Stock volatility and margin trading

Journal

of Monetary

Economics

26 (1990) 101-121.

North-Holland

Stock volatility and margin trading* Paul J. Seguin University of Rochester,

Received

October

Rochester,

NY 14627, USA

1989, final version

received

May 1990

This study examines additions of OTC issues to the list of marginable securities and tests the hypothesis that margin trading destabilizes prices and contributes to volatility. No detrimental effects are found. Instead, evidence suggests that margin eligibility increases the flow of information and enhances depth. Though volumes are 30% larger, volatility and noise decrease with the inception of margin trading. Further, margin eligibility is valuable: increases in value of 2% occur upon the announcement of eligibility. Analysis of a small sample of margin eligibility revocations provides no evidence that tightening margin restrictions reduces volatility.

1. Introduction

Since the market break of October 1987, there have been numerous suggestions for reducing the volatility of equity and derivative securities prices. One suggestion advocated by Congress, the Brady Commission, former SEC Chairmen John Shad and David Ruder, and others involves increasing initial margin requirements above the current 50% level. An initial margin requirement of x% indicates that a trader may borrow up to (1 -x)% of the price of a new purchase of securities from a commercial bank or broker. Maintenance requirements, which are not considered here, stipulate the amount by which the market value may fall before a ‘margin call’ is instigated. Maintenance requirements are designated by the exchanges themselves. Support for the proposition that margin regulation is an effective policy tool for combating ‘excessive’ volatility is found in Hardouvelis (1988a), who documents a negative relation between the level of margin restrictions and the volatility of the equity market. A version of this study, published by the New York Fed, has received prominent coverage in the financial press. *Financial support was provided by the T. Boone Pickens Jr. Research Fellowship at the Managerial Economics Research Center. I thank Mine;-chi Hu for her diligent assistance. Helpfil comments were received from Gene Fama (the-referee), Tony Greig,Shing-yang Hu, Gregg Jarrell, Bob King (the Editor), Haim Mendelson, Charles Plosser, Jill Pranner, Marlene Puff;;, Bill Schwert, Diug Skinner, and participants at workshops at Arizona State, Chicago, M.I.T., Michigan, Ohio State, Rochester, Utah, and Yale.

0304.3932/90/$03.50

0 1990-Elsevier

Science

Publishers

B.V. (North-Holland)

102

P. J. Seguin, Stock volatilityand margin trading

However, a number of subsequent studies including Ferris and Chance (1988), Hsieh and Miller (19901, Kupiec (19901, Salinger (19901, and Schwert (1990) have been critical of the methodology. After implementing corrections or enhancements to Hardouvelis’ methodology, these studies reach the conclusion that there is no relation between changes in margin requirements and subsequent equity volatility. Determining the effects of changes in equity margin restrictions is important for reasons other than controlling equity volatility. Since 1983, the Federal Reserve Board (the Fed) has debated turning initial margin requirements over to the exchanges. This deregulatory initiative is being greeted with consternation. Further, the Brady Commission and others have advocated an increase in the margin requirements on derivative securities. Following the break of October 1987, margins on futures and options were raised from 7 and 10% to 13 and 20%, respectively. Many parties are dissatisfied with these new levels and advocate restrictions that are still more stringent.’ This study examines the relation between margin restrictions and volatility. However, the changes in margin restrictions considered here differ from those examined by Hardouvelis (1988a, b) and related studies that examine a single time series of the volatility of an aggregate portfolio and attempt to measure the effect of changes in the official margin rate on aggregate volatility. In this study, I consider additions of OTC securities to the list of marginable securities. By comparing the volatility of returns of a security in a period before margin trading is allowed to the volatility in a period after margin trading is allowed, I can measure the marginal effect of margin trading on volatility. This experimental design offers many advantages. First, the magnitude of the change in margin requirements is larger for this sample. A cursory analysis of the 23 Fed changes suggests that a typical change is on the magnitude of 20% (i.e., the margin requirement is changed from 70% to 50% or vice versa). In my sample, the inception of margin eligibility is equivalent to a change in the requirement from 100% to 50%. Second, previous studies concentrated on the effects of margin requirements on exchange-listed firms. In this study, I examine the effects on OTC firms that are, on average, smaller and more thinly traded. If margin eligibility has detrimental effects on volatility, it is reasonable to expect that any effects would be more pronounced for these issues. Indeed, the Fed continually warned against extending margin eligibility to those firms which do not enjoy ‘sufficiently deep and liquid markets to insure that marginability would not be likely to increase volatility’ (SEC Release 34-21583, p. 65). Further, there are no listed options for any of the firms in this sample. Since options also allow investors ‘For example, the Wall Street Journal, March 1983, reports: ‘There is fear that industry self-regulation of credit will lead to snow-balling liberalization, which, in turn, will fuel speculative excesses reminiscent of the late 1920’s.’ Also see ‘Calming the Markets’ by John Shad, The Washington Post, July 25, 1988.

P. J. Seguin, Stock colatility and margin trading

103

the opportunity to take leveraged positions, the effect of margin eligibility on mitigating wealth constraints would be minimal had option products been available. The characteristics outlined above should lead to enhanced statistical power. That is, if a causal relation exists between margin restrictions and volatility, the characteristics of the sample should increase the probability of detection. However, as pointed out in numerous criticisms of Hardouvelis, detecting a statistical relation is not sufficient for concluding that a causal relation exists since the observed correlation may be generated by other factors including selection bias and changes in aggregate volatility for other reasons. There are two further characteristics of the sample examined here that aid in determining the extent to which an observed statistical relation can be interpreted as a causal relation. First, the inception of margin eligibility affects only a subset of listed securities at any one time so that those securities not affected (i.e., those already eligible for margin) can be employed as a control sample. In this study, I am able to examine changes in volatility surrounding the inception of margin eligibility relatiue to changes in volatility of a portfolio that is unaffected by changes in margin eligibility. Second, Officer (1973), Schwert (1990), and Salinger (1990) argue that one interpretation for the observed correlation of volatility and margin restrictions is that the Fed reacts to abnormally high levels of prices by restraining margin credit. Since volatility is lower when prices are higher [Black (1976) or Pagan and Schwert (199011, it is possible that an increase in the level of the market ‘causes’ both lower volatilities and higher margin restrictions. This selection bias is a smaller problem in this experiment. As discussed in the following section, the Fed closely follows a set of criteria in determining which firms are to be added to the list of marginable firms. There is little discretion by the Fed in the decision-making process. Further, these criteria are not related to recent returns or recent volatility. Consequently, the ability of the Fed to react to unusual circumstances is severely limited for this sample. In summary, if relaxing margin restrictions increases volatility, those attributes of the sample that amplify statistical power should allow for the detection of a statistical relation. Further, since I can control for changes in aggregate volatility and mitigate the selection bias problem, I would also expect to be able to attribute these increases to margin restrictions.2 ‘Attributing observed changes concomitant with eligibility to margin activity may be confounded by the effects of margin eligibility on short selling. Since shares purchased on margin are held under ‘street name’, the inventory of securities that can be sold short increases once a firm becomes eligible for margin. It is impossible to measure the increase in this inventory, but it should be noted that a substantial inventory exists before margin activity commences. This inventory is partially comprised of securities (i) held by market makers and dealers, and (ii) owned by individual traders with margin accounts who choose to keep both eligible and ineligible securities in one account.

