="display: inline;"> The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection by synergistically combining the advantages of graph neural networks (gnn), convolutional neural networks (cnn) and long short-term memory (LSTM) networks. This convergence enables multifaceted analysis of complex transaction patterns, improving detection accuracy and resilience agains… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.06529v1-abstract-full').style.display = 'inline'; document.getElementById('2407.06529v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.06529v1-abstract-full" style="display: none;"> The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection by synergistically combining the advantages of graph neural networks (gnn), convolutional neural networks (cnn) and long short-term memory (LSTM) networks. This convergence enables multifaceted analysis of complex transaction patterns, improving detection accuracy and resilience against complex fraudulent activities. A key novelty of this paper is the use of multilayer perceptrons (MLPS) to estimate node similarity, effectively filtering out neighborhood noise that can lead to false positives. This intelligent purification mechanism ensures that only the most relevant information is considered, thereby improving the model's understanding of the network structure. Feature weakening often plagues graph-based models due to the dilution of key signals. In order to further address the challenge of feature weakening, GNN-CL adopts reinforcement learning strategies. By dynamically adjusting the weights assigned to central nodes, it reinforces the importance of these influential entities to retain important clues of fraud even in less informative data. Experimental evaluations on Yelp datasets show that the results highlight the superior performance of GNN-CL compared to existing methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.06529v1-abstract-full').style.display = 'none'; document.getElementById('2407.06529v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.15214">arXiv:2006.15214</a> <span> [<a href="https://arxiv.org/pdf/2006.15214">pdf</a>, <a href="https://arxiv.org/format/2006.15214">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Statistical Finance">q-fin.ST</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> </div> </div> <p class="title is-5 mathjax"> Improving MF-DFA model with applications in precious metals market </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-fin?searchtype=author&query=Wang%2C+Z">Zhongjun Wang</a>, <a href="/search/q-fin?searchtype=author&query=Sun%2C+M">Mengye Sun</a>, <a href="/search/q-fin?searchtype=author&query=Elsawah%2C+A+M">A. M. Elsawah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2006.15214v1-abstract-short" style="display: inline;"> With the aggravation of the global economic crisis and inflation, the precious metals with safe-haven function have become more popular. An improved MF-DFA method is proposed to analyze price fluctuations of the precious metals market. Based on the widely used multifractal detrended fluctuation analysis method (MF-DFA), we compare these two methods and find that the Bi-OSW-MF-DFA method possesses… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.15214v1-abstract-full').style.display = 'inline'; document.getElementById('2006.15214v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.15214v1-abstract-full" style="display: none;"> With the aggravation of the global economic crisis and inflation, the precious metals with safe-haven function have become more popular. An improved MF-DFA method is proposed to analyze price fluctuations of the precious metals market. Based on the widely used multifractal detrended fluctuation analysis method (MF-DFA), we compare these two methods and find that the Bi-OSW-MF-DFA method possesses better efficiency. This article analyzes the degree of multifractality between spot gold market and spot silver market as well as their risks. From the numerical results and figures, it is found that two elements constitute the contributions in the formation of multifractality in time series and the risk of the spot silver market is higher than that of the spot gold market. This attempt could lead to a better understanding of complicated precious metals market. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.15214v1-abstract-full').style.display = 'none'; document.getElementById('2006.15214v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">23 pages, 17 figures, 6 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1810.09936">arXiv:1810.09936</a> <span> [<a href="https://arxiv.org/pdf/1810.09936">pdf</a>, <a href="https://arxiv.org/format/1810.09936">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Trading and Market Microstructure">q-fin.TR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Enhancing Stock Movement Prediction with Adversarial Training </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-fin?searchtype=author&query=Feng%2C+F">Fuli Feng</a>, <a href="/search/q-fin?searchtype=author&query=Chen%2C+H">Huimin Chen</a>, <a href="/search/q-fin?searchtype=author&query=He%2C+X">Xiangnan He</a>, <a href="/search/q-fin?searchtype=author&query=Ding%2C+J">Ji Ding</a>, <a href="/search/q-fin?searchtype=author&query=Sun%2C+M">Maosong Sun</a>, <a href="/search/q-fin?searchtype=author&query=Chua%2C+T">Tat-Seng Chua</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1810.09936v2-abstract-short" style="display: inline;"> This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model. The rationality of adversarial training here is that the input features to stock predictio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.09936v2-abstract-full').style.display = 'inline'; document.getElementById('1810.09936v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1810.09936v2-abstract-full" style="display: none;"> This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model. The rationality of adversarial training here is that the input features to stock prediction are typically based on stock price, which is essentially a stochastic variable and continuously changed with time by nature. As such, normal training with static price-based features (e.g. the close price) can easily overfit the data, being insufficient to obtain reliable models. To address this problem, we propose to add perturbations to simulate the stochasticity of price variable, and train the model to work well under small yet intentional perturbations. Extensive experiments on two real-world stock data show that our method outperforms the state-of-the-art solution with 3.11% relative improvements on average w.r.t. accuracy, validating the usefulness of adversarial training for stock prediction task. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.09936v2-abstract-full').style.display = 'none'; document.getElementById('1810.09936v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">IJCAI 2019</span> </p> </li> </ol> <div 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