Celebrity Twitter Portfolio

Nitesh Patel (20008726)

4

Executive Summary1. Research PurposeThe aim of this paper is to construct an alternative investment product based on celebrity tweets and the subsequent assessment of its performance. Over the course of this study, I will look to answer: How does the product perform in comparison to the benchmark and market portfolio and what conclusions can be drawn from the evaluation.2. Research DesignThe first steps to preparing the data; I chose a sample of celebrity Twitter users. Researching and find the most followed users; I then eliminated those whose data were unattainable, or not suitable, whilst also looking at finding a varied sample of users who are widely followed. The celebrities Twitter history is then retrieved and filtered for the mentioning of S&P500, FTSE350 and the Top 500 Global Brands. A portfolio is then created based upon the filtered tweets, and the portfolio performance is assessed using simple risk and return measures, as well as advanced relative risk measures. 3. FindingsA total of 173 different trade positions were identified and held, which consisted of 50 different companies. The strategy is long-only, with each trade identified by the strategy, being held for 52 weeks (1 year), and then sold. The subsequent performance assessment reveals that the product is profitable and outperforms its benchmark in many aspects. The reward to risk relation is very positively in favour of the created portfolio.4. Research LimitationsFirst of all the study carried out, is comparatively small in sample period, due to limited data availability. I can retrieve circa 3200 tweets from any Twitter user, however if they frequently tweet this may shorten the time horizon for that individual. Additionally the social media platform did not have widespread use until 2009/2010. The filtering system relies on the list input, and therefore many companies will be omitted. Also the Twitter users may mis-spell or spell differently the company which will mean their tweet doesnt pass the filter. 5. Research Implications (Theoretical & Practical)The performance assessment of the portoflio created in this study, provides abnormal returns. This is a good starting step, for further research, to improve the reliability and validity of the research carried out thus far. Further data can be mined and analysed to help improve the post-execution performance evaluation.6. Originality/ValueThis paper is the first to attempt to create a trading strategy and portfolio which is based solely upon celebrities mentioning the use of, endorsing or posting positively about a product, service or company. Previous studies have looked at financial experts influence on Twitter, on general moods amongst the public to predict the market, or posititvity to predict box office opening weekend outcomes, etc. This study shows signs of positive performance using the strategy formulated. In line with many studies carried out before, this study also shows a positive relationship between the activity on Twitter and stock returns.

Table of ContentsExecutive Summary21.Introduction42.Study Outline43.Data54.Portfolio Creation65.Product Assessment75.1Test for normality75.2. Return85.3Risk105.4Higher moments of return distribution105.5Correlation115.6. Capital Asset Pricing Model125.7. Advanced Absolute Risk Measures145.8. Advanced Relative Risk Measures156. Discussion and Conclusion16Sources17Appendix18

TablesTable 1: Celebrities used, and their corresponding number of followers and tweets.5Table 2: The final set of events which are used to create the portfolio6Table 3: The Jarque-Bera scores for the portfolios8Table 4: Standard Deviation of the Excess Return of the Portfolio. Weekly and Yearly Comparison.9Table 5: Portfolio skewness and kurtosis10Table 6: Advanced Absolute Risk Measures for Portfolio and MSCI World Index13Table 7: Advanced Relative Risk Measures for Portfolio and MSCI World14