104

P. J. Seguin, Stock volatilityand margin trading

I find no detrimental effects of relaxing margin restrictions. Comparisons of volatilities calculated in periods of no margin activity to those calculated during margin eligibility provide no evidence of an increase in any measure of volatility. Indeed, all point estimates of volatility decline once margin trading is introduced, though the statistical significance varies. This result is consistent with the hypotheses that speculation is inherently stabilizing [Friedman (1953)] and that relaxing margin restrictions enhances the abilities and efficacy of stabilizing traders [Moore (1966) and Hsieh and Miller (1990)]. Increases in volume of 30% are also documented. The volume increase and volatility decrease are consistent with the hypothesis that reducing traders’ wealth constraints enhances liquidity and the depth of the market [Conrad (1989) and Bessembinder and Seguin (1990)]. The conclusion is robust to the time frame examined, corrections for changes in aggregate volatility, and corrections for potential selection biases. Examinations of autocorrelations provide no evidence that the extent of noise trading increases with the inception of margin trading. Further, there is evidence that margin eligibility is valuable. Statistically significant increases in share value in the 1% to 2% range occur upon the announcement that a firm has been added to the eligible list. The hypothesis that the inception of margin activity has detrimental effects on volatility and firm value is inconsistent with the data. I also examine a small sample of firms that become ineligible for margin trading. The analysis of this sample provides no support for the hypothesis that limiting margin activity reduces excess volatility. In the following section, I review the literature that discusses the relation between margin and volatility. Section 3 contains a brief review of institutional issues and a description of the margin eligibility selection process. Sample selection is outlined in section 4. In section 5, methodology and results are presented. The final section contains conclusions. 2. Margin requirements

and volatility

Numerous empirical and theoretical studies have considered the relation between volatility and margin activity. One frequently discussed phenomenon is ‘pyramiding/depyramiding’. Pyramiding is triggered by a rise in share value and an increase in shareholder wealth. Increased wealth allows investors to purchase more securities on margin with the increase in demand leading to further price appreciation. This, in turn, leads to further margin purchases and so on. Depyramiding is triggered by a decline in share value which in turn triggers margin calls. Leveraged speculators subjected to margin calls flood the market with securities in an attempt to raise the necessary cash which exaggerates the fall in price. Again, the process iterates. The crucial link in this story is that margin activity attenuates past price movements. However, Garbade (1982, p. 320) points out that this link is

P. J. Seguin, Stock colatility and maqyin trading

105

inconsistent with the body of empirical evidence showing that, in the absence of new information, large amounts of a stock can be bought or sold with little price pressure. A second widely debated issue concerns the nature of speculation. Since the primary effect of margin is to enhance the ability of speculators to take leveraged positions in equities, predictions of the effects of margin requirements on volatility depend on assumptions concerning the nature of speculators. DeLong, Shleifer, Summers, and Waldman (1987) argue that margin requirements affect the trading of inherently destabilizing ‘noise traders’. Their model predicts that the introduction of margin trading in a security (or any relaxation of margin requirements) would allow destabilizing traders to lever their positions and increase the amount of ‘nonfundamental’ volatility. Alternatively, Moore (1966) and Hsieh and Miller (1990) argue that the nature of speculation is inherently stabilizing and that increased margin restrictions inhibit the abilities of price stabilizing traders. Consequently, they predict a positive relation between the degree of margin restriction and volatility. Goldberg (1985) reaches a similar conclusion based on the reasoning that if individuals are restricted from holding highly levered positions in equity they would instead seek to hold positions in highly levered firms. Consequently, margin restrictions induce firms to increase their financial leverage which increases the observed volatility of equity prices. Two early empirical works examine the link between margin restrictions and volatility. Moore (1966) reports a neg&:e correlation between the level of stock prices and the level of margin credit which is consistent with his hypothesis that speculators are stabilizing in nature. Officer (1973) finds that volatilities are lower after the Fed was empowered to limit margin. However, he concludes that the decline in volatility represents a return to more normal levels of volatility and cannot be attributed to any particular regulatory phenomenon. The number of empirical studies examining this relation increased following the publication of Hardouvelis (1988a). Ferris and Chance (1988) and Hsieh and Miller (1990) examine the ratio of variances calculated over periods preceding and following changes in the margin rate and find no evidence of a negative correlation between the change in the margin level and change in volatility. Kupiec (1990) fails to find any evidence of a relation between margin restrictions and volatilities estimated using a GARCH in Means model. Schwert (1990) and Hsieh and Miller (1990) examine the spurious regression problem inherent in Hardouvelis’ methodology. They find that evidence of a relation is a statistical artifact attributable to the fact that margin levels and volatility estimates are integrated. These studies measure the effects of changes in the official margin rate on aggregate volatility: the ‘event’ under consideration affects all marginable

