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according to the slides (milestone 7) and report (milestone 8) I gave. write the conclusion and future direction. 

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ABSTRACT
This paper shows the effect good and bad inflation news have on the S&P 500, it aims at
differentiating which news have positive and negative influence on the market returns using. It
is frequently observed that current inflation, predicted inflation, and unexpected inflation all
have a negative impact on stock returns, whether they are nominal or real. In this study, we
applied text mining methods on news from Reuters and we discover that both positive and
negative inflation news can result in a range of stock market reactions, depending on the status
of the economy. The results show that the impact of inflation news on the S&P 500 depends on
whether investors interpret inflation news favorably or negatively depending on the status of
the economy.
INTRODUCTION
This study was conducted to assess the connection between inflation news and the S&P
500 stock performance. This study’s basis is a hypothesis that there is a positive and negative
correlation between the news about inflation and the stock market yield. The theory is
expanded to include stock returns and inflation under the assumption of a non-stochastic
equity risk premium. As a result, it is anticipated that inflation and stock returns will be
positively correlated (Azar, S.A. 2010). There is a wealth of research literature on the existing
relationship between inflation news and stock performance. We extend earlier research on
inflation by controlling for positive and negative inflation shocks based on high, medium, and
low economic conditions. According to the health of the economy, positive (negative) inflation
shocks can be either good or bad news. According to McQueen and Roley (1993), stock
investors would view favorable shocks in industrial production as “good news” during the Great
Depression but as “bad news” in 1969, when output and employment were high. Their
empirical investigations, however, do not specifically examine the effects of good news versus
bad news macroeconomic surprises on stock returns. According to their argument, depending
on the status of the economy, a positive inflation shock may indicate different things to
investors. We also hypothesize that investors will view news of higher-than-expected inflation if
the economy is in a recession as positive news (e.g., higher prices due to increasing consumer
confidence and demand for goods and services). Investors, however, are predicted to see a
positive inflation shock in an expanding economy negatively (e.g., an increased likelihood of
potential central bank intervention to increase interest rates, decrease inflation rates, and slow
the economy). Boyd, Hu, and Jagannathan’s (2005) observation that rising unemployment is
viewed as good news by stock investors during economic expansions but bad news during
economic contractions provides precedent for differing market responses to good and negative
macroeconomic data. Veronesi (1999) makes the theoretical case that investors respond
differently to terrible news in good times compared to good news in bad times. Therefore,
although theory and research support the idea that the market reacts differently to good and
negative news, this problem has not been covered in earlier inflation studies. By looking at how
positive and negative inflation news impacts stock returns under various economic conditions,
we want to fill a gap in the research on inflation news.
Using text mining, we investigate how changes in inflation news announcements affect
S&P 500 returns. Our findings suggest that, in general, it is critical to distinguish between good
and bad news when it comes to inflation news to comprehend how inflation impacts S&P 500
stock returns more thoroughly. For instance, in the weeks following inflation news events, we
find that both positive and negative inflation news can have significant cumulative effects on
market returns. The impacts of inflation news on market returns, however, tend to be washed
out or muted when positive and negative inflation news are added together as in earlier studies
(Knif, Kolari, and Pynnönen, 2008). Additionally, given the current state of the economy, both
positive and negative inflation shocks might cause a variety of stock market reactions due to
the consequences of good and bad news.
LITERATURE REVIEW
Previous researchers have conducted studies to determine the effect of inflation news on stock
returns using event study methods. However, only a minority of the studies evaluated the
effect of inflation news on the S&P 500 based on positive and negative influences. Adams,
McQueen, and Wood (2004) used intraday stock returns more recently and discovered
significant inflation impacts for positive and negative inflation shocks including various
economic states. The authors total both positive and negative inflation shocks across economic
states, which is pertinent to the current investigation. Park and Jang (2022) in their study noted
the impact of emotion in our judgement of the news. They pointed out that most often we are
advised to make decisions based on data and not emotions, but we are humans and one of our
flaws is emotions. The impact of emotions has been discarded mainly because it seems difficult
to quantify (Park et al. 2022). The analysis of consumer sentiments attempts to analyze
consumer emotions and their effects on markets. The research was narrowed down to the
specialty clothing market in Seoul, South Korea. The researchers hoped that their model would
form the basis of similar studies to quantify, analyze and predict consumer emotions and their
impact on other markets. The study aimed at examining the relationship between emotional
information and traditional markets and how to use them to drive consumption in the market.
