宏觀經濟變量與股票交易
 
宏觀經濟變量和股票成交量之間的關系

這項研究探究的是從2004年1月至2009年8月間，每月KSE-100指數成交量所受公布的消費者價格指數(CPI)、批發價格指數(WPI)和利率的影響。研究發現這些宏觀經濟指標都不是影響KSE 100指數成交量的因素，而是長期滯后的交易量影響了KSE 100指數成交量。

關鍵詞：消費者價格指數(CPI)，批發價格指數(WPI)，利率公告，成交量，ARIMA。

本研究的目的是探究每月KSE-100指數成交量所受報告的宏觀經濟變量，如消費者價格指數(CPI)，批發價格指數(WPI)和利率的影響。
先前的研究已經說明了金融市場與這些報告的宏觀經濟變量有一定的關系。但這些研究只是假設“所有宏觀經濟變量和KSE 100指數成交量都是非重大關聯”。
宏觀經濟變量：
消費大國物價指數(CPI):
不同的價格指數可用于衡量通貨膨脹。價格指數是指衡量相對于選定的基準年來說的集體價格水平。巴基斯坦的消費者價格指數(CPI)、敏感的價格指標(SPI)和批發價格指數(WPI)都是以2000 – 01作為基準年。
 
Macroeconomic variables and Stock Trading
 
Relationship between macroeconomic variables and Stock Trading Volume 36
 
Abstract
 
This study examines monthly KSE-100 index trading volume response to announcements about Consumer price index (CPI), wholesaler price index (WPI) and interest rates beginning from January 2004 to august 2009.The findings significantly support that neither of the macro economic indicators causes KSE 100 index trading volume. Nevertheless, the non-seasonal lags (AR1) of trading volume causes the trading volume of KSE 100 index
 
Key words:
 
Consumer price index (CPI), wholesaler price index (WPI), interest rates announcements, trading volume, ARIMA.
 
CHAPTER-1
 
INTRODUCTION 引言
 
The purpose of this study is to examine the monthly KSE 100 index trading volume response to announcements of macro economic variables which are consumer price index (CPI), wholesale price index (WPI) and interest rates.
 
Previous studies have examined that the financial markets response to these announcements. But this research is only concerned with the hypotheses that “All macroeconomic variables has non-significant association with the KSE 100 index trading volume.
 
The Macro economic variables are as follow:
 
1-Consumer Price Index (CPI):
 
Different price indices are used to measure inflation. A price index is a measure of the collective price level relative to a chosen base year. In Pakistan a consumer price index (CPI), a sensitive price indicator (SPI) and a wholesale price index (WPI) are collected. They have commonly the base year 2000-01.#p#分頁標題#e#
 
CPI is a most important measure of price changes at retail level. It specifies the cost of purchasing a Representative fixed basket of goods and services consumed by private households. In Pakistan CPI covers the retail prices of 374 items in 35 major cities and reflect roughly the changes in the cost of living of urban areas.
 
Wholesale price index (WPI):
 
WPI is designed for those items which are mostly consumable in daily life on the primary and secondary level. These prices are collected from wholesale markets and also from mills at prearranged wholesale market level. The WPI covers the 106 commodities of the wholesale price. Existing in 18 major cities of Pakistan. The prices are regularly reported on monthly basis. WPI covers 425 items, divided in five major commodity groups via (i) Food, (ii) Raw material, (iii) Fuel, Lighting and Lubricants, (iv) Manufacturing, (v) Building material.
 
Interest rate:
 
A rate which is charged or paid for the use of money. An interest rate is frequently expressed as an annual percentage of the principal. It is considered by dividing the amount of interest by the amount of principal. Interest rates frequently change as a result of inflation and Federal Reserve policies.
 
CHAPTER-2
 
LITERATURE REVIEW 文獻綜述
 
Previous studies have shown that the stock market response to announcements about macro-economic variables.
 
Pearce and Roley (1985) find that the response of stock prices to the weekly money stock announcement is constant with the efficient markets theory because only the unexpected money stock change had a significant cause and this cause was complete within the trading day after the announcement. Pearce and Roley (1985) indicates that it is practical to view discount rate changes as unanticipated, at least in terms of timing and perhaps also in terms of magnitude. The economic news events are considered announcements on the money stock, inflation, real activity, and the discount rate. An unanticipated increase in money, inflation, and the discount rate may be viewed as bad news for the stock market and thus should be followed by a fall in stock prices. Unexpectedly strong real growth may be viewed as good news with stock prices rising as a result, unless future policy is assessed as being more restrictive.
 
In the pre October(1979) Surprises in announced values of the CPI, industrial production, the unemployment rate, and the discount rate are statistically unrelated to stock prices during this period, at least on announcement days. In the subsample beginning in October 1979, discount rate changes also have significant effects. Second, only limited evidence supports the view that either inflation or real economic activity surprises affect stock prices. In the pre- October 1979 subsample, PPI surprises have significant effects on the day of an announcement, but they are estimated to be offset by the end of one week. In both subsamples, surprises in announced levels of real economic activity have no significant impact on stock prices on the day of an announcement. Smirlock (1986) find a significant positive response of long-term rates to unexpected inflation. Smirlock finding that long-term rates rise in response to unanticipated inflation in the post-79 periods complements and expand the growing body of literature that has study the effect on financial markets of macro-economic information announcements and provides further support and evidence that a wide range of macroeconomic variables affect many different financial securities and markets. Taken by Smirlock (1986) results do not allow bias between the competing hypotheses in explaining the response of interest rates to unexpected inflation and money surprises. The differential response of long rates and short rates to CPI surprises combined with the relatively large price change associated with long- term bonds to inflation surprises seems to suggest market participants revise their inflation expectations. Accordingly this may lead one to conclude that the expected inflation hypothesis is valid. Such a conclusion however is tenuous. As it was noticed that an efficient market also implies that there should be no response of interest rates in the post-announcement period. Since the anticipated component of inflation announcements did not affect interest rates, to examine the speed of adjustment.#p#分頁標題#e#
 
Black (1986) Noise makes trading in financial markets possible and therefore allows watching prices for financial assets. Noise caused markets to be somewhat inefficient but often avoid from taking benefit of inefficiencies.
 
