Introduction to Stock Price Influencers

Have you ever pondered the elements that influence stock market fluctuations? A variety of factors shape stock prices, including economic conditions, interest rates, investor sentiment, insider trading, financial forecasts, revenue growth, earnings reports, dividends, and stock buybacks. This multitude of influences makes it challenging for both individuals and professionals to accurately predict stock price movements.

The current stock price is primarily determined by the balance of supply and demand at any moment. Supply represents the shares available for sale, while demand signifies how many shares buyers are interested in acquiring. An excess of buyers leads to increased prices as they compete to purchase shares, whereas more sellers than buyers results in price declines until the market finds a balance that attracts buyers.

According to David R. Harper from Investopedia, stock price movements can be categorized into three key factors: fundamental factors, technical factors, and market sentiment. Fundamental factors are linked to a company’s profitability and earnings from its goods and services. Technical factors involve a stock's price history and trader behavior, while market sentiment reflects the overall mood of investors towards a specific security or market, often visible through price fluctuations. Generally, rising prices suggest a bullish sentiment, while declining prices indicate a bearish outlook.

The advent of Machine Learning has introduced advanced algorithms that are increasingly applied to stock market analysis and predictions.

Roadmap for Sentiment Analysis

This article will outline a program designed to assess market sentiments derived from news headlines. We will compare these sentiments with stock price changes over a specified period using machine learning and Python. The steps include:

1. Import Necessary Software Libraries
2. Gather and Parse Data
3. Implement Sentiment Analysis
4. Visualize Sentiments
5. Correlate Sentiments with Price Changes
6. Draw Conclusions

Collecting and Parsing Data

FinViz serves as a valuable resource for investors, providing easy access to stock data. We will collect information for specific stock symbols, such as Amazon (AMZN).

As you scroll down, a list of news articles related to Amazon becomes visible.

We will create an array of stock symbols and the corresponding FinViz URL for data parsing.

symbols = ['MSFT', 'FB', 'NVDA']

Using the Request module in Python, we will send HTTP requests to obtain the data.

... (rest of the data processing code) ...

Visualizing Sentiments

Next, we will group the data by symbol and date to visualize the daily average compound sentiment scores.

plt.rcParams['figure.figsize'] = [9, 6]

news_mean = news.groupby(['symbol', 'date']).mean().unstack()

news_mean = news_mean.xs('compound', axis="columns").transpose()

news_mean.plot(kind='bar')

plt.show()

Visualizing Gains and Losses

We will visualize the correlation between average sentiment scores and stock price changes.

Observations and Conclusions

The findings reveal inconsistencies in the relationship between sentiment scores and stock price movements. For instance, slight positive sentiment was associated with a loss, whereas moderate positive sentiment correlated with gains. These results highlight the necessity for further research to clarify the relationship between sentiment and stock prices.

Thank you for reading! Feel free to leave your thoughts and feedback below.

The first video title is [Python Project] Sentiment Analysis and Visualization of Stock News - YouTube. This video delves into how to perform sentiment analysis on stock-related news articles and visualize the results through Python.

The second video title is Stock Sentiment Analysis using News Headlines - YouTube. This video explores the process of analyzing stock sentiment derived from news headlines and its implications for stock trading.