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Table 5 Sector level results

From: Characterizing financial markets from the event driven perspective

Model W Consumer discretionary Consumer staples Energy Financials Health care Industrials Information technology Materials and utilities Telecommunication services
   mae mae mae mae mae mae mae mae mae
(a) Errors of prediction models per sector with news
Decision tree cent 0.617 0.521 0.649 0.767 0.799 0.836 0.606 0.385 0.774
  clst 0.641 0.563 0.697 0.777 0.826 0.867 0.655 0.444 0.798
  cos 0.590 0.502 0.621 0.733 0.780 0.831 0.594 0.337 0.759
  count 0.644 0.565 0.698 0.800 0.850 0.903 0.629 0.408 0.792
Gaussian process cent 0.489 0.399 0.566 0.619 0.676 0.717 0.500 0.309 0.642
  clst 0.494 0.406 0.568 0.623 0.681 0.722 0.506 0.315 0.647
  cos 0.490 0.401 0.565 0.619 0.677 0.718 0.502 0.310 0.643
  count 0.489 0.400 0.550 0.620 0.674 0.716 0.500 0.309 0.647
KernelRidge cent 1.131 1.122 1.200 1.286 1.345 1.454 1.167 0.897 1.364
  clst 2.336 2.415 2.430 2.509 2.569 2.709 2.382 2.129 2.625
  cos 1.113 1.170 1.189 1.269 1.355 1.445 1.149 0.909 1.342
  count 2.620 2.571 2.635 2.813 2.790 2.887 2.619 2.393 2.858
Linear regression cent 3.020 3.127 3.185 3.296 3.358 3.618 3.118 2.758 3.504
  clst 3.938 4.196 4.050 4.142 4.188 4.405 4.037 3.746 4.275
  cos 3.165 3.465 2.979 3.394 3.405 3.824 3.340 2.843 3.772
  count 3.624 3.689 3.926 3.853 4.007 4.182 3.707 3.259 3.991
Nearest neighbors cent 0.539 0.461 0.604 0.669 0.721 0.774 0.556 0.376 0.701
  clst 0.564 0.480 0.630 0.692 0.742 0.796 0.583 0.390 0.737
  cos 0.537 0.455 0.606 0.666 0.720 0.775 0.557 0.356 0.712
  count 0.592 0.502 0.651 0.722 0.768 0.822 0.607 0.422 0.745
Neural net cent 0.620 0.489 0.780 0.771 0.845 0.855 0.594 0.361 0.799
  clst 0.643 0.543 0.757 0.822 0.881 0.904 0.651 0.484 0.845
  cos 0.660 0.532 0.840 0.826 0.901 0.918 0.668 0.396 0.831
  count 0.849 0.804 0.888 0.991 1.056 1.102 0.881 0.656 1.060
Random forest cent 0.545 0.465 0.580 0.703 0.732 0.794 0.555 0.340 0.717
  clst 0.560 0.482 0.609 0.701 0.740 0.795 0.572 0.377 0.720
  cos 0.554 0.478 0.580 0.705 0.736 0.804 0.566 0.345 0.731
  count 0.581 0.508 0.634 0.735 0.772 0.816 0.581 0.380 0.734
Model Consumer discretionary Consumer staples Energy Financials Health care Industrials Information technology Materials and utilities Telecommunication services
  mae mae mae mae mae mae mae mae mae
Errors of prediction models per sector with no news
Decision tree 0.630 0.564 0.680 0.817 0.841 0.881 0.612 0.367 0.772
Gaussian process 0.494 0.406 0.568 0.622 0.680 0.721 0.505 0.315 0.646
Kernel ridge 0.512 0.462 0.591 0.656 0.751 0.768 0.529 0.324 0.678
Linear regression 0.513 0.463 0.592 0.657 0.753 0.769 0.530 0.324 0.679
Nearest neighbors 0.563 0.497 0.577 0.762 0.747 0.858 0.592 0.390 0.730
Neural net 0.725 0.756 0.651 1.001 0.876 0.956 0.781 0.461 0.781
Random forest 0.580 0.505 0.614 0.755 0.767 0.824 0.565 0.359 0.720