Stock exchange reforms and market efficiency: the Italian experience

Transcription

1 European Financial Management, Vol. 7, No. 1, 2001, 93±115 Stock exchange reforms and market efficiency: the Italian experience Giovanni Majnoni Banca d'italia, Research Department and Massimo Massa 1 INSEAD Boulevard de Constance, Fontainebleau Cedex, France; massimo.massa@insead.fr Abstract This paper examines whether the reforms introduced by the Italian Stock Exchange from 1991 to 1994 creation of specialised intermediaries, obligation to trade on the official markets, screen-based trading and cash settlement) did increase market efficiency. The issue is addressed using both the traditional information efficiency model, which tests market efficiency by verifying the predictability of prices conditional on some information subset, and a microstructure approach that measures efficiency as the distance of the price movements from their efficient components, represented by a random walk process. The joint analysis of daily and intraday data on prices and volumes validates the hypothesis that most of the reforms have increased market efficiency over the sample period, except for cash settlement, which appears to have substantially reduced it. Keywords: Stock market; informational efficiency; trading systems. JEL classification: G14 1. Introduction In 1993 and 1994 the Italian stock market ended a protracted period of stagnation with vigorous growth involving share prices, trading volume and new issues. The overall size of the market, measured as the ratio of total capitalisation to GDP, rose by 2 percentage points to 18%. These signs are the first important manifestations of the effects of the stock exchange reforms undertaken in past years, namely the introduction of the obligation to trade in the official market, the creation of specialised intermediaries securities firms) and the introduction of screen-based 1 We thank the Milan Stock Exchange for the intra-daily data kindly provided and Fabrizio Bini for precious research assistance. We also thank F. Drudi, A. Locarno, R. Violi, an anonymous referee and J. Doukas the Editor) for their helpful comments. Naturally, we are the only ones responsible for any omissions or mistakes. # Blackwell Publishers Ltd 2001, 108 Cowley Road, Oxford OX4 1JF, UK and 350 Main Street, Malden, MA 02148, USA.

2 94 Giovanni Majnoni and Massimo Massa trading and cash settlement. Nonetheless, in terms of size, liquidity, and variety of instruments traded, the Italian share market is still far behind those of the leading industrial countries. The present paper seeks to determine whether there has been an overall gain in efficiency in the Italian share market in recent years, and the extent to which it has varied between market segments. The question can be addressed using the traditional scheme of the information efficiency of financial markets or, alternatively, by applying some variants to that approach recently proposed in research on the microstructure of markets. According to the former, efficiency is a feature of the market that is either present or absent, and its existence is verified on the basis of the predictability of the prices formed on a particular market. According to the latter, efficiency is rather qualitative and may therefore be present to differing degrees depending on the information and operational imperfections of the market. For traders, these imperfections incur a cost that increases as the market's efficiency diminishes. The present paper tests efficiency by both of these standards using a common model in which present returns may depend on the past record of returns and trading volume. With reference to the first standard, we test a semi-strong form efficiency. For the second, we derive an indication of market efficiency by decomposing the behaviour of prices into an `efficient' component, represented by a random walk process, and a `noise' component. Both tests define the cost of the market's inefficiency as the deviation of observed prices from those that would obtain in the absence of friction. The analysis is performed for two periods, 1987±90 and 1992±94, on daily data for two groups of shares characterised by differing liquidity. The traditional tests for information efficiency found a positive effect of the reform measures on the more heavily traded issues. In particular, for the more liquid securities the information content of trading volume declined, while no comparable effect is found for the less liquid securities. In general, there seems to be a more generalised gain in efficiency embracing both market segments, although more pronounced for the more liquid shares. The greater efficiency of this segment is confirmed by analysis of intraday data for the first half of Using a joint specification of the price and volume stochastic processes, we show that the set of market reforms enacted seems to have reduced the information value of trading volume; for the less liquid shares, with smaller market capitalisation, this positive effect does not seem to have emerged. Nevertheless, an important conclusion reached in this study is that the persistence of the causal chain running from volume to price has not prevented an improvement in market efficiency, as measured by the `noise' component of prices, even for the less heavily traded shares. At the same time, the `noise' incorporated in the prices of the less actively traded shares remains large compared with the more heavily traded shares, raising again the recurring question of whether it might not be advisable to promote different trading systems for securities of different liquidity: say, market makers for the less liquid and continuous auction for the more liquid. By contrast, a deterioration in the `quality' of prices, revealed by an increase in the `noise' component, is found for the shares that have recently been shifted from forward to cash settlement. For these securities, the decrease in trading volume has coincided with a reduction in the information content of prices. Among other factors, this may be due to the only partial changeover of listed shares to the new settlement system, which, risk and return being equal, produces an incentive to shift to shares

