The daily data for each bank and the TBFSIare regressed against the TASI for all observations. A series of beta coefficients are calculated. These betas identify the sensitivity of the bank and banking sector returnsto the market returns for each period (Period 1, Period 2 and Period 3) and the full sample phase (Periods 1 – 3). Risk is assessed by measuring beta coefficient. Table 5 represents the daily beta for banks and the TBFSI against the TASI across all three market cycles as well as the overall phase.
Al-Rajhi Bank has the highest beta 1.04 and Al-Bilad Bank the lowest beta of 0.70. Al-Rajhi Bank and Arab National Bank have the highest beta coefficient across other banks and TBFSI. The overall beta of 1.04 suggests that for every 1% increase in overall market returns, the returns for the Al-Rajhi Bank and Arab National Bank will increase by 1.04%.Betas of bankswith one or lower values, indicate a lower risk compared to the overall Saudi Stock Market. Therefore the results of these betas in Table 5 show banks such as Saudi Hollandi Bank and Al-Bilad Bank should be less sensitive to market movements compared to Al-Rajhi Bank and Arab National Bank. Banks with betas below one are interpreted as lower risk investment or a defensive type of stock. These stocks are therefore seen as potentially attractive to risk-averse investors. Click Here

Table 5 further reveals that the beta coefficientsfor each bank fluctuate over time, Cycle 1, Cycle 2 and Cycle 3. The variation in these‘cyclical’ betas compared to the ‘overall’ beta coefficient captures the individual short-term exposure of the market-moving events (systematic risks) identified earlier in Table 3. The efficacy of applying a single beta coefficient over a long period of time to signal future forecasts of asset performance and required returns challenges its reliability as a market signal. Clearly investors formulating an investment strategy based on beta values could be misled by relying on a single static beta value, particularly one which may encompass major systematic market-moving events.The final column on Table 5 uses the coefficient of variation (CV) to measure the stability of the beta across the different cycles. A higher CV implies higher volatility. Results show high relative volatility across all the banksin this sample. To improve the reliability of the beta coefficient signal and overcome the limitations of a single beta coefficient derived from a period of past returns, the authors suggest calculating a ‘rolling’ beta. Since economies are dynamic and the gathering of information is perpetual, a rolling regression technique is applied to generate daily beta values for each sector. A rolling regression of 100 daily returns is conducted and rolled on a daily basis throughout the entire sample period. By applying a rolling regression technique 772 daily beta estimates are generated instead of one single beta estimate which best fits the sample data. Descriptive statistics of the beta values across all banks are presented in Table 6.
The rolling beta coefficient identifies the daily risk-return relationship between the bank and the market, based on historical daily return data. The CV provides the relative volatility of the rolling beta. A high CV suggests high variability of the beta coefficient. Hence without reference to the CV, the beta alone disguises the true signal of the sector-market relationship.

Table-5.Beta values for all listed banks in TASI across each cycle

Bank Name Cycle 1Global FinancialCrisis


Cycle 2Recovery(Beta) Cycle 3Arab Spring (Beta) Cycle 1 – 3Overall Phase (Beta) CV – Beta Volatility
AlRajhi Bank 1.03 1.20 0.83 1.04 14.49%
Arab National Bank 1.08 0.97 1.00 1.04 4.58%
Bank Aljazira 0.96 1.03 1.05 0.99 3.89%
Banque Saudi Fransi 0.95 0.88 1.01 0.94 5.48%
TBFSI 0.91 0.99 0.92 0.93 3.56%
SAMBA Bank 0.77 1.09 1.20 0.91 19.25%
Alinma Bank 0.94 0.79 0.75 0.86 10.05%
Saudi British Bank 0.85 0.86 0.96 0.86 6.18%
Riyadh Bank 0.84 0.82 0.87 0.84 2.90%
Saudi Investment Bank 0.83 0.75 0.92 0.83 8.23%
Saudi Hollandi Bank 0.85 0.63 0.71 0.77 12.14%
Al-Bilad Bank 0.66 0.76 0.74 0.70 6.10%

Table-6.Descriptive Statistics of rolling daily Betas 2008 – 2011, by banks

Bank Name Mean StandardDeviation Kurtosis Skewness C.V.Volatility
AlRajhi Bank 1.06 0.19 2.47 -0.24 17.92%
Arab National Bank 0.99 0.15 3.01 -0.51 15.15%
Bank Aljazira 0.94 0.18 2.44 0.98 19.15%
Banque Saudi Fransi 0.87 0.28 2.04 0.04 32.18%
TBFSI 0.96 0.13 4.23 1.22 13.54%
SAMBA Bank 1.09 0.70 2.76 0.53 64.22%
Alinma Bank 0.75 0.14 2.65 -0.20 18.67%
Saudi British Bank 0.85 0.26 2.88 -0.58 30.59%
Riyadh Bank 0.82 0.16 6.61 1.37 19.51%
Saudi Investment Bank 0.77 0.17 2.03 -0.22 22.08%
Saudi Hollandi Bank 0.70 0.18 3.04 0.05 25.71%
Al-Bilad Bank 0.57 0.28 1.88 -0.35 49.12%

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