106

P. J. Seguin, Stock uolatilityand margin trading

securities. The event considered in this study, however, affects only a subset of securities at any one time, which may confound the applicability of these results for predicting the effects of aggregate changes. If the only effects of changes in margin requirements are direct and mechanical, i.e., volatility is related to the level of the initial margin requirement, then effects should be the same whether one firm or all firms are affected. However, changes in margin requirements generate indirect effects which may differ depending on the number of firms affected. For example, one potential difference concerns the amount of information inferred from the event. Granting eligibility to a subset of issues reflects the result of a process, outlined below, with little regulatory discretion, and therefore, limited opportunities for signalling [Grube, Joy, and Howe (198711. Alternatively, the decision to change the level of initial margin requirements comes only after extensive consideration by the Fed, and signals the Fed’s beliefs about the level of the market. However, many authors, including Officer (19731, conclude that the effect of any such information on subsequent volatility is negligible. A second potential difference involves substitution possibilities. If initial margin requirements are altered for only a subset of securities, the costs of attaining a leveraged position in the affected issues change relative to the costs associated with unaffected issues. Since investors are free to substitute between the two sets of issues, relative prices must adjust. However, substitution is also pertinent for aggregate changes. When initial margin requirements are changed, all eligible securities are affected, but ineligible issues and other securities (foreign equity, corporate, and government debt) are not, allowing substitution and shifts in relative transactions costs and asset prices. Consequently, the nature of direct and indirect effects are comparable regardless of whether initial margin restrictions are altered for a subset or universe of equities. In both situations, any mechanical and substitution effects are relevant, while the effects of any signalling by the Fed are limited. Potentially, magnitudes may differ, but it is unlikely that the direction of valuation or volatility changes would differ between the two types of events. 3. A brief regulatory history Before the introduction of the Securities Exchange Act in 1934, margin regulation was the jurisdiction of the exchanges. The Act stipulated that margin maintenance would remain a matter for the exchanges, but empowered the Federal Reserve Board with overseeing initial margin requirements. For 34 years, margin trading was available for exchange-listed securities only. The Over the Counter Market Act of 1968 allowed margin trading on OTC securities. Prior to this act, brokers were forbidden from lending funds for the purchase of OTC securities. Loans from commercial banks, however, were not subject to any restrictions. Under the Act, the same rules apply to stocks ineligible for margin trading. Once an issue is eligible, however, loans

P. J. Seguin, Stock uolatility and margin trading

107

by both brokers and commercial banks are subjected to margin constraints. The net effect of margin eligibility on the ease of securing loaned funds cannot be determined a priori and depends on whether the benefits to traders of allowing loans by brokers offsets the costs of restricting commercial bank loans. The 1968 Act stipulated initial criteria that an OTC issue had to satisfy before becoming eligible: a minimum price of $5 per share and $5 million in either capital plus surplus (book value) or the market value of equity. Once a firm was deemed to be eligible, the Fed would add the name to the OTC list, published between one and four times a year. In 1972, continued listing requirements were established. The Fed would ‘delist’ (remove from the list of margin eligible securities) any issue with a share price below $3, market value below $2.5 million, and book value below $2.5 million. Following an extensive review, the Fed amended the rules pertaining to margin eligibility in 1982. Market value constraints were removed, and the initial and continued listing criteria were eased. Under the old rules, an issue had to satisfy the minimum price requirement and either the market value or book value criteria. After the amendment, both the price requirement (which remained at $5) and the book value requirement (lowered to $4 million) became binding. The continued listing requirements were also lowered; the price constraint was lowered from $3 to $2 and the book value constraint was lowered from $2.5 million to $1 million. The stated goal of these amendments was to make eligibility criteria more comparable with the listing requirements of the American Stock Exchange (AMEX). At that time, the Fed began updating the list of eligible securities four times a year, and instituted a policy of announcing changes to the list two weeks before the effective date. The last major change occurred in 1984 when automatic margin eligibility was extended to any firm that joined NASD’s National Market System. Margin eligibility was extended immediately upon listing. 4. Sample selection Changes in the list of margin-eligible OTC securities, published in the Federal Register and the Wall Street Journal, were employed to generate a list of names of all equity additions between 1976 and 1987. American Depository Receipts (ADR’s) were removed from consideration, as were those firms that became automatically eligible for margin due to National Market System listing,3 resulting in a list of 2,380 firms. “Seguin (1989) examines the effects of National Market System listing on volatility and reports a statistically significant decline upon listing. There is no evidence, however, that the decline in volatility differs between those firms which become eligible for margin trading upon listing and those firms which were eligible before joining the NMS. Consequently, Seguin (1989) finds no evidence that margin eligibility has an additional effect on volatility given the effects of NMS listing.

108

P. J. Seguin, Stock volatilityand margin trading Table 1 Announcement

Announcement date 770104 770818 780405 781004 790404 791003 800409 801008 810408 811007 820303 820715 821007 830207 830606 831003 840206 840604 841026 850131 850430 850731 851031 860131 860428 860801 861030 870203 870501 870731

dates and number

of issues in original

Number of firms

of total

155 182 75 68 118 70 82 90 70 136 161 69 64 65 79 136 189 181 125 38 21 28 13 25 25 12 26 33 21 23

6.5 7.2 3.2 2.9 5.0 2.9 3.4 3.8 2.9 5.7 6.8 2.9 2.7 2.7 3.3 5.7 7.9 7.6 5.3 1.6 0.9 1.2 0.5 1.1 1.1 0.5 1.1 1.4 0.9 1.0

sample.a

Cumulative total 155 337 412 480 598 668 750 840 910 1046 1207 1276 1340 1405 1484 1620 1809 1990 2115 2153 2174 2202 2215 2240 2265 2277 2303 2336 2357 2380

Cumulative percent 6.5 14.2 17.3 20.2 25.1 28.1 31.5 35.3 38.2 43.9 50.7 53.6 56.3 59.0 62.4 68.1 76.0 83.6 88.9 90.5 91.3 92.5 93.1 94.1 95.2 95.7 96.8 98.2 99.0 100.0

aAnnouncement date of margin eligibility is defined as the earliest of (i) Wall Street Journal publication date, (ii) Federal Register publication date, or (iii) effective date as reported in the Federal Register. This list was created by examining all firm names reported by the Wall Street Journal or Federal Register as being added to the list of margin-eligible firms. American Depository Receipts L4DR’s) are excluded, as are those issues which became eligible for margin automatically by joining the National Market System (NMS).

Table 1 lists the number of firms that became eligible on each of the 30 announcement dates. The announcement date was defined as the earliest of (i> the publication date in the Federal Register, (ii) the publication date in the Wall Street .bumaZ, or (iii) the effective date as reported in the Federal Register. Prior to the 1982 amendments, publication in the Federal Register would occur roughly two business days after the effective date. Wall Street .humaE publication typically occurred on the effective date. Subsequent to the amendments, publication in both the Federal Register and the Wall Street Journal preceded the effective date by about 10 business days.

