Qualitative and Quantitative Risk Management (QQRM)
We have a new model for integrating qualitative and quantitative risk management (QQRM) in emerging equities markets. Practical experience has shown that emerging markets equities strategies that depend primarily on qualitative or quantitative approaches to portfolio development and risk management are insufficient. Neither qualitative nor quantitative models can fully address all of the pertinent concerns of political and fiscal stability, as well as dynamic economies and structural changes.
Over the years, our approach to integrating the two disparate philosophies in a proof of concept known as QQRM (qualitative and quantitative risk management) has demonstrated that investors can achieve a higher risk-adjusted return than an exclusively return-centric or risk-centric approach.
Most stock investing firms classify their investment method as either qualitative or quantitative, resulting in a spectrum that depends exclusively on judgment or numerically-based approaches. The corresponding risk monitoring and control procedures are embedded in these two alpha-creation philosophies.
A qualitative strategy involves visiting nations in the emerging markets investment universe, engaging with government and business leaders, and evaluating market risks. A quantitative risk framework is based primarily on correlation of matrices that reflect one asset's risk characteristics in relation to the returns of other assets in the investing universe.
In the context of our operations at The Baileys and Partners, we have proved that a hybrid method combining qualitative and quantitative approaches can be effectively utilized in emerging markets equities investing strategy to provide excellent returns while reducing risk. These two opposing ideas are not as diametrically opposed as they appear, and can be effectively blended in a complementary manner to strengthen the deficiencies of their counterpart.
The QQRM is developed and specified at the country level using data from a variety of sources, including surveys and assessments of risk and business environments. We test our country-level QQRM, apply it to security-level data, and run portfolio simulations from 2015 to 2020.
Using QQRM to adjust security-level alpha forecasts can add a risk premium to stocks in the emerging markets investment universe. Countries with higher risk levels may require a more attractive alpha forecast to be included in the portfolio. This technique is always evaluated using a quasi-sharpe ratio, which is a risk-adjusted statistic (annualized return divided by annualized standard deviation).
Recent events have highlighted the significance of factoring political, country, economic, and corporate risk into any value model. The current scenario in Libya, Sudan and the Ukraine-Russia war serve as excellent brief case study examples. As a result of the previous years' unexpected political and social unrest, Libya and Ukraine now faces the risk of total state collapse, terrorism, and civil war, as well as a severe east-west schism caused by the lack of a clear strategy to end the wars.
The preceding examples demonstrate the complex effects of risk factors that are difficult to quantify and are typically excluded from traditional investment models. This technique demonstrates how assessing and applying qualitative risks can affect firm valuation and investment in emerging and frontier markets.
In conclusion, qualitative risk management is to identify and assess risks based on their characteristics and potential impact on an investment or company. This method relies on subjective assessments, which are frequently supported by tools such as risk matrices, expert judgment, and risk categorization. The primary purpose is to prioritize risks based on their likelihood and potential effects, allowing decision-makers to devise methods to minimize or respond to the most serious risks.
Qualitative risk management is especially effective in the early stages of risk assessment, since it provides a comprehensive perspective of the risk landscape while also flagging areas that require more investigation for potential risks.
In contrast, quantitative risk management entails numerically analyzing risks in order to more precisely evaluate their possible impact. This strategy employs statistical methodologies, mathematical models, and numerical data to calculate the likelihood of risks and their potential financial or operational implications.
Monte Carlo simulations, sensitivity analysis, and decision tree analysis are prominent techniques used in quantitative risk management. This technique assists companies in successfully allocating investment money, developing robust risk mitigation strategies, and making educated decisions based on the possible cost and value of various risk solutions by offering a more complete and objective assessment of investment risks.
Note: If you would like to use this QQRM model for your future stock allocation in investments you can reply this email or contact us at zoebailey@substack.com