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Active ETF and AI: A Promising or Perilous Combination?

Active ETF and AI: A Promising or Perilous Combination?

Exchange-traded funds, or ETFs, are popular investment vehicles that offer exposure to a basket of securities, such as stocks, bonds, commodities, or cryptocurrencies. ETFs trade on exchanges like stocks, making them easy and convenient to buy and sell. Most ETFs are passively managed, meaning they track an index or a benchmark and aim to replicate its performance. However, some ETFs are actively managed, meaning they have a fund manager who makes decisions on what securities to include in the portfolio and when to buy or sell them. The goal of active ETFs is to outperform the index or the benchmark, rather than just match it.

Active ETFs have been around for more than a decade, but they have gained more attention and popularity in recent years, thanks to the emergence of new technologies and innovations. One of these innovations is artificial intelligence, or AI, which refers to the ability of machines or software to perform tasks that normally require human intelligence, such as learning, reasoning, and decision making. AI has been applied to various fields and industries, including finance and investing. Some active ETFs have started to use AI as a tool or a strategy to enhance their performance and gain an edge over the market.

But how does AI work in active ETFs? And what are the benefits and risks of this combination? I will offer my point of view on these questions, based on the information I gathered from various sources.

How AI Works in Active ETFs

There are different ways that AI can be used in active ETFs, depending on the type and the objective of the fund. Here are some examples:

  • AI can be used to analyze large amounts of data, such as market trends, economic indicators, company fundamentals, and social media sentiment, and generate insights and predictions that can help the fund manager make better investment decisions. For instance, the AI Powered Equity ETF (NYSE:AIEQ) uses an AI system called IBM Watson to build predictive models on the universe of U.S. equities and identify stocks that have the highest potential for capital appreciation. It is equal to 1,000 research analysts, traders and quants working around the clock.
  • AI can be used to automate the trading process, such as executing orders, rebalancing the portfolio, and adjusting the risk exposure, based on predefined rules and algorithms. This can reduce human errors, biases, and emotions, and increase efficiency and speed. For example, the BTD Capital Fund (NYSE:DIP) uses an AI algorithm to trade U.S. stocks based on momentum, volatility, and trend-following factors.
  • AI can be used to create new and innovative investment strategies, such as using natural language processing to analyze the transcripts of corporate earnings calls and identify signals of future performance, or using machine learning to discover hidden patterns and correlations among different asset classes and markets. For instance, the WisdomTree International AI Enhanced Value Fund (NYSE:AIVI) uses an AI model to enhance the value factor by incorporating alternative data sources, such as patent filings, web traffic, and news sentiment.

What Are the Benefits of AI in Active ETFs

The main benefit of using AI in active ETFs is that it can potentially improve the performance and the returns of the fund, by providing more accurate and timely information, by exploiting market inefficiencies and opportunities, and by adapting to changing market conditions. AI can also lower the cost and the risk of active management, by reducing the need for human intervention, by optimizing the portfolio allocation and the trading execution, and by diversifying the sources of alpha. Furthermore, AI can offer more transparency and accountability, by disclosing the methodology and the rationale behind the investment decisions, and by providing performance attribution and feedback.

Some evidence suggests that AI can indeed enhance the performance. For example, an article on CNBC mentioned that using IBM’s Watson platform, the AI Powered Equity ETF (AIEQ) is among the first ETFs to rely on AI for stock selection. It also mention that the information edge of AI is still unclear, but there are signs that some AI-based funds are doing better than their conventional peers. In another research paper published in 2022 by Rui Chen and Jinjuan Ren, titled ‘Do AI-powered mutual funds perform better’, which examined the broader AI capability in the mutual fund domain. They analysed the prospectuses from the EDGAR database of the US Securities and Exchange Commission. It matched them with AI-powered funds using mutual fund data from the CRSP Survivor-Bias-Free US Mutual Fund Database from January 2009 to December 2019. They discovered that these funds do not beat the market in general. However, a comparison reveals that AI-powered funds outperform human-managed peer funds significantly.

