Machine Learning Approaches for Investing Strategies in Stock Market

In the ever-evolving world of stock market investing, machine learning has emerged as a game-changing approach. This paper delves into the latest machine learning techniques utilized for making informed investment strategies in the stock market, with a specific focus on identifying the opportune moments to invest. Novel algorithms like Reinforcement Learning, Gradient Boosting Machines (GBM), Random Forest, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) Networksare examined for their potential to predict market trends, analyze stock price movements, and optimize investment decisions. Through the exploration of these cutting-edge methodologies, this research aims to shed light on the transformative impact of machine learning in the realm of stock market investing. With the introduction of robots in manufacturing, the imperative of continuous workplace learning emerges as a cornerstone for acquiring new knowledge and skills. While technical competence development plays a crucial role in understanding how the robot works and mastering skills like (re)programming the robot, this is not enough to create effective Human-Robot Collaboration (HRC). Organizational change frameworks emphasize the importance of preparing employees for change by increasing their readiness. However, current training practices frequently overlook this step. By combining different literature streams, we develop three propositions to guide the design of employee-centered HRC training. We propose that, in addition to training technical competencies (e.g., knowledge about robots), incorporating employee-centered components (e.g., fostering employee readiness) and promoting HRC skills (e.g., coordination) are essential for effective HRC training. Further, the implementation of robots in the workplace is a process in which training should be provided to employees throughout the different phases. This challenges current practices in which training is seen as a one-time event. The present paper aims to advance our understanding of HRC training and encourages the integration of technical and employee-centered elements when designing training for HRC. This will help design successful training initiatives and, thereby, support employee adaptation and organizational functioning when introducing robots in the workplace.