Iranian Financial Engineering Association(IFEA) International Journal of Finance & Managerial Accounting 2588-4379 8 29 2023 04 01 Study of the CoAnomaly in Tehran Stock Exchange 221 238 21139 10.30495/ijfma.2022.64105.1749 EN Vahid Faezinia Ph.D Student in Finance, Yazd Branch, Islamic Azad University, Yazd, Iran Hamid Khajeh Mahmoudabadi Assistant Professor, Department of Finance, Faculty of Management, Economics and Accounting, Yazd Branch, Islamic Azad University, Yazd, Iran Gholamreza Askarzadeh Darreh Assistant Professor, Department of Finance, Faculty of Management, Economics and Accounting, Yazd Branch, Islamic Azad University, Yazd, Iran Seyyed Yahya Abtahi Assistant Professor, Department of Finance, Faculty of Management, Economics and Accounting, Yazd Branch, Islamic Azad University, Yazd, Iran Journal Article 2021 11 02 Anomaly correlation is among the essential topics that must be paid attention to in investment. Given the investment portfolio, the return is considered to be more than the expected return, and the premium risk of portfolio components, and portfolio components, and the correlation between portfolio components are investigated so that one can eventually achieve the optimal portfolio. The present study investigates the CoAnomaly in Tehran Stock Exchange. For this purpose, a simple measure of time series risk was presented as CoAnomaly (anomaly correlation) for stock market trade anomalies. This measure is the mean time-varying made up of 12 anomalies. Since the correlation between the underlying assets determines the portfolio variance, CoAnomaly is an important state variable for arbitrators that have a diverse anomaly portfolio to enhance performance. The information and data required in this study were obtained from the information on the firms listed in Tehran Stock Exchange over 2011-2020. Empirically, We show that, CoAnomaly is persistent and forecasts long-run aggregate volatility of the diversified anomaly portfolio. CoAnomaly positively predicts future average anomaly returns in the time series. On the other hand, results revealed that an increase in the CoAnomaly increases anomaly variance over the short term and long term. http://www.ijfma.ir/article_21139_65213079f85e2832dc71e5eee3c09d6b.pdf