FiguresFigure 1: Histograms displaying the return distribution and the corresponding skewness and kurtosis7Figure 2: Arithmetic Mean Excess Return of the portfolios8Figure 3: Arithmetic Mean Return of the portfolios8Figure 4: Standard Deviation of the Excess Return from the Portfolio and the MSCI World Index9Figure 5: Returns of both Portfolio and MSCI World Index10Figure 6: Regression of Portfolio Returns using CAPM11Figure 7: Regression of MSCI WORLD Returns using CAPM111. IntroductionSocial media is a revolution, which we are currently experiencing. It has changed the way people communicate and interact with one another, and opens up many more avenues to share news, information, and just general chit chat. Social media is relatively quiet young, but is here to stay for the foreseeable future. We are now at a point where online, we can share, read and react to lots of individual information being posted on microblogging websites, such as Twitter, Facebook, Google+, Tumblr and more. Twitter in particular has been widely embracedFinancial theory dictates that markets are efficient, and therefore public information is already incorporated into pricing, Fama (1970). However, we are now at a point where online, we can share, read and react to lots of individual information being posted on microblogging websites, such as Twitter, Facebook, Google+, Tumblr and more. This information is public, could be relevant and if not fully analysed and traded upon, could give us pricing information not yet priced into the market.Using empirical data, we can analyse these online messages to find relationships with relating stock prices on a matching time scale and period. The celebrity community is sharing information and opinions on brands and companies, continuously throughout the day on Twitter. These users have significant followings and their opinions, endorsements and use of everyday items, can be read via tweets by millions. They have the ability to change public opinion, and therefore followers may purchase or appreciate certain companies and brands more. Therefore they could have a correlation with company stock. Positive tweets from celebrities regarding companies can be seen as a vote of confidence from the celebrity world. Alternatively, if a user tweets their dislike for a product, this could be early signs of product failure, as generally celebrities are trend setters, and can make or break product launches. This field of research looks to filter and analyse masses of freely available data to be able to gauge positivity regarding companies and brands from the celebrity world.The purpose of this research is to be able to answer whether positive celebrity tweeting correlates with positive stock gains in the following months. I will be creating a portfolio which is based on investing in companies which receive positive tweets from celebrities in the forms of endorsement, positively talking about a company or brand, or tweeting they use or have been using a companys product or service. 2. Study OutlineSeveral previous studies, which I reviewed in prior to this, found significant relationships between information derived from tweets and different stock market characteristics. This study looks to build on the area looking at influential Twitter users and whether, their tweets can lead to stock market gains due to their positivity to the companys products and services. The data required initially, is compiled lists of tweets from celebrities. These are then filtered using a list consisting of the S&P500, FTSE350 and The Top 500 Global Brands. The remaining lists of tweets are then human-read, to be able to identify tweets which relevant. This leaves a final sample of trigger events, which are then sorted and the portfolios are constructed accordingly. Once the portfolios are established their performance is assessed in terms of return and risk to return measures. The findings from the analysis will be summarised and discussed in chapter six, which will also include the limitations faced by the study, and how it could be improved with further study and analysis.

3. DataCelebrities due to their nature, have a high following on Twitter and therefore are a good proxy for highly influential users. A list of celebrities is created arbitrarily but consists of those highly followed, good Twitter usage and chosen to be a varied sample. The sample of celebrities is shown below.CelebrityTwitter UsernameFollowersNo. of Tweets

