arXiv - QuantFin - General Finance最新文献_第10页

Large Language Models in Finance: A Survey 金融中的大型语言模型:综述

arXiv - QuantFin - General Finance Pub Date : 2023-09-28 DOI: arxiv-2311.10723

Yinheng Li, Shaofei Wang, Han Ding, Hang Chen

{"title":"Large Language Models in Finance: A Survey","authors":"Yinheng Li, Shaofei Wang, Han Ding, Hang Chen","doi":"arxiv-2311.10723","DOIUrl":"https://doi.org/arxiv-2311.10723","url":null,"abstract":"Recent advances in large language models (LLMs) have opened new possibilities\u0000for artificial intelligence applications in finance. In this paper, we provide\u0000a practical survey focused on two key aspects of utilizing LLMs for financial\u0000tasks: existing solutions and guidance for adoption. First, we review current approaches employing LLMs in finance, including\u0000leveraging pretrained models via zero-shot or few-shot learning, fine-tuning on\u0000domain-specific data, and training custom LLMs from scratch. We summarize key\u0000models and evaluate their performance improvements on financial natural\u0000language processing tasks. Second, we propose a decision framework to guide financial professionals in\u0000selecting the appropriate LLM solution based on their use case constraints\u0000around data, compute, and performance needs. The framework provides a pathway\u0000from lightweight experimentation to heavy investment in customized LLMs. Lastly, we discuss limitations and challenges around leveraging LLMs in\u0000financial applications. Overall, this survey aims to synthesize the\u0000state-of-the-art and provide a roadmap for responsibly applying LLMs to advance\u0000financial AI.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"138 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523135","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

Predictive AI for SME and Large Enterprise Financial Performance Management 面向中小企业和大型企业财务绩效管理的预测性人工智能

arXiv - QuantFin - General Finance Pub Date : 2023-09-22 DOI: arxiv-2311.05840

Ricardo Cuervo

{"title":"Predictive AI for SME and Large Enterprise Financial Performance Management","authors":"Ricardo Cuervo","doi":"arxiv-2311.05840","DOIUrl":"https://doi.org/arxiv-2311.05840","url":null,"abstract":"Financial performance management is at the core of business management and\u0000has historically relied on financial ratio analysis using Balance Sheet and\u0000Income Statement data to assess company performance as compared with\u0000competitors. Little progress has been made in predicting how a company will\u0000perform or in assessing the risks (probabilities) of financial\u0000underperformance. In this study I introduce a new set of financial and\u0000macroeconomic ratios that supplement standard ratios of Balance Sheet and\u0000Income Statement. I also provide a set of supervised learning models (ML\u0000Regressors and Neural Networks) and Bayesian models to predict company\u0000performance. I conclude that the new proposed variables improve model accuracy\u0000when used in tandem with standard industry ratios. I also conclude that\u0000Feedforward Neural Networks (FNN) are simpler to implement and perform best\u0000across 6 predictive tasks (ROA, ROE, Net Margin, Op Margin, Cash Ratio and Op\u0000Cash Generation); although Bayesian Networks (BN) can outperform FNN under very\u0000specific conditions. BNs have the additional benefit of providing a probability\u0000density function in addition to the predicted (expected) value. The study\u0000findings have significant potential helping CFOs and CEOs assess risks of\u0000financial underperformance to steer companies in more profitable directions;\u0000supporting lenders in better assessing the condition of a company and providing\u0000investors with tools to dissect financial statements of public companies more\u0000accurately.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"88 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522772","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

Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies 平均绝对方向性损失作为算法投资策略中机器学习问题的新损失函数

arXiv - QuantFin - General Finance Pub Date : 2023-09-19 DOI: arxiv-2309.10546

Jakub Michańków, Paweł Sakowski, Robert Ślepaczuk

引用次数: 0

Cryptocurrency in the Aftermath: Unveiling the Impact of the SVB Collapse 事后的加密货币:揭示SVB崩溃的影响

arXiv - QuantFin - General Finance Pub Date : 2023-09-15 DOI: arxiv-2311.10720

Qin Wang, Guangsheng Yu, Shiping Chen

引用次数: 0

InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning InvestLM:一个使用金融领域指令调优的大型投资语言模型

arXiv - QuantFin - General Finance Pub Date : 2023-09-15 DOI: arxiv-2309.13064

Yi Yang, Yixuan Tang, Kar Yan Tam

{"title":"InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning","authors":"Yi Yang, Yixuan Tang, Kar Yan Tam","doi":"arxiv-2309.13064","DOIUrl":"https://doi.org/arxiv-2309.13064","url":null,"abstract":"We present a new financial domain large language model, InvestLM, tuned on\u0000LLaMA-65B (Touvron et al., 2023), using a carefully curated instruction dataset\u0000related to financial investment. Inspired by less-is-more-for-alignment (Zhou\u0000et al., 2023), we manually curate a small yet diverse instruction dataset,\u0000covering a wide range of financial related topics, from Chartered Financial\u0000Analyst (CFA) exam questions to SEC filings to Stackexchange quantitative\u0000finance discussions. InvestLM shows strong capabilities in understanding\u0000financial text and provides helpful responses to investment related questions.\u0000Financial experts, including hedge fund managers and research analysts, rate\u0000InvestLM's response as comparable to those of state-of-the-art commercial\u0000models (GPT-3.5, GPT-4 and Claude-2). Zero-shot evaluation on a set of\u0000financial NLP benchmarks demonstrates strong generalizability. From a research\u0000perspective, this work suggests that a high-quality domain specific LLM can be\u0000tuned using a small set of carefully curated instructions on a well-trained\u0000foundation model, which is consistent with the Superficial Alignment Hypothesis\u0000(Zhou et al., 2023). From a practical perspective, this work develops a\u0000state-of-the-art financial domain LLM with superior capability in understanding\u0000financial texts and providing helpful investment advice, potentially enhancing\u0000the work efficiency of financial professionals. We release the model parameters\u0000to the research community.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138542560","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

