EquBot LLC, in partnership with ETF Managers Group (ETFMG), recently debuted the world’s first Exchange Traded Fund (ETF) powered by articificial intelligence: the AI Powered Equity ETF (NYSE Arca: AIEQ).
AIEQ is an active ETF built on EquBot’s proprietary algorithms and is the world’s first artifical intelligence (AI) ETF, utilizing the cognitive and big data processing abilities of IBM Watson™ to analyze U.S.-listed investment opportunities.
EquBot’s approach ranks investment opportunities based on their probability of benefiting from current economic conditions, trends, and world- and company-specific events, and identifies those equities with the greatest potential for appreciation. EquBot and ETFMG expect the fund’s portfolio to typically consist of 30 to 70 of U.S. equities only and volatility comparable to the broader U.S. equity market.
With artificial intelligence, computer systems are able to perform tasks that would normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. In the case of AIEQ, the fund’s underlying technology is constantly analyzing information for approximately 6,000 U.S.-listed equities, including company management and market sentiment, and processes more than one million regulatory filings, quarterly results releases, news articles, and social media posts every day.
“ETFs have made beta ‘smart,’ but with AIEQ we’re looking to make investing intelligent,” said Chida Khatua, CEO and co-founder of EquBot LLC. “EquBot AI Technology with Watson has the ability to mimic an army of equity research analysts working around the clock, 365 days a year, while removing human error and bias from the process.”
Khatua notes that the approach underpinning AIEQ also includes machine learning, giving it the ability to automatically learn and improve from experience without being explicitly programmed.
“Machine learning is one of the most powerful applications of artificial intelligence,” he added. “As powerful as many algorithms underlying expensive quantitative hedge funds and other vehicles might be, unless they’re also built with AI and machine learning baked right in, mistakes can be propogated and opportunities for outperformance can be missed.”
“With the launch of AIEQ, we’re not only bringing our new fund to market,” said Art Amador, co-founder and COO of EquBot. “We believe we’re pioneering a whole new investment category; one that will soon have investors and advisors diversifiying their portfolios among passive, active and AI approaches.”
“Artificial Intelligence is making life better for individuals as its application expands across industries,” said Sam Masucci, CEO and Founder of ETF Managers Group. “We are excited to be the first asset manager to bring AI into portfolio management and the ETF space, providing investors with another first-to-market ETF product.”
AIEQ may invest in the securities of companies of any market capitalization and will have an expense ratio of 0.75%.
“Everyday, there is more information, not less,” added Amador. “That information explosion has made the jobs of portfolio managers, equity analysts, quantitative investors and even index builders more challenging. New technology in artificial intelligence helps solve those challenges and we’re very pleased to be bringing AIEQ to market to make an AI approach to investing available to all.”
This summer Volvo Cars revealed that its subsidiary Polestar will begin developing high-performance electric cars. In conjunction with this the company announced that its Senior Vice President of Design, Thomas Ingenlath, and Senior Vice President of Corporate Communications, Jonathan Goodman, will leave Volvo Cars to take over the new electric car brand.
Thomas Ingenlath assumes the role of CEO while Jonathan Goodman takes up the reins as COO.
The concept version of the first new electric car has now been unveiled and is on display at the car show in Shanghai, under the name Polestar 1 – but the company’s Instagram account already hosts a preview of the car, teasing with details displayed in 60 close-ups. Together they form a complete image of the car.
Thomas Ingenlath tells Radio Sweden that the long-term goal is to reach a production of 80,000 cars. The number of employees at Polestar now amounts to more than 110. Two years ago there were only 15, SvD Näringsliv reports.
At the same time, Volvo Cars is to launch two electric car models in 2019.
Volvo Cars, having bought Polestar the summer of 2015, describes the company as, “a new independent electrified high-performance car brand,” which probably means that a new Tesla challenger is headed for the market.
Capitalists focus on the financial returns from capital invested, and most business leaders prioritize issues that are financially material. For anyone with a pension linked to market performance, that is a good thing. But this single-minded focus can be a major problem when it comes to tackling slow-building, systemic challenges, like global warming, that could take down not just supply chains but, over time, entire economies.
