arXiv - QuantFin - Statistical Finance最新文献_第9页

Workplace sustainability or financial resilience? Composite-financial resilience index 工作场所的可持续性还是财务复原力？财务复原力综合指数

arXiv - QuantFin - Statistical Finance Pub Date : 2024-03-24 DOI: arxiv-2403.16296

Elham Daadmehr

引用次数: 0

Nonlinear shifts and dislocations in financial market structure and composition 金融市场结构和组成的非线性变化和混乱

arXiv - QuantFin - Statistical Finance Pub Date : 2024-03-22 DOI: arxiv-2403.15163

Nick James, Max Menzies

{"title":"Nonlinear shifts and dislocations in financial market structure and composition","authors":"Nick James, Max Menzies","doi":"arxiv-2403.15163","DOIUrl":"https://doi.org/arxiv-2403.15163","url":null,"abstract":"This paper develops new mathematical techniques to identify temporal shifts\u0000among a collection of US equities partitioned into a new and more detailed set\u0000of market sectors. Although conceptually related, our three analyses reveal\u0000distinct insights about financial markets, with meaningful implications for\u0000investment managers. First, we explore a variety of methods to identify\u0000nonlinear shifts in market sector structure and describe the mathematical\u0000connection between the measure used and the captured phenomena. Second, we\u0000study network structure with respect to our new market sectors and identify\u0000meaningfully connected sector-to-sector mappings. Finally, we conduct a series\u0000of sampling experiments over different sample spaces and contrast the\u0000distribution of Sharpe ratios produced by long-only, long-short and short-only\u0000investment portfolios. In addition, we examine the sector composition of the\u0000top-performing portfolios for each of these portfolio styles. In practice, the\u0000methods proposed in this paper could be used to identify regime shifts,\u0000optimally structured portfolios, and better communities of equities.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"267 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140301861","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

Modeling stock price dynamics on the Ghana Stock Exchange: A Geometric Brownian Motion approach 加纳证券交易所股票价格动态建模：几何布朗运动方法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-03-19 DOI: arxiv-2403.13192

Dennis Lartey Quayesam, Anani Lotsi, Felix Okoe Mettle

引用次数: 0

Advanced Statistical Arbitrage with Reinforcement Learning 利用强化学习进行高级统计套利

arXiv - QuantFin - Statistical Finance Pub Date : 2024-03-18 DOI: arxiv-2403.12180

Boming Ning, Kiseop Lee

引用次数: 0

From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing 从因子模型到深度学习：重塑经验资产定价的机器学习

arXiv - QuantFin - Statistical Finance Pub Date : 2024-03-11 DOI: arxiv-2403.06779

Junyi Ye, Bhaskar Goswami, Jingyi Gu, Ajim Uddin, Guiling Wang

{"title":"From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing","authors":"Junyi Ye, Bhaskar Goswami, Jingyi Gu, Ajim Uddin, Guiling Wang","doi":"arxiv-2403.06779","DOIUrl":"https://doi.org/arxiv-2403.06779","url":null,"abstract":"This paper comprehensively reviews the application of machine learning (ML)\u0000and AI in finance, specifically in the context of asset pricing. It starts by\u0000summarizing the traditional asset pricing models and examining their\u0000limitations in capturing the complexities of financial markets. It explores how\u00001) ML models, including supervised, unsupervised, semi-supervised, and\u0000reinforcement learning, provide versatile frameworks to address these\u0000complexities, and 2) the incorporation of advanced ML algorithms into\u0000traditional financial models enhances return prediction and portfolio\u0000optimization. These methods can adapt to changing market dynamics by modeling\u0000structural changes and incorporating heterogeneous data sources, such as text\u0000and images. In addition, this paper explores challenges in applying ML in asset\u0000pricing, addressing the growing demand for explainability in decision-making\u0000and mitigating overfitting in complex models. This paper aims to provide\u0000insights into novel methodologies showcasing the potential of ML to reshape the\u0000future of quantitative finance.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140107820","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

The entropy corrected geometric Brownian motion 熵校正几何布朗运动

arXiv - QuantFin - Statistical Finance Pub Date : 2024-03-10 DOI: arxiv-2403.06253

Rishabh Gupta, Ewa Drzazga-Szczȩśniak, Sabre Kais, Dominik Szczȩśniak

引用次数: 0

Calibrated rank volatility stabilized models for large equity markets 大型股票市场的校准秩波动率稳定模型

arXiv - QuantFin - Statistical Finance Pub Date : 2024-03-07 DOI: arxiv-2403.04674

