Analyzing Stock Brokers’ Trading Patterns: A Network Decomposition and Spatial Econometrics Approach

Figure 6

(a) Estimated coefficients associated with the negative spatial dependence variable in Probit regressions using only the network of traders with proprietary trading activities. The regressions are estimated with different thresholds defining the transaction network and different sizes of the IPSA shock used as the dependent variable in the Probit. Each regression controls for the variation of oil prices, dollar prices, VIX index, emerging market index, copper prices, 5-year credit default swaps S&P 500 stock index, month, and year. The size of the IPSA jump is measured in standard deviations (s.d.)

(b) Estimated coefficients associated with the negative spatial dependence variable in Probit regressions using the bipartite network of traders with and without proprietary trading activities. The regressions are estimated with different thresholds defining the transaction network and different sizes of the IPSA shock used as the dependent variable in the Probit. Each regression controls for the variation of oil prices, dollar prices, VIX index, emerging market index, copper prices, 5-year credit default swaps S&P 500 stock index, month, and year. The size of the IPSA jump is measured in standard deviations (s.d.)