Arguably Adequate Aqueduct Algorithm: Crossing A Bridge-Less Block-Chain Chasm 充分的渡槽算法:跨越无桥的区块链鸿沟

arXiv - QuantFin - General Finance Pub Date : 2023-09-12 DOI: arxiv-2311.10717

Ravi Kashyap

引用次数: 0

A New Framework to Estimate Return on Investment for Player Salaries in the National Basketball Association 一个估算nba球员工资投资回报的新框架

arXiv - QuantFin - General Finance Pub Date : 2023-09-11 DOI: arxiv-2309.05783

Jackson P. Lautier

{"title":"A New Framework to Estimate Return on Investment for Player Salaries in the National Basketball Association","authors":"Jackson P. Lautier","doi":"arxiv-2309.05783","DOIUrl":"https://doi.org/arxiv-2309.05783","url":null,"abstract":"The National Basketball Association (NBA) imposes a player salary cap. It is\u0000therefore useful to develop tools to measure the relative realized return of a\u0000player's salary given their on court performance. Very few such studies exist,\u0000however. We thus present the first known framework to estimate a return on\u0000investment (ROI) for NBA player contracts. The framework operates in five\u0000parts: (1) decide on a measurement time horizon, such as the standard 82-game\u0000NBA regular season; (2) calculate the novel game contribution percentage (GCP)\u0000measure we propose, which is a single game summary statistic that sums to unity\u0000for each competing team and is comprised of traditional, playtype, hustle, box\u0000outs, defensive, tracking, and rebounding per game NBA statistics; (3) estimate\u0000the single game value (SGV) of each regular season NBA game using a standard\u0000currency conversion calculation; (4) multiply the SGV by the vector of realized\u0000GCPs to obtain a series of realized per-player single season cash flows; and\u0000(5) use the player salary as an initial investment to perform the traditional\u0000ROI calculation. We illustrate our framework by compiling a novel, sharable\u0000dataset of per game GCP statistics and salaries for the 2022-2023 NBA regular\u0000season. A scatter plot of ROI by salary for all players is presented, including\u0000the top and bottom 50 performers. Notably, missed games are treated as defaults\u0000because GCP is a per game metric. This allows for break-even calculations\u0000between high-performing players with frequent missed games and average\u0000performers with few missed games, which we demonstrate with a comparison of the\u00002023 NBA regular seasons of Anthony Davis and Brook Lopez. We conclude by\u0000suggesting uses of our framework, discussing its flexibility through\u0000customization, and outlining potential future improvements.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"65 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522843","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

New News is Bad News 新消息就是坏消息

arXiv - QuantFin - General Finance Pub Date : 2023-09-11 DOI: arxiv-2309.05560

Paul Glasserman, Harry Mamaysky, Jimmy Qin

引用次数: 0

News-driven Expectations and Volatility Clustering 新闻驱动的预期和波动聚类

arXiv - QuantFin - General Finance Pub Date : 2023-09-09 DOI: arxiv-2309.04876

Sabiou Inoua

{"title":"News-driven Expectations and Volatility Clustering","authors":"Sabiou Inoua","doi":"arxiv-2309.04876","DOIUrl":"https://doi.org/arxiv-2309.04876","url":null,"abstract":"Financial volatility obeys two fascinating empirical regularities that apply\u0000to various assets, on various markets, and on various time scales: it is\u0000fat-tailed (more precisely power-law distributed) and it tends to be clustered\u0000in time. Many interesting models have been proposed to account for these\u0000regularities, notably agent-based models, which mimic the two empirical laws\u0000through a complex mix of nonlinear mechanisms such as traders' switching\u0000between trading strategies in highly nonlinear way. This paper explains the two\u0000regularities simply in terms of traders' attitudes towards news, an explanation\u0000that follows almost by definition of the traditional dichotomy of financial\u0000market participants, investors versus speculators, whose behaviors are reduced\u0000to their simplest forms. Long-run investors' valuations of an asset are assumed\u0000to follow a news-driven random walk, thus capturing the investors' persistent,\u0000long memory of fundamental news. Short-term speculators' anticipated returns,\u0000on the other hand, are assumed to follow a news-driven autoregressive process,\u0000capturing their shorter memory of fundamental news, and, by the same token, the\u0000feedback intrinsic to the short-sighted, trend-following (or herding) mindset\u0000of speculators. These simple, linear, models of traders' expectations, it is\u0000shown, explain the two financial regularities in a generic and robust way.\u0000Rational expectations, the dominant model of traders' expectations, is not\u0000assumed here, owing to the famous no-speculation, no-trade results","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138542559","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

Aggregation of financial markets 金融市场聚合

arXiv - QuantFin - General Finance Pub Date : 2023-09-08 DOI: arxiv-2309.04116

Georg Menz, Moritz Voß

引用次数: 0