The methodology used in this research was so unique that it was patented after its
development. The binary logistic analysis revealed that specialty clothing had lots of positive
keywords while memory had lots of negative keywords. Decision Tree analysis produced 61.3%
of negative and neutral sentiments and 38.7% of positive responses.
In the second quarter of 2020, according to the U.S. Bureau of Statistics, consumer
spending reduced by 9.8 percent (Matsumoto, Miller, and Monta, 2022). After one year, the
COVID-19 pandemic still had strong effects on the economy by the second quarter of 2021.
When businesses and consumers adapted to the situation, consumer expenditure was 15.7
percent higher in 2021 than the previous year. The authors of the article assessed how changes
in consumer purchasing and spending patterns affected indices of consumer inflation since the
COVID-19 epidemic started in 2019. The authors observed the effects of these changes on the
Consumer Price Index for All Urban Consumers (CPI-U). They also examined which expenditure
categories propel the difference between the fixed-weight CPI-U and the alternative indexes
constructed with contemporaneous weights. Finally, the authors compare the
contemporaneous-weight indexes with indexes constructed with real-time expenditure data.
The result showed that the CPI-U therefore became decoupled from actual spending patterns,
some prices rising significantly while the CPI-U remained constant. Despite falling prices, people
stopped buying some things while purchasing a lot of other ones. The researchers found that
people had turned to looking up real time price indexes on the internet to try to keep up with
the rapid changes.
Sun and Shi (2022) in their paper proposed the outbreak of coronavirus disease (COVID19) as a natural experiment that can provide insights into the effects of investor sentiment on
stock market reactions. Employing the event study methodology (ESM) and taking the date of
the Wuhan lockdown as the event date, the researchers found that average abnormal return
(AAR) and cumulative abnormal return (CAR) are significantly negative, and average trading
volume excesses far more than before within two days of the outbreak. Furthermore, the
researchers established a difference-in-differences (DID) model to investigate the differences
between Hubei and non-Hubei listed companies. The results show that for Hubei listed
companies, the change of excessive trading volume (ETV) between pre-event and post-event
period is significantly higher than that of non-Hubei listed companies, while there exhibits no
relationship between the change of AAR and registration place. Overall, their findings provide
new evidence for the interaction of local bias and investor sentiment affecting stock market
reactions. The results show that for listed companies registered in Hubei, the change of ETV
before and after the event date is significantly higher than that of non-Hubei listed companies,
while there exhibits no relationship between the change of AAR and registration place.
Janková and Dosko?il (2020) evaluated the impact of federal reserve system (FED) economic
reports on US financial market using text mining. Central bank communication can affect
market results through “reporting”, where changes in communication are reflected in market
prices according to Hüning (2020). Public reports and news often provide useful information on
the current state of the financial market, while news from the media often influence traders of
the stock market. The pace of today’s technological developments makes this news and
information travel faster and more efficiently according to Alamsyah et al. (2018). The news can
influence traders to buy more or sell their shares on the stock market. The authors of this paper
(Jankova et, al. 2020) examined the link between monetary policy reports and the stock index
prices. The goal was to perform text mining of published Federal Reserve System (FED) reports
and from these reports to determine the sentiment communicated by the central bank and to
identify the impact on stock markets. The authors’ motive for conducting the study is the
assumption that, for example, the Fed’s communicated policy regarding raising interest
rates/inflation or other similar elements may have a significant impact on the purchase or sale
of financial assets by investors. The paper is unique in that it connects the interdisciplinary
nature, which at the core includes both behavioral economic topics obtained from published
central bank reports and the Support Vector Machine method, which is used to determine
sentiment and mood scores communicated by the central bank of the USA. In conclusion, the
study shows that if the stock market’s higher fear of the VIX index persists, there is a weak
ability to predict the direction of US stock indices, despite the optimistic tone in the Fed’s
economic growth statement.
