Table 5.2 continued.
Data set N Yearly Mean Std.Dev. Skew. Kurt. t-ratio Sharpe Max.loss Q20 Adj Q20 Q20 r2
UK FTSE100 4042 0.0679 0.000261 0.011301 -0.822 14.148 1.47 0.005322 -0.4307 35.65b 17.41 1338.35a
Ireland ISEQ 3250 0.0221 0.000087 0.011081 -0.484 8.459 0.45 -0.008499 -0.3997 21.93 17.89 332.65a
Egypt CMA 1695 0.1205 0.000451 0.007462 0.214 14.211 2.49b 0.036029 -0.3609 196.80a 117.79a 42.62a
Israel TA100 2996 0.0676 0.00026 0.016554 -0.6 9.001 0.86 0.005322 -0.5359 137.64a 91.70a 263.49a

Table 5.3: Statistics best strategy: mean return criterion, 0% costs. Statistics of the best strategy, selected by the mean return criterion, if no transaction costs are implemented, for each index listed in the first column. Column 2 shows the strategy parameters. Columns 3 and 4 show the mean return and excess mean return on a yearly basis in %/100 terms. Columns 5 and 6 show the Sharpe and excess Sharpe ratio. Column 7 shows the largest cumulative loss of the strategy in %/100 terms. Columns 8, 9 and 10 show the number of trades, the percentage of profitable trades and the percentage of days these profitable trades lasted. The last column shows the standard deviation of returns during profitable trades divided by the standard deviation of returns during non-profitable trades. The results are computed for an US-based trader who applies the technical trading rule set to the local main stock market indices recomputed in US Dollars. The daily interest rate on 1-month US certificates of deposits is used to compute the Sharpe and excess Sharpe ratio in columns 5 and 6.

Data set Strategy parameters r re S Se ML # tr. %tr.>0 %d > 0 SDR
World MSCI [ MA: 1, 2, 0.000, 0, 0, 0.000] 0.5152 0.3980 0.1461 0.1349 -0.1868 2215 0.748 0.859 1.4453
Argentina Merval [ FR: 0.010, 0, 50, ] 0.2691 0.5789 0.0266 0.0648 -0.6139 61 0.656 0.854 1.4492
Brazil Bovespa [ MA: 1, 5, 0.000, 0, 0, 0.025] 0.4663 0.5036 0.0425 0.0528 -0.5389 455 0.686 0.809 1.1355
Canada TSX Composite [ MA: 1, 2, 0.001, 0, 0, 0.000] 0.3988 0.3347 0.0879 0.0894 -0.4481 1241 0.737 0.850 1.0806
Chile IPSA [ MA: 1, 2, 0.001, 0, 0, 0.000] 0.6768 0.8065 0.1046 0.1396 -0.3578 659 0.707 0.842 1.4096
Mexico IPC [ MA: 1, 2, 0.001, 0, 0, 0.000] 1.1004 0.9829 0.1026 0.1004 -0.3558 556 0.710 0.820 1.5576
Peru Lima General [ MA: 1, 2, 0.001, 0, 0, 0.000] 1.2380 1.0112 0.1447 0.1281 -0.3233 814 0.720 0.846 1.4390
US S&P500 [ FR: 0.005, 0, 0 ] 0.2310 0.1156 0.0434 0.0284 -0.3795 1409 0.725 0.822 1.2534
US DJIA [ MA: 1, 2 0.000, 0, 0, 0.000] 0.2060 0.0791 0.0351 0.0157 -0.4630 2667 0.695 0.795 1.2852
US Nasdaq100 [ MA: 1, 2 0.001, 0, 0, 0.000] 0.3972 0.2543 0.0480 0.0362 -0.8507 1821 0.717 0.821 1.1472
US NYSE Composite [ MA: 1, 2 0.000, 0, 0, 0.000] 0.2630 0.1486 0.0561 0.0410 -0.2849 2543 0.712 0.815 1.3261
US Russel2000 [ MA: 1, 2 0.001, 0, 0, 0.000] 0.5653 0.4423 0.1273 0.1134 -0.2872 989 0.762 0.874 1.1298
US Wilshire5000 [ MA: 1, 2 0.000, 0, 0, 0.000] 0.3202 0.2010 0.0711 0.0539 -0.5005 2115 0.722 0.825 1.2656
Venezuela Industrial [ MA: 1, 2 0.000, 0, 0, 0.000] 1.2107 1.3313 0.0835 0.0963 -0.4630 857 0.704 0.823 1.5636
Australia ASX All Ordinaries [ MA: 1, 2, 0.000, 0, 0, 0.000] 0.2837 0.2470 0.0506 0.0576 -0.4159 1855 0.699 0.814 1.1426
China Shanghai Composite [ FR: 0.005, 2, 0 ] 0.5693 0.3764 0.0558 0.0389 -0.4502 266 0.711 0.824 1.2988
Hong Kong Hang Seng [ MA: 1, 5, 0.000, 0, 0, 0.000] 0.6507 0.5112 0.0870 0.0788 -0.3691 870 0.721 0.858 1.3489
India BSE30 [ MA: 1, 2, 0.001, 0, 0, 0.000] 0.6566 0.7110 0.0775 0.0950 -0.4509 867 0.713 0.844 1.0448
Indonesia Jakarta Composite [ FR: 0.005, 0, 0 ] 1.2643 1.5135 0.0884 0.1109 -0.7013 739 0.747 0.878 0.8928
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