The Hypothesis About Market Volatility Effects To assess the effects of price and price movement, we included in our analysis the following information: 3 price of stock at end of August 1968 4 variation in stock price in August 1968 (range/price). This allows us to learn how price level for each security and recent volatility in the stock price has affected the probability of a fail. Another indicator-which we did not use in our study because of the high cost of getting the basic data-could be the amount and direction of price movement in the stock between trade date and settlement day. CONTROLLING FOR OTHER CONDITIONS Other conditions associated with a trade may mask the effects of the guessed causes, and we included a number of kinds of information in our analysis to control statistically for these conditions. Controlling for Broker/Dealer Size Several indicators of broker/dealer size were used, and still others were available for use. We included in our analysis the information: 17 number of exchange/association memberships for buyer 22 number of buyer's employees 24 buyer's total assets 30 number of exchange/association memberships for settler 41 settler's total assets Controlling for Broker/Dealer Business Mix Since both the mix of revenue producing work and the mode of doing the work varies from house to house, we attempted to take this into consideration by including the following information in our analysis: 19 buyer does underwriting or not 20 buyer clears for others or not 21 buyer clears through others or not 31 settler operates on agency basis with public or not 33 settler does underwriting or not 34 settler distributes mutual funds or not 35 settler makes third market in listed securities or not 36 settler clears for others or not 37 settler clears through others or not 38 settler has offices in NY, NJ, Pa., or not. Controlling for the Size of the Settlement Some trades and settlements are small, others large. Occasional comments from people in the industry suggested that the size of the settlement may even affect the probability of a fail. In any event, it seemed appropriate to control for the size of the settlement. We included in our analysis the following information: Since standards for listing on the three exchanges/associations are different, a rough statistical control for the differences in these standards was accomplished by including in our analysis the information: 7 security listed or not. Controlling for Short-Term Variation in Workload Trading volume fluctuates in the short term as well as the longer periods of time, and we controlled for the volume of trading on the three business days prior to the trade by calculating the combined trading volume in shares for the New York and American Stock Exchanges for the time immediately preceding each trade date. Our thought was that the increased or decreased work load in the operations offices could affect their performance and perhaps affect fails. We included the following information in our analysis: 1 trading volume for prior 3 days, NYSE and ASE. ANALYSIS OF THE DATA Samples with substantially complete data were chosen for analysis, and the relationship of each of these variables to all others was examined by calculating the correlations among the variables and examining these coefficients by factor analysis. The samples used in our analyses are described in Appendix B, and the method of analysis with some of its technical details is described in Appendix C. V. THE CAUSES OF FAILS In the previous chapter we showed how many different kinds of information were observed about each buy in our sample so that we could discover how they may affect the occurrence of a fail. We divided the buys about which we had complete information into two samples, and our analysis of the causes of fails is based on those samples.3 The relationship of each circumstance or variable affecting the trade to every other variable affecting the trade is represented by the correlation matrices presented in Table 6. Each number in Table 6 is a correlation coefficient. Each coefficient indicates the degree of relation between two conditions affecting the buy. For example, the buyer's number of employees and the buyer's assets are correlated +.57 in sample B and +.58 in sample C. These correlations, which are positive and much larger than .00, indicate that buyers with a large number of employees also have large dollar assets and a buying broker/ dealer with a small number of employees tends to have small dollar assets. The positive correlation between these two variables indicates that when one of the variables is large the other variable also is large. Since the correlation between dollar assets and number of employees in our sample of buying broker/ dealers is about +.58, we also know that these two variables are not perfectly correlated with each other. If that had been the case