Purpose: The purpose of the study is to systematically review and examine the effectiveness of corporate governance mechanisms in restraining earnings management among the listed firms of the Casablanca Stock Exchange. Research methodology: In this study, we used the modified Jones model to calculate discretionary accruals. Our sample comprises 27 firms covering the period from 2016 to 2018, analyzed by the EGLS estimator. Results: Our empirical results show that gender diversity, board size, and audit committee independence reduce the managers' discretion. Simultaneously, we found a significantly positive association between earning management and different corporate governance characteristics such as CEO duality, institutional investor ownership, and family ownership. We do not find any evidence that audit committee size, ownership concentration, and managerial ownership significantly influence discretionary accruals. Limitations: This study's main limitation is that we did not address the direction of discretionary accruals, which does not allow us to detect the motivational aspects behind earnings management. Contribution: The results of this study will help Moroccan authorities in their formulation of an appropriate regulatory framework because very few studies have been conducted in this area in the case of the Moroccan listed companies, especially with a large set of governance variables as our empirical model.

Purpose: This work aimed to investigate the impact of triple-A supply chain (SC) on SC performance in Bahir Dar & Kombolcha textile Share Company, Ethiopia. Research Methodology: The study used survey questionnaires as a data collection instrument. Statistical package for social science to purify measurement items & Partial least square structural equation model used to test whether SC agility, SC adaptability, and SC alignment have individual or joint effects on SC performance. Results: The finding indicates that SC adaptability, SC alignment, and SC agility have a significant effect on SC performance. The result also indicates that the joint triple-A SC had a strong impact on SC performance. Limitations: The study focused on two Ethiopian textile share companies and it does not include other companies in the country. Contribution: This study allows us to understand the joint triple-A SC, SC agility, adaptability, and SC alignment-SC performance relationships at a dimensional level and helps to develop a comprehensive research model.

Purpose: This paper discusses major stock market trends and provides information on stock market forecasting. Stock market forecasting is essentially an attempt to forecast the future value of the stock market. Doing this manually can be a strenuous task, and thus we need some software and algorithms to make our task easier. This paper also lists a few of those algorithms, formulas, and calculations associated with them. These algorithms and models primarily revolve around the concept of Machine Learning (ML) and Deep Learning. Research Methodology: This study is based on descriptive, quantitative, and cross-sectional research design. We used a multivariate algorithm model and indicators to examine stocks for investing or trading and their efficiency. It concludes with the recommendations for enhancing trading strategies using machine learning algorithms. Results: This study suggests that after comparing and combining the various algorithms using experimental analysis, the random forest algorithm is the most suitable algorithm for forecasting a stock's market prices based on various data points from historical data. Limitations: The applicability of the study was only hampered by unforeseeable tragic events such as economic crisis, market collapse, etc Contribution: Successful stock prediction will be a substantial benefit for stock market institutions and provide real-world answers to the challenges that stock investors face. As a result, gaining significant knowledge on the subject is quite beneficial for us.