The Intersection of AI and Stock Markets: Performance Analysis of the Nasdaq, S&P 500, and tech giants Nvidia, Intel, Amazon, Microsoft, and Meta

 

Dissertation title: The Intersection
of AI and Stock Markets: Performance Analysis of the Nasdaq, S&P 500, and
tech giants Nvidia, Intel, Amazon, Microsoft, and Meta

Structure: Master
Thesis
The dissertation will be split into 3 chapters: 1)
Introduction/Problem statement/research questions and objectives and literature
review.  2) research phlisophic,research approach,
data collection and analysis of the models (strategy) and limitations of the
research 3) discussion and results of the models, research
extension/improvements and conclusion.

Reference
style:
Harvard Style –
use only academic reputable books and journal and not websites and AI content.                       Deadline: 18.11.2024
is my preferred delivery time for at least the 2 chapters

Models: The
data can be attached by me, I have Bloomberg and Refinitiv access.

The excel file with the models and the R script must
be shared.

*Please professor comments and my master thesis
revised proposal for better understanding!

 

Professor Comments:

1. The motivation is interesting. However, it would be
quite difficult to attribute Nasdaq’s evolution to the “AI factor”.
Just so you know, many other factors are present, and these other factors will
blur any effect (you are aware of this, eg. “limitations”) . Still,
it is interesting to analyze what you mention. You focussed on two companies,
one indisputably linked to AI but the other not (you correctly highlight this
by mentioning the bandwagon effect); maybe it could be interesting to include
other firms such as Amazon, Microsoft, and Meta…(i.e., the “seven
magnificent”) and see what you find.

2. References could be better; you should not use
general references as Miller or Tsay but academic papers directly linked to
your research.  Please also beware of formal aspects, there are no page
numbers,

3.You want to include the investor sentiment by using
VIX but note that this does not give you an idea of the polarity but on the
intensity. Moreover, you should provide some hint of how you are going to
collect “AI news”, this point is crucial, you can not simply select a
bunch of news because you consider them relevant, you should do something
systematic.

4. Consider using other indexes besides NASDAQ and
-possibly- SP500 (e.g. technology indexes or AI specific Index)

 

 

 

 

Master thesis Proposal  (Revised after professor comments)

 

The Intersection of AI and Stock Markets: Performance
Analysis of the Nasdaq, S&P 500, and tech giants Nvidia, Intel, Amazon,
Microsoft, and Meta

 

Research Area of Interest:

My research interest lies in exploring the intersection
of artificial intelligence (AI) and financial markets
, with a particular
focus on how the widespread adoption of AI has influenced the performance of major
stock indices
like the Nasdaq (representing US technology and
innovation sectors), the S&P 500 (representing the US overall
economy), and potentially other technology-focused or AI indices. I aim
to understand how AI has influenced market behavior, investor
sentiment
, and stock price movements within these indices over the
long term (5 years).

In addition to analyzing indices, I will conduct a comparative
analysis of key tech companies
that are deeply involved in AI, such as Nvidia,
Intel, Amazon, Microsoft, and Meta
. While Nvidia is indisputably linked to
AI, Intel is a latecomer to the AI race, and this contrast makes for an
interesting comparison. Expanding the analysis to other tech giants (Amazon,
Microsoft, Meta) will help provide a broader view of how AI influences both
individual companies and stock market indices. My goal is to address the
broader implications of AI adoption
on the stock market and to draw
conclusions on AI’s importance in driving stock market performance.


Objectives:

  1. Evaluate
    the performance
    of Nasdaq, S&P 500, and
    other relevant technology-focused indices over the past 5 years,
    during a period of AI boom, using stock prices, returns, and volatility measures.
  2. Analyze
    the correlation
    between the performance of
    indices and AI-related companies, including Nvidia, Intel, Amazon,
    Microsoft, and Meta, to assess how AI adoption has influenced their stock
    performance.
  3. Compare
    and contrast
    the macro market results
    (indices) with the micro market results of Nvidia, Intel, Amazon,
    Microsoft, and Meta.
  4. Investigate
    market behavior
    (bullish vs. bearish,
    volatility) and investor sentiment (using both VIX and other
    alternative measures) in response to AI-related announcements, and observe
    their short-term impact on stock prices.
  5. Systematically
    collect and analyze AI-related news
    to
    study its correlation with stock movements across both indices and
    individual companies.