No accident, then, that we increasingly hear discussion about “framing” in boardrooms and C-suites. Political analyst George Lakoff, notably in his book Don’t Think of an Elephant!, has shown how the way we frame such challenges shapes our reasoning and priorities much more than most of us recognize. Crucially, he concludes that our unconscious mental frames “shape the goals we seek, the plans we make, the way we act, and what counts as a good or bad outcome of our actions.”
The message for business leaders, as for social change agents: To change our frames is to change the way we perceive, prioritize and invest time, effort and money. “Reframing,” Lakoff tells us, “is social change.”
A critical first step is to understand the different mental and political frames currently in play. My colleagues and I see at least six main frames at work in the sustainable business space. Each has its strengths and limitations. Having a clearer grasp of these mental models can help business leaders to work with others, both inside and outside their organizations, to build more sustainable businesses.
“You haven’t said anything,” Musk once said in a meeting, according to a former SpaceX employee. “Why are you in here?”
Most people don’t like to have their time wasted with unnecessary meetings.
But Elon Musk, the founder and CEO of SpaceX, has a strategy to keep things moving, a former SpaceX employee posted on Quora.
The former employee further explained Musk’s rationale for making such a blunt proclamation.
“That may be borderline rude, but it makes sense,” he wrote. “Don’t be in a meeting unless there’s a purpose for it; either to make a decision, or get people up to speed. In most cases, an email will suffice.”
Musk isn’t the only CEO who values running an efficient meeting – Jeff Bezos, the Amazon founder and CEO, employs the “two-pizza rule” to cut down on meeting bloat.
His thinking is simple: Meetings should be small enough that two pizzas would feed the entire group. If not, the meeting would probably be too big and unproductive.
Bezos also told Fortune in 2012 that some meetings with senior executives began with silent reading time, during which all attendees familiarized themselves with a memo describing the matter at hand, took notes, and mulled over the issues before beginning the discussion. That way, he said, he gets everyone’s attention immediately – because no one likes to waste time in a conference room.
Most team leaders try to build cohesion on their teams. Through team-building exercises and the careful establishment of norms and processes, leaders aim to create a culture of trust, psychological safety, and good feeling.
But should enterprise leadership teams also pursue cohesion? To explore this question, over the course of six years, we surveyed senior-most leadership teams 99 times at companies representing a variety of industries, including financial services, food and beverage, energy, technology, healthcare, and retail. Each team member responded to over 110 items that focused on their team’s capability and performance. The specific domains of capability and performance were based on previous research, including:
- Team structure
- Team processes
- Team results
- Team dynamics
Most teams (55 teams comprised of 700+ senior executives) also responded to items focused on organization performance —comparing themselves to industry peers — and included dimensions such as Sales and Revenue Growth, New Product Development, and Market Share.
We expected to replicate the importance of cohesion in this unique context. We were wrong. Results from our research indicate that while concepts like internal cohesion and psychological safety are important to team performance, they are not the most critical at the enterprise level. Rather, it is the ability to manage conflicting tensions — as opposed to seeking cohesion — that is the most predictive of top-team performance.
The amount raised for initial coin offerings of cryptocurrencies has surpassed earliest-stage venture capital so far in 2017—a ranking change that institutional investors would be smart to acknowledge, Goldman Sachs said in a note out Tuesday.
“Real dollars are at work here and warrant watching, especially in light of the growing world of ICOs and fundraising that now exceeds internet angel and seed investing,” the analysts, led by Robert Boroujerdi, said.
The total amount of crypto ICO cash raised in the year to date amounts to $1.25 billion, more than venture funding at the angel and seed stages, though excluding crowdfunding (see the chart below), the Goldman note said, quoting Coin Schedule. An ICO, similar to an initial public offering of stock, is a fundraising process through sales of tokens linked to existing cryptocurrencies.
The biggest offerings were blockchains Tezos with $232 million and Bancor with $150 million in bitcoin BTCUSD, +0.67% and its rival, Ethereum’s ether.
