CFA Institute

Themes in Alternative Investments. 2023. Shaen Corbet and Charles Larkin, eds. De Gruyter. The alternative investment space continues to grow beyond hedge funds and private equity to embrace various types of financial innovation. This volume affords the topic a rich and varied presentation from several authors, not only of investments but also of themes that occupy this realm of the investment universe. Opacity and illiquidity are part and parcel of this evolution. With new opportunities come challenges in performance measurement, due diligence, and regulation. Technological innovation proceeds apace, effective oversight less so. Analysts, portfolio managers, risk professionals, and regulators will find this work a timely and useful compendium. Officialdom in a regulation-averse incoming presidential administration in the United States that has promoted digital currency with abandon would do well to heed the lessons contained within its covers. CFA charterholders and candidates will also find value in this text as they will increasingly be confronted with the realities and challenges of the ever-changing alternative asset category. The selection of topics in this book appears at first blush to be random. Not so. Rather, the chapters represent a cross-section of issues relevant to the current state of nontraditional investments. Information asymmetry is a common thread, presenting an ongoing challenge to regulators and practitioners who aspire to a greater understanding of the complexities of this category. An account of the Mozambican tuna bond scandal underscores the risks inherent in less developed markets. This study on state-owned enterprise misappropriation of funds earmarked for tuna fishing and maritime security reminds us of how rapidly things can devolve. The revelation of the misused funds occasioned a collapse of the national currency and a sovereign debt default. Poor due diligence and oversight by lenders who approved these loans offer a cautionary tale for risk managers and regulators who deal with higher-risk economies. Relatedly, the discussion and analysis of Silicon Valley Bank’s rise and fall suggest ongoing deficiencies in regulation and policy. Regulatory surveillance and capital requirements arising from the Dodd-Frank Act, enacted in the wake of the 2007-2009 Global Financial Crisis, were intended to head off the collapses of financial institutions of the sort that led to that calamity. Yet a relaxation of the applicability of regulatory scrutiny and stress testing to banks with assets under $250 billion during the first Trump administration afforded SVB freer rein in its underwriting of loans to the technology sector, subjecting it to a far greater degree of industry-specific risks. A confluence of strategic choices, such as the bank’s vast pandemic-era accumulation of deposits that it invested largely in interest-rate-sensitive US Treasury and mortgage-backed securities, along with the exogenous shock of the Federal Reserve’s decision to raise rates to staunch inflation, served the bank poorly when it was hit with a surfeit of withdrawal requests. Finding itself caught out, SVB had to sell fixed-income holdings at a significant loss, which in turn, occasioned a vicious circle of ever-increasing withdrawal requests. This reverberative effect further eroded investor confidence and the bank’s share price, resulting in SVB’s implosion. The implications of this collapse were far-reaching: interest-rate risk management is critical, as is portfolio diversification to mitigate sector-specific risks. Centralized and decentralized finance appear to have more in common than would seem to be so at first glance. Opacity, illiquidity, and risk concentration are as relevant in the digital currency space as they are in the world of fractional-reserve banking. The book’s analysis of FTX’s rapid ascent and decline underscores the seemingly ephemeral nature of the burgeoning cryptocurrency industry. Indeed, the company’s travails and downfall should serve as a powerful reminder that the promise and potential of decentralized finance are as fraught with risk as their counterparts in the conventional kind. In this instance, fraudulent conduct was very much at work; the lure of innovation and subsequent disarray emphasizes the importance of rigorous due diligence. More intensive regulation and corporate governance will be critical prospectively. This necessary regulatory rigor should likewise apply to the novel seductiveness of the non-fungible token (NFT), a digitized innovation using the blockchain technology chassis that undergirds cryptocurrencies to create a distinct noninterchangeable item of value. NFTs have gained popularity in art, music, and real estate as a means of identifying a work’s originality and ownership. Yet these items are subject to various types of fraud—rug-pull schemes, price manipulation, illusory value creation, and so-called tech enamorment or undue fascination with the novelty of this technology with indifference to its potentially adverse impact on society. Market saturation of these tokens, the questionable promise of decentralized finance, and the precarity of their value in the wake of the FTX exchange collapse suggests that these are early days for a product requiring more scrutiny and oversight. Two chapters provide an interesting and fairly detailed examination of the challenges of investing in wine. Outside the expertise of most advisors, highly specialized knowledge of such industry dynamics as terroir, weather, vintages, and agriculture is essential, as is knowledge of industry dynamics. The lack of consistent data makes investing in wine a daunting task. And there are different ways to obtain exposure including direct investment, bespoke allocation through the guidance of a wine investment management company, and wine mutual funds managed like hedge funds. In addition, the sector lacks quality data, and there are varying opinions on risk and return measurement. Investment advisors would suggest a small allocation to this sector. Would it be better to imbibe than invest? Another chapter revisits what can be considered more traditional alternative investments. As the discussion of private equity and hedge funds makes plain, regulation is often uneven and incomplete. In the aftermath of the Global Financial Crisis, the private market space has been subject to greatly expanded regulation. Views contrast on its benefits in a realm where opacity is necessary to achieve alpha yet simultaneously presents risks to consumers. As hedge funds and private equity funds have grown since the crisis, they have presented systemic risks that regulation needs to address. The emergence of the Dodd-Frank Act

