Quantitative Investment Diversification Strategies via Various Risk Models

TL;DR

This paper develops and tests various high-dimensional risk models for portfolio diversification in the US stock market, aiming to optimize profits and minimize risks over a long-term period from 1970 to 2023.

Contribution

It introduces new high-dimensional risk models and evaluates their effectiveness in portfolio construction through extensive out-of-sample testing.

Findings

Certain risk models outperform traditional methods in risk-adjusted returns.

Diversification strategies based on these models reduce portfolio volatility.

Long-term tests confirm the robustness of the proposed models.

Abstract

This paper focuses on the developing of high-dimensional risk models to construct portfolios of securities in the US stock exchange. Investors seek to gain the highest profits and lowest risk in capital markets. We have developed various risk models and for each model different investment strategies are tested. Out of sample tests are performed on a long-term horizon from 1970 until 2023.