Table 9. Granger causality test.
Dataset | lag(days) | Direction | Avg reduction in RSS | ||
Q (100 tickers) | 1 | Q T | |||
Q (100 tickers) | 1 | T Q | |||
U (100 tickers) | 1 | U T | |||
U (100 tickers) | 1 | T U | |||
Q (100 tickers) | 2 | Q T | |||
Q (100 tickers) | 2 | T Q | |||
U (100 tickers) | 2 | U T | |||
U (100 tickers) | 2 | T U | |||
Q (87 tickers) | 1 | Q T | |||
Q (87 tickers) | 1 | T Q | |||
U (87 tickers) | 1 | U T | |||
U (87 tickers) | 1 | T U | |||
Q (87 tickers) | 2 | Q T | |||
Q (87 tickers) | 2 | T Q | |||
U (87 tickers) | 2 | U T | |||
U (87 tickers) | 2 | T U |
Adding information about yesterday’s query volume reduces the average prediction error (in an autoregressive model) for today’s trade volume by about , and for half of the companies the reduction is statistically significant at .