Katy Perry@katyperry52,491,5415528

Justin Bieber@justinbieber51,141,98526,666

Barack Obama@BarackObama42,663,79511,630

Taylor Swift@taylorswift1340,409,7222,188

Justin Timberlake@jtimberlake31,894,9862,250

Ellen DeGeneres@TheEllenShow28,626,9458,758

Cristiano Ronaldo@cristiano25,644,8311,878

Kim Kardashian@KimKardashian20,837,25017,302

Harry Styles@Harry_Styles20,242,3354,703

Kaka@kaka18,633,9163,444

Niall Horan@NiallOfficial18,056,0959,054

Pitbull@pitbull16,383,0365,475

Ashton Kutcher@aplusk15,985,3118,333

Drizzy (Drake)@Drake14,828,2001,539

Emma Watson@EmWatson12,854,363707

Paris Hilton@parishilton12,679,21419,435

Stephen Fry@stephenfry6,757,55019,115

Jessie J@JessieJ6,556,91515,912

Ricky Gervais@rickygervais5,758,77615,258

Table 1: Celebrities used, and their corresponding number of followers and tweets. Data from: http://Twittercounter.com/pages/100Using a python script with the use of Twitter developer access (further information in the appendix), I pull the last 3200 tweets from all the celebrities. The limitation of 3200 is due to the structure of Twitter and the maximum historic allowance for my basic developer status. The script is a heavily edited version of other freely available scripts on python repositories. These tweets are exported to an excel spreadsheet, which then with the use a VBA script; I can filter using a list. The list consists of the S&P500 company names, FTSE350 company names and the Global 500 Most Valuable Brands, retrieved from http://brandirectory.com/league_tables/table/global-500-2014. The final outputted list of tweets are then human read, to verify they are referring to a publicly listed company and if they are endorsing, positively talking about or mentioning their use of a product, service or company. For example, a celebrity tweeting their positive opinion on the new iPhone, would count as a trigger to go long Apple (APPL) stock.The tweets which pass the filtering system are then named events, which are collated together. All the events up to 1st Jan 2014, are then used to create a portfolio. The financial data for the companies is retrieved from DataStream and the data collection period is from April 2008 to the present day. The data is enough to cover a year prior to the first trigger event, which is required to create a portfolio of the previous 12months to the trading strategy portfolio. The data is then transformed into weekly returns for each stock between April 2008 and now.The benchmark being used is the MSCI World Index, which the data for has also been pulled from DataStream. The risk free data is obtained from http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html. The market portfolio chosen is the S&P500, due to majority of my trades for the portfolio being American companies. The S&P 500 weekly data has been obtained from http://research.stlouisfed.org/fred2/series/SP500/downloaddata.

Table 2: The final set of events which are used to create the portfolio

4. Portfolio Creation The strategy behind creating this portfolio is to invest in those firms which are being positively mentioned via Twitter from celebrities. In the preceding review of literature on this field, it is observed that positivity regarding certain subjects on Twitter, can in fact predict the outcomes, such as the study to predict box office movie returns on the basis of positivity surrounding the movie prior to the release, Asur, S. and Huberman, B. A. (2010). In this case we are looking for abnormal stock returns after celebrities have mentioned the stock in a positive light. The strategy is to create a long only, equally weighted portfolio, holding the corresponding stock for 12 months after the tweet has passed the filtering system. The stock will be held for 12 months, as time needs to be allowed, the tweet to disseminate throughout the rest of Twitter, or word of mouth, as the positivity surrounding the product or service, will generate further rounds of positive discussion between followers. A secondary portfolio is created with the same event dates but a portfolio of the previous 12 months. This allows for a comparison of the returns between the portfolio, before and after the tweet from the celebrity.5. Product AssessmentIn this section I will assess the portfolio using several return and risk-related performance measures from the time of the portfolios inception to the present day, the exception is the comparison portfolio created by holding each stock for 12 months prior the execution of trading for the strategies portfolio. The portfolio returns are weekly based, as is the data for the MSCI World Index, S&P 500. The risk-free rate which has be obtained is also the weekly rate.Each trade is calculated to be executed on the same day each week after the tweet. Due to the financial data retrieved, the weekly returns were based on a Wednesday to Wednesday timescale, and this is continued throughout the analysis, to keep the structure simpler.

5.1 Test for normalityTo assess whether skewness and kurtosis need to considered for the performance evaluation, I have created graphs displaying the return distribution of the created strategy portfolio, the previous 12 months prior portfolio and the MSCI World Index and S&P500. Figure 1: Histograms displaying the return distribution and the corresponding skewness and kurtosis

Portfolio ReturnsPortfolio of previous 12 monthsMSCI World IndexS&P 500

Jarque-Bera744.73863008.14344.893111.93281

In order to test for normality, I will test the portfolios using the Jarque-Bera test. At a confidence level of 95% a critical value of 5.99 is derived. If the test statistic exceeds this value then the returns are not normally distributed. The table below displays the Jarque-Bera values. Since all of the figures exceed 5.99 the returns are not normally distributed. This finding therefore implies that skewness and kurtosis need to be considered when assessing the performance.

Table 3: The Jarque-Bera scores for the portfolios

5.2. ReturnThe main performance evaluation of the portfolio created is the return. The portfolio return is created by taking the logarithmic returns from the portfolio. Logarithmic returns are used as they are less likely to negative skewed and more normally distributed than using normal returns. From this, I have created a nominal chart, as seen below, representing the value of the created portfolio, the market portfolio and the benchmark, from the point of the first trade executed by the strategy formulated for this study. This base value is at 1000, to make them easily comparable.