P. J. Seguin, Stock colatility and margin trading

109

From the initial list of 2,380 firms, the final sample was constructed by choosing those 1,803 firms that (i) were listed in the CRSP-NASDAQ tape for at least two hundred days on both sides of the Federal Register reported effective date and (ii) had at least 150 nonmissing sets of bid-ask quotes in each of the two 200 day periods. Of the 577 firms eliminated in this step, only 162 (28%) were eliminated due to insufficient data in the margin-eligible (post> period. Of these, only ten had insufficient data due to financial distress. 5. Methodology

and results

Transaction prices are reported for firms only during their tenure on the NMS. Throughout, however, closing quotes are available.4 Consequently, returns are calculated as rI = ln( bid-ask midpoint at t t bid-ask midpoint at t - I}, where ‘midpoint’ refers to the mean of the closing bid and ask quotes. Returns have been adjusted for stock distributions. 5.1. Volatility effects For each firm, two ‘raw’ standard deviations are calculated as s = TCr:, where T is the number of valid returns in the estimation period. F The ‘pre-margin’ standard deviation is calculated using daily returns from day - 200 to day - 5; the ‘post-margin’ standard deviation is calculated from day +5 to +200, where day 0 is the first day of margin eligibility. The distributions of pre-margin and post-margin standard deviations, and differences or ratios of the two are highly skewed, therefore inferences are generally based on the difference in the logs of these estimates. Taking logs not only alleviates the skewness problem but the differences can be loosely interpreted as percent changes. Table 2 presents the mean and median of the differences of logs, the percent of changes that are negative, and three tests of whether the changes are reliably different from zero. The first test, the common t-test, is strictly valid only when the underlying distribution is normal and the observations are independent. However, given the approximate symmetry of the sample distribution and the large number of estimates, sample means should be 4Prior to July 1980, the NASD reported ‘median’ or ‘representative’ quotes. Following date, closing quotes refer to the ‘best’ bid and ask quotes listed on the NASDAQ system at halt of trading. Though not reported, subsequent tests were run on a sample which excluded firms with a return sequence straddling the change in reporting regime. The results do change.

this the all not

110

P. .I. Sequin, Stock volatilily and margin trading Table 2 Tests of volatility Sample descrlptlon

Variable

changes

Sample s,ze

surrounding

M&Ill

I-statistic for F = 0

margin

eligibility”

Medun

PeWZtt lers than II

Binomial r-statistu

Bootstrap p-value

In(qx,,,7 q?J

Full

1.802

- O.tl248

-2.x0

- 0.0309

53.9

- 3.31

0.041

In(a,,,,,

No missmg returns

1.729

- 0.0272

- 3.01

-0.0371

54. I

-3.41

Mnll

712

- 0.0346

- 2.20

-0.0419

s4.x

I.(NO -0.0184 1.8~ - 0.0h30

- I.76

- 0.0248

53.4

- 3.37

- O.0758

54.x

- g&

In{un,,,,, - OPFL)

1

Pre h/X2

l”(Q<>\, - nnrL

Post h/X2

In(u p
Full

2.56 2.25 -4.0X

0.007

“All tests are comparisons of statwn computed over the period + 5 to + 200 (post) with those computed over the period -200 to -5 (pre). rr IS unadjusted standard deviation: ,r* is the standard deviation of a time bene\ of markrt-volatility-adlusted returns. This serw 1s constructed by dividing daily returns by contemporaneous expected market volatihty. Followmg Schwert (19x9). expected market volatility is generated by iterating between two regresswns: the first regrecses returns against daily dummlrs and lagged returns and volatditxr. The second regresses the absolute value of the residuals from the first regression against daily dummies and lagged volatility measures. The wie\ employed hrrc are fitted valuer from the second regression. Returns are estimated as the log-difference\ of bid-ask spread midpoint\. The reported statistics are the cross-sectional mean and median, a t-test of whether the umple mean equal\ zero which assume\ the hampIe mean is dlstnbuted normal. the percent of volatdity changes which arc lew than zero, the aawciated binomial rwtatistic of the null that 50% of the changes are negatwr. and a [~-value dewed from a hootstrap methodology a\ outlined in Efron and Tibshiranl (19X6) wth 1.000 replicatwns. Each te\t assumes cmwwctional independence. The full sample I\ compwed of those firma that (i) were listed on CRSP-NASDAO for at least 21)(1 days on r!thrr \idr of the margin rllgibllity announcement date, and (it) had at least IW non-“wing bid-a\k quote\ m each of thr two two-hundred day mtewals. Pre h/X2 refer\ to those firma which hrcame sllglblr brfwr the extenwe modlticatmn of margIn eliglbillty criteria in June 19x2.

drawn from a distribution that is approximately normal. The percent of volatility changes that are negative is also reported. If changes in volatilities are cross-sectionally independent, then a test statistic of whether p, the sample proportion of volatilities that decline, equals one-half can be computed by standard methods: namely, t = [(OS -p)J1804] + 0.5. A third test for changes in volatility, based on bootstrapping, does not require the specification of the underlying distribution [see Efron and Gong (1983) and Efron and Tibshirani (1986)]. Rather, a p-value associated with the null is derived solely from the empirical distribution.5 P-values are reported for the null that the log-difference increases upon the inception of margin trading. Results of the above tests appear in the first line of table 2. It is apparent from the analysis of the change in logs that there is a statistically significant decline of about 2-3% in return standard deviation once the firm is listed on margin, regardless of the test performed. The p-values associated with the two t-tests and with the bootstraps are all below the 1% level. Conclusions are robust to the choice of estimation intervals. For example, over the periods -400 to - 1 and + 1 to +400, the mean change for the 941 firms ‘Following Efron and Gong (1983) and Efron and Tibshirani (1986). a 1 -p% confidence interval for some statistic 0 can be calculated by: (i) collecting a dataset of size N, S(N), whose elements are s(l), s(2), , s(N); (ii) choosing N sample points from S(N) with replacement and estimating B from this sample; (iii) repeating (ii) A4 times yielding estimates O(l), 0(2),. , O(m), , O(M). The p-values reported here represent the proportion of those O(ml’s which satisfy the null hypothesis, Though this method is robust to the underlying distribution, its robustness to the lack of independence is not documented. I correct for dependence below.