Certainly, the $120 billion cryptocurrency market, with bitcoin at its helm, is not without scrutiny by authorities. Venture-backer The DAO, which was formed to invest in start-ups and projects linked to cypto platform Etherium’s ether, was shuttered following scrutiny by the Securities and Exchange Commission over its coin offerings earlier this year. The regulator said in July that ICOs should be considered as securities and fall under SEC oversight.
Yet regulatory formalization is not necessarily slowing all market expansion. Last month, LedgerX, a bitcoin options exchange, received regulatory approval from the Commodity Futures Trading Commission.
Underlying the practice and study of business is the belief that management is a science and that business decisions must be driven by rigorous analysis of data. The explosion of big data has reinforced this idea. In a recent EY survey, 81% of executives said they believed that “data should be at the heart of all decision-making,” leading EY to enthusiastically proclaim that “big data can eliminate reliance on ‘gut feel’ decision-making.”
Managers find this notion appealing. Many have a background in applied sciences. Even if they don’t, chances are, they have an MBA—a degree that originated in the early 20th century, when Frederick Winslow Taylor was introducing “scientific management.”
MBA programs now flood the business world with graduates—more than 150,000 a year in the United States alone. These programs have been trying to turn management into a hard science for most of the past six decades. In large measure this effort began in response to scathing reports on the state of business education in America issued by the Ford and Carnegie Foundations in 1959. In the view of the report writers—all economists—business programs were filled with underqualified students whose professors resisted the methodological rigor of the hard sciences, which other social sciences had embraced. In short, business education wasn’t scientific enough. It was in part to remedy this shortcoming that the Ford Foundation supported the creation of academic journals and funded the establishment of doctoral programs at Harvard Business School, the Carnegie Institute of Technology (the predecessor of Carnegie Mellon), Columbia, and the University of Chicago.
But is it true that management is a science? And is it right to equate intellectual rigor with data analysis? If the answers to those questions are no and no—as we will suggest in the following pages—then how should managers arrive at their decisions? We’ll set out an alternative approach for strategy making and innovation—one that relies less on data analysis and more on imagination, experimentation, and communication.
But first let’s take a look back at where—or rather with whom—science started.
Strains on the global labor force are becoming painfully evident. Market forces will fail to resolve demand and supply imbalances for tens of millions of skilled and unskilled workers.
Over the past three decades, as developing economies industrialized and began to compete in world markets, a global labor market started taking shape. As more than one billion people entered the labor force, a massive movement from “farm to factory” sharply accelerated growth of productivity and per capita GDP in China and other traditionally rural nations, helping to bring hundreds of millions of people out of poverty. To raise productivity, developed economies invested in labor-saving technologies and tapped global sources of low-cost labor
Today, the strains on this market are becoming increasingly apparent. In advanced economies, demand for high-skill labor is now growing faster than supply, while demand for low-skill labor remains weak. Labor’s overall share of income, or the share of national income that goes to worker compensation, has fallen, and income inequality is growing as lower-skill workers—including 75 million young people—experience unemployment, underemployment, and stagnating wages.
The McKinsey Global Institute (MGI) finds these trends gathering force and spreading to China and other developing economies, as the global labor force approaches 3.5 billion in 2030. Based on current trends in population, education, and labor demand, the report projects that by 2020 the global economy could face the following hurdles:
- 38 million to 40 million fewer workers with tertiary education (college or postgraduate degrees) than employers will need, or 13 percent of the demand for such workers
- 45 million too few workers with secondary education in developing economies, or 15 percent of the demand for such workers
- 90 million to 95 million more low-skill workers (those without college training in advanced economies or without even secondary education in developing economies) than employers will need, or 11 percent oversupply of such workers
The dynamics of the global labor market will make these challenges even more difficult. The population in China, as well as in many advanced economies, is aging, reducing the growth rate of the global labor supply; most of the additions to the global labor force will occur in India and the “young” developing economies of Africa and South Asia. Aging will likely add 360 million older people to the world’s pool of those not participating in the labor force, including 38 million college-educated workers, whose skills will already be in short supply.
