Predicting Stock Price Movement Using Subjective Cash Flow and Discount Rate Expectations in Iran's Sock Market

Document Type : Original Article

Authors

1 PhD student in Financial Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran.

2 Department of Financial Management, Yazd Branch, Islamic Azad University, Yazd, Iran.

3 Department of Economics, Islamic Azad University, Yazd, Iran

Abstract
Predicting Stock Price Movement Using Subjective Cash Flow and Discount Rate Expectations Modeling in Iran's Sock Market

Abstract
One of the ambiguations in assets pricing which has been ignored in assets pricing literature is this question that stock price movement is based on revision in expected cash flows or revision in discount rates? Therefore, in this research stock price movement in Iran’s stock market was studied for the first time using subjective cash flow and discount rate expectations modeling. For this purpose, required data was gathered form Tehran stock exchange, RahAvard Novin data bank and analysists consensus system during 2011-2023. Also, firstly subjective expectations of cash flow growth (SCF) and subjective expectations of discount rate (SDR) were predicted using Vector Auto-Regressive (VAR), Implied Cost of Capital (ICC) and Revisions in Analysts' Forecasts (RAF) models. Then, the performance of mentioned models was evaluated using Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). Results showed that ICC model is more efficient from VAR model and RAF model is more efficient that both VAR and ICC models in predicting SCF and SDR. Also, against the expectations, the covariance between P/E and SCF is negative and covariance between P/E and SDR is positive which indicate the inefficient algorithm in stock pricing in Iran’s stock market due to factors such as high inflation, economic sanctions and investors’ behavioral biases.
Keywords: subjective expectations of cash flow growth, VAR model, ICC model, RAF model.

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Articles in Press, Accepted Manuscript
Available Online from 02 June 2026

  • Receive Date 18 January 2026
  • Revise Date 05 May 2026
  • Accept Date 13 May 2026