An Investigation into Media and Market responses, looking at the condition of The Economist Headline relationship with the FTSE 100 Stock Market index

George Gazzard

I7953605

I7953605@bournemouth.ac.uk

Abstract: My research will determine the effect that a relevant media source (The Economist) condition of headline has on Quarterly UK closing Stock prices (FTSE 100) and will test whether there is a relationship present. For this purpose we divide the political headline news into two categories (positive news and negative or neutral news.)

We find the empirical evidence in fact contradicts my hypothesis and suggests that there is no significant correlation existent solely between the FTSE100 closing quarterly index and the condition of the Quarterly Economist headline. It goes as far as disproving any correlation with my continuous variable, unemployment and only shows statistical significance with an already known variable, within the US stock market, the Dow Jones.

Keywords:FTSE 100, Stock Index, Dummy Variable, Confidence, Claimant Count, ONS, Swiss International Finance Group, Dow Jones

George Gazzard i7953605

1. Introduction: Basis to my research is to establish whether one media source, in this case The Economist, can be held significant in affecting the latter closing Stock Index of the UK FTSE 100. Previous work exist which show effects of media on Stock Return and demonstrate that positive media sentiment within company-specific news articles has a significant positive relationship with UK stock returns and vice versa. (Ferguson 2011.)1 Further, Cutler et al. (1989)2 is a prior empirical study that explores the relation between stock prices and media coverage. This investigation highlighted the difficulty in explaining the variance in the stock prices, as roughly, only half of the asset-price volatility was able to be explained by news about fundamentals. Having accounted for significant macroeconomic news, the authors established that news incorporating fundamentals can account for up to 30% of stock price movements. However, they also noted that some of the most significant market action took place on days with no significant news. I intend to be more specific in targeting solely the relationship The Economist demonstrates in affecting the FTSE and how the FTSE will affect the headline of The Economist.

Intentions of investigation Establish whether specific media headlines have an effect on the FTSE 100 stock price Build a model for determinates of FTSE 100 fluctuations

2. Data SetMy data set incorporates data from 2000 through to 2012 for:

Raw FTSE100 Monthly Closing index (dependant variable) FTSE100 Monthly Closing index percentage change Raw FTSE100 Quarterly Closing index FTSE100 Quarterly Closing index percentage change Creation of weekly/monthly/quarterly dummy variables which are relevant to the condition

of the weekly Economist headline (independent variable) The weekly/monthly/quarterly unemployment taken by the claimant count (continuous

variable) with relevant percentage changes

Data CollectionThe economist provided weekly Cover Pages for each issue dating from January 2nd 2000 to December 3 2012. Source; (The Economist 2000)3; from here I created simple on/off weekly dummy variables for each headline. This therefore allowed me to covert categorical data, shown through The Economist issue headlines, into numerical data so I could compare its relationship and effect on the FTSE100.

1 Nicky J. Fergusona, Jie Michael Guoa, Herbert Y.T. Lamb, Dennis Philipa. (Jan 1, 2011). Media Sentiment and UK Stock Returns. Available: http://www.dur.ac.uk/resources/dbs/faculty/working-papers/WP2011-06--MediaSentimentUKStockReturns.pdf. Last accessed 11th April 20132 Cutler, D. M., Poterba, J. M., and Summers, L. H., „What moves stock prices?‟, The Journal of Portfolio Management , Vol. 15 (3), 1989, pp. 4-12.3 The Economist. (2000). Current and Previous Issues. Available: http://www.economist.com/printedition/covers?print_region=76981&date_filter%5Bvalue%5D%5Byear%5D=2000. Last accessed 3/04/2013.

George Gazzard i7953605

The Different condition of Headline is represented by dummy variables;

‘1’ – Positive Economist Headline

‘0’ – Negative or Neutral Economist Headline

In order to try and prove any significance between the two data sets, I had to ensure both sets of data could be extrapolated to show a monthly and quarterly basis. This would also be a more realistic time period for any condition of The Economist Headline to have any effect on the closing monthly or quarterly FTSE 100 index. Further it will also allow effect where a small time lag would exist.

My Data for unemployment is derived from the Source; (Office of National Statistics 2000)4 and also spanned from 2000 to 2012. I took the quarterly administrative unemployment rates within the UK which were produced from by the ONS who used a more developed Claimant Count method. It will allow me to run multiple regression analysis.

FTSE100 Quarterly Historical data, dating back to 2000 provided by the Swiss International (Finance Group 2000)5 will act as my dependant variable. The Percentage change from quarterly closing FTSE prices was important to establish as we are measuring responsiveness of FTSE to headline condition, therefore the change will show us if there is a relationship between headline condition and closing FTSE. I utilised the closing unadjusted stock price monthly and quarterly as it may potentially allow for more effect from my independent variables.