Noise in the form of uncertainty about future tastes and technology by sector causes business cycles, and makes them highly opposed to improvement through government interference. Noise in the form of expectations that require not follow rational rules causes inflation to be what it is, at least in the lack of a gold standard or fixed exchange rates. Noise in the form of uncertainty about what relative prices would be with other exchange rates makes us think wrongly that changes in exchange rates or inflation rates cause changes in trade or investment flows or economic activity. Most in general, noise makes it very hard to test either practical or academic theories about the way that financial or economic markets work.
 
In (Black) (1986) model of financial markets, noise is contrasted with information. People sometimes trade on information in the common way. Black (1986) model was correct in expecting to make earnings from these trades. On the other hand, people sometimes trade on noise as if it were information. If Black (1986) expect to make earnings from noise trading, it was wrong. However, noise trading is necessary to the existence of liquid markets.
 
In Black (1986) model of the way to study the world, noise is what makes our observations imperfect. It stays from knowing the expected return on a stock or portfolio. It stays from knowing whether monetary policy affects inflation or unemployment. It also stays from knowing what, if anything can do to make things better.
 
In Black (1986) model of inflation, noise is the arbitrary element in expectations that leads to an arbitrary rate of inflation consistent with expectations. In Black (1986) model of business cycles and unemployment, noise is information that hasn't arrived yet. It is simply uncertainty about future demand and supply conditions within and across sectors. When the information does arrive, the number of sectors where there is a superior match between tastes and technology is an index of economic activity. In Black (1986) model of the international economy, changing relative prices become noise that makes it difficult to see that demand and supply conditions are mainly independent of price levels and exchange rates. Without these relative price changes, it could seen that a version of purchasing power parity holds a large amount of the time.
 
Noise makes financial markets possible, but also builds them imperfect. If there is no noise trading, there would be very little trading in individual assets. People would hold individual assets, directly or indirectly, but they would infrequently trade them. People trading to change their exposure to broad market risks would trade in mutual funds, or portfolios, or index futures, or index options.In the pre October(1979) Surprises in announced values of the CPI, industrial production, the unemployment rate, and the discount rate are statistically unrelated to stock prices during this period, at least on announcement days. In the subsample beginning in October 1979, discount rate changes also have significant effects. Second, only limited evidence supports the view that either inflation or real economic activity surprises affect stock prices. In the pre- October 1979 subsample, PPI surprises have significant effects on the day of an announcement, but they are estimated to be offset by the end of one week. In both subsamples, surprises in announced levels of real economic activity have no significant impact on stock prices on the day of an announcement. Smirlock (1986) find a significant positive response of long-term rates to unexpected inflation. Smirlock finding that long-term rates rise in response to unanticipated inflation in the post-79 periods complements and expand the growing body of literature that has study the effect on financial markets of macro-economic information announcements and provides further support and evidence that a wide range of macroeconomic variables affect many different financial securities and markets. Taken by Smirlock (1986) results do not allow bias between the competing hypotheses in explaining the response of interest rates to unexpected inflation and money surprises. The differential response of long rates and short rates to CPI surprises combined with the relatively large price change associated with long- term bonds to inflation surprises seems to suggest market participants revise their inflation expectations. Accordingly this may lead one to conclude that the expected inflation hypothesis is valid. Such a conclusion however is tenuous. As it was noticed that an efficient market also implies that there should be no response of interest rates in the post-announcement period. Since the anticipated component of inflation announcements did not affect interest rates, to examine the speed of adjustment.#p#分頁標題#e#
 
They would have little reason to trade in the shares of an individual firm. People who would like cash to spend or who would like to invest cash they have received would increase or decrease their positions in short term securities, or money market accounts, or money market mutual funds, or loans backed by real estate or other assets.
 
Black (1986) someone with information or insights about person firms would want to trade, but would realize that only another individual with information or insights would take the other side of the trade. Taking the other side's information into account, is it still important trading? From the point of view of someone who knows what both the traders know, one side or the other must be making an error. If the one who is making an error declines to trade, there would be no trading on information. In other words, Black (1986) do not believe it makes sense to create a model with information trading but no noise trading where traders have different beliefs and one trader's beliefs are as good as any other trader's beliefs. Variation in beliefs must derive ultimately from differences in information. A trader with a special piece of information would know that other traders have their own special pieces of information, and would therefore not automatically run out to trade.
 
Black (1986) if there is small or no trading in person shares, there can be no trading in mutual funds or portfolios or index futures or index options, because there would be no practical way to price them. The whole structure of financial markets depends on relatively liquid markets in the shares of individual firms. Noise trading offered the essential missing ingredient. Noise trading is trading on noise as if it be information. People who trade on noise be willing to trade even though from an objective point of view they would be better off not trading. Possibly they think the noise they are trading on is information or maybe they just like to trade.
 
With a lot of noise traders in the market, it now pays for those with information to trade. It even pays for people to seek out costly information which they would then trade on. Most of the time, the noise traders as a group would lose money by trading, while the information traders as a grouping would make money.
 
The more noise trading, the more liquid the markets would be, in the sense of having common trades that allows observing prices. But noise trading actually place noise into the prices. The price of a stock imitates both the information that information traders trade on and the noise that noise traders trade on. As the amount of noise trading increases, it would become more profitable for individual to trade on information, but only because the prices have more noise in them. The increase in the amount of information trading does not mean that prices are more efficient. Not only would more information traders come in, but existing information traders would take larger positions and would spend more on information. Yet prices would be not as much of efficient. What's needed for a liquid market basis prices to be less efficient.#p#分頁標題#e#
 
The information traders would not take large enough positions to remove the noise. For one thing, their information gives them an edge, but does not guarantee a profit. Taking a better position means taking more risk. So there is a limit to how large a position a trader would take. For another thing, the information traders can never be sure that they are trading on information rather than noise. What if the information they have has already been reflected in prices? Trading on that kind of information would be just like trading on noise. Because the actual return on a portfolio is a very noisy estimate of expected return, even after adjusting for returns on the market and other factors, it would be difficult to show that information traders have an edge. For the similar reason, it would be difficult to show that noise traders are losing by trading. There would always be a lot of uncertainty about who is an information trader and who is a noise trader. The noise that noise traders put into stock prices would be cumulative, in the similar sense that a drunk tends to wander farther and farther from his starting point. Offsetting this, though, would be the research and actions taken by the information traders. The farther the price of a stock gets from its value, the more aggressive the information traders would become. More of them would come in, and they would take larger positions.
 