3 Stock Exchange Reforms and Market Efficiency 95 traded on an forward-settlement basis, which require less initial investment i.e., allow greater leverage). 2. Recent developments in the Italian share market The Italian share market has long been underdeveloped by comparison with those in the other leading industrial countries. Contributory factors have been the inefficient design of trading mechanisms, a set of disincentives to share issues by firms, and structurally insufficient demand. At the same time, it has faced competition from more efficient foreign stock markets, that of London in particular, as a result of the removal of the last foreign exchange controls First market reforms The reform of the Italian stock exchange itself appears as the outcome of a series of distinct measures affecting the market's efficiency at various levels: expanding its size, increasing its completeness and modernising its trading and settlement systems. Among the most important provisions were those of the 1991 securities market reform law, which made on-exchange trading compulsory from 1992 onwards and introduced new specialised intermediaries, namely securities firms. The ban on over-the-counter trading fostered the expansion of the market, while the subjection of securities firms to a wideranging set of disclosure requirements enhanced its transparency. The measures revising the trading and settlement mechanisms include, in order of implementation, the gradual transition from a call-over auction to a continuous screen-based auction, begun in November 1991 for an initial group of 5 securities, and completed in April 1994; the derogation from compulsory on-exchange trading for block trades, provided for in 1992; the unification of trades effected on the second markets in the national screenbased system in 1994; and the creation of an odd-lot market in A new settlement mechanism With regard to the settlement mechanism, in January 1994 cash settlement t 5 rolling settlement) was introduced for an initial group of 54 shares, and a specific cash trade guarantee fund was set up at the Clearing and Guarantee House. In July 1994, cash settlement was extended to an additional group of 147 securities. Taking account of the delisting of several securities, at the end of 1994 cash settlement was used for 188 of the total of 321 issues that had been listed as of 31 March The reduction in leverage connected with the transition to cash settlement tends to lessen the market's liquidity. The options and securities lending market that is expected to begin operating shortly should counter this effect. A first step in this direction was taken at the end of 1994 with the start of trading in the Mib 30 index futures. The effect of stock exchange reform on volume and prices was considerable. In particular, in the years 1993±94, there was an unusually rapid rise in both prices and volume, and a pronounced concentration of trades in the more liquid issues. At the beginning of 1992, with the inception of continuous screen-based trading for the first group of securities and the entry into force of compulsory on-exchange trading, activity in the share market began its fastest phase of expansion in the last 10 years. In the previous 5 years, between 1985 and 1991, turnover at constant share prices had remained broadly unchanged, reaching a high at the time of the 1986 price boom and

4 96 Giovanni Majnoni and Massimo Massa a low at the end of From 1992 to mid-1994 monthly turnover rose sharply, peaking in May 1994 at more than 24.8 trillion lire 8.5 trillion at 1980 share prices). Although volume subsequently fell back, in the first quarter of 1995 monthly turnover still exceeded 14.4 trillion lire averaging 5.7 trillion at 1980 share prices). On the other hand, and in contrast with the leading markets abroad, block trading grew only moderately, by 12.2%, between 1993 and In 1994 the average monthly value of block trades amounted to 1.2 trillion lire, or 7% of the total turnover compared with 52% on the New York Stock Exchange in 1992). The sharp rise in share prices until mid-1994 and the subsequent decline roughly coincided with expansion and then the contraction of trading volume. As shown in Table 1, the growth in volume was accompanied by a further concentration of trading on the largest securities. If we group the stocks on the basis of trading volume, we can see that in the 2 years 1989±90 the securities included in the first decile generated less than half of the total turnover and those in the first two deciles around two thirds. In the 2 Table 1 Turnover and market capitalization. Panel A: percentage distribution by class of trading volume Securities are grouped into deciles in decreasing order of trading volume. For each decile we compute the total trading volume generated by all the stocks belonging to the specific decile and their total market capitalization. Then we report the ratio of each one of them over the total across all the deciles. Trading volume Market capitalization DECILES 1987± ± ± ± ± ± ± ±94 lst nd rd th th th±10th TOTAL Panel B: turnover rate: distribution by class of trading volume Securities are grouped into deciles in decreasing order of trading volume. For each decile we compute the ratio between total trading volume generated by all the stocks belonging to the specific decile and their total market capitalization. DECILES 1987± ± ± ±94 1st nd rd th th th±10th AVERAGE

5 Stock Exchange Reforms and Market Efficiency 97 years 1993±94 the corresponding figures were respectively three quarters and nine tenths; in other words, 80% of the securities accounted for only 10% of the total turnover. The parallel increase of stock prices and trading volume raises several problems of interpretation. In the first place, does the strong positive correlation between prices and trading volume that has been observed in the most recent years tell us anything with regards to market efficiency? If the correlation were due to the fact that traders revise their expectations of returns on the basis of volume developments, it would be an indication of market inefficiency, at least in the informational sense. On the other hand, it could be the natural consequence of the dispersion of opinions among traders, which has increased with the entry of new market participants, and would thus be independent of the degree of efficiency of the market. Secondly, how should the growing concentration of trades on a limited number of securities be interpreted? Is it a sign of poor efficiency of the market as a whole or, rather, a built-in consequence of differing liquidity among the various categories of security? Lastly, how has the changeover from forward to cash settlement affected the informational efficiency of the market? 3. The link with the existing literature The relation between price and volume has been dealt with by two contiguous strands of research: the traditional analysis of the information efficiency of markets and the more recent work on market microstructure. The traditional analysis addresses the issue implicitly, since trading volume can be included in the set of publicly available information for predicting prices. Fama's definition of weak-form efficiency 2 has recently been questioned by growing evidence showing autocorrelation of returns, phenomena of seasonality the so-called `day-of-the-week effect', `holiday effect', etc.) and partial predictability of medium and long-term returns. The second strand of analysis originates from the consideration of the numerous types of frictions which characterise market activity, that is explicit trading costs commissions, stamp duty) and implicit costs associated with uncertainty in the market and the unequal distribution of information among traders. In this context, the common perception by traders of high price volatility will hold back the expansion of trading and, by inhibiting the deepening of the market, will confirm the expectations of price instability Pagano, 1989). Measures that alter expectations as to the dispersion of prices can expand the flow of orders and the number of traders i.e., market size), increasing the market's depth and enabling market equilibrium to be achieved at higher transaction levels. One implication of these analyses is that growth in trading volume, due to the entry of new categories of trader or to the increase in the number of transactions, can reduce volatility as measured by the absolute fluctuations of prices around their equilibrium value. 3 2 Fama says that a market is weak form efficient if future share prices cannot be predicted on the basis of past prices and semi-strong-form efficient if they cannot be predicted on the basis of all publicly available information. Although trading volume is not explicitly mentioned, it can be included in the set of such information. 3 The implications of this strand of research are not univocal, however: if the increased liquidity of the market makes prices more sensitive to the arrival of new information Epps and Epps, 1976), there will be both a positive correlation between volumes and absolute variations in returns and an increase in volatility.