HypothesisTan and Gannon (2002)6 state that the reaction of the closing stock index depends on the political

News - prices should increase if news leads to an increasing revision of investor’s expectation and similarly a downward movement if the investors respond to news in opposite way.

My prior knowledge leads me to propose that a relationship will exist between the condition of the Financial Times headline affecting the ensuing FTSE100 price. To test my hypothesis I shall provide evidence of whether any of my variables within my investigation have an effect on the FTSE100 index. This ultimately will provide me with a revised linear model of variables which are significant to affecting my dependant variable. I am aware other such variables may exist which have a correlation with the FTSE100 where Cowie,(2010)7 emphasizes the strength of inflation affecting the

4 Office of National Statistics. (). Unemployment statistics from 1881 to the present day. Available: https://www.google.co.uk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CDsQFjAB&url=http%3A%2F%2Fwww.ons.gov.uk%2Fons%2Frel%2Flms%2Flabour-market-trends--discontinued-%2Fjanuary-1996%2Funemployment-si. Last accessed 5/04/2013.

5 Swiss International Finance Group. (2000). investor tools-FTSE100 Historical Prices. Available: http://www.frankfurtstockexchange.de/investor-tools/world-stock-indices/symbol/2-indices/6-ftse.html?task=historical. Last accessed 6/04/2013.

6 Tan, O.G., & Gannon, G.L. (2002). Information Effect of Economic News: SPI futures.International Review of Financial Analysis, 11, 467-489.http://dx.doi.org/10.1016/S1057-5219(02)00065-07 Ian Cowie. (2010). Why rising inflation could push FTSE 100 through 6000. The Telegraph. 1 (1), 1.

George Gazzard i7953605

FTSE100, however my belief is The Economist media headline will have a significant correlation with the FTSE100.

Assumptions for regressions with time series dataWhen providing a monthly dummy variable from calculated weekly dummy variables I assume that a month with equal neutral/negative news ‘0’ to positive news ‘1’ is regarded as a positive month. This rational is based around my regard of British media and economists being largely pessimistic, who prove economic despair as the new norm. James Kirkup,( 2012)8 therefore any reference of a positive headline would surely outweigh the perceived value of a negative one.

Further I assume that one month time lag is significant to show changes in FTSE100 index resulting from a change in condition of The Economist Headline which is a large assumption considering it is a short time period to have any effect on market prices.

My model ignores the external effect of international stock market performances on the FTSE and essentially assumes that the main effect on the FTSE is UK media where analysts such as P. Inman (2013)9 highlight otherwise.

Gauss Markov Conditions:Assumption that the data is linear and Y is found by following the process of:

Y i=β0+β1X1 i+.. .+βk X ki+εi

Correlations

Dow Jones

Quarterly

Quarterly

Dummy Variable

Quarterly

Unemployment

Rate

Dow Jones Quarterly

Pearson Correlation 1 -.060 .217

Sig. (2-tailed) .676 .126

N 51 51 51

Quarterly Dummy Variable

Pearson Correlation -.060 1 -.183

Sig. (2-tailed) .676 .193

N 51 52 52

Quarterly Unemployment

Rate

Pearson Correlation .217 -.183 1

Sig. (2-tailed) .126 .193

N 51 52 52

8 James Kirkup. (2012). Sir Mervyn King is pessimistic. Is economic despair the new normal? . Politics. 1 (3), 1.

9 Phillip Inman & Dominic Rushe. (2013). Stock markets surge as Dow closes at all-time high. Business - Stock Markets. 1 (1), 2

George Gazzard i7953605

Table 2. Correlation between independent variables

Table 2 outlines the correlation between dependant variable and independent variables. Disproving my hypothesis the Quarterly FTSE100 price only shows a significant correlation level with the Dow Jones Quarterly.

There is no exact linear relationship between each independent variable with in my sample. The disturbance term has been proved having zero expectation where;

To prove my independent variables have no effect to disturbance within my model, the standardised residuals should have a mean of 0 (as shown below).

N MeanFTSE 100 Residuals 156 0.000

Visual inspection of Scatterplot 1 shows the residual plot of disturbance terms. They show no trend and are distributed evenly with a constant variance, so are not in violation of the homoscedasticity assumption.