What Are the Risks of AI in Active ETFs

However, using AI in active ETFs also comes with some risks and challenges. One of these risks is that AI is not infallible, and it can make mistakes or errors, especially when dealing with complex, uncertain, and dynamic situations. AI can also be affected by data quality and availability issues, such as noise, bias, or gaps, which can impair its accuracy and reliability. Moreover, AI can be vulnerable to cyberattacks, hacking, or manipulation, which can compromise its security and integrity.

Another risk is that AI can be difficult to understand and explain, especially when using advanced and sophisticated techniques, such as deep learning or neural networks. This can create a lack of trust and confidence among investors, regulators, and auditors, who may not be able to verify or validate the logic and the outcomes of the AI system. This can also raise ethical and legal issues, such as accountability, liability, and fairness, when the AI system makes decisions that have significant impacts or consequences.

A third risk is that AI can create new and unforeseen problems or risks, such as market instability, systemic risk, or social and environmental harm. For example, AI can amplify market volatility and contagion, by triggering feedback loops, herd behavior, or flash crashes, especially when many funds use similar or correlated AI strategies. AI can also disrupt the market structure and the competitive landscape, by creating new winners and losers, by increasing the concentration and the power of a few players, or by displacing or replacing human workers.

Summing Up

In conclusion, active ETFs and AI are a promising or perilous combination, depending on how they are used and regulated.

On the one hand, AI can offer significant advantages and opportunities, by enhancing their performance, lowering their cost and risk, and increasing their transparency and accountability. On the other hand, AI can pose significant challenges and threats, by introducing errors and uncertainties, creating trust and ethical issues, and generating new and unforeseen problems and risks.

Therefore, investors, fund managers, and regulators need to be aware and cautious of the benefits and the risks of AI in active ETFs, and adopt appropriate measures and safeguards to ensure that AI is used in a responsible and sustainable manner. For myself, I hope to see how this will work on Bitcoin ETFs. I will try to collect more data to give you a follow up analysis on that.

 

Source: https://za.investing.com/analysis/active-etf-and-ai-a-promising-or-perilous-combination-200596385

Anndy Lian is an early blockchain adopter and experienced serial entrepreneur who is known for his work in the government sector. He is a best selling book author- “NFT: From Zero to Hero” and “Blockchain Revolution 2030”.

Currently, he is appointed as the Chief Digital Advisor at Mongolia Productivity Organization, championing national digitization. Prior to his current appointments, he was the Chairman of BigONE Exchange, a global top 30 ranked crypto spot exchange and was also the Advisory Board Member for Hyundai DAC, the blockchain arm of South Korea’s largest car manufacturer Hyundai Motor Group. Lian played a pivotal role as the Blockchain Advisor for Asian Productivity Organisation (APO), an intergovernmental organization committed to improving productivity in the Asia-Pacific region.

An avid supporter of incubating start-ups, Anndy has also been a private investor for the past eight years. With a growth investment mindset, Anndy strategically demonstrates this in the companies he chooses to be involved with. He believes that what he is doing through blockchain technology currently will revolutionise and redefine traditional businesses. He also believes that the blockchain industry has to be “redecentralised”.

www.anndy.com
j j j

What the EU Gets Right with its New AI Rules

What the EU Gets Right with its New AI Rules

The European Union’s latest effort to rein in artificial intelligence, the AI Act, marks a pivotal step towards regulating a technology that is as pervasive as it is potent. With its public unveiling on January 21, the Act lays a framework that seeks to harness AI’s capabilities while safeguarding the fundamental tenets of trust, ethics, and human rights.

As we unpack the Act’s dimensions, we will weigh its merits against its potential impediments to the trajectory of AI, not just within the confines of Europe but as a precedent for the global stage. The discourse around this groundbreaking legislation is as much about its current form as it is about the dialogue it engenders concerning the future interplay of artificial intelligence with our societal mores and economic frameworks.

Does it strike the right balance?

The AI Act introduces a risk-based regulatory schema, categorizing AI systems into unacceptable, high-risk, limited-risk, and minimal-risk. The Act prohibits ‘unacceptable risk’ AI systems, such as manipulative social scoring and covert emotional manipulation, to protect individual rights. ‘High-risk’ AIs, pivotal in healthcare, education, and law enforcement, face rigorous requirements including human oversight. ‘Limited-risk’ AIs, like chatbots, must disclose their AI nature to users. Lastly, ‘minimal-risk’ AIs, like video games, have minimal regulatory constraints, promoting innovation while safeguarding against abuses.

