arXiv - QuantFin - Statistical Finance最新文献_第7页

Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds 预测激进基金下一个目标的可解释机器学习模型

arXiv - QuantFin - Statistical Finance Pub Date : 2024-04-24 DOI: arxiv-2404.16169

Minwu Kim

{"title":"Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds","authors":"Minwu Kim","doi":"arxiv-2404.16169","DOIUrl":"https://doi.org/arxiv-2404.16169","url":null,"abstract":"This work develops a predictive model to identify potential targets of\u0000activist investment funds, which strategically acquire significant corporate\u0000stakes to drive operational and strategic improvements and enhance shareholder\u0000value. Predicting these targets is crucial for companies to mitigate\u0000intervention risks, for activists to select optimal targets, and for investors\u0000to capitalize on associated stock price gains. Our analysis utilizes data from\u0000the Russell 3000 index from 2016 to 2022. We tested 123 variations of models\u0000using different data imputation, oversampling, and machine learning methods,\u0000achieving a top AUC-ROC of 0.782. This demonstrates the model's effectiveness\u0000in identifying likely targets of activist funds. We applied the Shapley value\u0000method to determine the most influential factors in a company's susceptibility\u0000to activist investment. This interpretative approach provides clear insights\u0000into the driving forces behind activist targeting. Our model offers\u0000stakeholders a strategic tool for proactive corporate governance and investment\u0000strategy, enhancing understanding of the dynamics of activist investing.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140797866","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

Arbitrage impact on the relationship between XRP price and correlation tensor spectra of transaction networks 套利对 XRP 价格与交易网络相关张量谱关系的影响

arXiv - QuantFin - Statistical Finance Pub Date : 2024-04-15 DOI: arxiv-2405.00051

Abhijit Chakraborty, Yuichi Ikeda

{"title":"Arbitrage impact on the relationship between XRP price and correlation tensor spectra of transaction networks","authors":"Abhijit Chakraborty, Yuichi Ikeda","doi":"arxiv-2405.00051","DOIUrl":"https://doi.org/arxiv-2405.00051","url":null,"abstract":"The increasing use of cryptoassets for international remittances has proven\u0000to be faster and more cost-effective, particularly for migrants without access\u0000to traditional banking. However, the inherent volatility of cryptoasset prices,\u0000independent of blockchain-based remittance mechanisms, introduces potential\u0000risks during periods of high volatility. This study investigates the intricate\u0000dynamics between XRP price fluctuations across diverse crypto exchanges and the\u0000correlation of the largest singular values of the correlation tensor of XRP\u0000transaction networks. Particularly, we show the impact of arbitrage\u0000opportunities across different crypto exchanges on the relationship between XRP\u0000price and correlation tensor spectra of transaction networks. Distinct periods,\u0000non-bubble and bubble, showcase different characteristics in XRP price\u0000fluctuations. Establishing a connection between XRP price and transaction\u0000networks, we compute correlation tensors and singular values, emphasizing the\u0000significance of the largest singular value. Comparisons with reshuffled and\u0000Gaussian random correlation tensors validate the uniqueness of the empirical\u0000tensor. A set of simulated weekly XRP prices, resembling arbitrage\u0000opportunities across various crypto exchanges, further confirms the robustness\u0000of our findings. It reveals a pronounced anti-correlation during bubble periods\u0000and a non-significant correlation during non-bubble periods with the largest\u0000singular value, irrespective of price fluctuations across different crypto\u0000exchanges.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140832985","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 Mergers and Acquisitions in Competitive Industries: A Model Based on Temporal Dynamics and Industry Networks 预测竞争性行业中的并购：基于时间动态和行业网络的模型

arXiv - QuantFin - Statistical Finance Pub Date : 2024-04-10 DOI: arxiv-2404.07298

Dayu Yang

{"title":"Predicting Mergers and Acquisitions in Competitive Industries: A Model Based on Temporal Dynamics and Industry Networks","authors":"Dayu Yang","doi":"arxiv-2404.07298","DOIUrl":"https://doi.org/arxiv-2404.07298","url":null,"abstract":"M&A activities are pivotal for market consolidation, enabling firms to\u0000augment market power through strategic complementarities. Existing research\u0000often overlooks the peer effect, the mutual influence of M&A behaviors among\u0000firms, and fails to capture complex interdependencies within industry networks.\u0000Common approaches suffer from reliance on ad-hoc feature engineering, data\u0000truncation leading to significant information loss, reduced predictive\u0000accuracy, and challenges in real-world application. Additionally, the rarity of\u0000M&A events necessitates data rebalancing in conventional models, introducing\u0000bias and undermining prediction reliability. We propose an innovative M&A\u0000predictive model utilizing the Temporal Dynamic Industry Network (TDIN),\u0000leveraging temporal point processes and deep learning to adeptly capture\u0000industry-wide M&A dynamics. This model facilitates accurate, detailed\u0000deal-level predictions without arbitrary data manipulation or rebalancing,\u0000demonstrated through superior evaluation results from M&A cases between January\u00001997 and December 2020. Our approach marks a significant improvement over\u0000traditional models by providing detailed insights into M&A activities and\u0000strategic recommendations for specific firms.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"48 13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140595901","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

