Background: Predicting stock prices in today’s complex financial landscape is asignificant challenge. An innovative approach to address this challenge is integrating sentiment analysis techniques with the well-established Autoregressive IntegratedMoving Average (ARIMA) model. Modern financial markets are influenced by various factors, including real-time news and social media trends, which demand accuratepredictions. This research recognizes the growing importance of market sentiment derived from news and aims to improve stock price prediction by combining ARIMA’sanalytical capabilities with sentiment analysis. This endeavor seeks to provide aclearer understanding of the intricate dynamics of stock price movements in an eramarked by abundant information and rapidly changing market conditions. The integration of these methods has the potential to enhance the accuracy of stock priceforecasts, offering benefits to investors and financial analysts alike. Objectives: The project involves three key components. It begins by gatheringhistorical stock data for a specific stock ticker and conducting essential data preprocessing. Next, it focuses on extracting news headlines from a prominent financial website and conducting a thorough sentiment analysis of these headlines. Thissentiment analysis provides valuable insights into public sentiment surrounding thechosen stocks, with visualizations representing positive, negative, and neutral trends.Finally, the project aims to combine the findings from both components using an Ensemble Method, resulting in a comprehensive suggestion to user whether to buy,holdor sell the stock. These components collectively aim to improve stock price predictions and assess the adaptability of the ARIMA model to changing market conditionsalong the time and significant events. Methods: This project explores an innovative approach to improve stock pricepredictions, combining the ARIMA model with sentiment analysis methods usingfinancial news data. The study involved collecting historical stock data from YahooFinance, employing moving averages like 5-day, 30-day and 90-day windows, andusing advanced models such as ARIMA for predictions. Our analysis also includestime series plots at various intervals, providing valuable perspectives. Through theEnsemble Method, which integrates quantitative predictions and sentiment analysis,we generated practical recommendations for a five-day forecast. Our work addressedgaps in integrating sentiment analysis into stock prediction models and adapting tochanging market conditions, contributing to the advancement of stock forecastingmethodologies. Results: The ensembled predictive model for stock prices demonstrates favorableoutcomes. The Mean Absolute Error (MAE) is 0.8659, indicating accuracy, and theRoot Mean Squared Error (RMSE) is 0.1732, showing the overall prediction error.The Mean Absolute Percentage Error (MAPE) is 1.8541, suggesting precision in comparison to actual stock prices. The R-squared value is 0.9804, indicating the model’sability to explain variation in stock price data. These findings highlight the model’seffectiveness in providing reliable insights for investors in the dynamic stock market. Conclusions: The analysis with the ARIMA model to enhance stock price predictions. It revealed that sentiment analysis complements traditional methods, providing valuable insights for decision-making. Evaluating ARIMA’s long-term performance suggests adaptable forecasting techniques. This work contributes to advancingfinancial analysis and improving stock price predictions.

The timberlands in the South provide a large resource base for forest-based industry. This resource base is utilized to provide major contributions to the southern economy. Aruna et al. (1997) examined southern forest-based industry economic contributions from the early 1990?s. This study examined the change in economic contributions primarily using 2001 data. In 1992, southern forest-based industries provided 633,367 (full- and part-time) jobs and this increased to 718,176 in 2001, accounting for only 1.3% of the total employment in the South versus 1.5%. Forest-based earnings in the South experienced a real increase of $181 million (1990 dollars) from 1990 to 1998 and accounted for 1.7% of total southern U.S. earnings in both years. The value of shipments attributed to southern forest-based industries increased $22.8 billion in real 1991 dollars which translated into a real increase of $11.0 billion (1991 dollars) in valueded between 1991 and 2001. In 2001, value of shipments increased to 9.6% of the South?s total from 7.8% in 1991 and valueded increased from 8.0% in 1991 to 9.1% in 2001. Although there were increases in the economic contributions of southern forest-based industries, overall there was little in the way of relative change over this time period.

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