{"title":"知情交易者的活动与股票回报","authors":"CheChun Hsu","doi":"10.1108/mf-10-2023-0613","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>Recent studies suggested the ratio of option to stock volume reflected the private information. Informed traders were drawn to the options market for its leverage effect and relatively low transaction costs. Informed traders use different intervals of option moneyness to execute their strategies. The question is which types of option moneyness were traded by informed traders and what information was reflected in the market. In this study, the authors focused on this question and constructed a method for capturing the activity of informed traders in the options and stock markets.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>The authors constructed the daily measure, moneyness option trading volume to stock trading volume ratio (MOS), to capture the activity of informed traders in the market. 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The firms in the highest moneyness option trading volume to stock trading volume ratio for call quintile outperform the lowest quintile by 0.575% per week (approximately 30% per year).</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>The authors first propose the measures, moneyness option trading volume to stock trading volume ratio, that combined with the trading volume and option moneyness. 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引用次数: 0
摘要
目的近期的研究表明,期权与股票成交量的比率反映了私人信息。知情交易者被期权市场的杠杆效应和相对较低的交易成本所吸引。知情交易者使用不同区间的期权货币性来执行他们的策略。问题是,知情交易者交易的是哪种类型的期权货币性,市场反映了哪些信息。在本研究中,作者重点研究了这一问题,并构建了一种方法来捕捉知情交易者在期权和股票市场中的活动。作者根据货币性期权交易量与股票交易量之比组成了五分位数投资组合,并提供了资本资产定价模型和法玛-法式五因子阿尔法。为了确定 MOS 在控制公司特征效应后是否具有预测未来股票收益的能力,作者组成了双重排序组合,并进行了法玛-麦克贝斯回归。原创性/价值作者首次提出了期权交易量与股票交易量比这一衡量指标,并将其与交易量和期权货币性相结合。作者提供的证据表明,这些指标具有预测未来股票收益的能力。
Recent studies suggested the ratio of option to stock volume reflected the private information. Informed traders were drawn to the options market for its leverage effect and relatively low transaction costs. Informed traders use different intervals of option moneyness to execute their strategies. The question is which types of option moneyness were traded by informed traders and what information was reflected in the market. In this study, the authors focused on this question and constructed a method for capturing the activity of informed traders in the options and stock markets.
Design/methodology/approach
The authors constructed the daily measure, moneyness option trading volume to stock trading volume ratio (MOS), to capture the activity of informed traders in the market. The authors formed quintile portfolios sorted with respect to the moneyness option to stock trading volume ratio and provided the capital asset pricing model and Fama–French five-factor alphas. To determine whether MOS had predictive ability on future stock returns after controlling for company characteristic effects, the authors formed double-sorted portfolios and performed Fama–Macbeth regressions.
Findings
The authors found that the firms in the lowest moneyness option trading volume to stock trading volume ratio for put quintile outperform the highest quintile by 0.698% per week (approximately 36% per year). The firms in the highest moneyness option trading volume to stock trading volume ratio for call quintile outperform the lowest quintile by 0.575% per week (approximately 30% per year).
Originality/value
The authors first propose the measures, moneyness option trading volume to stock trading volume ratio, that combined with the trading volume and option moneyness. The authors provide evidence that the measures have the predictive ability to the future stock returns.
期刊介绍:
Managerial Finance provides an international forum for the publication of high quality and topical research in the area of finance, such as corporate finance, financial management, financial markets and institutions, international finance, banking, insurance and risk management, real estate and financial education. Theoretical and empirical research is welcome as well as cross-disciplinary work, such as papers investigating the relationship of finance with other sectors.