Prediction of Cryptocurrency Prices through a Path Dependent Monte Carlo Simulation 通过路径依赖蒙特卡洛模拟预测加密货币价格

arXiv - QuantFin - Statistical Finance Pub Date : 2024-04-10 DOI: arxiv-2405.12988

Ayush Singh, Anshu K. Jha, Amit N. Kumar

引用次数: 0

Machine learning-based similarity measure to forecast M&A from patent data 从专利数据中预测并购的基于机器学习的相似性测量方法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-04-10 DOI: arxiv-2404.07179

Giambattista Albora, Matteo Straccamore, Andrea Zaccaria

引用次数: 0

Synchronization in a market model with time delays 有时间延迟的市场模型中的同步问题

arXiv - QuantFin - Statistical Finance Pub Date : 2024-04-09 DOI: arxiv-2405.00046

Ghassan Dibeh, Omar El Deeb

引用次数: 0

StockGPT: A GenAI Model for Stock Prediction and Trading StockGPT：用于股票预测和交易的 GenAI 模型

arXiv - QuantFin - Statistical Finance Pub Date : 2024-04-07 DOI: arxiv-2404.05101

Dat Mai

引用次数: 0

A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes 加密货币波动性比较--以新资产类别和成熟资产类别为基准

arXiv - QuantFin - Statistical Finance Pub Date : 2024-04-07 DOI: arxiv-2404.04962

Alessio Brini, Jimmie Lenz

{"title":"A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes","authors":"Alessio Brini, Jimmie Lenz","doi":"arxiv-2404.04962","DOIUrl":"https://doi.org/arxiv-2404.04962","url":null,"abstract":"The paper analyzes the cryptocurrency ecosystem at both the aggregate and\u0000individual levels to understand the factors that impact future volatility. The\u0000study uses high-frequency panel data from 2020 to 2022 to examine the\u0000relationship between several market volatility drivers, such as daily leverage,\u0000signed volatility and jumps. Several known autoregressive model specifications\u0000are estimated over different market regimes, and results are compared to equity\u0000data as a reference benchmark of a more mature asset class. The panel\u0000estimations show that the positive market returns at the high-frequency level\u0000increase price volatility, contrary to what is expected from the classical\u0000financial literature. We attributed this effect to the price dynamics over the\u0000last year of the dataset (2022) by repeating the estimation on different time\u0000spans. Moreover, the positive signed volatility and negative daily leverage\u0000positively impact the cryptocurrencies' future volatility, unlike what emerges\u0000from the same study on a cross-section of stocks. This result signals a\u0000structural difference in a nascent cryptocurrency market that has to mature\u0000yet. Further individual-level analysis confirms the findings of the panel\u0000analysis and highlights that these effects are statistically significant and\u0000commonly shared among many components in the selected universe.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"214 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596140","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 theoretical framework for dynamical fee choice in AMMs AMM 中动态费用选择的理论框架

arXiv - QuantFin - Statistical Finance Pub Date : 2024-04-05 DOI: arxiv-2404.03976

Abe Alexander, Lars Fritz

引用次数: 0

BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights BERT 主题驱动的股市预测：解读情绪洞察

arXiv - QuantFin - Statistical Finance Pub Date : 2024-04-02 DOI: arxiv-2404.02053

Enmin Zhu

{"title":"BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights","authors":"Enmin Zhu","doi":"arxiv-2404.02053","DOIUrl":"https://doi.org/arxiv-2404.02053","url":null,"abstract":"This paper explores the intersection of Natural Language Processing (NLP) and\u0000financial analysis, focusing on the impact of sentiment analysis in stock price\u0000prediction. We employ BERTopic, an advanced NLP technique, to analyze the\u0000sentiment of topics derived from stock market comments. Our methodology\u0000integrates this sentiment analysis with various deep learning models, renowned\u0000for their effectiveness in time series and stock prediction tasks. Through\u0000comprehensive experiments, we demonstrate that incorporating topic sentiment\u0000notably enhances the performance of these models. The results indicate that\u0000topics in stock market comments provide implicit, valuable insights into stock\u0000market volatility and price trends. This study contributes to the field by\u0000showcasing the potential of NLP in enriching financial analysis and opens up\u0000avenues for further research into real-time sentiment analysis and the\u0000exploration of emotional and contextual aspects of market sentiment. The\u0000integration of advanced NLP techniques like BERTopic with traditional financial\u0000analysis methods marks a step forward in developing more sophisticated tools\u0000for understanding and predicting market behaviors.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140595702","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