forecasting or predicting stock market price and the trend has been regarded as a challenging task because of its chaotic nature. The stock market is essentially a non-linear, non-parametric, noisy, and deterministically chaotic system because of liquid money, stock adequacy, human behavior, news related to the stock market, gambling, international money rate, and so on. In a country like Bangladesh, it is very difficult to find any prediction of the stock market especially the Dhaka stock market. Because its trends and forecasting depend on various factors. Understanding the pattern of the stock market and predicting their development and changes are research hotspots in academic and financial circles. Because financial data contain complex, incomplete, and fuzzy information, predicting their development trends is an extremely difficult challenge. Fluctuations in financial data depend on a myriad of correlated constantly changing factors. In this paper, financial productprice data are treated as a one-dimensional series generated bythe projection of a chaotic system composed of multiple factors into the time dimension, and the price series is reconstructed using the time series phase-space reconstruction (PSR) method. An RNN-based prediction model is designed based on the PSR method and long and short-term memory networks (LSTMs) for DL and used to predict stock prices and for predicting stock market data trend we use Facebook open-source model prophet The proposed and some other prediction models are used to predict multiple stock indices for different periods. A comparisonof the results shows that the proposed prediction model has a higher prediction accuracy.