Thus the price of a stock would tend to go back toward its value over time. The move would often be so slow that it is imperceptible. If it is fast, technical traders would perceive it and speed it up. If it is slow enough, technical traders would not be able to see it, or would be so uncertain of what they see that they would not take large positions.
 
Still, the farther the price of a stock go away from value, the faster it would tend to go back. This limits the degree to which it is likely to go away from value. All estimates of value are noisy, so Black (1986) can never know how far away price is from value.
 
Noise traders must trade to have their pressure. Since information traders trade with noise traders more than with other information traders, cutting back on noise trading also cuts back on information trading. Thus prices would not move as much when the market is closed as they move when the market is open. The relevant market here is the market on which most of the noise trader's trade. Noise traders may like better low-priced stocks to high-priced stocks. If they do, then splits would increase both the liquidity of a stock and its day-to-day volatility. Low-priced stocks would power is less efficiently priced than high-priced stocks. The price of a stock would be a noisy estimation of its value. The earnings of a firm (multiplied by a suitable price-earnings ratio) would give another estimation of the value of the firm's stock. This estimation would be noisy too. So long as noise traders do not always look at earnings in deciding how to trade, the estimation from earnings would give information that is not already in the estimation from price. Because an estimation of value based on earnings would have so much noise, there would be no easy way to use price-earnings ratios in managing portfolios. Even if stocks with low price-earnings ratios have higher expected returns than other stocks, there would be periods, possibly lasting for years, when stocks with low price-earnings ratios have lower returns than other similar stocks. In additional, noise build the opportunity to trade profitably, but at the same time makes it difficult to trade profitably.#p#分頁標題#e#
 
There is so much noise in the world, certain things are really unobservable, and Black (1986) cannot know what the expected return on the market is. There is every cause to believe that it changes over time, and no particular return as an estimation of the expected return, but it is a very noisy estimation. Similarly, the slopes of demand and supply curves are so hard to estimate that they are essentially unobservable; wealth is often a key variable in estimating any demand curve. But wealth is itself unnoticeable. It's not still clear how to define it. The market value of traded assets is part of it, but the value of non-traded assets and especially of human capital is a larger part for most individuals. There is no way to observe the value of human capital for an person, and it is not clear how Black (1986) might go about adding up the values of human capital for persons to obtain a value of human capital for a whole economy. Black (1986) suspects that if it were possible to observe the value of human capital, it would find fluctuating in much the same way that the level of the stock market fluctuates. In fact, Black (1986) think that it would find fluctuations in the value of human capital to be extremely correlated with fluctuations in the level of the stock market, though the magnitude of the fluctuations in the value of human capital is probably less than the magnitude of the fluctuations in the level of the stock market. It's actually easier to list observables than unobservable, since so many things are unobservable. The interest rate is visible. If there were enough trading in CPI futures, the real interest rate would be observable. So far, though, there are not enough noise traders in CPI futures to make it a viable market. Stock prices and stock returns are noticeable. The past volatility of a stock's returns is observable, and by using daily returns Black (1986) could come up to close to observing the current volatility of a stock's returns. Black (1986) could also come up to close to observing the correlations among the returns on different stocks. Economic variables appear generally less observable than financial variables. The prices of goods and services are hard to examine, because they are specific to location and terms of trade much more than financial variables. Quantities are hard to observe, because what is traded differs from place to place and through time. Thus econometric studies involving economic variables are hard to interpret for two reasons: first, the coefficients of regressions show slight about causal relations even when the variables are observable; and second, the variables are subject to lots of measurement error, and the measurement errors are probably related to the true values of the variable.
 
Perhaps the easiest economic variable to observe is the money stock, once agree on a definition for it. I think that accounts for some of the fascination it holds for economic theorists. In Black (1986) view, though, this easiest to observe of economic variables has no important role in the workings of the economy. Money is important, but the money stock is not. Still, the money stock is correlated with every measure of economic activity, because the amount of money used in trade is related to the volume of trade. This correlation implies neither that the government can control the money stock nor that changes in the money stock influence economic activity. Empirical studies in finance are easier to do than empirical studies in economics, because data on security prices are of generally higher quality than the available data in economics.#p#分頁標題#e#
 
Noise or uncertainty has its effects in economic markets because there are costs in shifting physical and human resources within and between sectors. If skills and capital can be shifted without cost after tastes and technology become known, mismatches between what we can do and what we want to do would not occur.
 
The stock prices would tell us that how investors think the event would affect the firms and Black (1986) thought that the noise and information both effects. Poitras (2004) find that announcements have a correlation with prices in financial markets that are statistically significant. However the relationship found, while statistically nonzero, is not a close one. The announcement variables Poitras (2004) examined explain together less than 2% of the variation of daily changes in the S&P 500, and no individual variable (other than the discount rate) explains more than about 3% of the variation occurring on its own announcement days. In spite of the considerable attention given to announcements, their weak explanatory power calls into question their economic significance.
 
Poitras (2004) states that, when economic activity is high, an announcement that employment and production are rising implies that the economy might be overheating. This leads the public to expect higher future inflation and interest rates, which reasons stock prices to fall. On the other hand, if the economy is experience a downturn, an announcement of rising employment and production is good news for stocks since it implies economic recovery and higher future earnings. Therefore, the impact of a macroeconomic announcement on the stock market is state contingent. A number of announcements demonstrate statistical significance, including nonfarm payrolls, the consumers' price index (CPI), the producer price index (PPI), and the discount rate. However the coefficient estimates for the interaction terms reveal that no announcement exhibits a structural shift that attains significance at the 0.05 level across models. Poitras (2004) find that nonfarm employment is statistically significant in most models, while the unemployment rate is never significant .As far as the state contingency of employment news is concerned, the present study finds the evidence to be weak and not robust. Poitras (2004) estimated marginal effect of nonfarm employment is significantly more positive during recessions, but only at the 0.10 level, not at the 0.05 level. Even this weak effect no longer holds if the model defines the economic state relative to the level of economic activity. Rotating to the unemployment rate, a stability test for this variable yields a Nyblom-Hansen statistic of 0.376, which rejects stability at the 0.10 level, but not at the 0.05 level. however if the impact of the unemployment announcement does understanding structural shifts, when these shifts occur is far from clear because none of the four economic-state models reveal any significant evidence of structural shifts. In addition, if the impact of employment news were state contingent, this property should be reflected not just by the unemployment rate but also by total employment.#p#分頁標題#e#
 
