The global investment landscape has undergone a paradigm shift, focusing on Environmental, Social, and Governance (ESG) factors as the major determinants of financial sustainability in investment decisions worldwide. This study uses predictive modeling to analyze the complex link between ESG variables and investment decisions. Focusing on three key sectors: IT, FMCG, and BFS, the study adopts a predictive modeling approach, recognizing the distinct characteristics and challenges within each sector. The information depends on the data obtained from different places like ESG details, and previous financial performance pointers – EBITDA, EPS, ROE, and P/E for 2018 to 2022, inclusive of general investor behavior. We can do this by working with relevant sources of data together with machine learning methods which show what happens in the Indian market in terms of ESG influencing the market thus leading to sustainable investment outcomes. This article seeks to comprehend why investors may favor sustainability as opposed to their conventional monetary units.

Stock market plays an important role on demonstrating economy direction and it provides good opportunities for people who wish to purchase a small portion of different firms & apos; shares. In this paper, we propose an empirical study to measure the impact of the market size and the ratio of book value on market value on excessive return. The study gathers the necessary information from some of active stock shares traded on Tehran Stock Exchange over the period of 2010-2011. The proposed model of this paper uses linear regression analysis to investigate the relationship between the excessive return and other factors. The study divides the information into seven equal groups and fits the regression model using ordinary least square technique. The results indicate that there is a negative relationship between size and excessive return and a positive relationship between the ratio of BV/MV and excessive return. Although the results of both tests are positive, we have to be more cautious about what have reported on the second hypothesis.

During the past few months, there has been a steady downside trend on oil price. From summer, 2015 to winter, 2016, the oil price has declined to the $20 range. Its rate of decline is amazing, having lost $10, one third of its value, in just one month. This paper presents a survey on recently published studies on relationship between oil price and stock market. The study covers several studies on the effects of oil price on China, India, Lebanon, United States and some other G7 stock markets. We also review the effects of oil price volatility on stock market. The results of most studies have indicated some strong relationships between oil price volatility and stock market return.

The purpose of this paper is to predict the S & P500 down moves with technical analysis indicators using learning vector quantization (LVQ) neural networks and probabilistic neural networks (PNN). In addition, entropy-based input selection technique is employed to improve the prediction accuracies. The out-of-sample simulations show that LVQ outperforms PNN. In addition, the Entropy-LVQ system achieved higher accuracy in comparison with the literature.

Technical analysis has been one of the most popular methods for stock market predictions for the past few decades. There have been enormous technical analysis methods to study the behavior of stock market for different kinds of trading markets such as currency, commodity or stock. In this paper, we propose two different methods based on volume adjusted moving average and ease of movement for stock trading. These methods are used with and without generalized regression neural network methods and the results are compared with each other. The preliminary results on historical stock price of 20 firms indicate that there is no meaningful difference between various proposed models of this paper.