David Itkin, Martin Larsson

{"title":"Calibrated rank volatility stabilized models for large equity markets","authors":"David Itkin, Martin Larsson","doi":"arxiv-2403.04674","DOIUrl":"https://doi.org/arxiv-2403.04674","url":null,"abstract":"In the framework of stochastic portfolio theory we introduce rank volatility\u0000stabilized models for large equity markets over long time horizons. These\u0000models are rank-based extensions of the volatility stabilized models introduced\u0000by Fernholz & Karatzas in 2005. On the theoretical side we establish global\u0000existence of the model and ergodicity of the induced ranked market weights. We\u0000also derive explicit expressions for growth-optimal portfolios and show the\u0000existence of relative arbitrage with respect to the market portfolio. On the\u0000empirical side we calibrate the model to sixteen years of CRSP US equity data\u0000matching (i) rank-based volatilities, (ii) stock turnover as measured by market\u0000weight collisions, (iii) the average market rate of return and (iv) the capital\u0000distribution curve. Assessment of model fit and error analysis is conducted\u0000both in and out of sample. To the best of our knowledge this is the first model\u0000exhibiting relative arbitrage that has statistically been shown to have a good\u0000quantitative fit with the empirical features (i)-(iv). We additionally simulate\u0000trajectories of the calibrated model and compare them to historical\u0000trajectories, both in and out of sample.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140069952","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 Using LSTM, SVM And Polynomial Regression 使用 LSTM、SVM 和多项式回归预测加密货币价格

arXiv - QuantFin - Statistical Finance Pub Date : 2024-03-06 DOI: arxiv-2403.03410

Novan Fauzi Al Giffary, Feri Sulianta

{"title":"Prediction Of Cryptocurrency Prices Using LSTM, SVM And Polynomial Regression","authors":"Novan Fauzi Al Giffary, Feri Sulianta","doi":"arxiv-2403.03410","DOIUrl":"https://doi.org/arxiv-2403.03410","url":null,"abstract":"The rapid development of information technology, especially the Internet, has\u0000facilitated users with a quick and easy way to seek information. With these\u0000convenience offered by internet services, many individuals who initially\u0000invested in gold and precious metals are now shifting into digital investments\u0000in form of cryptocurrencies. However, investments in crypto coins are filled\u0000with uncertainties and fluctuation in daily basis. This risk posed as\u0000significant challenges for coin investors that could result in substantial\u0000investment losses. The uncertainty of the value of these crypto coins is a\u0000critical issue in the field of coin investment. Forecasting, is one of the\u0000methods used to predict the future value of these crypto coins. By utilizing\u0000the models of Long Short Term Memory, Support Vector Machine, and Polynomial\u0000Regression algorithm for forecasting, a performance comparison is conducted to\u0000determine which algorithm model is most suitable for predicting crypto currency\u0000prices. The mean square error is employed as a benchmark for the comparison. By\u0000applying those three constructed algorithm models, the Support Vector Machine\u0000uses a linear kernel to produce the smallest mean square error compared to the\u0000Long Short Term Memory and Polynomial Regression algorithm models, with a mean\u0000square error value of 0.02. Keywords: Cryptocurrency, Forecasting, Long Short\u0000Term Memory, Mean Square Error, Polynomial Regression, Support Vector Machine","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140056979","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

"Digitwashing": The Gap between Words and Deeds in Digital Transformation and Stock Price Crash Risk "数字清洗"：数字化转型中的言行差距与股价暴跌风险

arXiv - QuantFin - Statistical Finance Pub Date : 2024-03-03 DOI: arxiv-2403.01360

Shutter Zor

{"title":"\"Digitwashing\": The Gap between Words and Deeds in Digital Transformation and Stock Price Crash Risk","authors":"Shutter Zor","doi":"arxiv-2403.01360","DOIUrl":"https://doi.org/arxiv-2403.01360","url":null,"abstract":"The contrast between companies' \"fleshy\" promises and the \"skeletal\"\u0000performance in digital transformation may lead to a higher risk of stock price\u0000crash. This paper selects a sample of Shanghai and Shenzhen A-share listed\u0000companies from 2010 to 2021, empirically analyses the specific impact of the\u0000gap between words and deeds in digital transformation (GDT) on the stock price\u0000crash risk, and explores the possible causes of GDT. We found that GDT\u0000significantly increases the stock price crash risk, and this finding is still\u0000valid after a series of robustness tests. In a further study, a deeper\u0000examination of the causes of GDT reveals that firms' perceptions of economic\u0000policy uncertainty significantly increase GDT, and the effect is more\u0000pronounced in the sample of loss-making firms. At the same time, the results of\u0000the heterogeneity test suggest that investors are more tolerant of state-owned\u0000enterprises when they are in the GDT situation. Taken together, we provide a\u0000concrete bridge between the two measures of digital transformation - digital\u0000text frequency and digital technology share - and offer new insights to enhance\u0000capital market stability.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140034856","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

ARED: Argentina Real Estate Dataset ARED：阿根廷房地产数据集

arXiv - QuantFin - Statistical Finance Pub Date : 2024-03-01 DOI: arxiv-2403.00273

Iván Belenky

引用次数: 0