The announcements as noticed above that are anticipated generally do not have significant descriptive power for changes in the S&P 500. This result concurs with theories of market efficiency. The results, though, indicate that announcements do have statistically significant explanatory power if unanticipated, which contradicts strong full informational efficiency. However the evidence might nonetheless be consistent with semi strong efficiency, which requires just the absence of unexploited profit opportunities. Correlation of announcements with stock prices can agree with semi strong efficiency so long as agents cannot profitably replicate the government survey in advance of the announcement. The profitability of duplication depends on the cost of conducting the survey, the survey's predictive power, the cost of acting on the information; and so on unanticipated announcements give explanation a fraction of daily variation in stock prices that is statistically nonzero yet rather small. For example, as indicated by the multiple R2 the eight unanticipated announcement variables together explain only about 1.6% of the variation in daily closing values of the S&P 500.
 
Poitras (2004) figure out each variable's partial R2 using only the observations for days on which the particular variable was announced (or the following market days if the variable was announced after hours). With the exception of the discount rate, Poitras computations show that no individual variable explains as much as 3% of the variation occurring on its own announcement days. In view of this result, the considerable press coverage devoted to government announcements appears misplaced. Exceptionally, the change in the discount rate explains more than 9% of the S&P variation occurring on its announcement days.
 
The result is intriguing because the discount rate also occurs to be the only variable in the set that is not produced by government survey. Instead of reflecting only pre existing information, the change in the discount rate most probably reflects the judgment and policy discretion of the authorities at the Federal Reserve. Therefore, unlike other announcements, changes in the discount rate can offer an exclusive source of news regarding discretionary shifts in public policy. This distinction might account for the discount rate's relatively better explanatory power.
 
Jain(1988) examine hourly stock returns and trading volume to investigate the response of the market participants about the money supply, consumer price index (CPI), producer price index(PPI), industrial production(IP), and the unemployment rate. Besides, more accurate estimates of stock market response can be obtained by using hourly prices. Announcements about economic variables may also influence trading volume if the market participants rebalance their portfolios based on new information. If market participants disagree about the effects of surprises in announcements, there should be increased trading activity in the market soon after the announcements. In comparison, if Jain (1988) would be in consensus about the effects of new information, trading activity may not be irregular even when prices change. Thus, examining the trading activity provides helpful information about the actions taken by the market participants based on macroeconomic news that stock returns alone can not. Trading volume response to economic news has not been examined before. Jain (1988) presents direct evidence on the validity of the efficient market theory by examining the speed of adjustment of stock prices to economic news. Empirical evidence on the speed of adjustment is indeed sparse. The use of hourly data should help to obtain more accurate estimation of the relation between inflation surprises and stock return. Two of the five announcement surprises have significant impact on stock prices. In particular, money-supply announcement and CPI-announcement surprises have significant negative effects on stock price. Ederington and Lee (1993) examine the impact of listed macroeconomic news. The bulk of the price adjustment to a main announcement occur within the first minute, volatility remains substantially higher than normal for roughly fifteen minutes and slightly important for several hours. However, these subsequent price adjustments are basically independent of the first minute's return.#p#分頁標題#e#
 
Ederington and Lee (1993) study the impact on interest rate and foreign exchange markets of scheduled macroeconomic news releases such as the employment report, the consumer price index (CPI), and the producer price index (PPI). Many market participants believe that such announcements have a most important impact on financial markets. Ederington and Lee(1993) believe that the results are generalizable to spot interest and exchange rate markets as well. In fact, Ederington and Lee (9993) feel many of the results are relevant to any scheduled announcement, i.e., one whose timing is known beforehand, such as earnings and dividend announcements. Ederington and Lee (1993) find that, within the first seventy minutes, volatility is not usually high at the opening (8:20) but at 8:30 when the announcements be made. More important, Ederington and Lee (1993) find that, when control for these announcements, volatility is basically flat both across the trading day and across the trading week.
 
In examining the importance of person announcements, Ederington and Lee (1993) find that the following seven announcements listed in order of decreasing impact have a significant (0.005 level) effect on T-bond futures prices: the employment, the PPI, the CPI, durable goods orders, industrial production-capacity utilization, construction spending-National Association of Purchasing Managers (NAPM) survey, and the federal budget. Employment, the PPI, the CPI, durable goods orders, construction spending-NAPM survey and industrial production-capacity utilization have a significant impact on Eurodollar futures, whereas employment, the U.S. merchandise trade deficit, the PPI, durable goods orders, GNP, and retail sales significantly impact the dollar-deutsche mark rates.
 
Ederington and Lee survey that the speed at which the market adjust to these news releases focusing on both market efficiency and volatility. Ederington and Lee (1993) find that the major price adjustment occurs within one minute of the release and the direction of subsequent price adjustments is basically independent of the first minute's price change. However, prices continue to be considerably more volatile than normal for roughly fifteen minutes and slightly more volatile for several hours. It appears that the traders with immediate access to the market rapidly form a basically unbiased estimate of the release's implication for market prices and that the price adjusts to this level almost immediately. Prices continue to adjust as details become available and as these and other traders reassess the news and its implication for prices. Though, these subsequent adjustments are generally independent of the initial price change. Attention is now turned to the question of how quickly the markets adjust to this new information. There are two aspects to this adjustment: efficiency and volatility persistence. Ederington and Lee (1993) expect that the standard deviation of returns to increase when the information first arrives and to return to normal once the full implications of the information for market prices is worked out. Ederington and Lee (1993) seek to measure how long it takes for the markets to completely incorporate the new information by measuring how long return volatility remains higher than normal. A second matter concerns the efficiency of the market adjustment. If the price adjusts slowly to the new information, then it may be possible to earn surplus returns based on the initial market reaction to the news release. Ederington and Lee (1993) observed that in the interest rate markets, 9:15 to 9:20 return volatility is not impacted by the 8:30 announcements. In fact, no announcements have a significant impact on volatility in any of the later periods.#p#分頁標題#e#
 
