• Ashutosh Kaddi

• Vasishta Kuchipudi

• Yashwanth Reddy Kariveda

• Pankaj Shinde

Primary data: Articles of top fortune 500 companies.

Secondary data: Historical stock data of fortune 500 companies using ‘marketstack’ or a similar API.

Using dplyr, tidyverse libraries to clean, transform and pivot longer dataset. Will implement more cleaning techniques once we work on actual data.

Rational choice theory states that individuals use rational calculations to make rational choices and achieve outcomes that are aligned with their own personal objectives. These results are also associated with maximizing an individual’s self-interest.

We believe that the rational choice theory is not absolute, sentiments and emotions will always play a crucial role in any system that is based on human actions. Every passing day more and more individual investors are becoming an active part of the stock market, this seems like an appropriate crossroad to gauge the effect of sentiments on the stock price.

Accurately outlining factors affecting price or even predicting stock prices is definitely not simple. The huge number of variables involved coupled with human inter-dependency makes stock market a virtually chaotic science. But the stock market also tends to be forward-looking, which means that it reflects investor outlook on the economy.

NLP (Sentiment Analysis): After we clean and stage our data, using VADER we will classify each article as POSITIVE, NEGATIVE or NEUTRAL. We will also try to factor in the polarity of the sentiment projected by the article. This can be done by refining a document and extracting keywords which will then be ranked by comparing these words to a predefined lexicon containing polarities.

Then we will plot these sentiments against the stock price for a period and try to establish a correlation.

The media ecosystem as a whole can be considered as the single most impactful factor in forming the sentiments of the ‘market’. Therefore, our evaluation of the study will be based on whether we were able to correlate a particular stock’s trend against the sentiment demanded by the company in the news.

We will be cautious about some obvious limitations like we can only collect articles for just a month which is a very small subset.