DATA COLLECTION

To gather data, we first looked at some major news sources. Our team gathered data from
Associated Press, Google News, Reuters, and Yahoo Finance. We gathered these articles using
the search term Inflation in the associated websites search bar. Then, we copied and pasted
the entire articles into an excel file. To ensure a proper timeline, we only gathered text data
from August 15th, 2022 through November 11th, 2022. We did not gather data on the weekend
due to a lack of financial reporting on the weekend. We also did not gather data on days when
the stock market was closed. This allowed us to focus our data based on actual daily
movements of the stock market.
At the same time, we gathered the adjusted daily close of the Standard and Poor’s 500
market index. This consisted of 64 days where the market was open. The S & P 500 is widely
seen as a benchmark of overall stock market movement. This was done to understand the daily
movement of the overall market during our data gathering phase. Once we collected this data,
we calculated the logarithmic difference of the adjusted daily closing price, giving us the daily
movement of the index. During this time period the maximum price was $4305.20, while the
minimum price was $3577.03. The largest daily upward movement was 4.42%, while the
largest daily downward movement was –5.40%. Positive gains consisted of 57.81% of the days,
while downward days made up 42.19% of the movements. (See Figure 1)
METHODOLOGY
For our text mining research, we used SAS OnDemand. Our group used default settings
for text parsing. Our Parse Variable was VAR1. We detected different parts of speech and
noun groups. For multi-word terms we used SASHELP.ENG_MULTI. Detect find entities is none.
We ignored parts of speech such as conjunctions. We also ignored types of attributes such as
number and punctuation. We allowed stem terms and used the synonym algorithm
SASHELP.ENGSYNMS. For filter we used a stop list algorithm SASHELP.ENGSTOP. The reported
number of Terms to Display was 20,000. (See Figure 2)
Next, we moved on to Text filtering. We began by allowing the program to check the
spelling of words. You used a Logarithmic frequency weighting system, and our term weighting
was default. The minimum number of documents was two. All terms were viewable. The total
number of terms to display was 20,000 again. (See Figure 3)
Finally, we conducted text clustering. To transform the text data we used SVD
resolution of high. We allowed the max SVD dimensions to be ten. Our settings allowed for a
maximum number of clusters. The maximum number of clusters we required was ten. We did
this to allow the program to find the natural clusters within the text. This allowed the data to
naturally find the maximum number of clusters. The cluster algorithm was set to ExpectationMaximization. The maximum number of descriptive terms in each cluster was 15. (See Figure 4)
INIATIAL DATA ANALYSIS
On our initial text data mining of the data we received the following results (See Figure
4):
Associated Press had an Initial text parsing returned the words be, year, and say as top 3
words which were present in 65, 63 and 61 articles respectively. The alpha attribute by
frequency was over 30,000. The role by frequency was approximately 15,000 for nouns, 8,000
for verbs, and 4,000 for adjectives and propositions. The most important word to our research
was inflation which appeared 350 times in 59 articles. The text filtering dropped some verbs. It
dropped the verb “be” and ”say” but kept key nouns like “inflation” and “year” and adjective
“high”. The nouns “year’’ has the highest frequency than this word in other three datasets.
AP has 4 clusters, the highest clusters is the fourth one with 35 percentage and 23
frequencies, it has key words like “ administration biden, share, family, house. These words did
not mean anything from a sentiment standpoint. The first cluster come with 22 percentage and
14 frequency, it has words like “ slow fed, risk, economist, loan”, which shows
negative sentiments. (See Figure 5/6/7)
Google News for text parsing the three most common words used in the analysis are inflation,
year and high with a frequency of 828, 284, and 215 respectively. Alpha attribute by frequency
was over 40,000. The role by frequency was right at 15,000 nouns, under 10,000 verbs, and
around 7,000 propositions. Google showed the highest word counts of the collected data.
Filtering resulted in an average number of words that were kept, not much different from the
other datasets. Nouns were of course the most kept, as they had the most meaning. The words
are financial in nature. The quality of the data is assessed as high.