Testing whether the values of the disturbance term hold independent distributions

George Gazzard i7953605

Scatter plot 1. Showing the disturbance term to be homoscedastic

Durbin- Watsons

d l du

Model

0.899 1.46 1.63 Positive autocorrelation in the residuals

E [εi ]=Cov [ X i , εi ]=0

The Durbin-Watson test will establish if there is any consistency present within the error terms (serial correlation) which would suggest there are in fact other variables which are not included in the model influencing the value. As my Durbin – Watson test revealed a 0.899 score. There is evidence of positive serial correlation. Small Durbin - Watson values in general indicate that successive error terms, on average, are close in value to each other or positively correlated. If however the score is larger than 2 successive error terms are much different on average in value from each other so have a negative correlation. In a regression, this can suggest underestimation in the level of statistical significance which undermines the value of my test.

The Shapiro-Wilk measures whether the disturbance terms are normally distributed. It is common for most time series studies to violate these criteria as often the value of the disturbance term in one observation is correlated with its value in its next. The results prove no significant correlation from one residual to the next in the FTSE100.

Least Squares Regression modelI present my regression model with the relevant variables in affecting the FTSE100;

FTSE100=β0+β1(DummyVariable)+β2Unemployment+β3(DowJones)+u

Statistical Results Table 5. Descriptive Statistics

Model R R Square Adjusted R Square

George Gazzard i7953605

Shapiro-Wilk

Statistic df Sig.

FTSE100_Residual .962 156 .000

N Minimum Maximum Mean Std. Deviation

Quarterly Unemployment

Rate52 4.60 8.50 6.0462 1.28909

Quarterly Dow Jones 51 7591.93 13895.63 10729.7945 1529.63036

Quarterly FTSE100 52 3567 6722 5349.02 804.954

1 .646a .417 .380

Therefore our least square regression can be shown as:

QuarterlyFTSE 100=( constant )+0 (Quarterly Dummy Variable )+0 (Quarterly Unemployment )+0.398 (Quar terly ClosingDow Jones )

FTSE100=0.398(Dow Jones)

Although, conclusively my regression model now appears very narrow in terms of the variables in question significance and thus effect on ‘y’ (FTSE index), I shall still provide an example where the effect of a 10 per cent increase in the closing index of the DowJones will mean a 43.7% rise in FTSE100 index.

FTSE100=0.398 (1.1 )=0.437

3. AnalysisAs the Quarterly Dummy Variable and Quarterly Unemployment Rate show significance levels of 0.887 and 0.889 respectively we reject them and eliminate them from our model. The Quarterly Dow Jones is therefore the only variable with a value that is statistically significant to the 95% confidence interval, with a value at 0. Shamah (2003) describes the correlation between the FTSE100 and the Dow Jones as happening to be one of the strongest within global markets10. The relation between quarterly DowJones and quarterly FTSE100 has proved that the Dow Jones is the only independent variable to accept the null hypothesis. This initial data, therefore proves that my hypothesis in assuming a relationship between my dummy variable and FTSE100 quarterly index to be incorrect

The creation of a dummy variable monthly and assumption in distinguishing the condition of The Economist headline with the correct dummy variable proved in effective in this study, as BJ Fogerty (1995)11 highlighted problematic areas when categorising dummy variables to the correct theme of

10 Shani Shamah. (2003). A foreign Exchange Priminister. Fundamental versus Technical Approaches. 1 (24.1), 159.

11 BJ Fogarty. (1995). Determining Economic News Coverage. The Explanations of Economic News Coverage. 1 (2), 4-8.

George Gazzard i7953605

Table 6. ParametersCoefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

(Constant) 1004.379 606.988 1.655 .105

Dow Jones Quarterly .398 .051 .758 7.812 .000

Quarterly Dummy Variable 22.429 156.306 .014 .143 .887

Quarterly Unemployment Rate 8.630 61.556 .014 .140 .889

a. Dependent Variable: QuarterlyFTSE100

the media outlet. The Dummy Variable I believe would have had more of a significance to the FTSE100, had the variables been calculated on an annual basis against the annual FTSE index. I assume there would be an existence of a greater correlation in the variables, with greater residuals in the data as with the increased time range.

Further, testing the percentage change annually of each of my variables would have caused a probable increase in the correlation between variables as it would show more of a fit to the economic business cycle as shown in Scatterplot 2. Assuming correlation and significance between variables on a quarterly and monthly basis was senseless as it did not provide a sufficient time lapse for The Economists headline to effect FTSE index.

James Kirkup (2012) highlighted how UK media is notoriously pessimistic leading to an unfair distortion in the real condition of the UK economy. This means that the headline of The Economist would never effectively provide as an indicator for the current performance in the domestic economy and thus the market – FTSE100.

Further nullifying my hypothesis the R squared figure which shows the value for how far the variation within Y (FTSE100) is as an outcome from the effect of the independent variables. It demonstrates a value of 0.380 which outlines that the effect of my independent variables is very insignificant in affecting the index of the FTSE100.

