SAUDI ARABIAN COMMERCIAL BANKS’ MARKET-RISK SENSITIVITY: Macroeconomic Cycles

SAUDI ARABIAN COMMERCIAL BANKS’ MARKET-RISK SENSITIVITY: Macroeconomic CyclesThe Samba Bank and the Al-Bilad Bank have the highest CV volatility results yet this is camouflaged by their relative low, stable overall beta value. These imply that both banks are less sensitive to market movements (because of low betas), which further implies lower risk which in turn would be attractive to risk-averse investors. However, the high rolling beta volatility (as measured by CV) implies high risk, which contradicts the readings of the low static beta value. It is therefore helpful to calculate the rolling beta, as well as calculating the CV, to allow a more informed investment decision. To further highlight the importance of calculating the rolling beta as opposed to the simple linear beta value, Figures 1and 2 illustrate how the beta value for each bankfor each day changes dramaticallyas it is rolled through the sample period. The two graphs illustrate two banks AlBilad Bank, lowest beta (0.66) value, and AlRajhi Bank with the highest (1.03) beta value, (these values are shown on Table 5). The graphs also compare the bank with the highest average beta value for the complete Banking Sector, and the highest and lowest average sector valuesof the 15 different sectors within the TASI.
Figure 1 shows the daily changes of the rolling beta for AlBilad Bank compared to the average banking sector static beta is 0.91; the highest sector, Petroleum, static beta, 1.37; the lowest sector Energy Utilities, static beta 0.59; and the average static beta value for AlBilad Bank of 0.66. Figure 2 showsAlRajhiBank also compares these data. Both Figures 1 and 2 clearly highlight how beta substantially deviates from the overall linear beta calculation presented earlier in Table 5.The static betas paint a very different picture of market risk compared to the rolling beta. (Further data of other banks are available from the authors).

This therefore underlines the limitation of using a static beta value over a period of time to guide risk management. A rolling beta better reflects any changes in current market conditions and provides a more accurate and reliable beta coefficient estimate. Risk-seeking investors choosing stocks/indices based on beta values above one, do so without knowing the relative volatility of the linear beta value. Paradoxically, a linear beta greater than one, may simultaneously have a rolling beta less than one. Hence the signal being sent to investors from the linear beta value may be completely misleading. The final part of this paper determines whether bank returns are over-valued or under-valued using the CAPM. Members of the financial community that don’t believe in the Efficient Market Hypothesis attempt to construct investment strategies that generate a positive alpha. Alpha measures the securities actual return minus the expected return as predicted by the CAPM. Positive alphas indicate a security which has outperformed its expected return. Simultaneously it provides an indication of securities/indices that are undervalued by the market. Using the rolling betas generated previously, daily alphas are constructed for each of the llbanks’ indices in the TASI. Utilizing average rolling beta values across each of the three macroeconomic cycles, expected returns for each of the banks are calculated using the CAPM model equation.

Figure-1

Figure-1. Albilad Bank rolling beta values and other comparative beta values

Figure-2

Figure-2.AlRajhi Bank rolling beta values and other comparative betas values

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