Enhancing Financial Data Visualization for Investment Decision-Making

arXiv:2403.18822v1 Announce Type: cross
Abstract: Navigating the intricate landscape of financial markets requires adept forecasting of stock price movements. This paper delves into the potential of Long Short-Term Memory (LSTM) networks for predicting stock dynamics, with a focus on discerning nuanced rise and fall patterns. Leveraging a dataset from the New York Stock Exchange (NYSE), the study incorporates multiple features to enhance LSTM's capacity in capturing complex patterns. Visualization of key attributes, such as opening, closing, low, and high prices, …

abstract adept arxiv cs.lg data dataset data visualization decision dynamics financial financial markets focus forecasting investment landscape long short-term memory lstm making markets memory movements networks paper patterns price q-fin.tr stock stock price type visualization