The AI Act is crafted with the dual goals of fostering technological innovation and upholding fundamental rights. The Act’s targeted regulatory focus seeks to minimize undue burdens on AI practitioners by emphasizing the control of applications with the most potential for harm. However, it is not without its detractors. Critics point to its ostensibly broad and ambiguous language, which may leave too much open to interpretation, potentially leading to legal uncertainties.

The Act’s broad definition of AI as a technology-neutral concept, its reliance on subjective terminology like “significant” risk, and the discretionary power it affords to regulatory bodies are seen as potential stumbling blocks, raising concerns over possible inconsistencies and confusion for stakeholders within the EU’s digital marketplace.

A significant challenge the EU’s AI Act faces is ensuring consistent enforcement across all member states. To address this, the Act constructs an elaborate governance structure that includes the European Artificial Intelligence Board and national authorities, bolstered by bodies responsible for market surveillance. The Act stipulates robust penalties for non-compliance, including fines of up to 7% of global annual turnover. Beyond punitive measures, it emphasizes the role of self-regulation, expecting AI entities to undertake conformity assessments and maintain risk management protocols. The Act also recognizes the importance of global cooperation, considering the divergent AI regulatory landscapes outside the EU.

The efficacy of the Act will ultimately hinge on the collective engagement and adherence of all parties to its stipulated frameworks.

Some pros and cons of the AI Act

The AI Act directly addresses the burgeoning field of advanced technologies, focusing on generative AI, biometric identification, and the nascent realm of quantum computing. These technologies hold transformative potential across diverse sectors including healthcare, education, entertainment, security, and scientific research.

Yet, with great potential comes a spectrum of challenges, particularly concerning ethical issues like bias and discrimination, as well as concerns over privacy, security, and accountability. The Act confronts these challenges head-on by instituting rules and obligations tailored to specific AI categories. For instance, generative AI systems — which can create new, diverse outputs such as text, images, audio, or video from given inputs — must adhere to stringent transparency obligations. This is particularly pertinent as generative AIs like ChatGPT and DALL-E find broader applications in content creation, education, and other domains.

The Act acknowledges the potential for malicious use of generative AI, such as spreading disinformation, engaging in fraudulent activities, or launching cyberattacks. To counteract this, it mandates that any AI-generated or manipulated content must be identifiable as such, either through direct communication to the user or through built-in detectability. The goal is to ensure that users are not deceived by AI-generated content, maintaining a level of authenticity and trust in digital interactions.

Additionally, the Act requires AI systems that manipulate content to be designed in such a way that their outputs can be discerned as AI-generated by humans or other AI systems. This provision aims to preserve the integrity of information and preclude the erosion of factual standards in the digital age.

The AI Act is intentionally crafted to harmonize technological progress with the protection of foundational societal norms and values. The Act’s efficacy is predicated on the meticulous application of these regulations, keeping pace with the rapid development of AI technologies.

Turning to biometric identification systems, these tools are capable of recognizing individuals based on unique physical or behavioral traits such as facial features, fingerprints, voice, or even patterns of movement. While they offer enhancements in security, border management, and personalized access, they simultaneously raise substantial concerns for individual rights, including privacy and the presumption of innocence.

The Act specifically addresses the sensitive nature of biometric identification, incorporating stringent controls over its deployment. It notably restricts the use of real-time biometric identification systems in public areas for law enforcement, barring a few exceptions where the circumstances are critically compelling — such as locating a missing child, thwarting a terrorist threat, or tackling grave criminal activity.

In cases where biometric techniques are employed for law enforcement, the Act mandates prior approval from an independent authority, ensuring that any use is necessary, proportionate, and coupled with human review and protective measures. This regulatory stance underlines a commitment to uphold civil liberties even as we advance into an era of increasingly sophisticated digital surveillance tools.