the coefficient would have been +1.00, a coefficient indicating the two variables are perfectly correlated with each other. Knowing the number of employees employed by a broker/dealer does not allow one to estimate precisely what the dollar assets of the broker/dealer are since these two variables are not perfectly correlated, but the relatively large positive correlation of +.58 indicates that broker/dealer firms of large size are characterized both by a large number of employees and large dollar assets. As another example, it can be seen in Table 6 that the correlation between the event that the buyer and settler are in the same town and the number of miles between the buyer and settler is -.72 in sample B and -.71 in sample C. We have represented the fact that the buyer and settler are in the same town by the digit 1 and the fact that buyer and settler are in different towns by the digit 0. We have represented the distance 3. See Appendix B for a discussion of samples B and C. Trading volume on prior 3 days וי 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 17 18 19 20 21 22 23 24,25 26 27 28 29 14 1 3 08-09 20 00 10 28 10 04.04-04.09 -05-11 11 00-17 01 02-01 00 08 -01-04 in Aug 168 4 10-14 18 13-02 43 02 14-04-14 18 town 5 -05-11-04 14 18-01 17-04-72 18-28 47-05 12 37 14-05-01 09 07 08 -11-07 -15-19 10 12 -08-03 04-13 01-06 09-02-08-10 04 13 14 is 16 17 Security is listed assy memberships Buyer number of employees Buyer employees to to 400 Buyer net capital ratio Buyer promptness in settling Buyer clerical accuracy Buyer promptness in paying 10-05 14 10-11 15 07-01-10-10 25 09 -05-11 00 02-23 01-01-09-14 01-07-04-13-03 06 01 -08 00-14 04 07-09-24 02-33-08 05 00-10 02 00 DO 34-0327-05 07: 17-08 08 04 02-02-0712 -28-01-21-14 04-21 18-08-05 30. 24 08-21 29-11 29-11 05.19.03 29 26-07-04-09 28-03 11 13-19 28-03 12 23-29-18-28 14 -02. 16 01 26-24 23-39-07 01-37-39-24-02 18-39 10-26 34-19 33 08-02 36 35 27 63 51-05 27 15-11 44 00 20 29-31-17-30 26 24-04 19 03 30 07 08 09 12 12-07 05 13 06 09-04 26-02 17-19-09 05-19-26-14 09 05-24-01-16 18-13 33 09-03 25 26 23 02 -11 08 02-03-18-08-06-01 00-08-03-11-19 06 00 08 00 04 03-06 00-02 00 01 00 15 -09 02-03-05-07-07-06-03-1402 05 05-09 -15-04-19 05-12-01 03-05-03-04 00-02-03 32-26 37 31-01 14 51-06 31 16-01-10 02-01-14-15 18 -0400-16-05 25-47-04 01-03 11-27 07-01 01 10-01 19 26-20 28 07-03 13 28-08 21 06-03 08 06 03-10-15 20 -06-15-11 16 25-28 14 09 00 14-07-04-02-03 01 02 08 03 10-23-29 43-09-03 25-03 14-18 10 09-06-09 14-20 17 37 29-2544-01 18 21-10-10-03-04-12-08 -10 20 07 03-38 29-02 07 01-16 26-05-06-03-04 07-09-34-48 19 07-23 -06 14 26 25 14 01 07-05 27 21-02 04 21-05 22 05 04-09 00-02-14-10 05-06 22 30 10-10 34-03 16 10-03-05-03-03-01-01 02 31-06-33-42 42-31 00 05-18 19-10 07 06 01-04 -02 27-01-18-42 36-20 01-06-24 22 00 04 01 00-07 -06 09-02-29-28 33-32 00 04-11 15-08 05 06-06-03 11-18 11 27 09-05 27-01 19 15 03-22 06-01-05-12 Boyer exchange Buyer hold's inventories Buyer does underwriting Buyer clears for others Buyer clears through others 21 22 23 24 25 26 27 28 29 Settler operates on Settler distributes mutual funds Settler clears for others Sattler assets Settler net capital ratio Clearing house used to settle 43 05-02 24 06 12 05 14-03 10 06-09 02-06-05-16-11 -04 02 02.05 15-1217 05-06 01-09 06 01-06-04-08 00 01 10 06 09-03-10 00-11 07-07-04-02 00-07 00 -02 02 06:04 00-05 08 06-04 01-01 07 00 00-01 00 14-01 23 01 05 07-03-06-11 02-01-03 13 00-04-07 46 -10-03 02 06 02 01 08 01 00-01-01 09-01-01-04-03 44 45 38-06 12 05 17 -50-57-66 64 55-06 34-32-04 57-06 46 11-03 30-20 56 13 37 -33-31-02-5570-67 26-05-51 -24-36-09-65 25-45 26 12-5972 -26-28-01-6154-52 15 06-67 72.77 -50 41 03-0947-16 58 00 16 65-51-50-52 71 71-50 76-49 -05 09-03-12 06-15-02-01 12 05-05-02-02 -08 04 04 04 04-05-01-07-06 02-02 02-05 -21 03-04-08 12-21 01-05-18 20 11 21-14 -11 06-07-08-02-13 04-04 02 05 01 00-05 -06-01 01-12 13-14 01 03 02 11 02 10-03 02 03-03 05-01 05 06 01 11-02-04-07 03 07 07-01 03-18 12-11 04 14-19-10-19 cb 05-03 04-02 03 02-04 06 01 01 01 00-05 05 01-11 18-45 25 00 05 11-37-10-31 08 06-02-11-02-04 01 08 08 21-07-06-09 17 00 11.03 15-16 03 00-08-04-13-07-10-10 12-03 02 00-06 09 01 09 16-10-06-12-13 -04-05-08 02-11 04-09-02 03-08-02-10 07 05 02 12-0113-01-01-02.00 05-03 03 C3 08-03 01 08-05 12 06 04 16-10-07-12 10 05-02 15-16 15-05-08 02 03 09 02 05-02 05-04-03 10-10 13-08 03-03-15-04-06 07 59 02 22 14-20 59-22 34 41-45-33-4236 16 07 12-12-15 02 06 08-08 01 22 00 07 -11-17-04-15 06-25-07-10-13 13 10 05-16 00 08 0/ 02-04 04-03 00-07 00 01-02-06 29-06 24-12 02 17-01 22 13-06 00-05 (3 -28 03-22 11-01-17-03-19-06 04-07-01-02 03 01 07-13 01 01-05 06 06 00-01-02-03 -01 01 00 01-02 00 05-02-04 03-01-03-06 -14-04-03-16 09-18 11-12-14 2025 20-18 -48 02-23-0609-42 07-32-34 23 17 29-23 in fail state 57 01:02-12-10-14 12-32-07-06-05 08 05. 14.06. 07-01-09-10 06-17:22-21-03-06-17, 20. 14. 27–12 57 |