Assessment of Google stock price movements in relation to financial market anticipations

Abstract

Trading in and out of publicly listed company shares on the stock market helps one to create financial gains. Since it shows both business circumstances and the general status of the economy, the stock market is an important gauge of the financial situation of a country. Although stock market investments have great long-term potential, they are also intrinsically dangerous depending on several elements including political events, economic situation, and market attitude. Forecasting methods raise the possibility of prediction mistakes and frequently bring more computing complexity even if they enhance accuracy. This paper suggests a new hybrid model combining Forensic-Based Investigation Optimization (FBIO) with variational mode decomposition (VMD) and Long short-term memory (LSTM) networks in view of these difficulties. Reflecting larger market anticipations obtained from daily trading data price, the VMD-FBIO-LSTM model is meant to capture the basic patterns in stock price volatility. Incorporating market patterns, daily opening and closing prices, and trading volumes, this paper made use of an eight-year Google stock dataset. While FBIO maximizes the model parameters to improve adaptation to dynamic market circumstances, the VMD approach finds latent patterns within the data that match important market indicators. The model's performance was evaluated using data from Google's stock between January 2, 2015, and June 29, 2023. Compared to the previous models, the VMD-FBIO-LSTM model is more efficient and has better-predicting accuracy and lower error rates. Given that the suggested methodology is always well-suited to handling complex time-series data in the stock market, these findings provide investors and financial experts with new, crucial information.