{"title":"The impact of business conditions and commodity market on US stock returns: An asset pricing modelling experiment","authors":"Fangzhou Huang, Jiao Song, N. Taylor","doi":"10.3934/qfe.2022019","DOIUrl":null,"url":null,"abstract":"In this paper, two comprehensive mathematical approaches: cubic piecewise polynomial function (CPPF) model and the Fourier Flexible Form (FFF) model are built into asset pricing models to explore the stock market risk, commodity market risk and overall business conditions in relation to US stock returns as a modelling experiment. A selection of knots and orders are applied on the models to determine the best fit coefficients, respectively, based on Akaike Information Criteria (AIC). The classic risk coefficient along with downside and upside counterparts are estimated in a non-linear time-weighted fashion and are subsequently adopted as risk factors to investigate the explanatory and predictive power to stock returns. It is found that time-weighted classic, downside and upside risk coefficients of all three domains provide significant explanatory power to current stock returns, while the predictive power appears to be weak. The findings fill the gap in literature, specifically on both investigating and pricing the time-weighted risk. This paper innovatively employs the Aruoba-Diebold-Scotti (ADS) real business index to measure the business conditions in macroeconomics context. The methodology proposed in this paper embeds advanced mathematical approaches to provide robust regression estimation. The application of proposed models enriches the dimension in pricing risk in stock market and wider financial market.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantitative Finance and Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3934/qfe.2022019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, two comprehensive mathematical approaches: cubic piecewise polynomial function (CPPF) model and the Fourier Flexible Form (FFF) model are built into asset pricing models to explore the stock market risk, commodity market risk and overall business conditions in relation to US stock returns as a modelling experiment. A selection of knots and orders are applied on the models to determine the best fit coefficients, respectively, based on Akaike Information Criteria (AIC). The classic risk coefficient along with downside and upside counterparts are estimated in a non-linear time-weighted fashion and are subsequently adopted as risk factors to investigate the explanatory and predictive power to stock returns. It is found that time-weighted classic, downside and upside risk coefficients of all three domains provide significant explanatory power to current stock returns, while the predictive power appears to be weak. The findings fill the gap in literature, specifically on both investigating and pricing the time-weighted risk. This paper innovatively employs the Aruoba-Diebold-Scotti (ADS) real business index to measure the business conditions in macroeconomics context. The methodology proposed in this paper embeds advanced mathematical approaches to provide robust regression estimation. The application of proposed models enriches the dimension in pricing risk in stock market and wider financial market.
本文将三次分段多项式函数(CPPF)模型和傅立叶灵活形式(FFF)模型两种综合数学方法构建到资产定价模型中,作为建模实验,探讨股市风险、商品市场风险和整体商业状况与美股收益的关系。根据赤池信息准则(Akaike Information Criteria, AIC),对模型分别应用选择的结和顺序来确定最佳拟合系数。以非线性时间加权的方式估计经典风险系数以及下行和上行对应系数,然后将其作为风险因素来研究对股票收益的解释和预测能力。研究发现,三个领域的时间加权经典、下行和上行风险系数对当前股票收益具有显著的解释能力,但预测能力较弱。这些发现填补了文献上的空白,特别是在调查和定价时间加权风险方面。本文创新性地采用了Aruoba-Diebold-Scotti (ADS)真实商业指数来衡量宏观经济背景下的商业状况。本文提出的方法嵌入了先进的数学方法来提供稳健的回归估计。所提出的模型的应用丰富了股票市场和更广泛的金融市场的风险定价维度。