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Impact of CPI on Stock Return

Info: 2358 words (9 pages) Example Literature Review
Published: 25th Jan 2022

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Tagged: Economics


The purpose of this study was to see the relationship between the Stock return of KSE-100 index and Consumer price index (CPI).

The well-organized stock market mobilizes the savings and activates the investment projects, which lead to economic activities in a country. The movements in the stock prices are an important indicator of the economy.

It is usually considered that financial markets respond to announcements about economic variables such as money supply, consumer price index (CPI), wholesale price index (WPI), Producer price index (PPI), unemployment rate, discount rate, industrial production etc.

Previous studies have examined the financial markets reaction to these announcements. But this study was only concerned with the hypothesis that change in CPI has significant association with the change in Stocks return. This study has shown that there is a negative relationship between consumer price index and KSE -100 index trading volume and the results were significant. Now we will see the relationship between the stock return and the inflation rate. This study is helpful for investors in decision making about changing their investment portfolios around these announcements (http://iqra.academia.edu/drsubhani/papers).

Consumer Price Index (CPI)

Consumer price index reflects changes in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly (http://www.investopedia.com).

Stock Return

Stock return is the ratio of the present year’s KSE-100 index and previous year’s KSE-100 index. It I calculated as:

Ln (St/St-1)

Where St is the KSE-100 index of present year and KSE-100 index of previous year.


This study mainly focuses on the impact of CPI that is the inflation on the stock returns.


The hypothesis shall be tested is as follow:

H0 : CPI has no effect on the stock returns

H1 : CPI has an effect o the stock returns.


Previous studies have examined the stock market re action to announcements about economic variables.

Schwert (1981) examined the everyday returns to the S & P composite portfolio around the C.P.I. announcement dates from 1953 -78 and found that stock market responds negatively to the announcement of unanticipated inflation in the CPI.

Schwert (1989) reported that there are at least three theories that predict a positive relation between volatility and volume. First if investors have heterogeneous beliefs, new information causes both the price changes and the trading. Second, if some investors use price movements as information on which to make trading decisions, large price movements cause large trading volume. Finally, if there is short -term “price pressure” due to illiquidity in secondary trading markets; large trading volume that is predominantly either buy or sell orders cause price movements.

Schwert (1989) results have shown a positive relation between stock volatility and trading activity and results supported the proposition that stock market volatility is higher when trading activity is higher and there was little evidence that financial volatility helps to predict future trading volume growth, except for stock volatility from 1920 to 1952.

Whereas, using hourly data Jain (1988) found that CPI announcement surprises have significant negative effects on stock prices and trading volume was not associated with surprises in the CPI announcements and the results were consistent with the hypothesis that market participants interpret the surprises in announcements in an analogous manner and do not engage in additional trading.

Castanias (1979) reported that the variance of stock prices rises around the days of most economic news events, which Castanias (1979) interpreted as a reflection of new information appearing. Using daily data Pearce and Roley (1985) did not find an association between surprises in consumer price index (CPI) announcements and stock market reaction. By using monthly data Chen, Roll and Ross (1986) found that inflation related variables w ere highly significant in the 1968-77 periods and insignificant both earlier and later. Carlton (1983) reported that the inflation has a tremendous negative effect on volume traded. It appeared that the level of inflation, rather than the unanticipated component of inflation, was more significantly correlated with volume traded. A related reason for a decline in trading as a result of increasing inflation has to do with the different types of the commodity that are deliverable on the futures market.

Smirlock (1986) found a significant positive response of long -term rates to unexpected inflation.

Smirlock (1986) reported that the unanticipated component of the announced change in both the PPI and the CPI have an immediate positive effect on long -term rates in the post-79 period, but no effect in the pre-79 period.

Using daily prices of indexed bonds Huberman and Schwert (1985) found that about 85 percent of the reaction of bond prices to unexpected inflation occurred contemporaneously with the sampling of individual commodity prices from 2 to 6 weeks prior to the announcement. The remaining 15 percent of the reaction to unexpected inflation occurred on the day following the announcement. Black (1986) stated that the noise makes trading in financial markets feasible and therefore allows traders to monitor prices for financial assets. Noise causes markets to be inefficient to some extent but often prevents traders from taking benefit of inefficiencies. In Black (1986) model of financial markets, noise was compared with information. Traders at times trade on information in the common way. Traders are correct in anticipating making profits from these types of trades. On the other hand traders at times trade on noise like if it were information.

