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Prediction and the stock market go hand in hand. Due to the inherent limitations of traditional forecasting methods and the pursuit to uncover the hidden patterns in stock market data, stock market prediction using data mining techniques has caught the fancy of academicians, researchers, and investors. Based on a systematic review of more than 143 research studies spanning 25 years, the present paper brings to light the major issues concerning forecasting of stock markets based on data mining techniques, such as usage of data mining techniques in the stock market, input data types, single versus hybrid techniques, instruments and stock markets researched, types of software and algorithms used, measures of forecast accuracy, and performance of various data mining techniques. Emerging patterns related to various dimensions have been critically analyzed by highlighting the existing limitations and suggesting future research paradigms. This analysis can be useful for academicians, researchers and investors looking for futuristic directions in a given research domain.

Supply chain finance (SCF) is concerned with capital flows throughout a supply chain, an area that has been overlooked in previous decades. Supply chain finance (SCF) is a proven way for lowering financing costs and increasing finance efficiency. The purpose of this research is to look at the most important aspects of supply chain financing that have recently emerged. To map the knowledge structure of this topic, SLR and network analysis on SCF were conducted. In order to perform this systematic review, a sample of 1086 papers from 2000-2021 were gathered from data sources such as Proquest using search terms. The most often occurring terms in the title and author keywords were determined using Knime Analytics software. Relevant networks were built with the use of Vos viewer to locate prominent nations where research in the connected subject is being conducted, and keyword co-occurrence, authors, journals, countries, and year-wise data were retrieved.

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