Week 1 - Welcome to my Senior Project!

Hello everybody and welcome to my Senior Project! Over the next few months, I will try to determine how much news articles affect the trends of the stock market. My plan is to take stock market data from March 1, 2023 to September 1, 2023 and try to predict stock market prices for the next 6 months using the ARIMA model. Then, I will take news articles (an external influence) from the next 6 months (September 1, 2023 to March 1, 2024) and determine if they can explain the discrepancies between the ARIMA model’s predictions and the real life stock market prices by conducting sentiment analysis on the news. Today, I’ll talk about the background and inspiration behind my project. 

If you invest in the stock market, you’ve probably read at least a couple articles on Yahoo Finance or CNN to try to predict which companies to put your money into. There are many theories and observations that the news has some sort of impact on the stock prices, but there isn’t any definite research stating the exact effects that these articles have on the market. There are also many different types of news articles that could have varying effects (ex. positive/negative earnings reports, mergers/acquisitions, scandals). In this project, I will focus on 7 stocks known as the “Magnificent 7” that are said to have the biggest influence on the stock market. These stocks are Google (GOOG), Amazon (AMZN), Apple (APPL), Meta (META) , Microsoft (MSFT), Nvidia (NVDA), and Tesla (TSLA). 

First, I will create a picture of what the market for my chosen stocks would look like if their prices fluctuated in a mathematical manner. To do so, I will use a statistical model to predict stock prices solely based on the numerical data inputted into it. The ARIMA model (Auto Regressive Integrated Moving Average) is a machine learning model that uses time series data to learn from past datasets to understand and predict future trends. Research has shown that the ARIMA model is one of the best for forecasting stock prices/trends. It has three main qualities that allow it to do so. First, it can look at and learn past trends. Second, it can factor in changes and patterns in the data into its predictions. Third, it has a moving average to consider error terms. However, the stock market is extremely volatile and may therefore unexpectedly change very quickly, a trend which the model may not be able to predict based on past data. This is expected because my results from the ARIMA model will serve as a mathematical prediction of the stock market eliminating the effect of outside factors (which is not the case in real life). 

Next, I will analyze news articles using a common algorithm to determine what kind of effect they’ll have on the market. Sentiment analysis is the process of analyzing digital text to determine if the tone is positive, negative, or neutral. Sentiment analysis has been proven a very reliable way to classify words based on their tone as linguists tend to agree 85% of the time (giving the model an 85% average accuracy rate). This shows that it is a much more effective method of word classification than other models, such as AutoML. There are multiple ways to conduct sentiment analysis (from asking LLMs such as Chat GPT to classify words/news headlines to creating your own sentiment analysis model. For the purposes of ensuring my model is accurate, I will create my own to conduct this experiment. I will refer to various articles and research papers such as “News Sentiment Analysis” by Samuels and Mcgonical to create my model, as theirs has a high accuracy rate and has proven to be reliable for the purpose that I need it for. After determining whether the news articles I analyze are positively, negatively, or neutrally skewed, I can make conclusions on whether or not they can account for the discrepancy between the predicted versus real life stock prices. 

Many people, including myself and my family, look to the news as the primary source for financial information. However, sometimes the news may not represent the full picture and may even mislead readers who utilize these articles to make their decisions. I want to determine whether primary financial news outlets are really the best method for learning about corporate actions and decisions or whether people should look to other sources instead. I hope that by conducting this project, I am able to help people make better financial decisions by informing them how influential the news is on stock prices. Stay tuned to discover what I find out!