Due to the variety of corporate risks in turmoil markets and the consequent financial distress especially in COVID-19 time, this paper investigates corporate resilience and compares different types of resilience that can be potential sources of heterogeneity in firms' implied rate of return. Specifically, the novelty is not only to quantify firms' financial resilience but also to compare it with workplace resilience which matters more in the COVID-19 era. The study prepares several pieces of evidence of the necessity and insufficiency of these two main types of resilience by comparing earnings expectations and implied discount rates of high- and low-resilience firms. Particularly, results present evidence of the possible amplification of workplace resilience by the financial status of firms in the COVID-19 era. The paper proposes a novel composite-financial resilience index as a potential measure for disaster risk that significantly and persistently reveals low-resilience characteristics of firms and resilience-heterogeneity in implied discount rates.
{"title":"Workplace sustainability or financial resilience? Composite-financial resilience index","authors":"Elham Daadmehr","doi":"arxiv-2403.16296","DOIUrl":"https://doi.org/arxiv-2403.16296","url":null,"abstract":"Due to the variety of corporate risks in turmoil markets and the consequent\u0000financial distress especially in COVID-19 time, this paper investigates\u0000corporate resilience and compares different types of resilience that can be\u0000potential sources of heterogeneity in firms' implied rate of return.\u0000Specifically, the novelty is not only to quantify firms' financial resilience\u0000but also to compare it with workplace resilience which matters more in the\u0000COVID-19 era. The study prepares several pieces of evidence of the necessity\u0000and insufficiency of these two main types of resilience by comparing earnings\u0000expectations and implied discount rates of high- and low-resilience firms.\u0000Particularly, results present evidence of the possible amplification of\u0000workplace resilience by the financial status of firms in the COVID-19 era. The\u0000paper proposes a novel composite-financial resilience index as a potential\u0000measure for disaster risk that significantly and persistently reveals\u0000low-resilience characteristics of firms and resilience-heterogeneity in implied\u0000discount rates.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"154 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140298765","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 develops new mathematical techniques to identify temporal shifts among a collection of US equities partitioned into a new and more detailed set of market sectors. Although conceptually related, our three analyses reveal distinct insights about financial markets, with meaningful implications for investment managers. First, we explore a variety of methods to identify nonlinear shifts in market sector structure and describe the mathematical connection between the measure used and the captured phenomena. Second, we study network structure with respect to our new market sectors and identify meaningfully connected sector-to-sector mappings. Finally, we conduct a series of sampling experiments over different sample spaces and contrast the distribution of Sharpe ratios produced by long-only, long-short and short-only investment portfolios. In addition, we examine the sector composition of the top-performing portfolios for each of these portfolio styles. In practice, the methods proposed in this paper could be used to identify regime shifts, optimally structured portfolios, and better communities of equities.
{"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}
Dennis Lartey Quayesam, Anani Lotsi, Felix Okoe Mettle
Modeling financial data often relies on assumptions that may prove insufficient or unrealistic in practice. The Geometric Brownian Motion (GBM) model is frequently employed to represent stock price processes. This study investigates whether the behavior of weekly and monthly returns of selected equities listed on the Ghana Stock Exchange conforms to the GBM model. Parameters of the GBM model were estimated for five equities, and forecasts were generated for three months. Evaluation of estimation accuracy was conducted using mean square error (MSE). Results indicate that the expected prices from the modeled equities closely align with actual stock prices observed on the Exchange. Furthermore, while some deviations were observed, the actual prices consistently fell within the estimated confidence intervals.
{"title":"Modeling stock price dynamics on the Ghana Stock Exchange: A Geometric Brownian Motion approach","authors":"Dennis Lartey Quayesam, Anani Lotsi, Felix Okoe Mettle","doi":"arxiv-2403.13192","DOIUrl":"https://doi.org/arxiv-2403.13192","url":null,"abstract":"Modeling financial data often relies on assumptions that may prove\u0000insufficient or unrealistic in practice. The Geometric Brownian Motion (GBM)\u0000model is frequently employed to represent stock price processes. This study\u0000investigates whether the behavior of weekly and monthly returns of selected\u0000equities listed on the Ghana Stock Exchange conforms to the GBM model.\u0000Parameters of the GBM model were estimated for five equities, and forecasts\u0000were generated for three months. Evaluation of estimation accuracy was\u0000conducted using mean square error (MSE). Results indicate that the expected\u0000prices from the modeled equities closely align with actual stock prices\u0000observed on the Exchange. Furthermore, while some deviations were observed, the\u0000actual prices consistently fell within the estimated confidence intervals.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"259 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140205590","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}
Statistical arbitrage is a prevalent trading strategy which takes advantage of mean reverse property of spread of paired stocks. Studies on this strategy often rely heavily on model assumption. In this study, we introduce an innovative model-free and reinforcement learning based framework for statistical arbitrage. For the construction of mean reversion spreads, we establish an empirical reversion time metric and optimize asset coefficients by minimizing this empirical mean reversion time. In the trading phase, we employ a reinforcement learning framework to identify the optimal mean reversion strategy. Diverging from traditional mean reversion strategies that primarily focus on price deviations from a long-term mean, our methodology creatively constructs the state space to encapsulate the recent trends in price movements. Additionally, the reward function is carefully tailored to reflect the unique characteristics of mean reversion trading.