FTSE100 index is as shown by the scatterplot below, a cyclical cycle. (Papadopoulos 2011)12 studies show that a more appropriate variable that could have been used to correlate against FTSE prices would have been analysis in empirical data of discretionary fiscal policy. The government use these to influence the economy’s business cycle. Further this revised study could show what effect a policy has depending on the position in the trade cycle. Baum (2011)13 outlines that the influence of the policy measure will be strongly dependent upon the position of the trade cycle, potentially being an interesting variable in terms of effect on the trade cycle and thus the FTSE100.

12 Panagiotis Papadopoulos. (2011). Recovery of Financial Markets and Institutions and Challenges in post-crisis era. Evidence from the stock markets in USA and Europe. 1 (1), 5.13 Anja Baum & Gerrit B. Koester. (2011). The impact of fiscal policy on. evidence from a threshold VAR analysis. 1 (1), Non Technical Summary.

George Gazzard i7953605

Scatterplot 2. Monthly trend of the FTSE100

ConclusionThe investigation proved ineffective in establishing a relation between my dependent variable FTSE100 and independent variable Dummy Variable for The Economist headline. However from this we can draw some conclusions.

Markets do not have a significant reaction to the condition of media headlines. Although market confidence can be influenced by media our findings show that it has little effect. A reason I give to this little significance between Headlines and markets is that The Economist choose their headlines to entice readers to continue reading, therefore increasing revenue sales. However we have shown how markets do react to each other. The Dow Jones influences the FTSE100 index due to contagion between markets. This shows how market confidence is derived from foreign markets. Speculation will push the FTSE100 price down if the Dow Jones falls. Also factors effecting US business’ may also be affecting UK business’ as globalisation links markets, as world demand fluctuations influence price and supply shocks are universal.

George Gazzard i7953605

ReferencesBaum, Anja & Koester, Gerrit B. r. (2011). The impact of fiscal policy on. evidence from a threshold VAR analysis. 1 (1), Non Technical Summary.

Cowie, Ian . (2010). Why rising inflation could push FTSE 100 through 6000. The Telegraph. 1 (1), 1.

Cutler, D. M., Poterba, J. M., and Summers, L. H.(1989),What moves stock prices?‟, The Journal of Portfolio Management , Vol. 15 (3), pp. 4-12.Ferguson Nicky J. a, Jie Michael Guoa, Herbert Y.T. Lamb, Dennis Philipa. (Jan 1, 2011). Media Sentiment and UK Stock Returns. Available: http://www.dur.ac.uk/resources/dbs/faculty/working-papers/WP2011-06--MediaSentimentUKStockReturns.pdf. Last accessed 11th April 2013

Fogarty, BJ y. (1995). Determining Economic News Coverage. The Explanations of Economic News Coverage. 1 (2), 4-8.

Inman, Phillip & Rushe, Dominic. (2013). Stock markets surge as Dow closes at all-time high. Business - Stock Markets. 1 (1), 2

Kirkup, James (2012). Sir Mervyn King is pessimistic. Is economic despair the new normal? . politics. 1 (3), 1.

George Gazzard i7953605

Panagiotis Papadopoulos. (2011). Recovery of Financial Markets and Institutions and Challenges in post-crisis era. Evidence from the stock markets in USA and Europe. 1 (1), 5.

Shamah, Shani. (2003). A foreign Exchange Priminister. Fundamental versus Technical Approaches. 1 (24.1), 159.

Tan, O.G., & Gannon, G.L. (2002). Information Effect of Economic News: SPI futures.International Review of Financial Analysis, 11, 467-489.http://dx.doi.org/10.1016/S1057-5219(02)00065-0

Data Sources

Source. The Economist. (2000). Current and Previous Issues. Available: http://www.economist.com/printedition/covers?print_region=76981&date_filter%5Bvalue%5D%5Byear%5D=2000. Last accessed 3/04/2013.

Source. Office of National Statistics. (-). Unemployment statistics from 1881 to the present day. Available: https://www.google.co.uk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CDsQFjAB&url=http%3A%2F%2Fwww.ons.gov.uk%2Fons%2Frel%2Flms%2Flabour-market-trends--discontinued-%2Fjanuary-1996%2Funemployment-si. Last accessed 5/04/2013.

Source. Swiss International Finance Group. (2000). investor tools-FTSE100 Historical Prices. Available: http://www.frankfurtstockexchange.de/investor-tools/world-stock-indices/symbol/2-indices/6-ftse.html?task=historical. Last accessed 6/04/2013.

George Gazzard i7953605