Abstract
Due to its suitable power to anticipate using Non-Linear forecasting methodologies, LSTM (Long Short-Term Memory) has changed the approach to time series prediction several folds. Process compatibilities of technical identifiers and various financial benchmarks that are defining financial decision-making in international markets are affecting Bangladesh Market as well. Issues like MACD and RSI as a technical investigator and financial ratio aspects of EPS and PE Ratio play an important role in the selection of assets in DSE. Given adequate training in line with intended functionality models, RNN has the potential to think through in a similar manner and the probable results are exhibited in this paper. Because of the Gated Structure, which refers to retaining important information and discarding irrelevant information through diminishing gradient and exploding gradient, LSTM has achieved significant advances in nonlinear forecasting that is based on human behavior. In this study, we compared two alternative portfolios that will be dependent on LSTM's future forecasting capabilities in terms of projecting the greatest potential output, which is demonstrated using Portfolio Optimization principles.
Highlights
Due to its suitable power to anticipate using Non-Linear forecasting methodologies, LSTM (Long ShortTerm Memory) has changed the approach to time series prediction several folds
LSTM can handle time series problems using a feed financial decisions always lead to better, profitable forward network with fixed time windows (Gers et financial decisions (Wang et al, 2011)
LSTM occurs inside the confines of training under a Various technical indicators like MACD and relative strength index (RSI)
Summary
The most important factor is volatility still plays a crucial role for financial investstill financial knowledge and risk perception in the ment (Bodie, 1995). Since human thinking ability determines the overall we will figure out which stock has both better investment outlook of market behavior Combined with non-linear methods, LSTM is feature selection when it comes to learning the pattern believed to be able to make accurate and positive of choices depending on diverse scenarios. It is built predictions about the future aspects of any growth on a dilated causal convolution network that seeks to model (Livieris et al, 2020). Standards for identifying the finest equities to invest Formula as follows: in
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