{"title":"Advanced Statistical Arbitrage with Reinforcement Learning","authors":"Boming Ning, Kiseop Lee","doi":"arxiv-2403.12180","DOIUrl":"https://doi.org/arxiv-2403.12180","url":null,"abstract":"Statistical arbitrage is a prevalent trading strategy which takes advantage\u0000of mean reverse property of spread of paired stocks. Studies on this strategy\u0000often rely heavily on model assumption. In this study, we introduce an\u0000innovative model-free and reinforcement learning based framework for\u0000statistical arbitrage. For the construction of mean reversion spreads, we\u0000establish an empirical reversion time metric and optimize asset coefficients by\u0000minimizing this empirical mean reversion time. In the trading phase, we employ\u0000a reinforcement learning framework to identify the optimal mean reversion\u0000strategy. Diverging from traditional mean reversion strategies that primarily\u0000focus on price deviations from a long-term mean, our methodology creatively\u0000constructs the state space to encapsulate the recent trends in price movements.\u0000Additionally, the reward function is carefully tailored to reflect the unique\u0000characteristics of mean reversion trading.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"162 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140169653","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}
Junyi Ye, Bhaskar Goswami, Jingyi Gu, Ajim Uddin, Guiling Wang
This paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing. It starts by summarizing the traditional asset pricing models and examining their limitations in capturing the complexities of financial markets. It explores how 1) ML models, including supervised, unsupervised, semi-supervised, and reinforcement learning, provide versatile frameworks to address these complexities, and 2) the incorporation of advanced ML algorithms into traditional financial models enhances return prediction and portfolio optimization. These methods can adapt to changing market dynamics by modeling structural changes and incorporating heterogeneous data sources, such as text and images. In addition, this paper explores challenges in applying ML in asset pricing, addressing the growing demand for explainability in decision-making and mitigating overfitting in complex models. This paper aims to provide insights into novel methodologies showcasing the potential of ML to reshape the future of quantitative finance.
本文全面回顾了机器学习（ML）和人工智能在金融领域的应用，特别是在资产定价方面的应用。文章首先总结了传统的资产定价模型，并探讨了这些模型在捕捉金融市场复杂性方面的局限性。它探讨了 1) 包括监督、无监督、半监督和强化学习在内的 ML 模型如何为解决这些复杂性提供多功能框架，以及 2) 将先进的 ML 算法纳入传统金融模型如何增强回报预测和投资组合优化。这些方法可以通过对结构变化进行建模并纳入异构数据源（如文本和图像）来适应不断变化的市场动态。此外，本文还探讨了在资产定价中应用 ML 所面临的挑战，以满足决策中对可解释性日益增长的需求，并减轻复杂模型中的过度拟合。本文旨在提供新颖方法的见解，展示 ML 重塑量化金融未来的潜力。
{"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}
The geometric Brownian motion (GBM) is widely employed for modeling stochastic processes, yet its solutions are characterized by the log-normal distribution. This comprises predictive capabilities of GBM mainly in terms of forecasting applications. Here, entropy corrections to GBM are proposed to go beyond log-normality restrictions and better account for intricacies of real systems. It is shown that GBM solutions can be effectively refined by arguing that entropy is reduced when deterministic content of considered data increases. Notable improvements over conventional GBM are observed for several cases of non-log-normal distributions, ranging from a dice roll experiment to real world data.
