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Table 1 Correlation coefficients between S&P 500 changes and the predictors with polynomial and linear regressions

From: Predicting stock market movements using network science: an information theoretic approach

Correlation matrix Act. S&P Act. S&P Sqrs. S&P Abs. S&P
Strength distribution     
KLD 3 0.5628 / 0.6081 0.0895 0.6705 0.6454
KLD 6 0.5752 / 0.5984 0.0837 0.6823 0.6360
KLD 9 0.5333 / 0.5334 0.0582 0.6498 0.6725
KLD 13 0.4794 / 0.4886 0.0408 0.6182 0.6219
KLD All 0.5582 / 0.5635 0.1175 0.6521 0.6587
RS 3 0.4185 / 0.4455 0.0173 0.3811 0.6630
RS 6 0.4326 / 0.4845 0.0159 0.3838 0.6615
RS 9 0.4196 / 0.4855 0.0093 0.3750 0.6526
RS 13 0.4385 / 0.4674 0.0024 0.4077 0.6685
RS All 0.4065 / 0.4447 0.0134 0.3649 0.6552
Mean 0.4189 / 0.4583 0.0129 0.3640 0.6536
Variance 0.1487 / 0.1641 0.0175 0.3548 0.6407
Skewness 0.5471 / 0.5610 0.0644 0.6265 0.5716
Kurtosis 0.5425 / 0.5581 0.0192 0.4047 0.6532
Eigenvector centrality     
Mean 0.1526 / 0.1795 0.0099 0.2591 0.5351
Median 0.3168 / 0.3200 0.0110 0.2720 0.5509
Maximum 0.4272 / 0.4494 0.0068 0.2175 0.4836
Betweenness centrality     
Mean 0.0435 / 0.0482 0.0111 0.2797 0.5482
Median 0.0288 / 0.0289 0.0102 0.0089 0.0332
Maximum 0.0288 / 0.0288 0.0162 0.2445 0.4350
Network modularity     
Modularity 0.2973 / 0.2982 0.0082 0.1503 0.3906
  1. Note. Correlations are significant at the 0.05 level
  2. Act.: Actual values of S&P 500 changes
  3. Sqrs.: Squared values of S&P 500 changes
  4. Abs.: Absolute values of S&P 500 changes
  5. *Polynomial regression second-order/third-order
  6. **Linear regression