Comparing Pure Stock Portfolio with Stock and Crypto-currency mixed Portfolio through LSTM to Compare &amp; Analyze Investment Opportunities for Portfolio Performance Measurement

doi:10.34104/ajeit.021.045056

Comparing Pure Stock Portfolio with Stock and Crypto-currency mixed Portfolio through LSTM to Compare & Analyze Investment Opportunities for Portfolio Performance Measurement

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https://doi.org/10.34104/ajeit.021.045056

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LSTM (Long Short-Term Memory) has revolutionized the approach to time series prediction many folds due to its appropriate capability to forecast through Non-Linear forecasting methods. It’s observed that RNN has the capability to similarly think through given enough training in accordance to desired functionality models. Due to the Gated Structure referring to storing relevant information and forgetting the irrelevant information’s LSTM made revolutionary accomplishments towards non-linear forecasting that is dependent on human-like behavior. In this research, we have focused on making a comparison between two different portfolio’s which will depend upon LSTM’s future forecasting capability in terms of predicting the best possible output which gets illustrated through Portfolio Optimization principles

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LSTM has the capability to solve time series processes through by feed forward network using permanent size time windows (Gers et al, 2002)
This research process would describe the following events: 1. Creating a pure Stock Portfolio a) Each stock would get selected under the pretense of solid financial ratio as evidence for investment as suggestion. b) Using LSTM for nonlinear forecasting processes suggests the best stocks for performing well in long only investment method for best portfolio performance under the judgment of best stocks to select under the boundary of a specific time period
If we carefully look at the gated structure of RNN unit in LSTM, we can observe that there are four gates present in the unit

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LSTM has the capability to solve time series processes through by feed forward network using permanent size time windows (Gers et al, 2002). B) Using LSTM for nonlinear forecasting processes suggests the best stocks for performing well in long only investment method for best portfolio performance under the judgment of best stocks to select under the boundary of a specific time period. It’s seen that LSTM is capable of effectively predicting the Bit coin price while learning and training was the only feature for making accurate predictions (Wu et al, 2018).

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