Ethical Challenges of Asset Allocation
For a long time economic and financial models have been crafted and maintained with a rather naive and perilous assumption - this artificial and complex system of economy and associated investments is completely detached from the impact of social inequality, environmental deterioration and corporate malfeasance. Even though financial pundits would like to convince us about the infinite nature of assets and extreme financial assets like “Synthetic” collateralised debt obligations (CDO) tried to create financial assets rather creatively, assets of Mother Earth are limited and finite. Through the reawakening of awareness around Sustainable and Responsible Investment (SRI), realization has finally struck the ivory tower of finance and economics that our corporations, our society, our ecology, our beautiful planet are inexplicably intertwined through a series of complex and multidimensional interactions, often delaying the manifestations of certain harmful actions by several years to surface. At this juncture, it’s paramount that individual investors and professional portfolio managers include ESG (Environmental, Social and Governance) drivers while constructing financial portfolios, evaluating risk-return trade-offs and especially deciding the optimal asset allocation.
In relation to the ethical challenges for portfolio asset allocation, primary objectives for individuals investors differ rather substantially than those from portfolio managers
Regulatory Obligations: For example, large institutional investors like Pension Funds or Superannuation Funds (as in Australia) often have legal or regulatory directives to ensure sustainable future cash flows for their members (retirees) forcing fund managers to focus singularly on long-term capital gains often downplaying or completely ignoring ESG drivers.
Risk Modeling: For most portfolios, inclusion of Responsible Investments (RI) elements (via sector allocation as an asset allocation strategy) introduces additional risk elements. Environmental Risks, Social Risks and Governance Risks are harder to quantify and highly stochastic in nature, making the risk modeling even more complicated. Many ESG datasets demonstrate strong tendencies of non-Normality or “long and fat tailed” distributions invalidating usual standard deviation as a measure of risk. cVaR (Conditional Value At Risk) is usually adopted by SRI experts as a candidate measure of risk as far as asset allocations of ESG investments are concerned.
Expected Returns of ESG Elements: As stochastic processes, impact manifestations of ESG elements are slow, subtle and convoluted. To complicate things even further, underlying neoclassical economic models never properly incorporated environmental assets and social influences - be it in microeconomics or in macroeconomics. This complicates the estimation of (or deducing the distribution for) expected returns (E[r]) from ESG investments or ESG equities. Even if quantitative challenges are somehow made tractable, the bigger question of sacrificing shorter-term gains at the cost of longer-term sustainable growth remains.
Motivation: Continuing from last section, why an investor or a portfolio manager would be interested in allocating assets towards ethically “good” investments? Why bother moving beyond mathematical eloquence of Optimal Risky Portfolio or Optimal Complete Portfolio? If Agrochemical sector in India is demonstrating an expected return of 25% (CAGR) with a standard risk (standard deviation; not ESG-accounted risk such as cVaR) of 10% what additional motivation can divert an investor away from allocating assets to equities from the Agrochemical sector which is one of the major contributors of environmental pollution and ecological toxicity? How do we measure the impact of ecological toxicity to begin with? Even with the assumption of the existence of a broad Ecological Toxicity Metric, how this metric would influence the bottom lines of various companies in the Agrochemical sector (and thus hypothetically reducing effective expected return from 25% to 12% for example) remains to be seen. In this regard, Triple Bottom Line concept introduced by John Elkington (in 1994) is a paradigm-shifting stride in order to establish a consolidated accounting framework incorporating social and environmental bottom lines in addition to purely economic bottom lines for for-profit companies. Governments can play a crucial role here by partially translating various ESG risks to several categories of Pigovian taxation policies (for example - carbon tax) penalising certain sectors and effectively reducing their relative attractiveness from an asset allocation perspective.
Investment Tenure: ESG risks and opportunities are usually long-term in nature and manifest their impacts over a time period much longer than quarterly or even annual financial reporting timelines. As investors fails to estimate ESG value-creation over 10-20 years, a continuous conflict of shorter-term financial goals and longer-term value-creation critically influences asset allocation strategies during portfolio construction of individual and institutional investors.
Quantifying and linking ESG drivers to bottom lines is a necessary but not a sufficient step to encourage individual and institutional investors to consider ESG attributes while allocating assets especially for longer time horizons. Much more important requirement is to provide shorter-term impetus either via strategic taxation policies or concentrated efforts towards Impact Investment going beyond the usual boundaries of traditional SRI. Allocating assets towards sectors addressing the dual purpose of providing satisfactory financial returns and ESG-impact investment (doing business by doing good as the fundamental business model) directives can be mandated by governments, especially for large institutional investors like Pension Funds. Till the complete quantitative framework elucidating various ESG risks gets standardized, allocating assets to Energy or Health sub-sectors (for example - BioTech sub-sector building drugs for economically challenged communities with lower cost-base, higher efficacy and higher manufacturing scale) can be a smart interim choice.