Characterizing financial markets from the event driven perspective

Torkar, Miha; Mladenic, Dunja

doi:10.1007/s41109-021-00417-z

Applied Network Science

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

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