with sufficient data is - 0.1126 with associated t-statistic of -5.06. The percent of volatility declines is 57.7% with an associated t-statistic of -4.72. To determine whether these results are in some way affected by the presence of missing values, the above analysis was replicated on a sample of 1,729 firms that contain no missing values over the period - 200 to + 200. The results, reported on the second fine of table 2 are also consistent with a decline in volatility: point estimates and corresponding p-values are lower than those computed with missing values. As a further robustness check, the sample was partitioned into two subsamples: one containing the 712 firms added to the eligible list before the regulatory amendments of 1982, the second containing the remaining 1,090. Results for each subsample appear in the third and fourth line of table 2. Again, the evidence is uniformly consistent with a dechne in volatility. Finally, numerous cross-sectional regressions are estimated to determine whether the change in volatility associated with margin eligibility systematically varies. The percent change in standard deviation is the dependent variable and the log-difference in the standard deviation of market returns calculated over the same estimation period is included as an independent variable. Increases in market value of equity during the pre-listing period is also included in the specification, to accommodate leverage effects. Regardless of the formulation employed, there is no evidence that the effect of margin eligibility on volatility varies with price, the number of outstanding shares, or the market value of equity. In light of the importance placed on these variables in determining eligibility criteria, it is surprising that there is no apparent relation. Conversely, the change in volatility concomitant with margin eligibility is negatively related to firm age; relatively old firms experience larger decreases in volatility once margin eligibility is extended. To determine whether young firms actually experience volatility increases, statistical tests outlined above were applied to subsamples based on age. No statistically significant increases in volatility were detected.h

Numerous studies have documented changes in market-wide volatility. Black (1976) and Schwert and Seguin (1990) demonstrate that the volatilities of portfolios vary with aggregate volatility. Following Seguin (19891, an adjusted returns sequence is employed:

hThe average change in loo-day volatilities for the 375 firms with ages below 200 days was 0.73% with an associated t-statistic of 0.32, while the average change in 200-day volatilities for the 482 firms with ages between 200 and 300 days was 0.97% with an associated t-statistic of 0.56. The point estimates associated with all other age groups were uniformly negative.

112

P. J. Seguin, Stock volutilityand margin trading

where the denominator series is from Schwert (1990).’ Once the series of rjF’s are formed, the analysis is identical to that performed on the raw returns above; standard deviations and their logs are calculated in the pre-listing and post-listing period and compared via t-tests and the bootstrap. Point estimates reported in table 2 are larger than those for the raw volatility measures with mean and median changes in the logs of about 6%, which is highly significant.

5.1.2. Dependence Since margin listings are clustered in time, the assumption of cross-sectional independence implicit in t-statistics presented above is violated; though there are over 1,800 events, they occur on only 30 distinct dates. Potentially, p-values reported above may overstate statistical significance. To examine the effect of clustering on statistical significance, an aggregate time series is constructed by first calculating a measure of firm volatility for a given day and a given firm. These series are then aggregated cross-sectionally before calculating standard errors to avoid biases of standard errors attributable to cross-sectional correlations [see Bernard (1987, esp. p. 2011. Schwert and Seguin (1990) suggest @(rjr - pjl as an estimate of the standard deviation for a given security j during period t, where p, is the sample mean of the series of returns. This estimator is chosen since it provides unbiased estimates of cjt, if rjl is distributed normal with constant mean. To accommodate market-wide changes in volatility, a time series of &?iflr,T - ~71 is calculated for each firm. Next, these series are aggregated, yielding a series of cross-sectional average, market-adjusted firm standard deviations, S,, for t = -200 to +200. Note that these numbers reflect the average market-adjusted standard deviation rather than the market-adjusted standard deviation of a portfolio comprised of these securities. Thus, firmspecific volatility is not eliminated via diversification. The time series S, is then analyzed in a regression framework. Defining MARDUM as an indicator variable that equals 1 for t zz 0 and 0 otherwise, the impact of margin listing can be inferred from a regression of S, on MARDUM. After explicitly accommodating residual autocorrelation, estimation yields (with t-statistics in parentheses): S, = 3.07388 (114.724)

-

0.076869 ( -2.038)

MARDUM.

‘Schwert (1990) generates predicted standard deviations by iterating between two regressions. The first regresses returns against daily dummies and lagged returns and volatilities. The second regresses the absolute value of the residuals from the first regression against daily dummies and lagged volatility measures. I use fitted values from the second regression. I thank G. William Schwert for this data. Schwert and Seguin (1990) examine the properties of returns deflated by expected aggregate volatility and show that deflated returns are less heteroskedastic.

P. J. Seguin, Stock Lbolatiliry and margin trading

113

Since t = 0 was defined to be listing day, the intercept can be interpreted as the standard deviation on day 0 had the firm not become eligible for margin. The coefficient associated with iX4RLNJM is the change in market-adjusted standard deviation upon margin eligibility. Note that this decline represents approximately 2.5% of the intercept, which is similar to the point estimates of the mean and median change in raw variances discussed previously. Further, this decline is statistically significant, even after explicitly accommodating the clustering of event dates.

5.1.3.

Volatility and volume

Numerous studies demonstrate that volatility is highly correlated with contemporaneous volume. Admati and Pfleiderer (1988) and Kyle (1985) argue that volatility and volume are jointly determined and reflect the flow of information. Consequently, a potential explanation for the documented decline in volatility is that the flow of information and volumes are lower once an issue is eligible for margin trading. An examination of volumes should also provide information on the role of margin trading in the determination of market depth; finding that volatilities are lower and volumes are higher once margin trading is introduced is consistent with the joint hypothesis that depth is in part determined by wealth constraints of traders and that allowing margin trading mitigates this constraint. Finally, information on changes in volume may indicate whether mitigating wealth constraints and allowing levered positions induce agents to increase information collection. The CRSP-NASDAQ tape provides continuous daily volume data beginning January 1983. From the original sample, 830 firms satisfy the constraint of at least 100 days of reported volumes before and after margin eligibility. To measure aggregate portfolio volume, an index of average adjusted volumes was constructed. The alternative of simply totalling volume across all securities was rejected since this metric would be dominated by the volumes of a few large firms. First, for each firm, the average daily volume over the event period from - 100 to - 1 was calculated, where day 0 is the effective date. Second, for each day (- 100 to + 1001, adjusted volume was calculated by deflating daily volume by the pre-margin average. Finally, for each day, cross-sectional averages were calculated, yielding a time series of average adjusted volumes, V,. The plot of this series, fig. 1, displays two important features. First, there is a surge in volume of about 30-40% over the pre-margin average during the first few days of margin eligibility. Second, volume remains higher throughout the margin-eligible period: only three days in the period 0 to + 100 have volumes smaller than the pre-margin period. To determine statistical significance, the time series V, is regressed against the indicator variable MARLXJM and a trend variable that equals zero on the