6 98 Giovanni Majnoni and Massimo Massa Also the link between prices, volume and efficiency has been analysed on the basis of the existence of traders with differing information sets liquidity traders and information traders). The link between trading volume and price changes is not different from that found by the first group of studies, but in this case it becomes a function of the degree of information asymmetries in the market and the trading system used. The positive correlation between volume and the absolute change in prices strengthens as the precision of private information decreases, and as the dispersion and unpredictability of the opinions of uninformed liquidity) traders increase. Within this framework, the role played by trading volume can be identified by testing, first, whether it contributes to price predictability the traditional test of efficiency) and, second, whether it permits a more accurate estimation of the noise component in relation to the overall variability of returns i.e., of the noise-to-signal ratio). Regarding the Italian market, most of the studies have concentrated either on investigating the effects of competition from London to assess whether growing foreign competition has resulted in trade diversion and whether the introduction of more efficient trading mechanisms has raised the competitiveness of the Italian market Pagano and Roell, 1992), 4 or the impact of the changeover from the call-over auction to continuous trading Murgia 1993) The data We used two different data sets on prices and returns: first on daily data, then on intraday data. The analysis based on daily data considered the prices and trading volume of securities that were listed throughout the period from January 1987 to December These securities were grouped into deciles according to the volume of transactions effected, and the first ten securities of the first and fifth deciles were then selected. The selection of two groups of securities was designed to test whether the effects of the changes that had taken place in the share market had differed depending on the security's degree of liquidity. For this purpose, we divided the sample period into two sub-intervals: the first spanning from 1987 to 1990, the second from 1992 to In this way, we constructed two samples not very dissimilar in terms of number of observations, and covering the period before and after the implementation of the reforms in the Stock Exchange. In fact, the choice of the two non-overlapping periods seems especially reasonable considering the structural break which affected the evolution of the Milan Stock Exchange in For the analysis based on intraday data, the interval considered was the first half of 1994, a period characterised by contrasting phases of price developments and hence particularly appropriate for econometric analysis. The division into deciles referred to the volumes traded in 1994 and, as with the previous sample, the first ten securities of the first and fifth deciles were selected. The prices and volumes of the entire sequence of transactions carried out in these securities were used. Some descriptive statistics on the unit value and average daily number of trades are given in Table 2. 4 They identify a trade diversion effect only for some securities but not for the market as a whole, and observe that the listing of Italian securities on SEAQ has had a net trade-generating effect. 5 He identifies a positive effect on volume only for large shares.

7 Stock Exchange Reforms and Market Efficiency 99 Table 2 Descriptive statistics of the estimation sample. For each security belonging to the first and fifth decile, we report the minimum, maximum and average size of the unitary daily trade and the number of trades. The unitary value of trades is expressed in millions of Liras, while trade is expressed in number of trades. For each decile the average and the standard deviation of the values for each security are reported. Unit value of daily trades Average number of SECURITIES Average Minimum Maximum daily trades First decile Montedison Fiat Telecom Italia Olivetti ord Stet Credito Italiano Pirelli spa Fiat priv Stet risp. port Gemina Ferruzzi Finanziaria Banca Commerciale Italiana Montedison risp. Nc Telecom Italia risp Cir-Comp Ind. Riunite Average Standard deviation Fifth decile Costa Crociere BNL risp. Port Attivita Immobiliari Rinascente priv Stefanel Costa Crociere Risp. Nc Unicem risp. port Marzotto Alitalia risp. Nc Ratti spa Fin. Agroindustriale Unicem Assitalia Montedison risp. Con Oliverti priv Average Standard deviation

