Abstract
The common goal for investors is to minimise the risk and maximise the returns on their
investments. This is often achieved through diversification, where investors spread their
investments across various assets. This study aims to use the MAD-Entropy model to minimise
the absolute deviation, maximise the mean return, and maximise the Shannon entropy of the
portfolio. The MAD model is used because it is a linear programming model, allowing it to
resolve large-scale problems and nonnormally distributed data. Entropy is added to the MAD
model because it can better diversify the weight of assets in the portfolios. The analysed
portfolios consist of cryptocurrencies, stablecoins, and selected world indices such as the
SP500 and FTSE obtained from Yahoo Finance. The models found that stablecoins pegged to
the US dollar, followed by stablecoins pegged to gold, are better diversifiers for traditional
cryptocurrencies and stocks. These results are probably due to their low volatility compared
to the other assets. Findings from this study may assist investors since the MAD-Entropy
model outperforms the MAD model by providing more significant portfolio mean returns with
minimal risk. Therefore, crypto investors can design a well-diversified portfolio using MAD
entropy to reduce unsystematic risk. Further research integrating mad entropy with machine
learning techniques may improve accuracy and risk management.
Keywords: Entropy; Stablecoins; Diversification; Mean-absolute deviation (MAD);
Mean-absolute deviation-entropy (MAD-Entropy); Mean Variance (MV); Risk; Return.