Ederington and Lee (1993) has covered considerable ground and touched on numerous issues. The conclusions and contributions which Ederington and Lee (1993) look upon as most important follow. First, the observed intraday and day-of-the-week volatility patterns in interest rate and exchange rate futures market are mostly due to the timing of major macroeconomic announcements. When the impact of these announcements is detached, volatility is essentially flat across the trading day and across the trading week. Second, the monthly economic information releases with the most impact on interest rates in the 1988 to 1991 period were in order of decreasing impact, the employment report, the PPI, the CPI, and durable goods orders. Those with the maximum impact on the dollar-deutsche mark exchange rates were the employment report, the merchandise trade deficit, the PPI, durable goods orders, GNP, and retail sales. Third, most of the price adjustment to these information releases occurs within one minute of the release and trading profits based on the initial reaction basically vanish within this period. Fourth, while most of the price change occurs within one minute, volatility remains considerably higher than normal for another fifteen minutes or so and slightly higher for several hours. This can be explained as either continued trading based on the initial information as its implications for market prices are worked out or as price reactions to the details of the release as they become obtainable. The impact that macroeconomic variables have on equity markets plays a crucial role in the risk management strategies of financial market participants.
 
Ewing (2002) identifies and examines the extent to which innovations in several key macroeconomic variables are transmitted to the performance of financial sector stock returns, specifically, the NASDAQ Financial 100 index. Ewing (2002) focus on four fundamental macroeconomic variables that previous findings have identified as important state variables in stock and bond returns-namely, the stance of monetary policy, inflation, market or default risk, and real economic activity. An innovation to any of the macroeconomic variables may be interpreted as unexpected economic news. The generalized response functions allow Ewing (2002) to compare and contrast the effects of unanticipated changes in the macroeconomic factors on changes in the NASDAQ Financial 100 index without imposing a priori restrictions about the relative importance of the macroeconomic variables. Obviously, financial institutions, financial services firms, and financial market participants may be affected by movements in the NASDAQ Financial 100 index. Knowledge of what guide to movements in financial companies returns and how long shocks may last is, therefore, important the associated estimated correlation matrix. The financial sector returns are negatively correlated with changes in the fed funds rate and inflation, and positively correlated with changes in real output and risk. An unexpected positive change in the fed funds rate has a negative and significant initial impact effect.#p#分頁標題#e#
 
The impact on financial sector stock returns is negative and significant Thus, a sudden monetary tightening, as evidenced by an unanticipated rise in the fed funds rate, lowers financial sector stock returns for about two months.
 
Ewing (2002) suggests that the Fed may have a direct way in which to influence the stock return behavior of financial companies. The initial impact effect of an unanticipated increase in real output is positive and statistically significant. However, the output effect dies off quickly as it disappears within one month. The performance of the financial sector come out to be directly affected by real output shocks, but only contemporaneously. Increases in economic growth should be associated with a lower number of defaults, increased business investment and borrowing, and possibly increases in deposits and fund accounts as the wealth of savers increases. All of these factors should make banking and related stocks more attractive. Though, consistent with an efficient market, the opportunity to reap excess returns in this regard quickly disappears. The positive shock to AP is translated into a negative and statistically significant initial impact for financial sector returns. The effect remains negative and significant through h = 1 before dying off by the second month after the shock. Therefore, positive inflation shocks have a negative contemporaneous effect on the performance of the financial sector firms and this response persists for one full month. Unexpected inflation may reduce the stock returns of financial institutions and financial services firms because of the detrimental effects of inflation on corporate and personal balance sheets. Even though unexpected inflation lowers the real cost of borrowing, the shock also lowers the real return from lending so that lenders find a given loan to be less profitable. In addition, the shock also raises the cost of goods and services. If higher input costs for businesses and higher consumption costs for households are such that cash flows are sufficiently hindered or that probability of default significantly rises, then financial companies may be 'punished by Wall Street' in the wake of an inflation shock.
 
A monetary policy shock reduces financial sector returns having a significant initial impact effect that continues to affect returns for around 2 months. Unexpected changes in economic growth have a positive initial impact effect but show no persistence. An inflation shock is associated with a negative and statistically significant initial impact effect which lasts for up to 1 month after the time of shock. The financial sector responds immediately to an unanticipated increase in risk but the effect does not persist into the future.
 
Chordia and Swaminathan (2002) find that trading volume is a significant determinant of the lead-lag patterns observed in stock returns. Daily and weekly returns on high volume portfolios lead returns on low volume portfolios, controlling in favor of firm size. Autocorrelations does not explain the findings that are non synchronous trading or low volume portfolio. The pattern arises because returns on low volume portfolios respond more slowly to information in market returns. The speed of adjustment of individual stocks verifies these findings. In general, the outcome indicates that differential speed of adjustment to information is a significant source of the cross-autocorrelation patterns in short-horizon stock returns.#p#分頁標題#e#
 
Chordia and Swaminathan (2002) find that this is the closely related to the positive autocorrelations in portfolio returns are due to positive cross-autocorrelations amongst individual security returns. Chordia and Swaminathan (2002) results show that trading volume has significant information about cross-autocorrelation patterns outside that contained in firm size. The reason that lead lag patterns do not get arbitrage because of the high transaction costs that any trading strategy designed to exploit these short horizon patterns would face.
 
Chen, Roll and Ross (1986) tests whether innovations in macroeconomic variables are risks that are rewarded in the stock market. The theory suggests that the following macroeconomic variables should systematically affect stock market returns: the spread between long and short interest rates, expected and unexpected inflation, industrial production, and the spread between high- and low-grade bonds. Chen et al (1986) find that these sources of risk are significantly priced. in addition, neither the market portfolio nor aggregate consumption are priced independently. Chen et al (1986) also find that oil price risk is not separately rewarded in the stock market.
 
Chen et al (1986) theory is clear that all economic variables are endogenous in some ultimate sense. Simply natural forces, such as supernovas, earthquakes, and the like, are truly exogenous to the world economy, but to base an asset-pricing model on these systematic physical factors is well beyond our current abilities. Chen et al (1986) goal is just to model equity returns as functions of macro variables and non equity asset returns. Therefore Chen et al (1986) would take the stock market as endogenous, relative to other markets. With the diversification argument that is understood in capital market theory, merely general economic state variables would influence the pricing of large stock market aggregates. Some systematic variables that affect the economy's pricing operator or that influence dividends would also influence stock market returns. In addition, any variables that are necessary to complete the description of the state of nature would also be part of the description of the systematic risk factors. Chen et al (1986) have proposed a set of relevant variables and they now identify their measurement and obtain time series of unanticipated movements. They could continue by identifying and estimating a vector autoregressive model in an attempt to use its residuals as the unexpected innovations in the economic factors. It is, though, more interesting and perhaps vigorous out of sample to utilize theory to find single equations that can be estimated directly.
 
