Multiple Criteria Decision Making and General Regression for Determining Influential Factors on S&P 500 Index Futures

We employ the DEMATEL-based analytic network process (D-ANP) to evaluate the weight of various factors on S&P 500 index futures. The general regression method is employed to prove the result. We then employed grey relational analysis (GRA) to examine predictive power of determinants suggested by 13 experts for fluctuations in S&P 500 index futures. This study yields a number of empirical results. (1) The explanatory power of macroeconomic factors for S&P 500 index futures outperforms that of technical indicators, as found in most of previous research papers; (2) The D-ANP revealed that five core factors (US dollar index, ISM manufacturing purchasing managers’ index (PMI), interest rate, volatility index, and unemployment rate) affect fluctuations in S&P 500 index futures, of which the US dollar index is the most important; (3) A casual diagram shows that the US dollar index and interest rate have mutual effects, and the US dollar index unilaterally affects ISM manufacturing PMI, unemployment rate, and the volatility index; (4) Granger causality test results confirmed some similar results obtained via the D-ANP that the US dollar index, interest rate, and the PMI have major impacts on the S&P 500 index futures; (5) The general regression results confirmed that four of five factors selected via the D-ANP (US dollar index, interest rate, volatility index, and unemployment rate) have strong explanatory power in forecasting the rate of return on S&P 500 index futures; (6) The GRA revealed that the explanatory power of various factors selected via the D-ANP was better for S&P 500 than for Dow Jones Industrial Average (DJIA) and Nasdaq 100 index futures; (7) The explanatory power is better for S&P 500 Industrial than for S&P 500 transportation, utility, and financial index futures.