P. .I. Seguin, Stock uolatilityand margin trading

114 I.8

i

I

I

0.8’ -100

/

0

100

EVEN1 DAY

Fig. 1. Standardized

volume

and margin

eligibility;

sample

size = 830 firms.

For each firm that becomes eligible for margin trading between 1983 and 1987, average daily volume is estimated over the period - 100 to - 1, where day 0 represents the first day on which the firm is eligible for margin trading. Next, for each firm, a series of adjusted volumes is constructed by deflating each daily volume for days - 100 through + 100 by the above average. These series are then cross-sectionally averaged in event time.

first day of margin V, =

eligibility

1.0529 (57.463)

yielding:

+ 0.00105 (3.715)

Event Day + 0.15702 (4.771)

MARDUM.

A trend was included to allow for gradual changes in volume unrelated to margin eligibility, perhaps due to increased firm age. If the trend is omitted from the specification, the coefficient associated with the margin dummy is 0.2634 with a t-statistic of 6.414. Again, estimation explicitly accommodates the autocorrelation of the residuals. Finding that trading volume increases by about 15% subsequent to the inception of margin trading is consistent with the hypothesis that margin eligibility increases the collection and dissemination of information. The increase in volume given a decline in volatility is also consistent with the hypothesis that depth in the market for an issue is enhanced when margin trading is allowed. 5.2. Autocorrela tions Theories that predict prices also make specific

that margin trading leads to instability in stock predictions about autocorrelations. First, the pyra-

P. J. Seguin, Stock colatility and matgin trading

0.15

115

1

O.II0.13.

0.12,

--0

-

-c

-_.

_-y-

‘-.__/-

01, ,‘,‘,‘I’!’

i

I

“‘S”‘,‘,

2

3

4

5

6

7

3

9

IQ

II

:2

;3

14

I5

16

I7

18

19

20

LAG

Fig. 2. Autocorrelation

structure

and margin

eligibility;

sample

size = 1,729 firms

Autocorrelations at lag 7 for firm j are calculated as cr,,r,,_,/cr; for each firm over the period +5 to + 200 (post) and over the period -200 to -5 (pre) for a sample of 1,729 firms that become eligible for margin trading between 1976 and 1987 with no missing data over the -200 to +200 period. Day 0 is the effective day of margin eligibility as defined in the Federal Register. Returns are calculated as the log of the ratio of closing bid-ask midpoints adjusted for dividends and distributions. The figure plots cross-sectional averages of autocorrelations across the 1,729 firms. The solid line plots average autocorrelations calculated over the pre-margin period, while the dashed line plots average autocorrelations constructed over the post-margin period.

miding/depyramiding hypothesis suggests that negative shocks to prices lead to margin calls that cause further price depreciations, while positive price shocks lead to increased speculative buying that causes further price appreciations. In short, pyramiding/depyramiding predicts that the introduction of margin activity would lead to higher positive autocorrelations at short lags. A second hypothesized link between margin trading and volatility posits that margin requirements affect the trading of inherently destabilizing ‘noise traders’. DeLong, Shleifer, Summers, and Waldman (1987) predict that the introduction of margin trading in a security increases the role of ‘nonfundamental’ traders in the determination of prices. French and Roll (1986) argue that if pricing errors are eventually corrected, noise trading should induce negative autocorrelations at higher lags. Alternatively, Amihud and Mendelson (1987) argue that autocorrelations measure the speed of adjustment of prices to new information while ‘noise’ is permanent and related to

116

P. .I. Seguin, Stock volatility and margin trading

volatility. Under this paradigm, if autocorrelations remain unchanged, the declines in volatilities documented above are sufficient to refute the notion that margin trading exacerbates noise. Autocorrelations to lag 20 are estimated over the intervals -200 to -5 and + 5 to + 200 for each of the 1,729 firms with no missing returns. In fig. 2, a plot of the mean autocorrelation at each lag estimated over the post-margin period is superimposed over the plot of mean autocorrelations estimated in the pre-margin period. There is little difference between these two plots, though average autocorrelations are slightly lower in the post-margin period for lags higher than 9. This provides little evidence that the inception of margin trading alters the autocorrelation structure of returns in general. Tests of whether autocorrelations at short lags increase upon the inception of margin trading were performed. Though the first autocorrelation is slightly larger in the post-margin period, the change is small and insignificant. The autocorrelation at lag 2 declines, but this change is also insignificant. Consistent with the predictions of Garbade (1982), there is no evidence that margin trading induces pyramiding/depyramiding. To determine the extent to which margin trading exacerbates the contribution of noise traders in the determination of prices, 1 calculated actual-to-implied variance ratios as suggested by French and Roll (1986). The denominator of an n-day ratio is II times the estimated variance of daily returns. The numerator is the estimated variance of a sequence of nonoverlapping n-day returns. If the first y1- 1 autocorrelations are close to zero, the ratio would be close to one. I calculated lo-day actual-to-implied ratios and tested whether these ratios remain unchanged after the inception of margin trading. Though the mean changes were negative, median changes are positive and test statistics are insignificant. This evidence is inconsistent with margin trading having any impact on the contribution of noise traders.