Google News data had four clusters in total. Cluster 1 shows a negative sentiment,
while cluster 4 shows a positive sentiment, we should be able to extrapolate that into the data
to see if there is a correlation between the dates and the S&P 500. (See Figure 8/9/10)
Reuters had the highest alpha attributes by frequency totaling over 10,000. It also had
the highest role by frequency totaling at least 4,000. Parsing resulted in the three highest
frequency words of price, u.s., and repo. These had a frequency of 99, 80, and 85 respectively.
This individual word frequency was very low compared to the other datasets. This dataset
resulted in the lowest word count of the datasets. Inflation ranked #7 of the kept terms, which
is the lowest of any of the datasets. The data seems skewed more to a political nature, as
opposed to a finance focus.
Reuters had the most clusters with nine total. We were unable to find a good sentiment
correlation in the word clusters. The highest percentage cluster was #1, the words being grant,
saudi, and summary. The large numbers and high alpha attribution frequency showed that this
dataset had the best. (See Figure 11/12/13)
Initial text parsing for Yahoo Finance returned the words be, min, and read as top three
words which were present in 65, 64, and 64 articles respectively. The most important word to
our research was inflation which appeared 514 times in 60 articles. The Alpha attribute by
frequency was just over 30,000. The roles by frequency were over 10,000 nouns, 7,500 verbs
and around 4,800 propositions. As a result of the text parsing, it became obvious that nouns
and verbs were very prominent in the analysis. The nouns represented commodities, time i.e.,
year and metrics like inflation. The text filtering dropped some of the nouns and verbs. It
dropped the verb “be” but kept key nouns like inflation and year. This points us in the direction
of commodity prices and their effects on economies.
The Yahoo finance data had seven total clusters. Cluster 5 was in the lead with 31% and
a frequency of 20. It had key words and phrases like chair of fed, central bank, slow, economic,
point central, rate and economy. These have negative sentiments which align with the global
economic recession the world finds itself in. Cluster #2 formed 15% of the analysis. It had
keywords like average, industrial Nasdaq, stick, S&P 500, share, article, yield and gain. They had
a collective frequency of 10. Cluster #2 also has negative sentiments representing recent stock
crashes arisen from poor quarterly reports of major corporations. (See Figure 14/15/16)
FINAL DATA ANALYSIS
After our initial text mining analysis, our group decided to only continue with the
Reuters dataset and drop the other three. This was due to the dataset’s high alpha attribution
frequency which led us to the conclusion that we should focus our analysis on the Reuters
dataset. This would allow us to focus our efforts and allow us to have nonbiased results. At
this point we addressed the issue of sentiment correlation in the clusters with further text data
analysis.
We used the price movement data we had found during our data gathering phase to
find select the articles which occurred on positive movement days and negative movement
days. First, we separated the positive movement days from the negative movement days. We
then ran the two excel documents through the SAS OnDemand text mining tool.
We used the same settings on the positive movement days data as we used on the
overall data. As there were more positive movements, there was more text data in this
analysis. The positive movement analysis cluster analysis showed a result of 3 clusters. The
first cluster included the words +consumer +pressure +point +chief growth +rate +fall +bank
+cost +dollar +economy inflation +’commodity price’ +hike +low. The second cluster contained
the words +source +supply +million +barrel +russia +additional +group europe +concern
+analyst +oil energy +hit +demand +week. The final third cluster included the words +congress
+discuss +involve +leftist brazil +minister +contribute +president +bolster +election +budget
+broad +fund +sector +hold. (See Figure 17)
Next, we moved on to the negative movement days text data. As the movement
analysis above showed, this data included fewer days. The negative movement analysis
showed four clusters of data. The first cluster consisted of the words +stock crude +investor
bengaluru +day +week +analyst +rate +big +point interest +oil +head +fall +rise. The next
cluster included the words +stop +winter +inventory +head +import +keep +pipeline +region
+move +several +unit +level +state +further +gas. The third cluster contained the words +sign
+business +people +slow +increase +first +decline +remain data reuters +consumer +economic
+bank +state +year. The fourth cluster included the words ‘iron ore’ +iron +ore tuesday +export
fiscal +risk +financial +top likely +forecast billion government +import +estimate. (See Figure
18)
The cluster analysis of the two new datasets allowed our group to associate each of the
initial nine clusters into positive or negative sentiment based on the words that are in each
cluster. The positive clusters could be associated with Clusters #2, #4, #5, and #7. The negative
clusters include clusters #1, #3, #6, #8, and #9 (See Figure 19). Positive words account for 37.5%
of the data, while negatively associated words account for 67.5%. The overall market
movement was approximately 58% positive and 42% negative.