Chen et al (1986) study the relation between non-equity economic variables and stock returns. Though, because of the smoothing and averaging characteristics of most macroeconomic time series, in short holding periods, such as a single month, these series cannot be predictable to capture all the information available to the market in the same period. Stock prices, on the other hand, react very quickly to public information. The effect of this is to assurance that market returns would be, at best, weakly related and very noisy relative to innovation in macroeconomic factors. This should bias the results in favor of finding a stronger linkage between the time-series returns on market indices and other portfolios of stock returns than between these portfolio returns and innovations in the macro variable.#p#分頁標題#e#
 
McQueen and Roley (1993) explain that after allowing for different stages of the business cycle, a stronger relationship between stock prices and news is evident. In adding up to stock prices, McQueen and Roley (1993) examine the effect of real activity news on proxies for expected cash flows and equity discount rates. McQueen and Roley (1993) find that when the economy is strong the stock market react negatively to news about higher real economic activity. This negative relation is because of the larger increase in discount rates relative to expected cash flows.McQueen and Roley (1993) study whether the response of stock prices to macroeconomic news varies over different stages of the business cycle. By allowing the response to differ over different states of the economy, McQueen and Roley (1993) can test the good news/bad news story and suggest unbiased estimates of the effects of fundamental information about the economy. Economic announcements affect daily share price movements if the new information exposed by announcements affects either expectations of future dividends or discount rates or both.
 
McQueen and Roley (1993) need not expect that real economic activity surprises would affect cash flows and discount rates in the similar way across different states of the economy. As consequences, stock prices may well react differently to surprises of this nature, depending on whether the economy is operating below capacity. When the economy is booming, a real economic activity surprise could result in a larger increase in discount rates than cash flows, causing stock prices to fall. In this case, high capacity utilization and employment may limit further increases in output and, therefore, cash flow in the absence of new investment in plant and equipment.
 
McQueen and Roley (1993) also consider possible asymmetric effects of announcement surprises other than those related to real economic activity. This other economic information is, though, less closely related to the possible business-conditions effects. The announcements McQueen and Roley (1993) consider are for foreign trade, inflation, and money.
 
McQueen and Roley (1993) used daily percentage changes in the closing value of the S&P 500 Index to estimate the reaction of stock prices to new macroeconomic information. For economic announcements occurring either before or while the stock market is opens, they use the percentage change in the index from the previous business day's closing price to the closing price on that day. For announcements made after the stock market is closed, they use the percentage change in the index from that day's closing quote to the next business day's closing quote. Throughout the sample, the stock market closed at 4:00 P.M. EST. McQueen and Roley (1993) use EST for all closing and announcement times.McQueen and Roley (1993) observe the impact of new economic information on stock prices, interest rates, and other discount rate proxies without conditioning on the state of the economy. The outcomes for interest rates, the term spread, and the default spread are useful because they provide evidence that economic announcements contain relevant information for financial markets. To consider the role expected cash flows play in the stock market's response, McQueen and Roley (1993) estimate autoregressive models of announced and expected industrial production growth. McQueen and Roley (1993) use autoregressive models to account for the autocorrelation of industrial production growth. McQueen and Roley (1993) allow the effect of lagged industrial production to vary over different economic states because of the observed asymmetric behavior of real activity over the business cycle.#p#分頁標題#e#
 
McQueen and Roley (1993) provide evidence that the stock market's reaction to macroeconomic news depends on the state of the economy. In particular, news of higher-than-expected real activity when the economy is already strong results in lower stock prices, while the same surprise in a weak economy is associated with higher stock prices. This result helps to explain the insignificance of macroeconomic news, apart from monetary information. The source of the varying response of stock prices across economic states appears to be expected cash flows. The responses of equity discount rate proxies to new economic information are not significantly different across economic states. In contrast, unanticipated increases in economic activity in a weak economy raise expectations about future economic activity and cash flows. This same information in a strong economy does not lead to higher expected cash flows.
 
Carlton (1983) goal of this research is to measure how futures trading in existing contracts changes in response to changes in uncertainty caused by inflation. A Several different measures for PI (a measure of inflation that affects uncertainty) were used to reflect different beliefs about how inflation causes uncertainty. The different measures of PI that were used are (a) inflation and its absolute value, (b) inflation squared, (c) the absolute deviation of inflation from a four-year average, and (d) the absolute deviation of inflation from an ARIMA model of inflation estimated in levels (with one or two lags) and in first differences (one lag). The first two measures reflect the idea that inflation generates uncertainty about prices whereas the last two measures reflect the belief that it is only unexpected inflation that generates uncertainty about prices. Carlton (1983) examined the important link between uncertainty created by inflation and the volume of futures trading. The interrelationship between different futures markets was examined and it was shown that interrelationship could be used to analyze the likelihood of various futures markets dying.
 
Chordia, Roll,and Subrahmanyam (2001) liquidity and trading activity are significant features of financial markets, yet little is known about their evolution over time or about their time series determinants. A better understanding of these determinants should increase investor assurance in financial markets and thus enhance the usefulness of corporate resource allocation.
 
