Predicting trends in the stock market is becoming complex and uncertain. In response, various artificial intelligence solutions have emerged. A significant solution for predicting the trends of a stock’s volatile and chaotic nature is drawn from deep learning. The present study’s objective is to compare and predict the closing price of the NIFTY 50 index through two significant deep learning methods—long short-term memory (LSTM) and backward elimination LSTM (BE-LSTM)—using 15 years’ worth of per day data obtained from Bloomberg. This study has considered the variables of date, high, open, low, close volume, as well as the 14-period relative strength index (RSI), to predict the closing price. The results of the comparative study show that backward elimination LSTM performs better than the LSTM model for predicting the NIFTY 50 index price for the next 30 days, with an accuracy of 95%. In conclusion, the proposed model has significantly improved the prediction of the NIFTY 50 index price.

This paper identifies the CEO characteristics that have an impact on the performance of family businesses listed in the Euronext in the post-COVID 19 period. CEO characteristics are evaluated on two dimensions, i.e., personal characteristics and corporate governance mechanisms. A sample of 137 firm-year observations from Portugal, Luxembourg, the Netherlands, Ireland, France, and Belgium was chosen. CEO attributes of age, gender, education, and family membership were combined with corporate governance mechanisms of ownership concentration, CEO duality, CEO directorships, and CEO tenure, to predict return on assets and return on equity, using OLS regression. GMM estimation and Two-Stage Least Squares were employed to establish the robustness of the results. Among CEO personal characteristics, CEO family membership has a positive impact on return on assets, and a positive impact on return on equity. Among corporate governance mechanisms, CEO duality had a negative impact on return on assets, and a negative impact on return on equity. CEO ownership, and CEO tenure had a positive impact on return on assets, and a positive impact on return on equity. This paper’s value lies in its evaluation of the under-researched area of family businesses of Euronext-listed firms. It can be used by family businesses in the region, for the selection and training of CEOs to fulfill the goal of achieving superior financial performance.

Artificial intelligence (AI) is transforming the dimension of financial technology, also known as fintech, by automating procedures, boosting risk assessment, increasing user experience, and making transaction-level fraud detection easier to carry out in real time. Nevertheless, the speedy adoption of AI by the finance sector has raised a lot of concerns on ethical AI governance, data privacy, AI decision-making bias, cybersecurity threats, and regulatory compliance. If poorly regulated, AI in fintech presents prospective risks of financial instability, privacy abuses, and discriminatory lending. This chapter focuses on a framework for responsible AI adoption in which regulators, financial organizations, and AI developers can ensure AI deployment meets ethical, transparent, and accountable standards when deploying with similar technology in fintech. It examines trends in AI adoption in Europe, North America, Asia, Africa, and Latin America as well as best practices from around the world. It also examines strategies for algorithmic transparency, regulatory compliance, and AI risk mitigation. It also talks about global regulatory harmonization, XAI (explainable AI), and ethical AI framework needed for a trustable AI in finance.

This book discusses the financial impact of Artificial Intelligence (AI) on the finance and business sectors. By providing readers with comprehensive insights,