This work develops a predictive model to identify potential targets of activist investment funds, which strategically acquire significant corporate stakes to drive operational and strategic improvements and enhance shareholder value. Predicting these targets is crucial for companies to mitigate intervention risks, for activists to select optimal targets, and for investors to capitalize on associated stock price gains. Our analysis utilizes data from the Russell 3000 index from 2016 to 2022. We tested 123 variations of models using different data imputation, oversampling, and machine learning methods, achieving a top AUC-ROC of 0.782. This demonstrates the model's effectiveness in identifying likely targets of activist funds. We applied the Shapley value method to determine the most influential factors in a company's susceptibility to activist investment. This interpretative approach provides clear insights into the driving forces behind activist targeting. Our model offers stakeholders a strategic tool for proactive corporate governance and investment strategy, enhancing understanding of the dynamics of activist investing.
{"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}
The increasing use of cryptoassets for international remittances has proven to be faster and more cost-effective, particularly for migrants without access to traditional banking. However, the inherent volatility of cryptoasset prices, independent of blockchain-based remittance mechanisms, introduces potential risks during periods of high volatility. This study investigates the intricate dynamics between XRP price fluctuations across diverse crypto exchanges and the correlation of the largest singular values of the correlation tensor of XRP transaction networks. Particularly, we show the impact of arbitrage opportunities across different crypto exchanges on the relationship between XRP price and correlation tensor spectra of transaction networks. Distinct periods, non-bubble and bubble, showcase different characteristics in XRP price fluctuations. Establishing a connection between XRP price and transaction networks, we compute correlation tensors and singular values, emphasizing the significance of the largest singular value. Comparisons with reshuffled and Gaussian random correlation tensors validate the uniqueness of the empirical tensor. A set of simulated weekly XRP prices, resembling arbitrage opportunities across various crypto exchanges, further confirms the robustness of our findings. It reveals a pronounced anti-correlation during bubble periods and a non-significant correlation during non-bubble periods with the largest singular value, irrespective of price fluctuations across different crypto exchanges.
{"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}
M&A activities are pivotal for market consolidation, enabling firms to augment market power through strategic complementarities. Existing research often overlooks the peer effect, the mutual influence of M&A behaviors among firms, and fails to capture complex interdependencies within industry networks. Common approaches suffer from reliance on ad-hoc feature engineering, data truncation leading to significant information loss, reduced predictive accuracy, and challenges in real-world application. Additionally, the rarity of M&A events necessitates data rebalancing in conventional models, introducing bias and undermining prediction reliability. We propose an innovative M&A predictive model utilizing the Temporal Dynamic Industry Network (TDIN), leveraging temporal point processes and deep learning to adeptly capture industry-wide M&A dynamics. This model facilitates accurate, detailed deal-level predictions without arbitrary data manipulation or rebalancing, demonstrated through superior evaluation results from M&A cases between January 1997 and December 2020. Our approach marks a significant improvement over traditional models by providing detailed insights into M&A activities and strategic recommendations for specific firms.
{"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}
In this paper, our focus lies on the Merton's jump diffusion model, employing jump processes characterized by the compound Poisson process. Our primary objective is to forecast the drift and volatility of the model using a variety of methodologies. We adopt an approach that involves implementing different drift, volatility, and jump terms within the model through various machine learning techniques, traditional methods, and statistical methods on price-volume data. Additionally, we introduce a path-dependent Monte Carlo simulation to model cryptocurrency prices, taking into account the volatility and unexpected jumps in prices.
{"title":"Prediction of Cryptocurrency Prices through a Path Dependent Monte Carlo Simulation","authors":"Ayush Singh, Anshu K. Jha, Amit N. Kumar","doi":"arxiv-2405.12988","DOIUrl":"https://doi.org/arxiv-2405.12988","url":null,"abstract":"In this paper, our focus lies on the Merton's jump diffusion model, employing\u0000jump processes characterized by the compound Poisson process. Our primary\u0000objective is to forecast the drift and volatility of the model using a variety\u0000of methodologies. We adopt an approach that involves implementing different\u0000drift, volatility, and jump terms within the model through various machine\u0000learning techniques, traditional methods, and statistical methods on\u0000price-volume data. Additionally, we introduce a path-dependent Monte Carlo\u0000simulation to model cryptocurrency prices, taking into account the volatility\u0000and unexpected jumps in prices.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141149254","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}
Giambattista Albora, Matteo Straccamore, Andrea Zaccaria
Defining and finalizing Mergers and Acquisitions (M&A) requires complex human skills, which makes it very hard to automatically find the best partner or predict which firms will make a deal. In this work, we propose the MASS algorithm, a specifically designed measure of similarity between companies and we apply it to patenting activity data to forecast M&A deals. MASS is based on an extreme simplification of tree-based machine learning algorithms and naturally incorporates intuitive criteria for deals; as such, it is fully interpretable and explainable. By applying MASS to the Zephyr and Crunchbase datasets, we show that it outperforms LightGCN, a "black box" graph convolutional network algorithm. When similar companies have disjoint patenting activities, on the contrary, LightGCN turns out to be the most effective algorithm. This study provides a simple and powerful tool to model and predict M&A deals, offering valuable insights to managers and practitioners for informed decision-making.
