Forecasting Stock Prices: A Machine Learning-Based Approach for Predictive Analytics Through a Case Study

Stock price prediction has always been a challenging task, requiring careful observation of trends and dynamics of the market because of the volatile and complex nature of financial markets. Various factors affect market behavior all the time. Even some unquantifiable factors like 25 Oct 2025 (Published Online) emotions of the masses, social and political dynamics, etc., also play a great role. So perfect Machine Learning, Deep Learning, behaviors into consideration is crucial for better prediction of the ups and downs of prices. SMA, EMA, RSI, MACD, Bollinger Various machine learning and deep learning models have been proposed to tackle the challenges Bands, RFE, Random Forest by capturing and interpreting complex patterns and relationships in historical price data. Regressor, Multivariate Analysis, Technical features are important for understanding market trends and thus improving the LSTM. accuracy of stock price predictions. In this paper, we calculate key technical indicators such as Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Bollinger Bands, and others. We then focus on selecting the most relevant indicators by employing feature selection methods from these to enhance the extraction of meaningful features reflecting underlying market behavior and increase the probability of more precise prediction. Here, Recursive Feature Elimination (RFE) and Random Forest Regressor-based importance ranking methods have been applied for the feature selection task. To get a better forecast of market price, it is important to capture long- term dependencies and patterns over time. Long Short-Term Memory (LSTM) networks are well- suited for modeling and predicting sequential data like stock prices. By leveraging an LSTM model and taking the selected features, we do a multivariate analysis to forecast stock price based on historical data, identifying the trends fairly accurately with some lags here and there.

Digital Transformation in Business: Strategies and Implications for Organizational Change

By MD Ahsan Ullah Imran

Advanced algorithms, robotics, and analytics, among other digital technologies, are revolutionizing the dynamics of the workforce in organizations. Hence, the writers of this study have examined the consequences of emerging technology on Organizational Behavior. A significant proportion of the existing research on this topic has primarily examined the technology aspects, while neglecting the comprehensive perspective and its impact on organizational behavior. The uniqueness of this study resides in its ability to offer a comprehensive overview of the key digital technologies and assess their impact on employees and leadership. In order to achieve this objective, and considering the current relevance of the subject, the authors chose to examine the effects of digital technologies on organizational behavior. They accomplished this by conducting a thorough analysis of existing literature and organizing it according to the specific technologies and their implications. The article is divided into three sections. Firstly, the definitions of Organizational Behavior and digitalization were examined to establish a theoretical framework. This was followed by an analysis of the impacts and effects of digitalization on leadership and employees. Finally, the findings were summarized in a structured scheme.

Perioperative Medicine: Investigating Preoperative and Postoperative Management, Including Reducing Complications in Diabetic and Obese Patients

By Sheikh Ummey Salma Tonu

Perioperative Medicine: Investigating Preoperative and Postoperative Management, Including Reducing Complications in Diabetic and Obese Patients

Virtual Classrooms: An Inclusive Approach to Educate the Children with Autism

By Raiyan

Virtual Classrooms: An Inclusive Approach to Educate the Children with Autism

Green Finance and Its Impact on Sustainable Investment Strategies in the US

This research aims to analyze green finance's applicability in forming sustainable investment policies in the USA. This research fills a literature gap to uncover the long-run equilibrium co- integration between FDI inflows, CO2 emissions, renewable energy, and renewable electricity. 25 Oct 2025 (Published Online) Using VECM and Johansen co-integration tests, this paper discusses the long-run relationship of Green Finance, Foreign Direct gathered from the World Bank. The analysis reveals that the two variables are co-integrated over Investment (FDI), Sustainable the long run, though there is a short-run time-varying co-integration relationship. For instance, Investment, Renewable Energy, and the co-integration test results present a trace statistic of 72.77, and its p-value is 0.0001, which CO₂ Emissions justifies the existence of co-integration, which is a long-term equilibrium. The IRF analysis also shows that renewable energy consumption positively affects FDI, and levels off at 0.28 after 4 periods, whereas CO2 emissions have a negative long-run effect on FDI with a coefficient of - 4.9153. Based on these findings, applying green finance policies for renewable energy import can encourage foreign investments in the short run. However, the cost involved in shifting to renewable energy sources may lead to a restricted number of long-term investments. This motivated the study to recommend a search for more information on such sector dynamics.

Technology-Assisted Parent Training Programs for Autism Management

By Rayhan Khan

Technology-Assisted Parent Training Programs for Autism Management