Sentence examples for trade classification from inspiring English sources

The Lee and Ready (1991) trade classification algorithm used in many previous quote-driven studies involving order imbalance is therefore not needed.

An advantage of the German data over most US data is that all trades are identified as either buyer- or seller-initiated, thus avoiding errors from the use of trade classification algorithms.

This avoids any need for applying the Lee and Ready (1991) trade classification algorithm used in many previous quote-driven studies, see e.g., Chan and Fong (2000, p. 254), Chordia and Subrahmanyam (2004, p. 494), Yamamoto (2012, p. 9).

The amounts were determined on the basis of underpayments in the various trade classifications -- which also included plumbers, sheet-metal workers and laborers -- and the time worked.

Foods were then grouped into categories reflecting manufacturing and trade classifications [ 13].

But a tweak to the terms means Premier is now paying $15m, which changes the deal's classification and means the company's shares are back trading earlier than expected.

Experimental results confirm that the novel method improves over conventional approaches, especially in the trade-off between classification accuracy and machine complexity (i.e., the dimensionality of the remapped space).

One tradeoff in fixed window segmentation schemes is the window size, which basically trades confidence in classification accuracy for temporal accuracy of the segment boundaries and noise in the segmentation.

Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers.

More specifically, it requires: (1) to determine which classifier algorithms can handle the amount and the variability of data; (2) to evaluate the stability of classifier parameters; (3) to select the best feature set used as input data in order to find the good trade-off between classification accuracy and computational time; and (4) to establish the classifier accuracy over large areas.

Dummy variable that takes value 1 if the longest job a person has held has taken place in the following CNAE sector classifications: Trade (50 to 52), Restoration (Hostelería) (55), Transport (60 to 64), other services, including education y health (65 to 67, 70 to 74, 80, 85 and 90).