Harnessed from the enigmatic realm of quantum physics, quantum computing emerges as a technological titan capable of calculations that dwarf the prowess of traditional computers. With the power to sift through vast data and unlock solutions to hitherto intractable problems, its potential spans the spectrum from cryptography to complex simulations, and from optimization to machine learning. Yet, this same capability ushers in novel risks: the crumbling of current cryptographic defenses, the birth of unforeseen security breaches, and the potential to tilt global power equilibria. The European Union’s AI Act, while not directly addressing quantum computing, encompasses AI systems powered by such quantum techniques within its regulatory embrace, mandating adherence to established rules based on the assessed risk and application context. Moreover, the Act presciently signals the need for persistent exploration and innovation in this sphere, advocating for the creation of encryption that can withstand the siege of quantum capabilities.

The Act’s influence on the vanguard of technology is paradoxical. It affords a measure of predictability and a compass for AI practitioners and end-users alike, weaving a safety net for the digital citizenry. Conversely, it may erect hurdles that temper the speed of AI progress and competitive edge, leaving a mist of ambiguity over the governance and stewardship of AI. The true measure of the Act’s imprint will reveal itself in the finesse of its enforcement, its interpretative flexibility, and its dance with the ever-evolving tempo of AI innovation.

Ethical considerations

The ethical tapestry of the AI Act is rich and intricate, advocating for an AI that is at once robust, ethical, and centered around human dignity, reflecting and magnifying the EU’s core values. It draws inspiration from the Ethics Guidelines for Trustworthy Artificial Intelligence, which delineate seven foundational requirements for the ethical deployment of AI, from ensuring human agency to nurturing environmental and societal flourishing. These principles are not merely aspirational; they are translated into tangible and binding mandates that shape the conduct of AI creators and users.

This ambitious ethical framework, however, does not come without its conundrums and concessions. It grapples with the dynamic interplay of competing interests and ideals: the equilibrium between AI’s boon and bane, the negotiation between stakeholder rights and obligations, the delicate dance between AI autonomy and human supervision, the reconciliation between market innovation and consumer protection, and the symphony of diverse AI cultures under a unifying regulatory baton. These quandaries do not lend themselves to straightforward resolutions; they demand nuanced and context-sensitive deliberations.

The ethical footprint of the Act will also depend on its reception within the AI community and the wider public sphere. Its legacy will be etched in the collective commitment to trust and responsibility across the AI ecosystem, involving developers, users, consumers, regulators, and policymakers. The vision is a Europe — and indeed, a world — where AI is synonymous with trustworthiness and accountability. This lofty goal transcends legal mandates, reaching into the realm of ethical conviction and societal engagement from every stakeholder.

In an era where artificial intelligence weaves through the fabric of society, the AI Act emerges as a pioneering and comprehensive legislative beacon, guiding AI towards a future that harmonizes technological prowess with human values.

The Act casts a wide net, touching on policy formulation, regulatory architecture, and the ethical lattice of AI applications across and beyond European borders. It stands as a testament to opportunity and foresight, yet it is not without its intricate tapestry of challenges and quandaries. The true measure of its influence lies not in its immediate enactment but in the organic adaptability and robust enforcement as the landscape of AI shifts and expands.

It’s crucial to articulate that this Act doesn’t represent the terminus of regulatory dialogue but inaugurates a protracted era of AI governance. It necessitates periodic refinement in lockstep with the march of innovation and the unveiling of new horizons and prospects. This legislative framework calls for a symphony of complementary endeavors: the investment in research, the enrichment of education, the deepening of public discourse, and the cultivation of global partnerships.

Embarking on this audacious path to an AI domain that is dependable, ethical, and human-centric is a collective venture. It demands a concerted commitment from all corners of the AI sphere — developers, users, policymakers, and citizens alike. It is an invitation to contribute to and bolster this trailblazing expedition into the domain of artificial intelligence — an odyssey that we all are integral to shaping.

 

 

Source: https://intpolicydigest.org/what-the-eu-gets-right-with-its-new-ai-rules/

Anndy Lian is an early blockchain adopter and experienced serial entrepreneur who is known for his work in the government sector. He is a best selling book author- “NFT: From Zero to Hero” and “Blockchain Revolution 2030”.