并购（M&A）的定义和最终确定需要复杂的人工技能，因此很难自动找到最佳合作伙伴或预测哪些公司将达成交易。在这项工作中，我们提出了 MASS 算法，这是一种专门设计的公司间相似性度量方法，我们将其应用于专利活动数据，以预测并购交易。MASS 算法基于对基于树的机器学习算法的极度简化，自然地融入了交易的直观标准；因此，它是完全可解释和可说明的。通过将 MASS 应用于基于 Zephyr 和 Crunch 的数据集，我们发现它优于 "黑盒 "图卷积网络算法 LightGCN。相反，当相似公司的专利活动互不关联时，LightGCN 则是最有效的算法。这项研究为模拟和预测并购交易提供了一个简单而强大的工具，为管理者和从业者做出明智决策提供了有价值的见解。
{"title":"Machine learning-based similarity measure to forecast M&A from patent data","authors":"Giambattista Albora, Matteo Straccamore, Andrea Zaccaria","doi":"arxiv-2404.07179","DOIUrl":"https://doi.org/arxiv-2404.07179","url":null,"abstract":"Defining and finalizing Mergers and Acquisitions (M&A) requires complex human\u0000skills, which makes it very hard to automatically find the best partner or\u0000predict which firms will make a deal. In this work, we propose the MASS\u0000algorithm, a specifically designed measure of similarity between companies and\u0000we apply it to patenting activity data to forecast M&A deals. MASS is based on\u0000an extreme simplification of tree-based machine learning algorithms and\u0000naturally incorporates intuitive criteria for deals; as such, it is fully\u0000interpretable and explainable. By applying MASS to the Zephyr and Crunchbase\u0000datasets, we show that it outperforms LightGCN, a \"black box\" graph\u0000convolutional network algorithm. When similar companies have disjoint patenting\u0000activities, on the contrary, LightGCN turns out to be the most effective\u0000algorithm. This study provides a simple and powerful tool to model and predict\u0000M&A deals, offering valuable insights to managers and practitioners for\u0000informed decision-making.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140595818","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}
We examine a system of N=2 coupled non-linear delay-differential equations representing financial market dynamics. In such time delay systems, coupled oscillations have been derived. We linearize the system for small time delays and study its collective dynamics. Using analytical and numerical solutions, we obtain the bifurcation diagrams and analyze the corresponding regions of amplitude death, phase locking, limit cycles and market synchronization in terms of the system frequency-like parameters and time delays. We further numerically explore higher order systems with N>2, and demonstrate that limit cycles can be maintained for coupled N-asset models with appropriate parameterization.
{"title":"Synchronization in a market model with time delays","authors":"Ghassan Dibeh, Omar El Deeb","doi":"arxiv-2405.00046","DOIUrl":"https://doi.org/arxiv-2405.00046","url":null,"abstract":"We examine a system of N=2 coupled non-linear delay-differential equations\u0000representing financial market dynamics. In such time delay systems, coupled\u0000oscillations have been derived. We linearize the system for small time delays\u0000and study its collective dynamics. Using analytical and numerical solutions, we\u0000obtain the bifurcation diagrams and analyze the corresponding regions of\u0000amplitude death, phase locking, limit cycles and market synchronization in\u0000terms of the system frequency-like parameters and time delays. We further\u0000numerically explore higher order systems with N>2, and demonstrate that limit\u0000cycles can be maintained for coupled N-asset models with appropriate\u0000parameterization.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140832983","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}
This paper introduces StockGPT, an autoregressive "number" model pretrained directly on the history of daily U.S. stock returns. Treating each return series as a sequence of tokens, the model excels at understanding and predicting the highly intricate stock return dynamics. Instead of relying on handcrafted trading patterns using historical stock prices, StockGPT automatically learns the hidden representations predictive of future returns via its attention mechanism. On a held-out test sample from 2001 to 2023, a daily rebalanced long-short portfolio formed from StockGPT predictions earns an annual return of 119% with a Sharpe ratio of 6.5. The StockGPT-based portfolio completely explains away momentum and long-/short-term reversals, eliminating the need for manually crafted price-based strategies and also encompasses most leading stock market factors. This highlights the immense promise of generative AI in surpassing human in making complex financial investment decisions and illustrates the efficacy of the attention mechanism of large language models when applied to a completely different domain.