5.3. Announcement

effects

Amihud and Mendelson (1988) argue that the market value of a security is in part affected by liquidity, the cost or ease with which a position can be taken in an issue. Consequently, if margin trading represents a substantial enhancement to liquidity, the market value of the affected firms should increase upon announcement. To measure the effect of margin eligibility on firm value, standard event study methodology is employed. For each firm, Scholes-Williams (1977) estimates of market model parameters are calculated using returns for days - 200 to - 50 and + 50 to + 200. Abnormal returns for each firm (defined as the difference between the arithmetic return and the expected return conditional on the contemporaneous market return, or r,* - birmt) are then

I? J. Seguin, Stock volatilityand margin trading

117

Table 3 Price reaction

to announcement

of margin

Abnormal

eligibility;

sample

(%) (A) Abnormal Returns Standard error = 0.001078

Event day

-5 -4 -3 -2 -1

return

- 0.053 0.074 0.137 0.271 0.151 0.616 0.186 0.148 0.070 0.141 0.172 0.058 - 0.014 0.060 0.170 0.012

0 1 2 3 4 5 6 7 8 9 10

size: 1,803.” T-statistic

- 0.49 0.69 1.27 2.51 1.42 5.82 1.76 1.40 0.66 1.33 1.62 0.55 -0.13 0.57 1.61 0.12

___ (8) Cumulative abnormal refurns Standard - 10 to 10 -5t05 -1to1 0 to 1 -1 to10

error = 0.001078~~ 2.233 1.826 0.946 0.813 1.833

+ 2(~ - 1)0.297 3.61 4.11 4.29 4.68 3.95

aAbnormal returns are calculated over the period - 50 to +50 for each of 1,803 firms by subtracting from the raw return the product of the market return and a beta estimated from the methodology of Scholes and Williams (1977). Estimation period for each firm beta is -200 to - 50 and + 50 to + 200. Abnormal returns are then cross-sectionally averaged to yield a series of portfolio returns. This series has a standard error (calculated over -200 to +200) of 0.001059 and a first-order autocorrelation of 0.297. Since returns are cross-sectionally aggregated before standard errors are computed, inference is not affected by cross-sectional dependence or clustering [see Bernard (1987)]. ‘n’ is the number of days in a cumulative abnormal return. Day zero is the date of announcement of margin eligibility, defined as the earliest of(i) publication in the Wall Street Journal, (ii) publication in the Federal Register, or (iii) effective date as stated in the Federal Register.

calculated for the period -49 to + 49 and aggregated cross-sectionally. Throughout, ‘market’ returns refer to the returns to the CRSP equal-weighted NYSE-AMEX portfolio. Using returns from a market portfolio comprised of securities which are always marginable avoids a possible endogeneity problem. The results are summarized in table 3. Concomitant with the announcement that a firm will be added to the list of margin eligible securities, there appears to be an increase in value in the 1% to 2% range. Since eligibility is

118

P. J. Seguin, Stock colatility and margin trading

based on easily measured criteria, it is reasonable to expect that eligibility is partially anticipated by investors. Since these expectations are reflected in the share price, the value effect associated with announcement represents only the surprise to investors and can be interpreted as a lower bound on the total change in value attributable to margin eligibi1ity.s To evaluate the statistical significance of this increase, cumulative abnormal returns are constructed over a variety of ranges. T-statistics are computed by dividing by an estimate of the standard error that accommodates first-order autocorrelation, 04n + 2( y1- 1)~ , where u and p are estimates of the standard deviation and first autocorrelation of the portfolio, and y1 is the number of days accumulated. For the aggregate portfolio, estimates of the announcement effect are uniformly positive and significant. For example, over the period - 1 to + 10, the abnormal return is 1.83% with an associated t-statistic of 3.95. This finding is consistent with Grube, Joy, and Howe (1987) who also document a positive announcement effect associated with margin eligibility for their sample of 90 firms. It is also consistent with Largay (1973) and Eckhardt and Rogoff (1976) who document a negative price reaction associated with exchange-imposed margin restrictions. Since these restrictions result in a temporary prohibition of margin trading, imposition should have an effect opposite to the effect of the introduction of margin trading. Finally, Conrad (1989) documents a positive price reaction to the underlying equity security upon the inception of options trading which she interprets as evidence that options trading reduces the costs of taking positions in the underlying security. There is at least one alternative explanation. The certification hypothesis [Grube, Joy, and Howe (1987)] states that by adding a particular firm to the list of margin-eligible issues, the Fed has signalled an implied certification of quality. However, it should be pointed out that the Fed disclaims any such certification. Further, the extent of any signalling must be limited by the lack of discretion and flexibility in the selection process. For the major objective of this study, however, it is not crucial to distinguish which of these two hypotheses is responsible. It is sufficient to note that an increase in value seems inconsistent with the hypothesis that margin trading causes instability or increased volatility, unless economic agents on average u&e excess volatility.

‘The existence of an unanticipated component on widely available information, investors should Further, I performed some auxiliary analysis and criteria, it would almost certainly appear on one that the documented abnormal return is typically

is surprising. Since margin eligibility is based be able to accurately identify eligible firms. determined that once a firm met the eligibility of the next two eligibility lists. Note, however, less than the bid-ask spread for these firms.

P. J. Seguin, Stock volatiliry and margin trading

5.4.

119

Margin revocation

Since 1969, the Fed has been responsible for delisting, or removing issues from the list of margin-eligible securities if those issues fail to meet maintenance requirements outlined in section 3. As an additional test of robustness of the results presented above, I calculate the effects of margin delisting on volatility and volume. This extension is also of interest since numerous current debaters recommend tightened margin restrictions. Since firms that are ‘delisted’ experience a significant tightening of margin requirements (i.e., the initial margin requirement is raised from 50% at lOO%), their experiences provide information on the ability of tightened margin restrictions to curb ‘excessive’ volatility. Over the 1976-1987 period, 530 firms with identifiable CUSIP’s were delisted. However, only 170 (32%) could be employed. A total of 305 firms had stopped trading on NASDAQ by day +200 and could not be used, and an insufficient number of nonmissing returns could be found for a further 55 firms. Since this sample is roughly one-tenth the size of the margin-eligible sample, a reduction in precision and power can be expected. Power is also lower due to the inherent volatility of these firms; the average variance for the sample of margin delistings is roughly 2.6 times the average variance of the sample of newly eligible firms. Every test described above was replicated on the sample of delistings. Though point estimates for the mean change in variance were uniformly positive (the average change in log volatility is 0.5% for unadjusted volatilities and 2.2% for market-adjusted volatilities), none were significant (the associated t-statistics are 0.121 and 0.273, respectively). Tests for changes in volumes and autocorrelations also failed to detect any reliable effects. Measuring the announcement effects on firm value is complicated by the Fed’s policy of informing firm management of an impending delisting up to 30 calendar days in advance. Cumulative abnormal returns are calculated beginning on event day - 25. Regardless of termination date, the value effect appears to be around - l%, though no t-statistic exceeds 1. To conclude, from a statistical standpoint, margin delisting appears to be a ‘nonevent’, though this is partially attributable to the lack of power inherent in the sample. From a regulatory perspective, there is no evidence that tightening margin requirements can be employed to combat ‘excessive’ volatility.

