AbstractMulti-factor patterns used to predict stock returns are static patterns, and dynamic changes over time are influenced by hidden factors such as government economic policies, crises, and the like that lead to a breakdown in returns. Stock and changes are no
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AbstractMulti-factor patterns used to predict stock returns are static patterns, and dynamic changes over time are influenced by hidden factors such as government economic policies, crises, and the like that lead to a breakdown in returns. Stock and changes are not like a price bubble or a sharp drop in price. Using the Fama-McButt regression in a dynamic estimation of the factors influencing factors, and in particular the distinction between the effects of hidden and overt factors affecting the company's future performance, a more accurate estimate can be made in the turbulent conditions of Iran's economy. A random sample of Tehran Stock Exchange companies during the monthly periods in a 10-year period ending on 2019, have been tested. The results showed that, based on the Gibbons test (1987), only a pattern based on the Q pattern can be able to explain anomalies in stock returns. Also, the Q-factor model is able to explain the anomaly of optional commitment items, research and development and return (PTH) costs.
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