8 100 Giovanni Majnoni and Massimo Massa Table 3 presents the number of trades and the number of stocks transacted for the securities that moved from forward to cash settlement. These are reported for the 6 months prior 1=1=94±17=7=94) and the same period following the change of settlement system 18=7=94±1=12=94). The daily estimates use indices of total return adjusted for operations in respect of capital e.g., mergers) and dividend payments. The prices of forward-traded securities were translated into cash prices using the repo rates. Given the high frequency of trades, it was not deemed necessary to make such adjustments for the intraday data. To deal with the problem 6 of sampling and treatment of observations that occur at irregular intervals we used clock time, constructing the sequence of the average prices and total volume of transactions recorded in five-minute intervals. To determine the appropriate number of lags for the VAR estimation we carried out two sets of estimations with different samplings, using 5-minute and 10-minute intervals. For both of them we estimated the optimal number of lags, using a Schwartz criterion and found that the results are consistent. In fact, for most of the stocks there seems to be a structure of lags revealing a delay between 50 and 60 minutes, with a maximum of 70 minutes. This is represented by 7 lags in the case of 10 minute samplings and 14 lags in the case of 5 minute samplings. We estimated the model using both the 5- minute 14-lag VAR specification and the 10-minute 7-lag VAR 7 specification groupings, and the results do not seem to differ at all. 8 Also, as further evidence of the correct choice of the number of lags, we chose, within the 50±70 minute delay structure and 5-minute sampling, to perform the analysis using two alternative specifications: a 14-lag structure highest number of lags) and a 10-lag structure minimum number of lags). Given that the results strikingly agreed we adopted the 14- lag specification. In the case of daily data, on the contrary, we chose a specification with 5 lags, in order to cover a full week and to include the end-of-the-week market closing. 9 To check stationarity in a situation with high autocorrelation in the data, we used Philips-Perron and Augmented-Dickey-Fuller tests. They confirmed the assumption of non-stationary prices: they rejected the unit root hypothesis for both volumes and returns. 10 Seasonal effects were found at both the daily and intraday 6 This question is particularly relevant for the Milan Stock Exchange, where the flow of transactions is highly discontinuous, with bursts of trading activity being followed by rather long lulls. Three solutions are generally adopted in the literature: transaction time, which considers all transactions to be recorded at the time they are actually carried out; clock time, involving the construction of time intervals of standard length; and the so-called scaling law method, whereby the intervals are scaled to contain an equal number of transactions. The first and third methods would vitiate an investigation of the causal links between returns and trading volume. 7 Tests on the whiteness of the VAR's estimations' residuals confirm that the chosen specification is the most appropriate and parsimonious one. 8 The results for the most liquid stocks are strikingly consistent for both mean and standard deviation, while there seems to be a slight difference for the less liquid stocks, due, perhaps to the thinness of this segment of the market. 9 Tests on the whiteness of the residuals confirm the appropriateness of the choice. 10 Given that both Augmented-Dickey-Fuller and Phillips-Perron tests rejected the hypothesis of unit roots for all the stocks in the sample and for all the samples, we did not consider it worth reporting the results.

9 Stock Exchange Reforms and Market Efficiency 101 Table 3 Securities shifted to cash settlement: trading statistics. In this table we report the number of trading transactions and the number of shares involved for the stocks which have been moved to cash settlement for the 6 months before 1=1=94±17=7=94) and the 6 months after 18=7=94±31=12=94) the shift to cash settlement. The number of stocks transacted is expressed in terms of thousands of stocks. Estimation period Number of trades Number of stocks transacted 1=1=94± 17=7=94 18=7=94± 1=12=94 1=1=94± 17=7=94 18=7=94± 1=12=94 Bca pop. di Brescia 6,703 1,420 14,045 3,639 Montefibre 7,233 1,879 92,335 26,940 LLoyd Adriatico 9,152 5,075 12,855 9,274 Unicem risp. 6, , Unicem 6,043 1,772 11,522 4,078 Franco Tosi 3,355 1,162 3,355 1,070 Magneti Marelli 7, ,325 17,773 Cart. Sottrici Binda 7, , Tecnost spa 3, ,517 8,315 Calcestruzzi spa 3, , Stefanel 5,408 1,120 14,376 4,410 Marzotto 3,545 1,472 11,261 5,823 Attiv. imm. 5, ,200 2,460 Ericsson 7,768 1,381 6,088 1,472 Costa Crociere mc. 2, ,487 5,502 L'espresso 4, ,443 5,963 Comau fin. 5, ,425 6,080 Vittoria ass. 3, , Safilo spa 2,426 1,267 4,730 4,015 Alitalia cat. A 6, ,550 10,520 Edit. La Repubblica 4,330 1,172 21,063 5,745 Imm. Metanopoli 5, ,062 3,559 LLoyd Adriatico mc 2,309 5,075 2,371 1,342 Costa Crociere 3, ,487 5,197 Ratti spa 4, ,748 1,231 Teleco cavi risp.nc 3, , Montefibre risp.nc 7, ,335 26,940 Alitalia priv. 4, ,570 4,720 Saffa risp.nc 2, , Magneti Marelli risp. 1,755 2,166 61,325 2,760 Average , Standard deviation ,723 11,520