Figure 2: Portfolio Values Since Fund Inception

Figure 4: Arithmetic Mean Excess Return of the portfoliosFigure 3: Arithmetic Mean Return of the portfolios

As seen from the charts above, all four portfolios when unadjusted for the risk free rate have positive average weekly return. When taking into account the risk free rate (obtained from http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) it is visible that the created portfolio, benchmark and market portfolio still have positive average returns, however the portfolio of the previous 12 months, created as the comparison, has negative average returns.The analysis shows, for nominal and real return, the portfolio created, is considered a good investment. The portfolio is a much better investment once the tweets have been posted by celebrities, as the portfolio of stocks since the tweet performs, much better nominally and in real returns than the portfolio if held prior to the tweet.This return does not take into account the inherent risk attached to the portfolios and therefore the next section will focus on introducing some risk measures.5.3 RiskTable 4: Standard Deviation of the Excess Return of the Portfolio. Weekly and Yearly Comparison.Risk for this portfolio can be considered the possibility of a return not meeting the expected return. A simple way to measure this risk would be to analyse the return variance and the return standard deviation of the portfolio. To account for the risk free asset class, I will look at the excess return variance and standard deviation.Excess Return StDev WeeklyExcess Return StDev Yearly

Portfolio3.47%24.99%

MSCI World Index2.25%16.23%

S&P 5002.16%15.56%

Figure 5: Standard Deviation of the Excess Return from the Portfolio and the MSCI World IndexBoth charts show that the portfolio created, exceeds the MSCI World Index, in terms of absolute risk. This is to be expected, as the returns are higher for the portfolio than the benchmark. Therefore, the added risk of the portfolio is compensated by additional return over the benchmark. To be able to compare the risk-return ratios, we need to analyse this further using applicable risk to performance measures, these will be discussed later alongside other advanced risk measures.

5.4 Higher moments of return distributionThe return distribution from the created portfolios, of the strategy portfolio, the comparison portfolio of the previous 12 months prior to execution and the benchmark, can be described using skewness and kurtosis. The following table displays the empirical values for skewness and kurtosis for the portfolios constructed, including the benchmark being used.PortfolioPortfolio of the previous 12 monthsMSCI World IndexS&P 500

Skewness0.41679-1.780550168-0.49720721-0.37941

Kurtosis11.3141118.371022214.7943991243.736823

Table 5: Portfolio skewness and kurtosisThe created portfolio has a positive skew, whilst the benchmark , market and comparison portfolios are all negatively skewed. This means that the created portfolio using the portfolio strategy will experience positive deviations from the expected return. In respect to performance, this shows that the created portfolio is more beneficial than benchmark and market, and shows improvement after the tweet event by being much higher skewed than the comparison previous 12 month portfolio.All of the kurtoses display a heavy positive excess kurtosis, except for the market portfolio. This is interesting as only the market portfolio is similar to a normal distribution, being only 0.73 in excess kurtosis. All of the distributions are leptokurtic, showing they have higher peaks than a normal distribution which can result in larger fluctuations in the fatter tails. This can therefore impact the reliability of Value at Risk tests when assuming normal distribution, as there extremities can vary a lot more relative to a normal distribution due to fatter tails.

5.5 CorrelationTo be able to examine the relative movement of the created portfolio and the chosen market portfolio and benchmark the MSCI World Index, we need to use a covariance and correlation. This will show us the diversification in the portfolio and how the portfolio returns relative to the market returns.

Figure 6: Returns of both Portfolio and MSCI World Index In the above chart, I have used a two period moving average line, to be able to show the correlation, a little easier, due to the many weekly periods assessed in this study. The correlation between the two return sets, is 0.53, this shows they are moderately correlated. The positive correlation shows they tend to increase and decrease together.

5.6. Capital Asset Pricing ModelAnother performance evaluation method is to apply the capital asset pricing model to the returns. Using CAPM, we can regress the two sets of data, the portfolio and the market adjusted for the risk free rate. The slope will be the beta value between the two return sets and the y intercept displaying Jensens Alpha. This is because: Return(Portfolio/Benchmark) Risk Free = Jensens Alpha + Beta(Portfolio,Market) x [Return(Market) Risk Free] + Residual Error Term.