Currently, he is appointed as the Chief Digital Advisor at Mongolia Productivity Organization, championing national digitization. Prior to his current appointments, he was the Chairman of BigONE Exchange, a global top 30 ranked crypto spot exchange and was also the Advisory Board Member for Hyundai DAC, the blockchain arm of South Korea’s largest car manufacturer Hyundai Motor Group. Lian played a pivotal role as the Blockchain Advisor for Asian Productivity Organisation (APO), an intergovernmental organization committed to improving productivity in the Asia-Pacific region.

An avid supporter of incubating start-ups, Anndy has also been a private investor for the past eight years. With a growth investment mindset, Anndy strategically demonstrates this in the companies he chooses to be involved with. He believes that what he is doing through blockchain technology currently will revolutionise and redefine traditional businesses. He also believes that the blockchain industry has to be “redecentralised”.

www.anndy.com
j j j

Why Blockchain could be the solution for data quality and ethics in AI

Why Blockchain could be the solution for data quality and ethics in AI

Artificial intelligence (AI) is transforming the world in unprecedented ways, offering new possibilities for innovation, efficiency, and social good. It also poses significant challenges and risks, especially when it comes to the quality and ethics of the data used to train and operate AI systems. Data is the fuel that powers AI, and the quality and ethics of data directly affect the accuracy, reliability, and fairness of AI outcomes. Poor data quality can lead to errors, biases, and inefficiencies, while unethical data collection and usage can violate privacy, security, and human rights. Therefore, it is imperative to ensure that the data that feeds AI is trustworthy, transparent, and accountable. This is where blockchain technology can play a vital role.

Blockchain is a distributed ledger that records transactions in a secure, verifiable, and immutable way. It can provide a decentralised and tamper-proof platform for storing and sharing data among multiple parties, without the need for intermediaries or central authorities. It can also enable smart contracts, which are self-executing agreements that can trigger actions based on predefined rules and conditions. Blockchain can enhance the quality and ethics of data in AI in several ways, such as:

  • Authenticity: Blockchain can verify the origin and provenance of data, ensuring that it is authentic and reliable. The ability to track the changes and modifications made to data over time, creating an audit trail that can help detect and prevent fraud, manipulation, and corruption. For example, it can help verify the identity and credentials of data providers, as well as the consent and permissions of data subjects. It can also help validate the quality and accuracy of data sources, as well as the integrity and consistency of data processing and analysis.
  • Augmentation: I believe it can augment the intelligence and capabilities of AI by providing access to large and diverse datasets that can enrich the learning and performance of AI models. It can also facilitate data sharing and collaboration among different stakeholders, such as researchers, developers, regulators, and users, enabling cross-domain and cross-border data exchange and interoperability. The technology can also leverage smart contracts to automate data transactions and operations, such as data acquisition, aggregation, annotation, cleaning, and labeling, as well as data monetisation, compensation, and governance.
  • Automation: As mentioned in my speeches, blockchain helps to automate the ethical and legal aspects of data and AI, such as compliance, accountability, and transparency. It can embed ethical principles and values into the design and development of AI systems, as well as the data that feeds them. It can also enforce ethical rules and regulations through smart contracts, such as data protection, privacy, security, and consent. It can also provide mechanisms for monitoring, auditing, and reporting the impacts and outcomes of data and AI, as well as for resolving disputes and addressing grievances.

Blockchain and AI are complementary technologies that can create synergies and benefits for each other. I have mentioned very briefly in my earlier article on trends to look at in 2024. Blockchain can improve the trustworthiness of data resources that AI models pull from and increase the speed of AI operations by connecting models to automated smart contracts. AI can enhance the efficiency and scalability of blockchain by optimising its performance, security, and usability. Together, they can create a more trustworthy, transparent, and accountable data and AI ecosystem that can foster innovation, value creation, and social good.

However, this combination is not a silver bullet that can solve all the challenges and risks of data and AI. They also have their own limitations and drawbacks, such as technical complexity, performance issues, energy consumption, and governance challenges. Therefore, it is important to adopt a holistic and balanced approach that considers the opportunities and challenges of both technologies, as well as the ethical and social implications of their integration and application.