10 102 Giovanni Majnoni and Massimo Massa levels. 11 While such irregularities are present in the Italian market, they do not appear to be as pronounced as in other markets. The volume of trading, based on transaction prices, tends to rise slightly during the week, reaching a peak on Wednesday. In order to account for such seasonal effects, the daily estimates used two sets of additive dummies: one to account for the different days of the week and the other to purge off the effects of holiday and weekend closings of the stock exchange. 12 Also, we have controlled for the fact that trades carried out at the beginning of the day are significantly affected by the overnight closure of the market and that overnight volatility differs from volatility during the daytime, when the market is open. To this end, the estimates were carried out with different specifications: using additive dummies for the first transactions of the day equal to the number of VAR lags) and multiplicative dummies for the volumes and returns of those transactions. The final functional specification of the VAR was estimated using 5 lags for the daily estimates and 14 lags for the intraday estimates in order to best capture causal relationships induced with long lags. 4. The estimation model A model that lends itself to assessing market efficiency in terms of both the sensitivity of returns to the set of available information and the deviation of prices from the random-walk path, consists of the following bivariate VAR: r t ˆ a 1 r t 1 a 2 r t 2 b 1 x t 1 b 2 x t 2 1t 1) x t ˆ c 1 r t 1 c 2 r t 2 d 1 x t 1 d 2 x t 2 2t where r t represents the percentage change in share prices between t and t 1andx t the volume of trades effected in the same time interval. Using the data on trading volume and price changes, it allows us to test a) whether there are causal relations running from volume to returns or vice versa and whether such relations have changed in the most recent years and b) the importance of the imperfections of the market's microstructure in determining the overall variability of prices. The first test is based on the classic analysis of causality following Granger, for which x causes r if the null hypothesis that all the coefficients b of x are equal to zero is rejected. The second test is based on a multivariate version of the Beveridge-Nelson decomposition that makes it possible to reduce a non-stationary process in this case the price of a security) to the sum of a random walk and a stationary process. The application of this decomposition to the behaviour of prices, proposed by Hasbrouck 1993), allows us to separate the unpredictable efficient) component from the 11 Among the most notable anomalies at the daily level in the US market is the tendency of returns to be greater on Monday than on other days of the week French, 1980; Gibbons and Hess, 1981) and on particular days, such as the day preceding a holiday Ariel, 1990), the last day of the month and the last trading day in December Keim, 1989; Roll 1983). In addition, the highest returns are recorded in January and are concentrated on shares with the smallest market capitalization Keim, 1988). For calender anomalies in the Italian market, see Barone 1990). 12 The literature has found that trading is in general concentrated at the beginning and the end of the day, lending a characteristic U shape to the volume of transactions over the trading period Harris, 1986). This effect, which is also found in the Italian market Impenna et al., 1995), may partly be due to the suspension of trading overnight.

11 Stock Exchange Reforms and Market Efficiency 103 stationary component, attributable to market imperfections, thereby identifying the component of the change in share prices returns) due to the arrival of new information and the component attributable to `noise'. To describe the decomposition, it is useful to start out with the following expression of the prices in the share market: p t ˆ m t s t 2) where the observed price of the security p) consists of the sum of an `efficient price' m), given by the expected value of the security on the basis of publicly available information at time t, and a component of pricing error s), which measures the deviation of the price actually observed in the market from the efficient price. This error is justified by the presence of imperfections in the structure of the market, such as the lack of a perfect competitiveness in the retail and wholesale trading and the existence of inventory control costs for market makers, and it is a component of transaction costs. 13 Assuming, moreover, that efficient prices follows a random walk, its movement can be described as: m t ˆ m t 1 w t 3) where w t represents non-autocorrelated increments, such that: Ew t ˆ 0; Ew 2 t ˆ 2 w ; Ew tw ˆ 0; for t 6ˆ and that error s in 2) is distributed as a zero-mean stationary stochastic process defined as: s t ˆ w t t 4) where represents an error not correlated with w. We can interpret w as the component correlated with the arrival of information on the market and as the component induced by factors not correlated with it. Rewriting the model in terms of returns r), it follows: r t ˆ p t p t 1 ˆ m t m t 1 s t s t 1 ˆ w t s t s t 1 5) Since w and s are not autocorrelated, 5) can also be written as a moving average of a white noise process ": r t ˆ " t a" t 1 6) Adopting a Beveridge-Nelson specification whereby ˆ 0, i.e., the deviation of the observed price from the efficient price represented by the random walk is entirely due to information shocks Glosten, 1987), we can obtain the following expressions of the standard deviations of the two components relating to pricing error ˆ 0 and the random walk w ) see Appendix): w ˆ 1 a) " and s ˆ a " 7) The relations in 7) permit the standard deviation of the price change return) to be expressed as the sum of the two components. In this model, the ratio of the variance of 13 Transaction costs can be explicit commissions) or implicit. The latter consist of the difference between the price at which a transaction is effected and a price that can be interpreted as being efficient.

12 104 Giovanni Majnoni and Massimo Massa the pricing error to that of the random-walk component becomes an indicator of market efficiency: it will decrease as efficiency increases. In a perfect and informationefficient market, i.e., one capable of fully and immediately incorporating available information, the ratio will be equal to zero. Equations 6) and 7) can be obtained by transforming a vector autoregression of returns into a Vector Moving Average. 14 The model's explanatory power is increased by using the information content of trading volume. In particular, it can be assumed that the information capable of influencing the price change in 3) can be extracted from the behaviour of volume. Accordingly, the changes w of the random-walk component of prices can be expressed as a function of the information that traders extract from volume x), as follows: w t ˆ x t u t 8) In this case, the pricing error can be decomposed as follows: s t ˆ x t u t t 9) so that, continuing to assume that the term of error not due to information ) is equal to zero, the variances of the random-walk and pricing-error components will be equal to: 2 s ˆ b 2 2 x a 2 " and 2 w ˆ c 2 2 x d 2 2 " 10) which, thanks to the bivariate extension of the Beveridge-Nelson decomposition, can be decomposed into a part represented by the information extracted from volume 2 x ) and a function of white noise 2 "). In particular, given a VAR specification of returns and volumes, r t ˆ a 1 r t 1 a 2 r t 2 b 1 x t 1 b 2 x t 2 1t x t ˆ c 1 r t 1 c 2 r t 2 d 1 x t 1 d 2 x t 2 11) and having calculated its VMA representation: r t ˆ e 0 1; t e 1 1; t 1 f 0 1; t 2 f 1 2; t 1 x t ˆ g 0 1; t g 1 1; t 1 h 0 2; t h 1 2; t 1 12) the variance of the random-walk and pricing-error components can be derived on the basis of the following expressions Hasbrouck, 1993): h 2 w ˆ X X i h e i fi cov ) X X i 0 e i bi 13) and 2 s ˆ X1 [ j j ]cov )[ j ] 0 14) where j ˆ j ˆ 0 X1 k ˆ j 1 e 0 k e j ˆ X1 k ˆ j 1 f k : 14 The procedure to transform a VAR into a VMA can be found among others in Hasbrouck 1991) or in Judge et al. pp. 657±62).