Figure 7: Regression of Portolfio Returns using CAPM

Figure 8: Regression of MSCI WORLD Returns using CAPM

As the Figure 6 above shows, the Beta is -0.23 and this shows it offers a lower risk to the market portfolio as it is less volatile than the S&P 500. It is almost 80% less volatile over the period of analysis for this created portfolio. This beta also backs up the previous correlation statistics, showing there is a negative relationship between the S&P 500 and the portfolio created. The y-intercept or Jensens Alpha of 0.004 represents the portfolios systemic return when the market return is zero or the level of return from the created portfolio over the market for any given level of systematic risk.The R squared value of 2%, shows that 2% of the variance in this model, is explained by the data.Unsurprisingly the MSCI World Index and S&P500, has a beta of 0.5, which shows there is a positively relationship, however the MSCI World Index is less volatile, as to be expected by the nature of being more geographically diversified portfolio.

5.7. Advanced Absolute Risk MeasuresFor a holistic approach to assessing the performance of the portfolio, we need to analyse the return with regards to the risks associated, and whether the performance is good enough to justify the additional risk for the return over the risk free rate. Portfolio Returns (LN)MSCI WORLD LN Returns

VaR0.05350.0362

Expected Shortfall-0.0592-0.0424

Drawdown-0.3002-0.1575

LPM(rp)0.00060.0003

SSD(rxp)0.02410.0169

Shortfall Risk0.47240.4764

Table 6: Advanced Absolute Risk Measures for Portfolio and MSCI World IndexThe first measure of risk, which is commonly user, is Value at Risk. This is down by ranking the returns from the lowest to highest and identifying the 5% cumulative figure. This gives us the expected loss not to exceed the VaR with a probability of 95%. As shown in the table, the VaR level for the created portfolio is much higher than the benchmark. This means on any given day there is a probability of 5%, that the created portfolio will incur losses of 5.35% or more, whilst the benchmark would lose 3.62% or more.Another measure expected shortfall, can be used to analyse further the tails in the VaR measure. The expected shortfall is the average loss experienced on the occasions that the portfolio or benchmark has exceeded the VaR level. The VaR level is the minimum loss on any given day with a 5% probability, but the average loss deviates further from the minimum loss in the benchmark compared to the created portfolio.Drawdown is another risk measure that tells us the maximum decline in portfolio value before any recovery. The created portfolio suffers a drop in 30% of the total value without any recovery, at some stage during the life of the portfolio. A loss of almost a third of the portfolio value without seeing any positive gains on the weekly time period is not good. The drawdown on the benchmark is not as harsh, at only 15.7%. Lower Partial Moment is a measure of only downside risk. Standard deviation is commonly used, however it also considers the uncertainty of the upside chances as well as the downside risk. In our test of the portfolio and the benchmark, the portfolio has the higher downside risk. Semi-standard deviation is another measure, which follows on from LPM. SSD is the square root of the LPM, which means the squares deviations from the mean excess return. The portfolios SSD is valued at 2.4%, whilst the benchmark registers 1.7%, overall the downside risk is lower for the benchmark than the portfolio.Shortfall risk is another measure which can be derived from the LPM. By taking the LPM to the power 0, you can calculate the probability of a return falling below the minimal acceptable level, in this case falling below the mean excess return of the market portoflio, S&P 500. The values are very similar for the portfolio and benchmark, at 47.2% and 47.6% respectively.5.8. Advanced Relative Risk MeasuresFollowing on from the absolute risk measures, we can compute relative risk indicators, as shown in the table below.Portlfio Returns (LN)MSCI WORLD LN Returns