Conclusion and future direction
FIGURES
FIGURE 1:
FIGURE 2:
FIGURE 3:
FIGURE 4:
FIGURE 5:
FIGURE 6:
FIGURE 7:
FIGURE 8:
FIGURE 9:
FIGURE 10:
FIGURE 11:
FIGURE 12:
FIGURE 13:
FIGURE 14:
FIGURE 15:
FIGURE 16:
FIGURE 17:
FIGURE 18:
FIGURE 19:
REFERENCES
Alamsyah, A., Arasyi, M. T., & Rikumahu, B. (2018). Supporting Investment Decision Using SocioEconomic Issues Exploration and Stock Price Prediction. In: 2018 International Symposium on
Advanced Intelligent Informatics (SAIN). IEEE, 2018, 20-25. doi: 10.1109/SAIN.2018.8673343.
Brett Matsumoto, Christopher B. Miller, and Hugh Montag, “The impact of changing consumer
expenditure patterns at the onset of the COVID-19 pandemic on measures of consumer
inflation,” Monthly Labor Review, U.S. Bureau of Labor Statistics, April 2022,
https://doi.org/10.21916/mlr.2022.12
“Changes to Consumer Expenditures during the Covid-19 Pandemic.” U.S. Bureau of Labor
Statistics, U.S. Bureau of Labor Statistics, https://www.bls.gov/opub/ted/2022/changes-toconsumer-expenditures-during-the-covid-19-pandemic.htm.
Eskici, Hatice Burcu, and Necmettin Alpay Koçak. “A Text Mining Application on Monthly Price
Developments Reports.” Central Bank Review, vol. 18, no. 2, June 2018, pp. 51–60. EBSCOhost,
https://doi.org/10.1016/j.cbrev.2018.05.001.
Hüning, H. (2020). Swiss National Bank communication and investors’ uncertainty. The North
American Journal of Economics and Finance, 51. doi: 10.1016/j.najef.2019.101024
“The Impact of Changing Consumer Expenditure Patterns at the Onset of the COVID-19
Pandemic on Measures of Consumer Inflation.” Monthly Labor Review, Apr. 2022, pp. 1–17.
EBSCOhost,
https://search.ebscohost.com/login.aspx?direct=true&AuthType=sso&db=eft&AN=156778312
&site=eds-live&scope=site.
Janková, Z., & Dosko?il, R. (2020). Impacts of Federal Reserve System (FED) Economic Reports
on US Financial Market Using Text Mining. In Scientific Conference INPROFORUM (p. 39).
Karadag, Engin. “Effect of COVID-19 Pandemic on Grade Inflation in Higher Education in
Turkey.” PLoS ONE, vol. 16, no. 8, Aug. 2021, pp. 1–16. EBSCOhost,
https://doi.org/10.1371/journal.pone.0256688.
Kostoff, Ronald N., et al. “Duplicate Publication and ‘Paper Inflation’ in the Fractals Literature.”
Science and Engineering Ethics, vol. 12, no. 3, Sept. 2006, pp. 543–54. EBSCOhost,
https://doi.org/10.1007/s11948-006-0052-5.
Park, Sang Hun, and Seongman Jang. “Analysis of Consumer Sentiment in Traditional Market
Through Sentiment Information Analysis: Focusing on Seoul’s Gwangjang Market.” Journal of
Asian Sociology, vol. 51, no. 1, 2022, pp. 1–28. JSTOR, https://www.jstor.org/stable/27126205.
Accessed 6 Nov. 2022.
Sun, Lin, and Wei Shi. “Investor Sentiment and Stock Market Reactions to COVID-19: Evidence
from China.” Discrete Dynamics in Nature & Society, May 2022, pp. 1–10. EBSCOhost,
https://doi.org/10.1155/2022/8413916.