Existing literature usually has treated liquidity as a fixed property of an individual stock. In contrast to the existing literature, Chordia, Roll,and Subrahmanyam(2001) has study liquidity and trading activity for a comprehensive sample of NYSE-listed stocks over an 11-year period. Aggregate market spreads, depths, and volume are yet more volatile than returns. Daily changes in these variables are negatively in succession correlated. There has been a secular downtrend in spreads and an upward trend in depth and volume, although there have been major expeditions around these trends and at least one important structural break, when the minimum tick size was reduced from one-eighth to one-sixteenth in mid-1997. Chordia, Roll,and Subrahmanyam(2001) come across that liquidity and trading activity are influenced by several factors. Based on theoretical paradigms of price formation (inventory and asymmetric information) and on intuitive reasoning, they selected candidates as possible determinants. The explanatory variables contain short- and long-term interest rates, default spreads, market volatility, recent market movements, and indicator variables for the day of the week, for holiday effects, and for major macroeconomic announcements. Equity market returns and recent market volatility persuade liquidity and trading activity. Short-term interest rates and the term spread significantly have an effect on liquidity as well as trading activity. There are physically powerful day-of-the-week regularities in liquidity and in trading activity. A mostly intriguing result is the asymmetric response of bid-ask spreads to market movements. Both quoted and effective spreads increase severely in down markets, but decrease only marginally in up markets. In fact, the down-market variable is the most significant one in their analysis. In addition, contrary to intuition, recent market volatility tends to decrease spreads. Even though informal speculation about these results is possible, a formal theoretical investigation of this result would be desirable. Trading activity and market depth increase prior to scheduled macroeconomic announcements of GDP and the unemployment rate, whereas they fall back toward normal levels on the announcement day itself. This is consistent with bigger trading induced by differences of opinion prior to the announcement, which, being conducted by uninformed traders, is accommodated by dealers offering greater depth. The depth pattern would also be reliable with an increase in the number of informed traders as the announcement day approaches. The determinants investigated here explain among 18 and 33 percent of daily changes in liquidity and trading activity. Though, the sample period here, 1988 to 1998 inclusive, is a relentless bull market. It seems likely that liquidity and trading activity might behave differently in a bear market. Rising markets attract more investors and there is indeed sufficient evidence of steadily increasing liquidity over the past decade. Prolonged bear markets, on the other hand, could be subject matter to falling liquidity. Even though liquidity levels could vary with market trends, the determinants of day-to-day changes in liquidity are probably the same in most environments, though their explanatory power might very well fluctuate. If macro-variables foresee economic downturns, they might also foresee lower liquidity and trading activity in equity markets.#p#分頁標題#e#
 
CHAPTER-3
 
THEORETICAL FRAMEWORK 理論框架
 
ARIMA technique is applied on the 68 months period data of KSE-100 index trading volume and macro economic variables to test the hypothesis that “Consumer price index(CPI), Wholesale price index (WPI) and interest rate has non-significant association with the KSE 100 index trading volume”In the pre October(1979) Surprises in announced values of the CPI, industrial production, the unemployment rate, and the discount rate are statistically unrelated to stock prices during this period, at least on announcement days. In the subsample beginning in October 1979, discount rate changes also have significant effects. Second, only limited evidence supports the view that either inflation or real economic activity surprises affect stock prices. In the pre- October 1979 subsample, PPI surprises have significant effects on the day of an announcement, but they are estimated to be offset by the end of one week. In both subsamples, surprises in announced levels of real economic activity have no significant impact on stock prices on the day of an announcement. Smirlock (1986) find a significant positive response of long-term rates to unexpected inflation. Smirlock finding that long-term rates rise in response to unanticipated inflation in the post-79 periods complements and expand the growing body of literature that has study the effect on financial markets of macro-economic information announcements and provides further support and evidence that a wide range of macroeconomic variables affect many different financial securities and markets. Taken by Smirlock (1986) results do not allow bias between the competing hypotheses in explaining the response of interest rates to unexpected inflation and money surprises. The differential response of long rates and short rates to CPI surprises combined with the relatively large price change associated with long- term bonds to inflation surprises seems to suggest market participants revise their inflation expectations. Accordingly this may lead one to conclude that the expected inflation hypothesis is valid. Such a conclusion however is tenuous. As it was noticed that an efficient market also implies that there should be no response of interest rates in the post-announcement period. Since the anticipated component of inflation announcements did not affect interest rates, to examine the speed of adjustment.
 
Same model was used by Carlton (1983) for examining the effects of such announcement on financial markets. Carlton (1983) examined the important link between uncertainty created by inflation and the volume of futures trading. The interrelationship between different futures markets was examined and it was shown that interrelationship could be used to analyze the likelihood of various futures markets dying.
 
Thus econometric studies involving economic variables are hard to interpret for two reasons: first, the coefficients of regressions show slight about causal relations even when the variables are observable; and second, the variables are subject to lots of measurement error, and the measurement errors are probably related to the true values of the variable.#p#分頁標題#e#
 
Jain(1988) Announcements about economic variables may also influence trading volume if the market participants rebalance their portfolios based on new information
 
CHAPTER-4
 
RESEARCH METHOD 研究方法
 
Data collection
 
The sample period used in this study covers 68 monthly observation beginning from January 2004 to august 2009. The monthly trading volume of KSE-100 index data was obtained from Karachi stock exchange and monthly macro economic variables data was obtained from Federal bureau of statistics.
 
Methodological model
 
Following model is used to find the relationship between macroeconomic variables” and KSE-100 index trading volume. And to test the hypothesis that “Macro economic variables has significant association with the KSE-100 index trading volume”.
 
In its simplest form, an ARIMA model is typically expressed as:Melard's algorithm was used for estimation.
 
Interpretation of econometrical findings:
 
The parameter estimates table provides estimates of the model parameters and associated significance values, including both the AR and MA orders as well as any predictors
 
The parameter representing the Non- seasonal moving-average component (labeled AR1) is significant. Today's trading volume is effected by the yesterday's trading volume and this effect is highly significant.
 
