Comparing the ARIMA and LSTM Models on the Stock Price of FinTech Companies

Abstract

FinTech companies have emerged as a new force in the financial industry in recent years. Their innovative business models, high growth performance, and broad market prospects have attracted the attention and pursuit of numerous investors. Therefore, predicting the future trend of FinTech company stocks is significant for investors. This paper selects PayPal as the prediction target, collects the closing prices from 2018 to 2023, and uses the Auto-regressive Integrated Moving Average Model (ARIMA) and Long Short-Term Memory (LSTM) to compare the accuracy of the prediction results and select the optimal prediction model. Empirical results show that both the ARIMA and the LSTM models have specific effects in predicting the stock prices of the FinTech industry, with the LSTM model having higher prediction accuracy than the ARIMA model. However, it should be noted that stock price prediction is not entirely accurate but based on historical data and market trends. Therefore, when making investment decisions, it is necessary to consider various factors comprehensively and make scientific investment decisions to achieve better investment returns.