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The effect of annual report narratives on the cost of capital in the Middle East and North Africa: A machine learning approach. / Mousa, Gehan ; Elamir, Elsayed; Hussainey, Khaled.
In: Research in International Business and Finance, Vol. 62, 101675, 01.12.2022.

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Mousa G, Elamir E, Hussainey K. The effect of annual report narratives on the cost of capital in the Middle East and North Africa: A machine learning approach. Research in International Business and Finance. 2022 Dec 1;62:101675. Epub 2022 May 13. doi: 10.1016/j.ribaf.2022.101675

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Mousa, Gehan ; Elamir, Elsayed ; Hussainey, Khaled. / The effect of annual report narratives on the cost of capital in the Middle East and North Africa: A machine learning approach. In: Research in International Business and Finance. 2022 ; Vol. 62.

RIS

TY - JOUR

T1 - The effect of annual report narratives on the cost of capital in the Middle East and North Africa: A machine learning approach

AU - Mousa, Gehan

AU - Elamir, Elsayed

AU - Hussainey, Khaled

PY - 2022/12/1

Y1 - 2022/12/1

N2 - This paper contributes to accounting literature by reexamining the impact of the quantity and readability of annual report narratives on cost of capital. This study employs a machine learning technique, namely, the model-based (MOB) recursive partitioning, while the least absolute shrinkage and selection operator is used to select variables from a sample of 720 bank–year observations from eight Middle Eastern and North African countries between 2008 and 2019. The model-based (MOB) recursive partitioning works with local and global models to explore hidden information in the data that leads to better results in both linear and nonlinear relationships. Our analysis shows that, on one hand, the readability of annual report narratives has an insignificant impact on cost of capital. On the other hand, it shows that the greater the amount of narrative disclosure, the lower the cost of capital, a result that varies between countries and according to corporate profitability.

AB - This paper contributes to accounting literature by reexamining the impact of the quantity and readability of annual report narratives on cost of capital. This study employs a machine learning technique, namely, the model-based (MOB) recursive partitioning, while the least absolute shrinkage and selection operator is used to select variables from a sample of 720 bank–year observations from eight Middle Eastern and North African countries between 2008 and 2019. The model-based (MOB) recursive partitioning works with local and global models to explore hidden information in the data that leads to better results in both linear and nonlinear relationships. Our analysis shows that, on one hand, the readability of annual report narratives has an insignificant impact on cost of capital. On the other hand, it shows that the greater the amount of narrative disclosure, the lower the cost of capital, a result that varies between countries and according to corporate profitability.

U2 - 10.1016/j.ribaf.2022.101675

DO - 10.1016/j.ribaf.2022.101675

M3 - Article

VL - 62

JO - Research in International Business and Finance

JF - Research in International Business and Finance

SN - 0275-5319

M1 - 101675

ER -