The variable representing the monthly CPI, WPI and KIBOR are non-significant i-e .935,.607,.784 respectively. ARIMA model, since model is integrated of order 0 so as it becomes the ARMA model (1,0)
 
The ARMA parameter estimate and the regression parameter estimate are asymptotically uncorrelated.The sign of the correlation coefficient indicates the direction of the relationship (positive or negative).In the pre October(1979) Surprises in announced values of the CPI, industrial production, the unemployment rate, and the discount rate are statistically unrelated to stock prices during this period, at least on announcement days. In the subsample beginning in October 1979, discount rate changes also have significant effects. Second, only limited evidence supports the view that either inflation or real economic activity surprises affect stock prices. In the pre- October 1979 subsample, PPI surprises have significant effects on the day of an announcement, but they are estimated to be offset by the end of one week. In both subsamples, surprises in announced levels of real economic activity have no significant impact on stock prices on the day of an announcement. Smirlock (1986) find a significant positive response of long-term rates to unexpected inflation. Smirlock finding that long-term rates rise in response to unanticipated inflation in the post-79 periods complements and expand the growing body of literature that has study the effect on financial markets of macro-economic information announcements and provides further support and evidence that a wide range of macroeconomic variables affect many different financial securities and markets. Taken by Smirlock (1986) results do not allow bias between the competing hypotheses in explaining the response of interest rates to unexpected inflation and money surprises. The differential response of long rates and short rates to CPI surprises combined with the relatively large price change associated with long- term bonds to inflation surprises seems to suggest market participants revise their inflation expectations. Accordingly this may lead one to conclude that the expected inflation hypothesis is valid. Such a conclusion however is tenuous. As it was noticed that an efficient market also implies that there should be no response of interest rates in the post-announcement period. Since the anticipated component of inflation announcements did not affect interest rates, to examine the speed of adjustment.#p#分頁標題#e#
 
The correlation coefficients on the main diagonal are always 1.0, because each variable has a perfect positive linear relationship with itself.
 
Hypothesis summary
 
Hypothesis Regression significance Empirical
 
Coefficient β value Conclusion
 
CPI causes the trading volume -.007 .935 Rejected
 
WPI causes the trading volume -.040 .607 Rejected
 
Interest rate causes the trading volume -.061 .784 Rejected
 
CHAPTER-5
 
RESULTS 結果
 
ARIMA results are statistically non-significant; it means that macroeconomic variables have no cause on KSE 100 Index trading volume.
 
Black(1986) econometric studies involving economic variables are hard to interpret for two reasons: first, the coefficients of regressions show slight about causal relations even when the variables are observable; and second, the variables are subject to lots of measurement error, and the measurement errors are probably related to the true values of the variable. Pearce and Roley (1985) indicates that it is practical to view discount rate changes as unanticipated, at least in terms of timing and perhaps also in terms of magnitude. The economic news events are considered announcements on the money stock, inflation, real activity, and the discount rate. An unanticipated increase in money, inflation, and the discount rate may be viewed as bad news for the stock market and thus should be followed by a fall in stock prices. McQueen and Roley (1993) provide evidence that the stock market's reaction to macroeconomic news depends on the state of the economy. In particular, news of higher-than-expected real activity when the economy is already strong results in lower stock prices, while the same surprise in a weak economy is associated with higher stock prices. This result helps to explain the insignificance of macroeconomic news, apart from monetary information Existing literature usually has treated liquidity as a fixed property of an individual stock. In contrast to the existing literature, Chordia, Roll,and Subrahmanyam(2001) has study liquidity and trading activity for a comprehensive sample of NYSE-listed stocks over an 11-year period. Aggregate market spreads, depths, and volume are yet more volatile than returns. Daily changes in these variables are negatively in succession correlated. There has been a secular downtrend in spreads and an upward trend in depth and volume, although there have been major expeditions around these trends and at least one important structural break, when the minimum tick size was reduced from one-eighth to one-sixteenth in mid-1997. Chordia, Roll,and Subrahmanyam(2001) come across that liquidity and trading activity are influenced by several factors. Based on theoretical paradigms of price formation (inventory and asymmetric information) and on intuitive reasoning, they selected candidates as possible determinants. The explanatory variables contain short- and long-term interest rates, default spreads, market volatility, recent market movements, and indicator variables for the day of the week, for holiday effects, and for major macroeconomic announcements. Equity market returns and recent market volatility persuade liquidity and trading activity. Short-term interest rates and the term spread significantly have an effect on liquidity as well as trading activity. There are physically powerful day-of-the-week regularities in liquidity and in trading activity. A mostly intriguing result is the asymmetric response of bid-ask spreads to market movements. Both quoted and effective spreads increase severely in down markets, but decrease only marginally in up markets. In fact, the down-market variable is the most significant one in their analysis. In addition, contrary to intuition, recent market volatility tends to decrease spreads. Even though informal speculation about these results is possible, a formal theoretical investigation of this result would be desirable. Trading activity and market depth increase prior to scheduled macroeconomic announcements of GDP and the unemployment rate, whereas they fall back toward normal levels on the announcement day itself. This is consistent with bigger trading induced by differences of opinion prior to the announcement, which, being conducted by uninformed traders, is accommodated by dealers offering greater depth. The depth pattern would also be reliable with an increase in the number of informed traders as the announcement day approaches. The determinants investigated here explain among 18 and 33 percent of daily changes in liquidity and trading activity. Though, the sample period here, 1988 to 1998 inclusive, is a relentless bull market. It seems likely that liquidity and trading activity might behave differently in a bear market. Rising markets attract more investors and there is indeed sufficient evidence of steadily increasing liquidity over the past decade. Prolonged bear markets, on the other hand, could be subject matter to falling liquidity. Even though liquidity levels could vary with market trends, the determinants of day-to-day changes in liquidity are probably the same in most environments, though their explanatory power might very well fluctuate. If macro-variables foresee economic downturns, they might also foresee lower liquidity and trading activity in equity markets.#p#分頁標題#e#
 
CHAPTER-6
 
CONCLUSION 結論
 
This study has examined the monthly trading volume of KSE 100 index response from the beginning of January 2004 to august 2009 to announcements of Macro economic variables. ARIMA results support the hypothesis that “Consumer price index (CPI) have non-significant association with the KSE 100 index trading volume”, “Wholesale price index (WPI) have non-significant association with the KSE 100 index trading volume”, “Interest rate also have non-significant association with the KSE 100 index trading volume” but the non-seasonal lags (AR1) of trading volume causes the trading volume of KSE 100 index.
 
A Several different measures for PI (a measure of inflation that affects uncertainty) were used to reflect different beliefs about how inflation causes uncertainty. The different measures of PI that were used are (a) inflation and its absolute value, (b) inflation squared, (c) the absolute deviation of inflation from a four-year average, and (d) the absolute deviation of inflation from an ARIMA model of inflation estimated in levels (with one or two lags) and in first differences (one lag). The first two measures reflect the idea that inflation generates uncertainty about prices whereas the last two measures reflect the belief that it is only unexpected inflation that generates uncertainty about prices.
 
Future research can be done to check the relationship of macroeconomic variables with stock prices and stock returns or with different financial markets like Bond market or foreign exchange market.