If traders anticipate making profits from the noise trading then it is incorrect. Though, noise trading is important to the existence of liquid markets. Black (1986) further stated that an individual with information or insights regarding individual firms like d to trade but realize that only another individual with information or insights get to the other side of the trade. From the viewpoint of someone who knows what both the traders know one side or the other must be making a mistake. In other words, it does not make sense to make a model with the information trading but no noise trading where the traders have diverse beliefs and beliefs of one trader are as good as any other trader’s beliefs. Dissimilarities in beliefs should derive finally from differences in information. A trader with an exceptional piece of information knows that the other traders have their own exceptional pieces of information, and for that reason traders do not automatically rush out to trade. Black (1986) further mentioned that there was always a lot of vagueness regarding who is the noise trader and who is the information trader. Noise creates the possibility to trade profitably, but at the similar time makes it hard to trade profitably. Kandel,

Ofer and Sarig (1993) found that the variance of the inflation anticipation errors declines with trading days in the period examined. The decrease in the variance indicates that traders learn by frequently observing prices around the distribution of other traders’ information.

Kim and Verrecchia (1991) stated that the traders achieve their most favorable portfolios prior to the announcement through trading on what each one knows in the preannouncement period.

Announcements change the traders’ viewpoint and induce the traders to enter in a new round of trade. It is believed that traders are diversely informed and vary in the precision of their personal prior information hence traders respond in a different way to the announcement and it leads to the positive volume. When the new public information is released in period all traders revise their beliefs, and this revision is reflected in the change in market price. Relatively better informed traders revise their beliefs less because the new information is relatively less important to those traders than to those who are more poorly informed. The presence of differential precision thus causes differential belief revision among traders, which in turn creates trading volume.

Huang (2008) investigated the impact of US economic news on German stock index futures and compared it with the impact of domestic German news. Huang (2008) found that US economic news affects German stock futures on multiple dimensions including prices, trading volume, volatility, quoted spreads, inventory holding costs and the informational role of trading.

Huang (2008) investigated the impact of 17 types of German economic news announcements and 24 types of US economic news. For most US announcements Huang (2008) sample covered the entire investigation period from 1991 to 2005. Huang (2008) findings strongly suggested that German traders actively form private opinions about the implications of US economic news rather than just ‘free riding’ on the US market’s response (http://iqra.academia.edu/drsubhani/papers).


Data and Variable

The data used in this research paper is secondary data and gathered from different sources. A monthly data from the month of January 2002 to month of April 2011, 118 months has been taken. There are two variables that have been taken are CPI and stock return in order to show the relationship, one is Karachi’s stock return and another is Consumer price index which is a very important macro economic indicator. Consumer price index is independent variable where as stock returns are dependent.

Theoretical Framework

Following model was used to find the relationship between CPI “Consumer price index” and stock returns and to test the hypothesis that Change in CPI has an insignificant association with the change in stocks return.

Stock return = α + β (CPI) + e

Where Stock return = Monthly Stocks return, CPI = Monthly CPI “Consumer price index” and the coefficients α and β are regression parameters for the independent variable and e is the error.

Dependent Variable

Stock returns

Independent Variable

Consumer Price Index (CPI)Model

Simple linear regression model is used to analyze the impact of CPI on the Stock returns.


Model Summary



R Square

Adjusted R Square





a. Predictors: (Constant), CPI


The results show that the value of co-efficient of determination, R2 is equal to 0.019 which shows that 1.90% of the variable impact on the CPI. The remaining 98.10% is unexplained.



Sum of Squares


Mean Square















a. Predictors: (Constant), CPI

b. Dependent Variable: Stock Return


The value of ANOVA table is highly insignificant (.136) which shows that the model is not a good fit.



Unstandardized Coefficients

Standardized Coefficients



Std. Error












a. Dependent Variable: StockReturn


The result shows that coefficient of CPI is highly insignificant and it has no impact on the stock returns. However the regression test proves the Ho, that there is no effect of CPI on the stock returns. Therefore,

Stock returns = .061 + .000(CPI)


The main purpose of this paper has been to study that is there any effect of inflation on the stocks return. We ran the linear regression model for two variables CPI and Stock returns and cover the year starts from the July 2001 to March 2011.

We summarize some of the key findings. First of all, the R2 obtained from the stock returns is not at all impacted or affected by the inflation. The result implies that there is no effect of CPI on the stock returns.

We found that the data is highly insignificant and the variables have an insignificant no impact on the stock returns.


After conducting the research and running the linear regression test on the basis of data of CPI and stock returns, we came up with the conclusion that there is no impact of CPI on the stock returns. However if we compare the KSE-100 index with the CPI then the result will be totally opposite as they are highly significant.










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