#Callforpapers The stock market is a complex and dynamic environment that constantly fluctuates. Accurate trend prediction is crucial for investors to make informed decisions. We invite researchers and practitioners to contribute chapters to an upcoming book titled *Unveiling Stock Market Insights: Harnessing Big Data, Machine Learning, and Investor Psychology for Effective Trend Prediction*. Edited by Dr Pushan Kumar Dutta, Prof. Debashis De and Prof Subir Gupta. This book will be submitted to Springer Nature and aims to provide comprehensive insights into leveraging advanced technologies and psychological factors in predicting stock market trends. This book aims to highlight the potential of harnessing big data, machine learning techniques, and investor psychology in predicting stock market trends more effectively. By discussing benefits offered by these approaches, collection/analysis of big data, integration of machine learning algorithms with predictive modeling techniques; this chapter seeks to inspire researchers, policymakers' traders to leverage emerging technologies and psychological insights for enhancing decision-making processes within the dynamic realm of stock markets. **Objectives** The primary objectives of this book editorial are: 1. To introduce the concept of harnessing big data, machine learning, and investor psychology in predicting stock market trends. 2. To discuss the benefits offered by utilizing these approaches over traditional methods. 3. To explore how big data can be collected from various sources to uncover valuable insights. 4. To explain different machine learning algorithms used in analyzing stock market data for trend prediction. 5. To highlight the influence of investor psychology on stock price movements and its integration into predictive models. **Submission Guidelines** Interested authors are invited to submit an extended abstract (500-800 words) outlining their proposed chapter content. The abstract should clearly state the objectives, methodology, and expected outcomes of the chapter. Please include a brief author biography along with contact details. Abstracts should be submitted via email to [email protected] by 1st September, 2023 . Authors whose abstracts are accepted will be notified by [10th September, 2023] and requested to submit their full chapters (approximately 15-20 pages) for further review. We look forward to receiving your contributions and collaborating on this exciting book project that aims to advance knowledge in predicting #stockmarket trends using #bigdata, #machinelearning techniques, and #investorpsychology.