MIT Sloan Asst. Prof. Haoxiang Zhu
From Oxford Business Law Blog
On January 3, 2018, the revised Markets in Financial Instruments Directive, or MiFID II, became effective across EU member states. This comprehensive and far-reaching regulation will shape European capital markets in years to come. Among other things, MiFID II puts several restrictions on dark pools in European equity markets: (i) Broker Crossing Networks are essentially banned; (ii) dark pools that rely on “reference prices” on exchanges can only execute trades at the midpoint of exchange best bid and offer; and (iii) dark pools are subject to volume caps of 4% for a single venue and 8% across all dark pools (colloquially referred to as the double volume caps). On the other hand, MiFID II keeps the “Large in Scale” (LIS) waiver, so sufficiently large transactions can still go through without being counted toward, or affected by, the double volume caps.
Jargon and technical details aside, these MiFID II rules essentially push dark trading to return to basics: the matching of large institutional orders to reduce price impact (for both sides). Price impact—the very act of buying or selling moves prices adversely—can be quite costly for institutional investors, especially in today’s market where alphas are hard to generate and high-frequency traders watch every market movement at the microsecond level. By reducing the price impact of trades, investors enhance returns. Read More
MIT Sloan Senior Lecturer Steven Spear
En las primeras semanas de la Administración Trump, han surgido dos polémicas separadas con asuntos concomitantes. Una es la supuesta protección del suelo estadounidense frente a una amenaza extranjera, en forma de una muy polémica prohibición de los viajes a EE UU de ciudadanos de siete países mayoritariamente musulmanes. La segunda es la intimidación de Trump a los fabricantes para que aumenten la presencia de sus fábricas en Estados Unidos y la reduzcan en el resto de lugares.
Implícitas en ambas cuestiones hay dos visiones claramente diferentes sobre cómo conseguir una seguridad y prosperidad duraderas para EE UU. Una postura es que competimos mediante la localización y la acumulación de cosas: recursos, instalaciones, y el acceso a ellas. La postura alternativa es que una ventaja sostenida depende de la superioridad sostenida en la generación, identificación y aplicación de buenas ideas en un mundo cada vez más globalizado.
Según el primer punto de vista, “transaccional”, la competitividad se apoya en la conservación de la ventaja posicional y mediante la construcción de barreras que eviten que molestos competidores tengan acceso a mercados y clientes a los que uno ya está intentando atender y para evitar que los clientes actuales se marchen a fuentes alternativas de bienes y servicios. Puede que no sea una coincidencia que alguien que construyó su carrera comercial en el sector inmobiliario, caracterizado por el mantra “localización, localización, localización”, tenga esta visión de la competencia.
Sandy Pentland, MIT Sloan Information Technology Professor
New technologies that make it possible to reinvent our financial system have exploded over the past decade.
Bitcoin BTCUSD, ethereum and other cryptocurrencies are proof that there’s a market for alternatives to the big, powerful players. And yet, it’s unclear how these cryptocurrencies will affect the economic landscape. Problems like bubbles, financial crashes and inflation aren’t going away any time soon. (Ahem, note recent events.)
But in the future, things could be different. These digital currencies and their supporting infrastructure hold great promise for deepening our understanding of the monetary circuit. With newfound clarity, we can build tools for minimizing financial risk; we can also learn to identify and act on early-warning signals, thus improving system stability. In addition, this new level of transparency could broaden participation in the economy and reduce the concentration of wealth.
A crypto alternative
How might this work? Leading cryptocurrencies, with bitcoin being perhaps the most famous, or infamous, example, have considerable logistical limitations. An alternative is needed. Read More
MIT Sloan Visiting Lecturer Irving Wladawsky-Berger
From The Wall Street Journal
Artificial intelligence is now applied to tasks that not long ago were viewed as the exclusive domain of humans, matching or surpassing human level performance. But, at the same time, productivity growth has significantly declined over the past decade, and income has continued to stagnate for the majority of Americans. This puzzling contradiction is addressed in “Artificial Intelligences and the Modern Productivity Paradox,” a working paper recently published by the National Bureau of Economic Research.
As the paper’s authors, MIT professor Erik Brynjolfsson, MIT PhD candidate Daniel Rock and University of Chicago professor Chad Syverson, note: “Aggregate labor productivity growth in the U.S. averaged only 1.3% per year from 2005 to 2016, less than half of the 2.8% annual growth rate sustained from 1995 to 2004… What’s more, real median income has stagnated since the late 1990s and non-economic measures of well-being, like life expectancy, have fallen for some groups.”
After considering four potential explanations, the NBER paper concluded that there’s actually no productivity paradox. Given the proper context, there are no inherent inconsistencies between having both transformative technological advances and lagging productivity. Over the past two centuries we’ve learned that there’s generally a significant time lag between the broad acceptance of new technology-based paradigms and the ensuing economic transformation and institutional recomposition. Even after reaching a tipping point of market acceptance, it takes considerable time, often decades, for the new technologies and business models to be widely embraced by companies and industries across the economy, and only then will their benefits follow, including productivity growth. The paper argues that we’re precisely in such an in-between period.
Let me briefly describe the four potential explanations explored in the paper: false hopes, mismeasurements, concentrated distribution, and implementation and restructuring lags.
MIT Sloan Professor Thomas Kochan
Artificial intelligence is quickly coming of age and there remain lingering questions about how we will manage this change.
AI will eliminate some jobs, there’s no question, but it will also create some new ones. So the first question we will face as business people, workers and citizens is about balance: are we going to create more jobs than we eliminate or not?
The second and much more fundamental question is: how are we going to proactively manage our AI investments so we can use AI to create new jobs or career opportunities for the future? And how will we make sure those jobs reach out to various sectors of our society increasing our overall wealth and well being and not overly increasing the inequities that already exist in our society.
I believe if we think about it strategically and if we engage more people in the design of AI systems, we’ll be able to make this transition successfully. It will require a proactive strategy. The American public and people all over the world have been shown the negative consequences of not being proactive—take global trade for example. The benefits of global trade have not been widely shared and we are now witnessing the effects of the anger and frustrations this has produced in the movement to more extreme politics and the deeper social divisions laid bare by recent events. We can’t make the same mistake about the future developments of technology.