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Diversification is a fundamental strategy aimed at managing risk by spreading investments across different asset classes, industries, or geographic regions. The core principle is to avoid putting all eggs in one basket, reducing the impact of poor performance in a particular investment on the overall portfolio. By holding a variety of assets that respond differently to market conditions, diversification seeks to achieve a balance where the positive performance of some holdings can offset the negative impact of others. This approach helps investors navigate market uncertainties and fluctuations, contributing to the preservation of capital and the potential for more consistent returns over the long term.
So far, the majority of research has focused on diversification within various asset classes, with little or no attention given to diversification within investment strategies.
Reference [1] investigated diversification within investment strategies, specifically exploring diversification based on various types of assets, different trading models, and a combination of both. The authors pointed out,
Based on the results for five different assets (BTC, GLD, SPX, UNG, and ZWF), in the period from 2007 to 2022, we verified a few different research questions focusing on individual and ensemble algorithmic investment strategies using various types of theoretical models. The ensemble process used in this research for the first time focused on 3 different surfaces of single strategies combination, i.e. based on 1) various types of assets, 2) various theoretical models, and 3) a combination of both of them.
We verify the diversification potential of investment strategies for the equity index (S&P 500 index) based on various theoretical concepts against other investment strategies (RQ1). Therefore, referring to RQ1: Which of the tested groups of assets (energy commodities, cryptocurrencies, gold, or soft commodities) have the largest diversification potential in the complex algorithmic investment strategies, built with machine learning models and ARIMA-GARCH models for equity indices?, based on the results presented in Table 5 and Figure 4, we can state that only ensemble BTC has the diversification potential that increases the efficiency of ensemble models for the equity index. Moreover, taking into account that the distribution of returns for other equity indices is quite similar to that of the S&P 500 we are sure that our conclusions can be extended to them, as well.
In short, BTC strategies serve as the most effective hedge for SP500-based strategies.
To our best knowledge, this paper is the first to formally address diversification within investment strategies.
Let us know what you think in the comments below or in the discussion forum.
References
[1] Michańków, Jakub and Sakowski, Paweł and Ślepaczuk, Robert, Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices (September, 2023). https://ssrn.com/abstract=4585494
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