• We investigate the effect of big data technologies on entrusted loans of non-financial enterprises using text analysis method. • The empirical results robustly show that big data technologies have an inhibitory effect on entrusted loans of non-financial enterprises. • One plausible underlying mechanism is that big data technologies promote non-financial enterprises’ R&D, improve total factor productivity, and increase operating income, which attenuate incentives for non-financial enterprises to crowd out real investment and to act as entity intermediaries. The inhibitory effect is more prominent in the group with high industry competition or enterprises that receive less market attention. Big data technologies have complexified shadow banking activities. We explore the effect of big data technologies on entrusted loans of non-financial enterprises using a text analysis method. The regression results robustly show that big data technologies have an inhibitory effect on entrusted loans of non-financial enterprises and the inhibitory effect is not a confounding bias caused by ownership structure. The inhibitory effect is further verified by decision tree algorithm. A plausible underlying mechanism is that big data technologies promote non-financial enterprises’ R&D, improve total factor productivity, and increase operating income, which attenuate incentives for non-financial enterprises to crowd out real investment and to act as entity intermediaries. In addition, the inhibitory effect is more pronounced in the group with high industry competition or enterprises that receive limited market attention. This is the first paper to examine entrusted loans of non-financial enterprises using firm-level data. The findings provide empirical evidence for corporate business strategy choice and systemic financial risk prevention.
Sun et al. (Wed,) studied this question.