{"title":"The entropy corrected geometric Brownian motion","authors":"Rishabh Gupta, Ewa Drzazga-Szczȩśniak, Sabre Kais, Dominik Szczȩśniak","doi":"arxiv-2403.06253","DOIUrl":"https://doi.org/arxiv-2403.06253","url":null,"abstract":"The geometric Brownian motion (GBM) is widely employed for modeling\u0000stochastic processes, yet its solutions are characterized by the log-normal\u0000distribution. This comprises predictive capabilities of GBM mainly in terms of\u0000forecasting applications. Here, entropy corrections to GBM are proposed to go\u0000beyond log-normality restrictions and better account for intricacies of real\u0000systems. It is shown that GBM solutions can be effectively refined by arguing\u0000that entropy is reduced when deterministic content of considered data\u0000increases. Notable improvements over conventional GBM are observed for several\u0000cases of non-log-normal distributions, ranging from a dice roll experiment to\u0000real world data.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140107812","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 framework of stochastic portfolio theory we introduce rank volatility stabilized models for large equity markets over long time horizons. These models are rank-based extensions of the volatility stabilized models introduced by Fernholz & Karatzas in 2005. On the theoretical side we establish global existence of the model and ergodicity of the induced ranked market weights. We also derive explicit expressions for growth-optimal portfolios and show the existence of relative arbitrage with respect to the market portfolio. On the empirical side we calibrate the model to sixteen years of CRSP US equity data matching (i) rank-based volatilities, (ii) stock turnover as measured by market weight collisions, (iii) the average market rate of return and (iv) the capital distribution curve. Assessment of model fit and error analysis is conducted both in and out of sample. To the best of our knowledge this is the first model exhibiting relative arbitrage that has statistically been shown to have a good quantitative fit with the empirical features (i)-(iv). We additionally simulate trajectories of the calibrated model and compare them to historical trajectories, both in and out of sample.
{"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}
The rapid development of information technology, especially the Internet, has facilitated users with a quick and easy way to seek information. With these convenience offered by internet services, many individuals who initially invested in gold and precious metals are now shifting into digital investments in form of cryptocurrencies. However, investments in crypto coins are filled with uncertainties and fluctuation in daily basis. This risk posed as significant challenges for coin investors that could result in substantial investment losses. The uncertainty of the value of these crypto coins is a critical issue in the field of coin investment. Forecasting, is one of the methods used to predict the future value of these crypto coins. By utilizing the models of Long Short Term Memory, Support Vector Machine, and Polynomial Regression algorithm for forecasting, a performance comparison is conducted to determine which algorithm model is most suitable for predicting crypto currency prices. The mean square error is employed as a benchmark for the comparison. By applying those three constructed algorithm models, the Support Vector Machine uses a linear kernel to produce the smallest mean square error compared to the Long Short Term Memory and Polynomial Regression algorithm models, with a mean square error value of 0.02. Keywords: Cryptocurrency, Forecasting, Long Short Term Memory, Mean Square Error, Polynomial Regression, Support Vector Machine
{"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}
The contrast between companies' "fleshy" promises and the "skeletal" performance in digital transformation may lead to a higher risk of stock price crash. This paper selects a sample of Shanghai and Shenzhen A-share listed companies from 2010 to 2021, empirically analyses the specific impact of the gap between words and deeds in digital transformation (GDT) on the stock price crash risk, and explores the possible causes of GDT. We found that GDT significantly increases the stock price crash risk, and this finding is still valid after a series of robustness tests. In a further study, a deeper examination of the causes of GDT reveals that firms' perceptions of economic policy uncertainty significantly increase GDT, and the effect is more pronounced in the sample of loss-making firms. At the same time, the results of the heterogeneity test suggest that investors are more tolerant of state-owned enterprises when they are in the GDT situation. Taken together, we provide a concrete bridge between the two measures of digital transformation - digital text frequency and digital technology share - and offer new insights to enhance capital market stability.
{"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}
The Argentinian real estate market presents a unique case study characterized by its unstable and rapidly shifting macroeconomic circumstances over the past decades. Despite the existence of a few datasets for price prediction, there is a lack of mixed modality datasets specifically focused on Argentina. In this paper, the first edition of ARED is introduced. A comprehensive real estate price prediction dataset series, designed for the Argentinian market. This edition contains information solely for Jan-Feb 2024. It was found that despite the short time range captured by this zeroth edition (44 days), time dependent phenomena has been occurring mostly on a market level (market as a whole). Nevertheless future editions of this dataset, will most likely contain historical data. Each listing in ARED comprises descriptive features, and variable-length sets of images.
{"title":"ARED: Argentina Real Estate Dataset","authors":"Iván Belenky","doi":"arxiv-2403.00273","DOIUrl":"https://doi.org/arxiv-2403.00273","url":null,"abstract":"The Argentinian real estate market presents a unique case study characterized\u0000by its unstable and rapidly shifting macroeconomic circumstances over the past\u0000decades. Despite the existence of a few datasets for price prediction, there is\u0000a lack of mixed modality datasets specifically focused on Argentina. In this\u0000paper, the first edition of ARED is introduced. A comprehensive real estate\u0000price prediction dataset series, designed for the Argentinian market. This\u0000edition contains information solely for Jan-Feb 2024. It was found that despite\u0000the short time range captured by this zeroth edition (44 days), time dependent\u0000phenomena has been occurring mostly on a market level (market as a whole).\u0000Nevertheless future editions of this dataset, will most likely contain\u0000historical data. Each listing in ARED comprises descriptive features, and\u0000variable-length sets of images.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140034608","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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