13 Stock Exchange Reforms and Market Efficiency 105 The model thus offers a way of distinguishing between the part of residual variance attributable to the security's own features, which cannot be eliminated, even in the case of perfect and highly efficient markets variance of the random-walk component), from the part induced by the imperfections of the market variance of pricing error). The ratio between the two standard deviations indicates the degree of market perfection. The first step is the analysis of causality for all shares in the first and fifth deciles, using daily data for the periods 1987±90 and 1992±94 and intraday data for the first Table 4 Indicators of market quality: daily estimates. For the stocks belonging to the first and fifth decile we calculate the ratio of the variance of the pricing error s to that of the random-walk component random walk w ). The ratio is an indicator of market efficiency: it decreases as efficiency increases. In a perfect and informationefficient market, i.e., one capable of fully and immediately incorporating available information, the ratio will be equal to zero. The average across the stocks belonging to the same decile as well as their dispersion are reported. The estimates are based on daily data for the two sub-periods: 1987±90 and 1992±94. Estimation period 1987± ±1994 First decile Ras Fiat ord Fiat risp Generali Mediobanca Gemina Fiat priv Olivetti Ifi priv Italcementi Mean Standard deviation Fifth decile Marzotto Sorin Linificio Cantoni Snia Fibre Editoriale Snia BPD risp Attivita'Immobiliari Olcese Smi metalli Mean Standard deviation

14 106 Giovanni Majnoni and Massimo Massa Table 5 Indicators of market quality: intradaily data. 14 lag specification) For the stocks belonging to the first and fifth decile we calculate the ratio of the variance of the pricing error " to that of the random-walk component random walk w ). The ratio is an indicator of market efficiency: it decreases as efficiency increases. In a perfect and informationefficient market, i.e., one capable of fully and immediately incorporating available information, the ratio will be equal to zero. The average across the stocks belonging to the same decile as well as their dispersion are reported. Three specifications are used in the VAR. In the first one I) we include additive temporal dummies to take account of overnight and holiday closing of markets. In the second specification II) we use multiplicative temporal dummies to take account of overnight and holiday closing of markets. In the third specification III) we use both additive and multiplicative temporal dummies to take account of overnight and holiday closing of markets. Intra-daily data for the whole sample has been used. The estimation is based on 5- minute sampling and a specification with 14 lags. Estimation method I II III First decile Montedison Fiat Telecom Italia Olivetti ord Stet Credito Italiano Pirelli spa Fiat priv Stet risp. port Gemina Ferruzzi Finanziaria Banca Commerciale Italiana Montedison risp. Nc Telecom Italia risp Cir-Comp Ind. Riunite Mean Standard deviation Fifth decile Costa Crociere BNL risp. port AttivitaÁ Immobiliari Rinascente priv Stefanel Costa Crociere Risp. Nc Unicem risp. port Marzotto Alitalia risp. Nc Ratti spa Fin. Agroindustriale Unicem Assitalia Montedison risp. Conv Olivetti priv Mean Standard deviation

15 Stock Exchange Reforms and Market Efficiency 107 Table 6 Indicators of market quality: intradaily data. 10 lag specification) For the stocks belonging to the first and fifth decile we calculate the ratio of the variance of the pricing error " to that of the random-walk component random walk w ). The ratio is an indicator of market efficiency: it decreases as efficiency increases. In a perfect and informationefficient market, i.e., one capable of fully and immediately incorporating available information, the ratio will be equal to zero. The average across the stocks belonging to the same decile as well as their dispersion are reported. Three specifications are used in the VAR. In the first one I) we include additive temporal dummies to take account of overnight and holiday closing of markets. In the second specification II) we use multiplicative temporal dummies to take account of overnight and holiday closing of markets. In the third specification III) we use both additive and multiplicative temporal dummies to take account of overnight and holiday closing of markets. Intra-daily data for the whole sample has been used. The estimation is based on 5- minute sampling and a specification with 10 lags. Estimation method I II II First decile Montedison Fiat Telecom Italia Olivetti ord Stet Credito Italiano Pirelli spa Fiat priv Stet risp. port Gemina Ferruzzi Finanziaria Banca Commerciale Italiana Montedison risp. Nc Telecom Italia risp Cir-Comp Ind. Riunite Mean Standard deviation Fifth decile Costa Crociere BNL risp. port AttivitaÁ Immobiliari Rinascente priv Stefanel Costa Crociere Risp. Nc Unicem risp. port Marzotto Alitalia risp. Nc Ratti spa Fin. Agroindustriale Unicem Assitalia Montedison risp. Conv Olivetti priv Mean Standard deviation