The potential of this intersection is not only theoretical, but also practical and observable. Recently, several developments have highlighted the emerging synergy between AI and cryptocurrency, which is a subset of blockchain technology that enables digital currencies and payments. For instance, Grayscale Investments, the world’s largest digital asset manager, published a research report that reveals the impressive performance of AI-related crypto assets, which are up 522% in the last year, outperforming the Utilities and Services Crypto Sector (+86%) over the same period. The report also discusses how blockchain and AI can address future AI-related societal issues, such as the rise of deepfakes, concerns around data privacy, and concentration of power.

Another example of the convergence of AI and cryptocurrency is the AI fever that took over the World Economic Forum in Davos, Switzerland, in January 2024, pushing crypto aside as the new cool kid on the block. Some of the world’s biggest companies, such as Intel and Salesforce, showcased their AI products and services, while the AI House hosted events and discussions on various topics related to AI and blockchain, such as verifying content authenticity, reducing model bias, and improving access and competition within AI development. The AI dominance at Davos reflects the rapid rise in AI investments and interest last year, sparked by the explosion of popularity of ChatGPT, the AI chatbot developed by OpenAI, and launched at the end of 2022. ChatGPT is an AI system that can generate natural language responses to any text input, using a large dataset of internet conversations. It has been widely praised for its ability to produce coherent, engaging, and sometimes humorous dialogues, as well as for its potential applications in various domains, such as education, entertainment, and customer service.

In my opinion, it also raises some ethical and technical challenges, such as the risk of generating harmful or misleading content, the lack of transparency and accountability of its algorithms, and the difficulty of verifying and controlling its data sources. This is where blockchain technology can come in handy, as it can provide solutions for ensuring the quality and ethics of the data and AI. For instance, blockchain can help verify the origin and validity of the data used to train and operate ChatGPT, as well as the consent and preferences of the users and data subjects. Blockchain can also help track and audit the changes and outcomes of its interactions, as well as enforce ethical rules and regulations through smart contracts. Blockchain can also help augment and automate ChatGPT’s capabilities, by providing access to more diverse and reliable data sources, facilitating data sharing and collaboration, and enabling data monetisation and governance.

In conclusion, blockchain technology can offer a valuable solution for enhancing the quality and ethics of data and AI, as well as for creating synergies and benefits with AI and cryptocurrency. This intersection is not only theoretical, but also practical and observable, as evidenced by the recent developments in the field, such as Grayscale’s new study and the AI fever at Davos. These developments indicate a transformative phase where AI and cryptocurrency coalesce, fostering a landscape ripe for innovation and societal benefit.

This union is not only redefining blockchain’s utility, but also addressing critical challenges in AI governance and development. However, this union also requires a careful and balanced approach that considers the opportunities and challenges of both technologies, as well as the ethical and social implications of their integration and application.

 

 

 

Source: https://ciosea.economictimes.indiatimes.com/blog/why-blockchain-could-be-the-solution-for-data-quality-and-ethics-in-ai/107618432

 

Anndy Lian is an early blockchain adopter and experienced serial entrepreneur who is known for his work in the government sector. He is a best selling book author- “NFT: From Zero to Hero” and “Blockchain Revolution 2030”.

Currently, he is appointed as the Chief Digital Advisor at Mongolia Productivity Organization, championing national digitization. Prior to his current appointments, he was the Chairman of BigONE Exchange, a global top 30 ranked crypto spot exchange and was also the Advisory Board Member for Hyundai DAC, the blockchain arm of South Korea’s largest car manufacturer Hyundai Motor Group. Lian played a pivotal role as the Blockchain Advisor for Asian Productivity Organisation (APO), an intergovernmental organization committed to improving productivity in the Asia-Pacific region.

An avid supporter of incubating start-ups, Anndy has also been a private investor for the past eight years. With a growth investment mindset, Anndy strategically demonstrates this in the companies he chooses to be involved with. He believes that what he is doing through blockchain technology currently will revolutionise and redefine traditional businesses. He also believes that the blockchain industry has to be “redecentralised”.

www.anndy.com
j j j