In the ever-evolving landscape of artificial intelligence (AI), the trends point toward an insatiable appetite for larger, more powerful models. Large language models (LLMs) have become the torchbearers of this trend and epitomize the relentless quest for more data, more parameters, and inevitably, more computational power. But this progress comes at a cost, one not adequately accounted for by Silicon Valley or its patrons — a carbon cost. The equation is straightforward yet alarming: Larger models equate to more parameters, necessitating increased computations. These computations, in turn, translate to higher energy consumption and a more substantial carbon footprint. While the benefits of AI, which range from predicting weather disasters to aiding in cancer research, are clear, the environmental viability of less critical applications, such as generating AI-based superhero selfies, are more open to question.  This predicament brings us to the heart of a significant challenge in modern computing: Moore’s Law. For decades, this axiom has anticipated the exponential growth in computing power. However, this growth has not been matched by a proportional increase in energy efficiency. Indeed, the environmental impact of computing, especially in the field of AI, is becoming increasingly untenable.  These ecological costs are profound. Data centers, the backbone of AI computations, are notorious for their high energy demands. The carbon emissions from these centers, which often rely on fossil fuels, contribute significantly to global warming and stand at odds with the growing global emphasis on sustainability and environmental responsibility.  In the era of net zero, corporate environmental responsibility is under intense scrutiny, and numerous companies are quick to trumpet their commitment to energy efficiency. Often they acquire carbon credits to balance their carbon footprint, even as critics dismiss such measures as mere accounting maneuvers rather than a substantive change in operational behavior. In contrast, Microsoft and other select industry leaders are pioneering a more proactive approach. These firms are optimizing their energy consumption by conducting energy-intensive processes during off-peak hours and synchronizing their operations with periods of maximum solar output and other times of higher renewable energy availability. This strategy, known as “time-shifting,” not only mitigates their environmental impact but also underscores a tangible shift toward sustainability. Enter the realm of environmental, social, and governance (ESG) regulation, a framework that encourages companies to operate in a socially responsible way and consider their environmental costs. ESG scores, which rate companies based on their adherence to these principles, are becoming a crucial part of investment decisions. AI development, with its high energy demands, faces a unique challenge in this regard. Companies involved in AI research and development must now reconcile their pursuit of technical innovation with the necessity of maintaining a favorable ESG score. But have the ESG vendors caught on to this hot problem?  In response to these challenges, carbon aware, green AI, and eco AI and other concepts are gaining traction. These initiatives advocate for more energy-efficient algorithms, the use of renewable energy sources, and more environmentally conscious approaches to AI development. This shift is not just a moral imperative but also a practical necessity, as investors and consumers increasingly favor companies that demonstrate a commitment to sustainability.  The AI community is at a crossroads. On one hand, the pursuit of larger and more complex models is propelling us toward new frontiers in technology and science. On the other, we cannot ignore the associated environmental costs. The challenge, therefore, is to strike a balance — to continue the pursuit of groundbreaking AI innovations while minimizing their ecological toll. This balancing act is not just the responsibility of AI researchers and developers. It extends to policymakers, investors, and end-users. Policy interventions that encourage the use of renewable energy sources in data centers, investment in green AI start-ups, and a conscious effort by users to favor environmentally friendly AI applications can collectively make a positive difference.  The journey of AI is a story of technological achievement, but it must also be one of environmental responsibility. As we continue to push the boundaries of what AI can accomplish, we must also innovate in how we power these advancements. The future of AI should not just be smart; it must also be sustainable. Only then can we ensure that the benefits of AI are enjoyed not just by current generations but by the many generations to come. If you liked this post, don’t forget to subscribe to Enterprising Investor and the CFA Institute Research and Policy Center. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images / Jordan Lye Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source