16 I I 108 Giovanni Majnoni and Massimo Massa Table 7 Indicators of market quality. Comparing using 5 and 10 minute sampling For the stocks belonging to the first and fifth decile we calculate the ratio of the variance of the pricing error " to that of the random-walk component random walk w ). The ratio is an indicator of market efficiency: it decreases as efficiency increases. In a perfect and informationefficient market, i.e., one capable of fully and immediately incorporating available information, the ratio will be equal to zero. The average across the stocks belonging to the same decile as well as their dispersion are reported. Three specifications are used in the VAR. In the first one I) the estimation is based on 10-minute sampling and a 7±1ag VAR specification. In the second one II) the estimation is based on 5-minute sampling and 14±1ag VAR specification. Specification First decile Montedison Fiat Telecom Italia Olivetti ord Stet Credito Italiano Pirelli spa Fiat priv Stet risp. port Gemina Ferruzzi Finanziaria Banca Commerciale Italiana Montedison risp. Nc Telecom ltalia risp Cir-Comp Ind. Riunite Mean Standard deviation Fifth decile Costa Crociere BNL risp. port Attivita Immobiliari Rinascente priv Stefanel Costa Crociere Risp. Nc Unicem risp. port Marzotto Alitalia risp. Nc Ratti spa Fin. Agroindustriale Unicem Assitalia Montedison risp. Conv Olivetti priv Mean Standard deviation

17 Stock Exchange Reforms and Market Efficiency 109 Table 8 Effects of change from forward to cash settlement. In this table we report the indicator of market quality for the stocks that have been moved from forward to cash settlement. The indicator of market quality is calculated as the ratio of the ratio of the variance of the pricing error " to that of the random-walk component random walk w ). The ratio is an indicator of market efficiency: it decreases as efficiency increases. In a perfect and information-efficient market, i.e., one capable of fully and immediately incorporating available information, the ratio will be equal to zero. The average across the stocks belonging to the same decile as well as their dispersion are reported. Transactions are grouped using 5 minute intervals. Estimation period 1=1=94±17=7=94 18=7=94±31=12=94 Bca pop. di Brescia Montefibre LLoyd Adriatico Unicem risp Unicem Franco Tosi Magneti Marelli Cart. Sottrici Binda Tecnost spa Calcestruzzi spa Stefanel Marzotto Attiv. Imm Ericsson Costa Crociere rnc L'espresso Comau fin Vittoria ass Safilo spa Alitalia cat. A Edit. La Repubblica Imm. Metanopoli LLoyd Adriatico rnc Costa Crociere Ratti spa Teleco cavi risp.nc Montefibre risp.nc Alitalia priv Saffa risp.nc Magneti Marelli risp Average Standard Deviation

18 110 Giovanni Majnoni and Massimo Massa half of We used the Granger definition of causality for which x causes y if y can be forecast better by using past y and past x rather than just past y. This is tested by using likelihood ratio tests. 15 The results 16 confirmed the assumption that trading volumes affect returns but returns do not affect volumes. Finally, the VAR estimates were used to disaggregate the total variance of returns and calculate the component attributable to noise, i.e., the information and non-information imperfections produced by the structure of the market. The estimates of the ratio of the variance of the stationary component attributable to noise) to the non-stationary component of the shares in the daily sample are shown in Table 4; those for the intraday sample are shown in Tables 5, 6 and 7, and those for shares settled on a cash basis are given in Table 8. For all the VAR estimations used for the causality tests and for variance decomposition, we checked the hypothesis of whiteness of residuals. 6. The results The causality tests at the daily level produce different results for the most and least liquid shares as regards the evolution of market efficiency. There was an overall improvement in the information efficiency of the market for the most liquid shares between 1987± 90 and 1992± 94. The causal effect of volumes on returns, while confirmed for a large proportion of shares, tends to weaken and be rejected for an increasing number of shares. At the same time, the inverse causal relationship, running from returns to volumes, is negligible in both periods. 17 On the contrary, no clear sign of an improvement in efficiency for least liquid shares, has been found. The effect of volumes on returns tended to strengthen, while that of returns on volumes decisively weakened. 18 The flattening of the autocorrelation curve, which is practically nil after the initial shock, is common to both of the shares examined. The results of the causality analysis of intraday data differ from those for daily data. This is no surprise if one bears in mind that the underlying stochastic processes are not the same and are generated by the behaviour of investors with differing, albeit related, time horizons. 19 In particular, the improved efficiency revealed in the daily data is not present in intraday data for both liquid and illiquid shares: for the former, the causal relationship between volumes and returns is quite strong in both directions, probably owing to the speculative behaviour of investors who have to close their positions 15 For a definition of the test and for the specification of the restricted and unrestricted equations see Luetkepohl, 1990, pp. 35±40). 16 Not reported but available upon request from the authors. 17 The analysis of the impulse-response function for single specific shares confirms the considerable weakening of the causal effect of volumes on returns and the substantial absence of effects on volumes of a returns shock for the most liquid stocks. 18 The analysis of the impulse-response functions for specific shares reveals a slight increase in the impact of a volume shock on returns and a reduction in the impact of a returns shock on volumes. 19 See the recent study by Muller et al., 1995), which identifies a correlation between returns and volatility defined over a number of time intervals in the over-the-counter foreign exchange market, such that the volatility over the longer intervals, and thus characteristic of investors with longer time horizons, guides the volatility over shorter intervals, which can be identified with the investment horizons of traders who must close their positions at the end of the day.

