Financial Market Volatility & Investor Sentiment Analysis Blog Operation-(How I Will Run the Sentiment Analysis Blog)

Objective

Analyze financial market volatility and investor sentiment using news articles and social media-data, and present quantitative links between them so that readers can grasp the emotional and psychological factors driving financial decisions.

Key Takeaways

• Data collections: Web scraping gathers financial headlines, articles, and social‑media posts to build a rich sentiment dataset.

• Quantitive Insight: Statistical models reveal how sentiment scores correlate with volatility indices (e.g., VIX) and market returns.

• Knowledge Sharing: Findings are published as regular posts augmented with clear visualisations, code, and open data.

Expected Impact

Consistent, visually driven posts obscure emotion-variability relationships and empower students, investors, and researchers to make evidence-based decisions rather than intuition.

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Preface – Solving the Feelings Behind the Numbers

The blue and red candlesticks jump across price charts every second, but their slopes and volatility are not random at all. Each movement ultimately embeds collective human emotion and interpretation.

Witnessing the extreme market swings of the COVID era, I kept asking: “Can these chaotic patterns be expressed in the language of mathematics?”

This blog sets out to answer that question. By combining mathematical rigor with investor psychology, I will quantify market sentiment with data and reveal its mathematical connection to price dynamics.

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Why Math × Investor Sentiment?

1-1 Psychology: The Hidden Driver of Returns

• If you look at the same interest rate hike headline, some investors shout "Buy the bull market!" while others shout "Crisis is ahead!" Prices move more by the sum of interpretations than by simple facts.

• Quantifying this interpretation (emotion score) allows us to compare and model with market metrics.

  
  

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1-2 Three Mathematical Toolkits

Field	Role in Sentiment Analysis	example
Probability & Statistics	Identify correlation or regression between sentiment scores and market volatility	Correlation coefficient, linear regression
Linear Algebra	Summarize high-dimensional sentiment vectors using Principal Component Analysis	Keyword vectors → top 2–3 principal components
Calculus & Optimization	Quantify changes in sentiment by measuring the area under the curve before and after an event	Definite integrals to compute sentiment variation

Mathematics translates emotions into measurable variables, replacing intuition with quantitative evidence.

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Analytical Framework

The overall workflow consists of the following four steps:

1. Data Collection

   • News/Media: collect headlines and body texts from Chosun, Yonhap, and other sources.

   • Social Media: : Gather posts from Korean investment communities.

   • Market Indicators: Synchronize major metrics such as KOSPI,KOSDAQ

2. Sentiment Scoring

   • Use free positive/negative word list

   • Count positive words (P) and negative words (N).

   • Compute as a single score.

   • Average the scores for all items collected

3. Basic Statistical Analysis and visual posting

   • Scatter Plot & Correlation: Plot Daily Sentiment vs. VIX l; use the built‑in CORREL function to see the strength

   • Event Comparison: Pick key dates. Compare average sentiment three days before vs. three days after using a two‑sample t‑test.

   • Create line charts of Sentiment Index and VIX side‑by‑side; annotate major events manually.