{"title":"StockGPT: A GenAI Model for Stock Prediction and Trading","authors":"Dat Mai","doi":"arxiv-2404.05101","DOIUrl":"https://doi.org/arxiv-2404.05101","url":null,"abstract":"This paper introduces StockGPT, an autoregressive \"number\" model pretrained\u0000directly on the history of daily U.S. stock returns. Treating each return\u0000series as a sequence of tokens, the model excels at understanding and\u0000predicting the highly intricate stock return dynamics. Instead of relying on\u0000handcrafted trading patterns using historical stock prices, StockGPT\u0000automatically learns the hidden representations predictive of future returns\u0000via its attention mechanism. On a held-out test sample from 2001 to 2023, a\u0000daily rebalanced long-short portfolio formed from StockGPT predictions earns an\u0000annual return of 119% with a Sharpe ratio of 6.5. The StockGPT-based portfolio\u0000completely explains away momentum and long-/short-term reversals, eliminating\u0000the need for manually crafted price-based strategies and also encompasses most\u0000leading stock market factors. This highlights the immense promise of generative\u0000AI in surpassing human in making complex financial investment decisions and\u0000illustrates the efficacy of the attention mechanism of large language models\u0000when applied to a completely different domain.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596208","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}
The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility. The study uses high-frequency panel data from 2020 to 2022 to examine the relationship between several market volatility drivers, such as daily leverage, signed volatility and jumps. Several known autoregressive model specifications are estimated over different market regimes, and results are compared to equity data as a reference benchmark of a more mature asset class. The panel estimations show that the positive market returns at the high-frequency level increase price volatility, contrary to what is expected from the classical financial literature. We attributed this effect to the price dynamics over the last year of the dataset (2022) by repeating the estimation on different time spans. Moreover, the positive signed volatility and negative daily leverage positively impact the cryptocurrencies' future volatility, unlike what emerges from the same study on a cross-section of stocks. This result signals a structural difference in a nascent cryptocurrency market that has to mature yet. Further individual-level analysis confirms the findings of the panel analysis and highlights that these effects are statistically significant and commonly shared among many components in the selected universe.
{"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}
In the ever evolving landscape of decentralized finance automated market makers (AMMs) play a key role: they provide a market place for trading assets in a decentralized manner. For so-called bluechip pairs, arbitrage activity provides a major part of the revenue generation of AMMs but also a major source of loss due to the so-called informed orderflow. Finding ways to minimize those losses while still keeping uninformed trading activity alive is a major problem in the field. In this paper we will investigate the mechanics of said arbitrage and try to understand how AMMs can maximize the revenue creation or in other words minimize the losses. To that end, we model the dynamics of arbitrage activity for a concrete implementation of a pool and study its sensitivity to the choice of fee aiming to maximize the value retention. We manage to map the ensuing dynamics to that of a random walk with a specific reward scheme that provides a convenient starting point for further studies.
{"title":"A theoretical framework for dynamical fee choice in AMMs","authors":"Abe Alexander, Lars Fritz","doi":"arxiv-2404.03976","DOIUrl":"https://doi.org/arxiv-2404.03976","url":null,"abstract":"In the ever evolving landscape of decentralized finance automated market\u0000makers (AMMs) play a key role: they provide a market place for trading assets\u0000in a decentralized manner. For so-called bluechip pairs, arbitrage activity\u0000provides a major part of the revenue generation of AMMs but also a major source\u0000of loss due to the so-called informed orderflow. Finding ways to minimize those\u0000losses while still keeping uninformed trading activity alive is a major problem\u0000in the field. In this paper we will investigate the mechanics of said arbitrage\u0000and try to understand how AMMs can maximize the revenue creation or in other\u0000words minimize the losses. To that end, we model the dynamics of arbitrage\u0000activity for a concrete implementation of a pool and study its sensitivity to\u0000the choice of fee aiming to maximize the value retention. We manage to map the\u0000ensuing dynamics to that of a random walk with a specific reward scheme that\u0000provides a convenient starting point for further studies.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140595695","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}
This paper explores the intersection of Natural Language Processing (NLP) and financial analysis, focusing on the impact of sentiment analysis in stock price prediction. We employ BERTopic, an advanced NLP technique, to analyze the sentiment of topics derived from stock market comments. Our methodology integrates this sentiment analysis with various deep learning models, renowned for their effectiveness in time series and stock prediction tasks. Through comprehensive experiments, we demonstrate that incorporating topic sentiment notably enhances the performance of these models. The results indicate that topics in stock market comments provide implicit, valuable insights into stock market volatility and price trends. This study contributes to the field by showcasing the potential of NLP in enriching financial analysis and opens up avenues for further research into real-time sentiment analysis and the exploration of emotional and contextual aspects of market sentiment. The integration of advanced NLP techniques like BERTopic with traditional financial analysis methods marks a step forward in developing more sophisticated tools for understanding and predicting market behaviors.
{"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}
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