Sharpe Ratio0.10050.0382

RAPA0.0021697280.000823608

Treynor0.01510.0016

Sortina6.530666482.906925796

RoPS0.0073860090.00180046

Table 7: Advanced Relative Risk Measures for Portfolio and MSCI WorldThe most common risk measure used is the sharpe ratio, it is the ratio of the excess mean return of the portfolio over the risk of the portfolio, valued as the standard deviation of the excess returns. As shown in the table, the excess return per unit of deviation is higher for the portfolio than the benchmark. For this simple measure the risk-adjusted returns are far superior for the portfolio relatively.The treynor ratio is similar to the sharpe ratio, but differs slightly in that is accounts for only the systemic risk of the portfolio rather than taking the total risk of the portfolio. Therefore the ratio is the excess mean return of the portfolio over the beta of the portfolio. The beta is taken from the regressions done earlier, of the portfolio against the market portfolio, the S&P500. Again the portfolio performs very well and far better than the benchmark.The sortina ratio is the relative risk measuring following on from the Lower Partial Moment. This measure computes the return on semi-standard deviation. As for the results of 6.5 and 2.9 for the portfolio and benchmark respectively, this shows, that whilst the return for the portfolio is better, it is also has a much better return for the level of risk (semi-standard deviation) when compared to the benchmark.The last measured used is the Return on Probability of Shortfall. RoPS measures the return over the probability that the return fails to exceed the mean excess return of the market portfolio. This follows on from the Shortfall calculation in the previous section. The RoPS is far superior for the portfolio over the benchmark, and this means for investors, the risk of shortfall is much greater compared to the level of return when investing in the benchmark, and should invest in the portfolio created.A have used no relative risk measures involving drawdown, as the nature of my portfolio, is a fixed 12 month hold period, and therefore any drawdown suffered, I would not be able to react and cut my losses. These risk measures would offer no value as they are more suited to higher frequency trading, where the aim is to allow profits to run and cut losses. In my case the strategy is for the trade to run regardless of the market movement.The relative measures used, all agree that the portfolio created by the strategy outlined in this study, has a much better level of return per unit risk. When comparing this to the previous section, it shows compared to the benchmark, the portfolio created, has a higher potential for losses, due to increased volatility, but with the returns it generates for investors, it has a better risk-reward relationship.

6. Discussion and ConclusionThe preceding study is carried out using a strategy formulated using messages posted to Twitter from celebrities. The strategy after all of the assessment done, looks to have found a positive relationship between the messages regarding companies and the corresponding stocks return over the following 12 months. Over the course of the portfolio, using the 173 trade executions, spanning 50 different companies, and each stock being held for 12 months, we have beaten the benchmark, MSCI World Index, on several measures. The created portfolio outperforms in the market portfolio and the benchmarks return over the life of the portfolio to date, but also the relative risk associated it also outperforms the benchmark, showing a very positive reward to risk relationship. The portfolio has a higher return, but suffers from a high value at risk, but due to having great relative risk measure, you can see the portfolio rewards the investor much better per unit of risk taken relative to the benchmark. However, the portfolio constituents and study all together, could be increased and improved.The direct comparison between the before tweet event portfolio and the post tweet event portfolio shows a heavy performance improvement after the tweet. At this point without further research and widening the data set, it is possible to conclude, that there is a relationship between celebrities tweeting and stock prices for the following 12 months. This could be improved with another study to find the degree of effect on stock price improvement dependant on the weighting of the number of followers and quantifying the positivity of the tweets.The CAPM regression used could have been replaced with a Fama French three factor model or Cahart model to test the drivers in the portfolio returns, to a much more detailed level. A further question to the validity of the portfolio created, is the relatively short observation period. This would need to analysed in the forthcoming years, as Twitter itself is a relatively new platform for sharing this public information, and therefore we can not just extend our data back further. A vital assumption for many of the risk measures is normal distribution. As we saw the Jarque Bera test was very high, showing the distribution is far form being normal, and therefore this could improved with a further data in the datasets, that should lower the JB score, and help improve the reliability of the measures used.The portfolio created, could be expanded to data mine more celebrities, and potentially move onto all public Twitter users whose messages are then retweeted. This would increase the reliability, and help the portfolio become more diversified, as more tweets would be passing through the filter system created. Additionally, I could add more public companies into the filtering list, and a wholescale upgrade in the filtering system could be employed, as the python and VBA scripting employed in this study, could be deemed relatively simplistic. I was limited by the computing power available and the limited developer license issued by Twitter.

