There is almost no correlation under any circumstances between non-financial news sources and price movements, however there is some correlation between financial news sentiment and stock price movements. Our results indicate that there is very limited correlation between Twitter sentiment and price movements and that this does not change much when returns are taken relative to the market or when the market is calm or turbulent. In this paper we examine whether daily news sentiment of several companies and Twitter sentiment from their CEOs have an impact on their market performance and whether traditional news sources and Twitter activity of heads of government impact the benchmark indexes of major world economies over a period spanning the outbreak of the SAR-COV-2 pandemic. Twitter has been responsible for some major stock market news in the recent past, from rogue CEOs damaging their company to very active world leaders asking for brand boycotts, but despite its impact Twitter has still not been as impactful on markets as traditional news sources. However, classifying analyst reports into opinion and non-opinion sentences is insignificant for the forecasts.

Consequently, we obtain an indication that the analyst profile effectively improves the model forecasts. As analyst profiles, we used the name of an analyst, the securities company to which the analyst belongs, the sector which the analyst covers, and the analyst ranking. In addition to analyst reports, we input analyst profiles to the networks. Then, we employ the proposed method to forecast the movements of analysts' estimated net income and stock price by inputting the opinion and non-opinion sentences into separate neural networks. First, we apply the proposed method to extract opinion sentences from the analyst report while classifying the remaining parts as non-opinion sentences. Our methodology is based on applying natural language processing and neural networks in the context of analyst reports.

This article proposes a methodology to forecast the movements of analysts' estimated net income and stock prices using analyst profiles.

It may be concluded that positive sentiments nullified the negative sentiments where the overall sentiments have a mean subjectivity score that is more leaning towards the less-opinionated side, which may be accounted for the lack of abnormality was found on the event day as well as the adjustment period. This study observes that the range between the positive and negative sentiments is minimal and there is no abnormal returns found post the budget announcement in the event study. Event Analysis identifies whether any abnormal returns are seen on the event day and the subsequent 10 days. This has been made possible by using a supervised machine learning algorithm. Sentimental analysis tries classify the tweets on Bitcoin on the budget day into Positive or Negative and deciphers the overall sentiment of the bitcoin investors on the Budget Day and the subsequent 2 days. This study aims to understand the corroboration of the results of Sentimental analysis with Event Analysis on the Indian Budget 2022 announcement on cryptocurrency.

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