To understand where these gaps are likely to arise and have the greatest impact, MGI looked at the 70 countries that account for 96 percent of global GDP and are home to 87 percent of the world’s population. By plotting their populations’ educational and age profiles, as well as per capita GDP, we can see how prepared their national labor forces are to meet future demand, how easily they can grow their labor forces, and how productive their labor is. This yields eight clusters of countries: four in developing economies, three in advanced economies, and one group comprising Russia and Central and Eastern European states.
While market forces will move to eliminate projected imbalances before their full impact is felt, they cannot be avoided entirely without a concerted, global effort by governments and businesses to raise educational attainment and provide job-specific training. Advanced economies will need to double the pace at which the number of young people earning college degrees is rising—and find ways to graduate more students in science, engineering, and other technical fields; these workers will be in high demand, and their contributions will be critical for meeting the rising productivity imperative. Secondary and vocational training must be revamped to retrain mid-career workers and to provide job-specific skills to students who will not continue on to college.
Even then, in the next two decades, the world is likely to have too many workers without the skills to land full-time employment. In both developing and advanced economies, policy makers will need to find ways not only to produce high-skilled workers but also to create more jobs for those who aren’t as highly educated. Solutions include moving up the value chain in developing economies (food processing creates more employment than growing export crops, for example) and finding opportunities for workers without a college education to participate in fast-growing fields—such as health care and home-based personal services—in advanced economies.
Businesses operating in this skills-scarce world must know how to find talent pools with the skills they need and to build strategies for hiring, retaining, and training the workers who will give them competitive advantage. This will include finding ways to retain more highly skilled women and older workers. Businesses will also need to significantly step up their activities in shaping public education and training systems in order to build pipelines of workers with the right skills for the 21st-century global economy.
In MBA programs, students are taught that companies can’t expect to compete on the basis of internal managerial competencies because they’re just too easy to copy. Operational effectiveness—doing the same thing as other companies but doing it exceptionally well—is not a path to sustainable advantage in the competitive universe. To stay ahead, the thinking goes, a company must stake out a distinctive strategic position—doing something different than its rivals. This is what the C-suite should focus on, leaving middle and lower-level managers to handle the nuts and bolts of managing the organization and executing plans.
The Conventional Wisdom
It’s a truism among strategists that you can’t compete on the basis of better management processes because they’re easily copied. Operational excellence is table stakes in the competitive marketplace.
What the Data Shows
There are three problems with this thinking. First, effective management processes are highly correlated with measures of strategic success. Second, differences in process quality persist over time. Third, there’s little evidence that best-in-class processes can be imitated. GM tried for years to adopt Toyota’s superior production system and failed miserably.
Organizations need competent management just as much as they need analytical brilliance. We should stop teaching business school students that operational issues are beneath the CEO—and should encourage firms to invest in strengthening management throughout the organization.
Michael Porter articulated the difference between strategy and operational effectiveness in his seminal 1996 HBR article, “What Is Strategy?” The article’s analysis of strategy and the strategist’s role is rightly influential, but our research shows that simple managerial competence is more important—and less imitable—than Porter argued.
If you look at the data, it becomes clear that core management practices can’t be taken for granted. There are vast differences in how well companies execute basic tasks like setting targets and grooming talent, and those differences matter: Firms with strong managerial processes perform significantly better on high-level metrics such as productivity, profitability, growth, and longevity. In addition, the differences in the quality of those processes—and in performance—persist over time, suggesting that competent management is not easy to replicate.
Nobody has ever argued that operational excellence doesn’t matter. But we contend that it should be treated as a crucial complement to strategy—and that this is true now more than ever. After all, if a firm can’t get the operational basics right, it doesn’t matter how brilliant its strategy is. On the other hand, if firms have sound fundamental management practices, they can build on them, developing more-sophisticated capabilities—such as data analytics, evidence-based decision making, and cross-functional communication—that are essential to success in uncertain, volatile industries.
Achieving managerial competence takes effort, though: It requires sizable investments in people and processes throughout good times and bad. These investments, we argue, represent a major barrier to imitation.