Portfolio Optimization Model for Gold and Bitcoin Based on Weighted Unidirectional Dual-Layer LSTM Model and SMA-Slope Strategy

Abstract

Portfolio optimization is one of the most complex problems in the financial field, and technical analysis is a popular tool to find an optimal solution that maximizes the yields. This paper establishes a portfolio optimization model consisting of a weighted unidirectional dual-layer LSTM model and an SMA-slope strategy. The weighted unidirectional dual-layer LSTM model is developed to predict the daily prices of gold/Bitcoin, which addresses the traditional problem of prediction lag. Based on the predicted prices and comparison of two representative investment strategies, simple moving average (SMA) and Bollinger bands (BB), this paper adopts a new investment strategy, SMA-slope strategy, which introduces the concept of k-slope to measure the daily ups and downs of gold/Bitcoin. As two typical financial products, gold and Bitcoin are opposite in terms of their characteristics, which may represent many existing financial products in investors’ portfolios. With a principle of $1000, this paper conducts a five-year simulation of gold and Bitcoin trading from 11 September 2016 to 10 September 2021. To compensate for the SMA and BB that may miss buying and selling points, 4 different parameters’ values in the k-slope are obtained through particle swarm optimization simulation. Also, the simulation results imply that the proposed portfolio optimization model contributes to helping investors make investment decisions with high profitability.

Identification of runner fatigue stages based on inertial sensors and deep learning

“…The number of layers of the LSTM determines the number of training model parameters, and the more layers, the more parameters (Xue et al, 2022). A single-layer model tends to imply a simple model structure, but is not flexible enough and easily underfitted, while the probability of overfitting increases as the number of model layers increases, and too many layers can easily weaken the generalization ability of the model.…”

Dynamic Portfolio Rebalancing with Lag-Optimised Trading Indicators Using Serofam and Genetic Algorithms

“…Agrawal, Khan, and Shukla (2019) introduce their research using the SMA for technical analysis with a long short-term memory (LSTM) deep learning method for prediction of stock market trends. A portfolio optimization model for trading gold and Bitcoin is reported using a LSTM model and an SMA-slope investment strategy to measure their daily price movements (Xue, Ling, & Tian, 2022). The asset re-allocations are conducted based on risk taking attitudes of investors and several technical indicators, such as moving average, Stochastics oscillator, etc.…”