Data Sources and Platforms:

  • Historical
    stock price data
    , returns, liquidity, VIX,
    trading volume, and relevant company/market news will be sourced from
    platforms such as Bloomberg, Reuters, Investing.com, Yahoo Finance, Google
    Finance, and company-specific press releases.
  • Excel
    and R-Studio will be used for data analysis and statistical modeling.
  • The data
    collection process
    for AI-related news will be systematized by
    using news agencies’ archives and AI-related keyword filters on financial
    news platforms. This will ensure a consistent and unbiased selection of
    news articles.


Methodology:

  1. Data
    Collection
    :
    • Gather
      historical data on stock prices, returns, and volatility for the Nasdaq,
      S&P 500, and other technology-focused indices (e.g., Dow Jones
      U.S. Technology Index
      ).
    • Collect
      financial data and news on Nvidia, Intel, Amazon, Microsoft, and Meta,
      focusing on AI-related developments over the past 5 years.
  2. Time
    Series Analysis
    :
    • Perform
      time series analysis on the collected data using R or Excel,
      ensuring that the data is cleaned and preprocessed appropriately (Tsay,
      2002).
    • Develop
      statistical models to analyze relationships between stock/index
      performance and AI-related factors, using methods such as correlation
      and regression – multi regression
  3. Descriptive
    Statistics and Visualizations
    :
    • Generate
      descriptive statistics and charts to visualize the patterns
      and relationships between indices and AI-related companies over time.
    • Use VIX
      and or Implied Volatility to capture the intensity of investor
      sentiment and explore alternative measures (e.g., sentiment analysis
      of news articles
      ) to gauge the polarity of sentiments toward
      AI-related announcements.
  4. AI News
    Analysis
    :
    • Develop
      a systematic approach to collect AI-related news by using
      predefined search filters on platforms like Bloomberg, Reuters, and
      Google News.
    • Perform
      sentiment analysis on AI-related news to assess how it impacts stock
      prices and indices.
  5. Comparative
    Analysis
    :
    • Compare
      the performance of Nvidia and Intel, examining how their differing AI
      strategies have influenced their market valuation.
    • Expand
      the comparison to Amazon, Microsoft, and Meta to provide a broader
      perspective on AI’s impact within the tech sector.
  6. Earnings
    and Revenue Analysis
    :
    • Analyze
      earnings reports and financial statements of Nvidia, Intel,
      Amazon, Microsoft, and Meta to determine the contribution of AI-related
      product lines to their revenue growth.


Expected Outcomes:

  • I
    expect to find positive correlations between AI announcements and
    stock/index movements, particularly among tech giants like Nvidia, Amazon,
    and Microsoft.
  • The Nasdaq
    is anticipated to outperform the S&P 500, given its heavy
    weighting toward AI-driven companies.
  • Nvidia is
    expected to show the most significant gains, while Intel may
    underperform, as it has been slower in adopting AI technologies.
  • The
    inclusion of other AI leaders such as Amazon, Microsoft, and Meta
    will provide a more comprehensive picture of AI’s impact across the tech
    sector.
  • AI is
    likely to have been a significant driver of stock market
    performance over the past 5 years, but the analysis will also account for
    other macroeconomic factors such as interest rates.


Limitations:

  • It is
    difficult to isolate AI’s impact on stock performance due to the
    presence of other factors such as interest rates, inflation, and global
    economic events. Therefore, while AI may be a significant factor, it is
    not the sole driver of stock market trends.
  • The VIX
    measures the intensity of investor sentiment and not its polarity;
    alternative methods will be explored to capture the positive vs.
    negative sentiment
    surrounding AI-related news.
  • The
    inclusion of additional indices and companies may add complexity to the
    analysis, which will be carefully managed to ensure the scope remains
    feasible.


Reference List:

  • Guillen,
    S. (2023). The Impact of AI on Company Stock Returns.
    SSRN. https://ssrn.com/abstract=4595946.
  • Lee,
    J., & Malik, M. (2021). Artificial intelligence and semiconductor
    firms: A comparative analysis of Nvidia and Intel
    . Journal of
    Technology Management & Innovation, 16(2), 23-35.
  • Tsay,
    R. (2002). Analysis of Financial Time Series. John Wiley &
    Sons.
  • Smith,
    A., & White, B. (2020). The Role of AI in Modern Financial Markets:
    An Empirical Study
    . Journal of Financial Economics, 105(3), 567-589.
  • Brown,
    K., & Green, M. (2022). AI, Stock Markets, and Investor Sentiment:
    A Quantitative Analysis
    . Finance Research Letters, 18(4), 345-356.

 

 

 

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