SourcesAndreas Hoepner (2014). TRADING ON BRAND EVALUATIONS: Corporate Reputation Total Return Fund: Development and Assessment of an Alternative Investment Product. Alternative Investments - Investment Product Carolin.Asur, S. and Huberman, B. A. (2010). Predicting the future with social media. Proceeding WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology 1 pp. 492--499.Bodie, Kane and Marcus (2009): Investments, 8. Edition, Irvin: McGraw-Hill.Datastream 2014, Weekly stock data 2008-2014, Weekly MSCI World Index data 2008-2014. Available from: Datastream.Fama/French Factors [Weekly]. Available from: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.Global 500 Brands 2014, Brand Finance. Available from: http://brandirectory.com/league_Tables/table/global-500-2014.Hoepner, A., Rammal, H. and Rezec, M. (2011). Islamic mutual funds financial performance and international investment style: evidence from 20 countries. The European Journal of Finance, 17(9-10), pp.829--850.Kempf, A. and Osthoff, P. (2007). The effect of socially responsible investing on portfolio performance. European Financial Management, 13(5), pp.908--922.Malkiel, B. G. and Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work*. The journal of Finance, 25 (2), pp. 383--417.Twitter Statistics, Twitter Counter. Available from: http://twittercounter.com/pages/100.S&P 500 Weekly Data, FRED Economic Data. Available from: http://research.stlouisfed.org/fred2/series/SP500/downloaddata

AppendixFront end python code used to execute tweet download and create arrays for excel output. Tweepy and xlsxwriter are modules which are available to be installed to run the background processes using additional functions. This code is available online, and has been edited by myself to function in a way I could use it for this study.consumer_key = "2US2nBhbbSR3f8lN72PHb2Isd"consumer_secret = "uCAvXL1NY2IZgRTHbYFnIcTkGwEuh7jS1J35X6EcXGnnNHZSFn"access_key = "2310455635-RTYxmilTEuhYdzrOJdV6tsCo5cfjd77yG1e0oT7"access_secret = "iWb4q2SgSVDsuGXgtLnLQ17yKvZE0gPCmiirQbBOOrlcK"

def get_all_tweets(screen_name):

auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_key, access_secret) api = tweepy.API(auth)

alltweets = [] new_tweets = [] outtweets = []

new_tweets = api.user_timeline(screen_name = screen_name,count=200)

alltweets.extend(new_tweets)

#save the id of the oldest tweet less one oldest = alltweets[-1].id - 1

#keep grabbing tweets until there are no tweets left to grab while len(new_tweets) > 0: print "getting tweets before %s" % (oldest)

#all subsiquent requests use the max_id param to prevent duplicates new_tweets = api.user_timeline(screen_name = screen_name,count=200,max_id=oldest)

#save most recent tweets alltweets.extend(new_tweets)

#update the id of the oldest tweet less one oldest = alltweets[-1].id - 1

print "...%s tweets downloaded so far" % (len(alltweets))

#transform the tweepy tweets into a 2D array outtweets = [[tweet.id_str, tweet.created_at, tweet.coordinates,tweet.geo,tweet.source,tweet.text] for tweet in alltweets]

return outtweets

def write_worksheet(twitter_name):

#formating for excelformat01 = workbook.add_format()format02 = workbook.add_format()format03 = workbook.add_format()format04 = workbook.add_format()format01.set_align('center')format01.set_align('vcenter')format02.set_align('center')format02.set_align('vcenter')format03.set_align('center')format03.set_align('vcenter')format03.set_bold()format04.set_align('vcenter')format04.set_text_wrap()

out1 = []header = ["id","created_at","coordinates-x","coordinates-y","source","text"]

worksheet = workbook.add_worksheet(twitter_name)

out1 = get_all_tweets(twitter_name)row = 0col = 0

worksheet.set_column('A:A', 20)worksheet.set_column('B:B', 18)worksheet.set_column('C:C', 13)worksheet.set_column('D:D', 13)worksheet.set_column('E:E', 20)worksheet.set_column('F:F', 120)