19 Stock Exchange Reforms and Market Efficiency 111 during the day. For illiquid shares, there is a larger number of cases that do not exhibit a causal relationship; this may be the consequence of the infrequency of trades perhaps the result of market imperfections or block trading), which tends to mask causal relationships. An explanation of the differences between the daily and intraday efficiency analyses may be provided by examining a better indicator of market efficiency, one based on the ratio between the two components of overall variability the noise to signal ratio). Daily rations, as shown in Table 4, indicate a larger, but not solely, improvement in efficiency for more liquid shares. If we compare the results in the 1987±90 period for the two groups of stocks, we see immediately that those in the fifth decile have both higher mean and higher standard deviation that the ones in the first decile. This result survives even after we control for the outlier stock Snia BPD risp. In the second period, mean and volatility drop for both groups. In particular, in the 1992±94 period, the noise component was smaller and more concentrated around the mean for both groups mean equal to 1.12 and standard deviation equal to 0.15 for the first group, and mean equal to 1.30 and standard deviation equal to 0.13 for the second group). It is interesting to note that, while the more traded stocks have an even higher mean than that for less-frequently traded stocks, they display lower standard deviation. The results of the study of daily figures are confirmed at the intraday level, with greater efficiency for more frequently traded securities, marked by lower and less dispersed values for noise volatility and overall volatility. In particular, Table 5 reports the results for the case when we control for overnight and holiday closing of markets using both additive and temporal dummies. We find that the mean of the first group of stocks is lower than that of the second group 1.65 for the first group versus 3.66). The results substantiate the increase in market efficiency, especially for more liquid securities, in line with the findings of previous studies e.g., Murgia, 1993), which show more liquid shares as the main beneficiaries of stock exchange innovations, especially the introduction of continuous trading. It is interesting to note that in this case, the results based on daily observations coincide with the ones based on high frequency data, and that both point in the direction of improved market efficiency. Also, notice that the results agree regardless of the number of lags specified 5±10 lags) and of the grouping chosen 5± 10 minutes). Table 6 reports the results for the case where the specification is based on 10 lags and Table 7 compares the results for estimations based on 5 and 10 minutes sampling. In particular, in the specification based on 10 lags, the mean value of our measure of market inefficiency is lower for the first decile than for the second one for the specifications where both multiplicative and additive dummies are used to control for holiday and weekend effects with mean and standard deviations equal to 1.43 and 0.49 for the first decile and 2.78 and 1.59 for the fifth one). Also, in the case where we use 5 and 10 minute samplings, both the mean values and the standard deviations are higher for the first decile than for the fifth one. The analysis has also been carried out for stocks that have recently changed from forward to cash settlement, in order to discover if the fall in trading volumes is associated with a decline in market efficiency. The findings, shown in Table 8, refer to estimates made in the 6 months prior to and the same period following the reform of the settlement system. They show an increase in the average noise component with respect to overall volatility, and hence, ceteris paribus, a decline in market efficiency.

20 112 Giovanni Majnoni and Massimo Massa In particular, we see that the mean value of our measure of market inefficiency has risen from 5.95 to Also, the standard deviation has increased from 7.72 to One cause of this result, which appears to be in conflict with those for the other market segments, might be deterioration in the `quality' of prices a larger noise component) owing to the fact that not all shares are settled through the new system. At equal levels of risk and return, this may have encouraged a shift in trading towards shares settled forward, which enable investors to operate with lower initial financing i.e., greater leverage). Conclusion The joint analysis of prices and volumes which underlies the measurement of market efficiency has enabled us to address whether the efficiency of the Italian stock market increased after the main structural reforms were implemented, over the 1991 ±94 period. Our analysis shows that the strong positive correlation between price changes and trading volume is due to a significant causal relationship between trading volumes and price formation. This implies that trading volume has its own information content. The market reform measures weakened this relationship, but only for more liquid securities, strengthening information efficiency, in the conventional sense of the term, for that segment of the market alone. However, the persistence of the causal relationship has not prevented a significant improvement in market efficiency for lessfrequently traded shares either, as measured by the noise component of prices. We have also shown that increasing concentration of trading should not be interpreted as an indication of poor market efficiency, since the component of price volatility, due to the actions of uninformed investors or market imperfections, has declined as a proportion of total volatility, even for infrequently traded stocks. At the same time, our analysis shows that the prices of more illiquid shares are of significantly poorer quality than more actively traded securities. This raises the question of whether different trading systems are required for shares with differing liquidities: for instance, market makers for less liquid shares and continuous auction trading restricted to the most actively traded securities. This is an open topic that deserves to be examined in greater depth by future research. References Admati, A. R. and Pfleiderer, P., `Sunshine trading and financial market equilibrium', Review of Financial Studies, Vol. 4, 1991, pp. 443±81. Amihud, Y. and Mendelson, H., `Asta a chiamata ed asta continua: effetti sulla volatilitaá e la liquiditaá del mercato', Finanza imprese e mercati, 1989, pp. 393±419. Ariel, R., `High stock returns before holidays: existence and evidence on possible causes', Journal of Finance, Vol. 45, 1990, pp. 1611±26. Bagehot, W., `The only game in town', Financial Analysts Journal, March-April, 1971, pp. 12± 15. Barone, E., `The Italian stock market: efficiency and calendar anomalies', Journal of Banking and Finance, Vol. 14, 1990, pp. 483±510. Beveridge, S. and Nelson, C., `A new approach to the decomposition of economic time series into permanent and transitory components with particular attention to the measurement of the business cycle', Journal of Monetary Economics, Vol. 7, 1981, pp. 151±74.