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This study investigates the complex interplay between public sentiment, as captured through news titles and descriptions, and the stock prices of three major tech companies: Microsoft (MSFT), Tesla (TSLA), and Apple (AAPL). Leveraging advanced analytical methods including Pearson correlation, wavelet coherence, and regression analysis, this research probes the degree to which stock-price fluctuations can be attributed to the polarity of media sentiment. The methodology combines statistical techniques to assess sentiment's predictive power for stock opening and closing prices, while wavelet coherence analysis unveils the temporal dynamics of these relationships. The results demonstrate a significant correlation between sentiment polarity and stock prices, with description polarity affecting Microsoft's opening prices, title polarity influencing Tesla's opening prices, and a positive impact of title polarity on Apple's closing prices. However, Tesla's stock showed no significant coherence, indicating a potential divergence in how sentiment affects stock behavior across companies. The study highlights the importance of sentiment analysis in forecasting stock-market trends, revealing not only direct correlations but also lagged influences on stock prices. Despite its focus on large-cap tech firms, this research provides a foundational understanding of sentiment's financial implications, suggesting further investigation into smaller firms and other market sectors. [ABSTRACT FROM AUTHOR]

(1) Background: Since the current crises that has inevitably impacted the financial market, market prediction has become more crucial than ever. The question of how risk managers can more accurately predict the evolution of their portfolio, while taking into consideration systemic risks brought on by a systemic crisis, is raised by the low rate of success of portfolio risk-management models. Sentiment analysis on natural language sentences can increase the accuracy of market prediction because financial markets are influenced by investor sentiments. Many investors also base their decisions on information taken from newspapers or on their instincts. (2) Methods: In this paper, we aim to highlight how sentiment analysis can improve the accuracy of regression models when predicting the evolution of the opening prices of some selected stocks. We aim to accomplish this by comparing the results and accuracy of two cases of market prediction using regression models with and without market news sentiment analysis. (3) Results: It is shown that the nonlinear autoregression model improves its goodness of fit when sentiment analysis is used as an exogenous factor. Furthermore, the results show that the polynomial autoregressions fit better than the linear ones. (4) Conclusions: Using the sentiment score for market modelling, significant improvements in the performance of linear autoregressions are showcased. [ABSTRACT FROM AUTHOR]

The article aims to provide a perspective on economic growth by relying on the influence and use of the stock market as an economic lever. Two methods will be used: a quantitative one, determined by a multiple linear regression model, and a qualitative one that encumbers a sustainable vector model for generating economic growth. The data panel covers 36 states, for a period of 21 years. The paper manages to identify the main control functions that the stock exchange has over the macroeconomic context, through the quantitative and qualitative method, and to highlight the most important positive and negative attributes of using qualitative methods, in contrast to quantitative ones. The results show a predominant probabilistic characteristic of quantitative methods, in contrast to the flexibility and complexity of the qualitative method, which has been used. Additionally, the quantitative method offers a strictly cartesian perspective for determining future scenarios, while the sustainable vector model, based on a fractalized vision of reality, manages to capture a plurality of perspectives, as well as the interrelationships between the determining parameters, thus being a complex system of simple equations, as opposed to the quantitative method which is defined as a simple system of complex equations. [ABSTRACT FROM AUTHOR]