for h_item in header:worksheet.write(row, col, h_item, format03)col = col + 1

row += 1col = 0for o_item in out1:write = []cord1 = 0cord2 = 0write = [o_item[0], o_item[1], o_item[4], o_item[5]]

if o_item[2]:cord1 = o_item[2]['coordinates'][0]cord2 = o_item[2]['coordinates'][1]else:cord1 = ""cord2 = ""

format01.set_num_format('yyyy/mm/dd hh:mm:ss')worksheet.write(row, 0, write[0], format02)worksheet.write(row, 1, write[1], format01)worksheet.write(row, 2, cord1, format02)worksheet.write(row, 3, cord2, format02)worksheet.write(row, 4, write[2], format02)worksheet.write(row, 5, write[3], format04)row += 1col = 0

workbook = xlsxwriter.Workbook('Twitter_timelineX.xlsx')

write_worksheet('katyperry')write_worksheet('BarackObama')

workbook.close()

This VBA code is used within Excel, to filter the list of tweets produced by the script above. This script creates a temporary array in column Z of the list file Filtering List and uses it for an advanced filter of the column of tweets, it will then output in another column, all tweets which have at least one word matching the list.Sub Filtr() Dim c00 As Variant Dim mText As Variant Dim Sentences As Range Dim mCriteria As Range Dim mAddy As String Const mTextFile As String = "C:\Python27\Filtering List.txt" mText = Split(CreateObject("scripting.filesystemobject").getfile(mTextFile).openastextstream.readall, vbCrLf) If Len(mText(UBound(mText))) = 0 Then ReDim Preserve mText(UBound(mText) - 1) Set Sentences = Range("F1:F" & Range("F" & Rows.Count).End(xlUp).Row) Range("Z1") = Range("F1") mAddy = Range("Z2").Resize(UBound(mText) + 1).Address Range(mAddy) = Application.Transpose(mText) c00 = Evaluate(Chr$(34) & "* " & Chr$(34) & "&" & mAddy & "&" & Chr$(34) & " *" & Chr$(34)) Range(mAddy) = c00 Set mCriteria = Range("Z1:Z" & Range("Z" & Rows.Count).End(xlUp).Row) Sentences.AdvancedFilter xlFilterCopy, mCriteria, Range("H1"), False With Range("G2:G" & Range("H" & Rows.Count).End(xlUp).Row) .Formula = "=INDEX(B:B, MATCH($H2, F:F, 0))" .Value = .Value .NumberFormat = "YYYY/MM/DD HH:MM:SS" End With

Set mCriteria = Range("Z2:Z" & Range("Z" & Rows.Count).End(xlUp).Row)mCriteria.Replace "~* ", "", xlPartmCriteria.Replace "~ *", "", xlPart

Call RtoEnd SubSub Rto()

Dim c00 As VariantDim Filters As VariantDim iRow As LongDim iC As LongDim findME As Integer' sentencesiRow = Range("H" & Rows.Count).End(xlUp).Rowc00 = Range("H2:H" & iRow).Valuec00 = addS(c00)

Range("Z1:Z" & Range("Z" & Rows.Count).End(xlUp).Row).RemoveDuplicates 1, xlYesFilters = Range("Z2:Z" & Range("Z" & Rows.Count).End(xlUp).Row)

For iRow = LBound(c00, 1) To UBound(c00, 1) For iC = LBound(Filters, 1) To UBound(Filters, 1) findME = InStr(1, c00(iRow, 1), Chr$(32) & Filters(iC, 1) & Chr$(32), vbTextCompare) If findME > 0 Then With Range("H" & iRow + 1).Characters(findME, Len(Filters(iC, 1))).Font .ColorIndex = 3 .Bold = True End With End If NextNext

End Sub

Private Function addS(V As Variant) As Variant Dim i As Long For i = LBound(V, 1) To UBound(V, 1) V(i, 1) = Chr$(32) & V(i, 1) & Chr$(32) Next addS = VEnd Function