Mahanty, Sampriti, et al. “An Investigation of Academic Perspectives on the ‘Circular Economy’
Using Text Mining and a Delphi Study.” Journal of Cleaner Production, vol. 319, Oct. 2021.
EBSCOhost, https://doi.org/10.1016/j.jclepro.2021.128574.
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EBSCOhost, https://doi.org/10.22215/timreview/1284.
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Boyd, J. H., J. Hu, and R. Jagannathan, 2005, The stock market’s reaction to unemployment
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Financial Studies 6, 683–707.
IMPACT OF
INFLATION NEWS ON
THE S&P 500
Milestone 7
IST 6443
Team Members: Charles Williams, Ben Bonsu, Cheng Zhou,
Emmanuel Adeosun
Type of Project
• How does Inflation sentiment affect the S&P 500
• Use articles to text mine, assess the sentence of the articles, and
see their impact, if any, on the market
https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.shutterstock.com%2Fsearch%2Finflation&psig=AOvVaw2ChtD0vushgezp7_gQscL&ust=1666644060275000&source=images&cd=vfe&ved=0CAsQjRxqFwoTCMj-3Yub9_oCFQAAAAAdAAAAABAE
https://www.goldavenue.com/en/blog/newsletter-precious-metals-spotlight/what-can-we-learn-from-the-s-p-500-to-gold-ratio
Team Members
? Charles Williams
? Ben Bonsu
? Cheng Zhou
? Emmanuel Adeosun

What is the S&P 500, and Why Does It Matter to Traders at Banks?

Literature Review
Consumer Sentiment in Traditional Market Through Sentiment Information Analysis: Focusing
on Seoul’s Gwangjang Market
?
This study was conducted on the consumer sentiment analyzing consumer emotions and their effects
on Seoul, Korea’s clothing market
?
As countries develop, more urban areas develop. This results in a shift of markets from market
centers to more decentralized systems in the market.
?
Many governments have instituted measures to save these markets but it’s mostly proven futile.
?
The study hopes to find emotional and sentimental cords that visitors of traditional markets
experience when they visit these markets.
?
The methodology used in this research was so unique that it was patented after its development. The
choice of the Gwangjang Market was tactical as it was able to attract tourists as well as regular
shoppers. The data used spans a period of 8 years; from 2010 to 2017. These periods were when
attempts to restore the Gwangjang Market to its old glory received lots of media attention.
?
Data was collected from blogs, forums, newspaper reports, Twitter, and Facebook with Gwangjang
Market set as keywords.
Results and conclusions
?
Decision Tree analysis produced 61.3% of negative and neutral sentiments and 38.7% of positive
responses.
?
The government intends to use memory-related topics to review the market’s past goodwill. It will be
important for the government to take the results of this research into account in coaching and presenting
its message since it’s likely to strike a negative chord with prospective visitors.
The Impact of Changing Consumer Expenditure Patterns at the Onset of the COVID-19
Pandemic on Measures of Consumer Inflation.
?
In the second quarter of 2020, according to the U.S. Bureau of Statistics, consumer spending reduced by
9.8 percent.
?
As the pandemic began, people started to change their spending because of the closure of most
businesses.
?
When businesses and consumers adapted to the situation, consumer expenditure was 15.7 percent higher
in 2021 than the previous year.
?
During the pandemic, the government found that how they calculated the consumer price index of urban
consumers (CPI-U) was causing a bias.
?
The way the government calculated the CPI-U had a built-in lag due to collection of data, which was
unable to keep up with rapid changes in the CPI-U.
?
The researchers found that there were increases in the quantity of items purchased, more than changes in
price. People were purchasing large quantities of needed items, which was causing stock-outs at stores.
?
They also examined which expenditure categories propels the difference between the fixed-weight CPI-U
and the alternative indexes constructed with contemporaneous weights. Finally, the authors compare the
contemporaneous-weight indexes with indexes constructed with real-time expenditure data.
Investor Sentiment and Stock Market Reactions to COVID-19.
?
Treating the outbreak of coronavirus disease 2019 (COVID-19) as a natural experiment that can
provide insights into the effects of investor sentiment on stock

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