Over the past few days, alarm bells have been ringing for the risk of recession in the world’s leading economies (Germany, United Kingdom, Italy, Brazil and Mexico). Deceleration is affecting several regions in the world and might even become more widespread, exacerbating investor mistrust and financial market instability produced by the U.S.-China trade war, among other uncertainty factors. Investors have transferred part of their investment portfolios to safe haven assets, such as sovereign bonds, with investor mistrust at record lows in recent years.
Managing your investment portfolio when an economic cycle is drawing to an end can be tricky. Even though some experts recommend rebalancing asset allocation, buying treasury bonds, focusing on raw materials, commodities or real estate investment, the most important thing is to learn how to manage risk.
In the current context of uncertainty and adverse market conditions, assessing investment portfolio performance optimization, as well as liquidity risk, is crucial, as explained in the chapter “Theoretical and practical foundations of liquidity-adjusted value-at-risk (LVaR): optimization algorithms for portfolio selection and management” of the recently published book Expert Systems in Finance. Smart Financial Applications in Big Data Environments (Routledge, Taylor & Francis Group, 2019)
The concept of “portfolio optimization” was first introduced in 1952 by Markowitz, referring to finding the optimal investment portfolio in relation to the distribution of risk and returns for a given period. However, this approach has its limitations, since optimized portfolios do not normally perform as well in practice as in theory.
As a result, the use of robust and novel Value at Risk (VaR) modeling algorithms and techniques has become increasingly popular, particularly since the 2007-2009 global financial crisis. Although market risk measurement methodologies are fairly standardized, academics have not paid much attention to liquidity risk. Nevertheless, evaluating this type of risk is of utmost importance in emerging markets, such as Latin America, which are typically more liquid than developed markets.
Measuring and predicting liquidity risk is complex, since it depends on numerous interconnected factors. The optimization algorithm I have formulated seeks to improve the asset distribution process in multiple-assets portfolios, combining robust LVaR (Liquidity Value-At-Risk) models with advanced expert systems modeling techniques. This modeling algorithm attains better results than Markowitz's mean-variance method, being a more solid portfolio selection and management tool that includes real-world applications for investment funds, risk managers and financial institutions, as well as for regulators and legislators in both developed and emerging economies, in particular after the last 2007-2009 global financial crisis.
These optimization algorithms and smart financial applications have the potential to produce realistic risk-return profiles, making it easier to understand the implicit risks and build better investment multiple-assets portfolios. They are also useful for asset managers since they emphasize a more rational asset allocation, considering the last subprime or high-risk mortgage crisis.