6. Conclusions In his conclusion, Hardouvelis (1988a) states: ‘At a minimum, the evidence shows that the presence of margins contributes to a more stable market.’ It is

120

P. J. Seguin, Stock volatilityand margin trading

difficult to conceive of an experiment more likely to detect this effect than the experiment considered here. This study investigated changes in the stability of prices surrounding the instigation of margin trading. Since changes in margin restrictions are large and the firms are generally small, it is reasonable to expect that the tests performed here should detect any destabilization. However, test results indicate that margin trading does not lead to destabilization. Estimates suggest that, if there is an effect, it is value, stability, and liquidity enhancing: trading volumes increase by about 30%, volatility declines by about 2%, and value increases by 2%. The public policy implications suggested by these results are clear. This study can be viewed as another refutation of the hypothesis that margin trading is destabilizing, recently popularized by Hardouvelis (1988a, b). My conclusions suggest that margin restrictions are not an effective policy tool for controlling volatility. Instead, I find that margin activity is beneficial: margin eligibility is typically associated with an increase in firm value and an increase in market depth. Consequently, extension of margin eligibility to issues currently ineligible would probably be applauded by the shareholders and management of the firms affected.

References Admati, Anat R. and Paul Pfleiderer, 1988, A theory of intraday patterns: Volume and price variability, Review of Financial Studies 1, l-40. Amihud, Yakov and Haim Mendelson, 1988, Liquidity and asset prices: Financial management implications, Financial Management, Spring, 5-15. Amihud, Yakov and Haim Mendelson, 1987, Trading mechanisms and stock returns: An empirical investigation, Journal of Finance 42, 533-553. Bernard, Victor L., 1987, Cross-sectional dependence and problems of inference in market-based accounting research, Journal of Accounting Research 25, l-48. Bessembinder, Hendrik and Paul J. Seguin, 1990, Futures trading, liquidity and stock price volatility, Unpublished manuscript (University of Rochester, Rochester, NY). Black, Fischer, 1976, Studies of stock price volatility changes, Proceedings of the 1976 meetings of the Business and Economics Section, American Statistical Association, 177-181. Conrad, Jennifer, 1989, The price effects of option introduction, Journal of Finance 44, 487-498. DeLong, Bradford, J., Andrei Shleifer, Lawrence H. Summers, and Robert J. Waldman, 1987, The economic consequences of noise traders, NBER working paper no. 2395. Eckardt, Walter L. Jr. and Donald L. Rogoff, 1976, 100% margins revisited, Journal of Finance 31, 996-1000. Efron, Bradley and Gail Gong, 1983, A leisurely look at the bootstrap, the jackknife, and cross-validation, The American Statistician 37, 36-48. Efron, Bradley and R. Tibshirani, 1986, Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy, Statistical Science 1, 54-77. Ferris, S. and D. Chance. 1988, Margin and stock market volatilitv, Economics - reauirements . Letters 28, 251-254. French, Kenneth R. and Richard Roll, 1986, Stock return variances: The arrival of information and the reaction of traders, Journal of Financial Economics 17, 5-26. Friedman, Milton, 1953, The case for flexible exchange rates, in: Essays in positive economics (University of Chicago Press, Chicago, IL).

P. J. Seguin, Stock uolatili~ and margin trading

121

Garbade, Kenneth D., 1982, Federal reserve margin requirements: A regulatory initiative to inhibit speculative bubbles, in: Paul Wachtel, ed., Crises in economic and financial structure (Lexington Books, Lexington, MA). Goldberg, M.A., 1985, The relevance of margin requirements, Journal of Money, Credit and Banking 17, 521-527. Grube, R. Corwin, 0. Maurice Joy, and John S. Howe, 1987, Some empirical evidence on stock returns and security credit regulation in the OTC equity market, Journal of Banking and Finance 11, 17-31. Hardouvelis, Gikas A., 1988a, Margin requirements and stock market volatility, Federal Reserve Bank of New York Quarterly 13, 80-89. Hardouvelis, Gikas A., 1988b, Margin requirements, volatility and the transitory component of stock prices, Unpublished manuscript (Federal Reserve Bank of New York, NY). Hsieh, David A. and Merton H. Miller, 1990, Margin regulation and stock market variability, Journal of Finance 45, 3-30. Kupiec, Paul H., 1990, Initial margin requirements and stock market volatility: Another look, Journal of Financial Services Research 3, 287-301. Kyle, Albert S., 1985, Continuous auctions and insider trading, Econometrica 53, 1315-1335. Largay, J. A., 1973, 100% margins: Combating speculation in individual security issues, Journal of Finance 28, 927-952. Largay, J. A. and R. R. West, 1973, Margin changes and stock price behavior, Journal of Political Economy 81, 328-339. Moore, Thomas G., 1966, Stock market margin requirements, Journal of Political Economy 81, 328-339. Officer, Robert R., 1973, The variability of the market factor of the New York Stock Exchange, Journal of Business 46, 434-453. Pagan, Adrian R. and G. William Schwert, 1990, Alternate models for conditional stock volatility, Journal of Econometrics, forthcoming. Salinger, Michael A., 1990, Stock market margin requirements and volatility: Implications for regulation of stock index futures, Journal of Financial Services Research 3, 121-138. Scholes, Myron and J. T. Williams, 1977, Estimating betas from nonsynchronous data, Journal of Financial Economics 5, 309-328. Schwert, G. William, 1990, Margin requirements and stock volatility, Journal of Financial Services Research 3, 153-164. Schwert, G. William and Paul J. Seguin, 1990, Heteroskedasticity in stock returns, Journal of Finance, forthcoming. Seguin, Paul J., 1989, Exchange listing, liquidity and volatility: An empirical investigation of National Market System listing, Unpublished manuscript (University of Rochester, Rochester, NY).