Tag Archives: Chicago Booth

Updates from Chicago Booth

The real cost of discrimination: A case study from Nazi Germany
By Robin I. Mordfin -Policies such as the Trump administration’s ban on visitors from a string of majority-Muslim countries are likely to harm American companies, research suggests.

Chicago Booth’s Kilian Huber and University of Munich’s Volker Lindenthal and Fabian Waldinger draw their conclusion from a study of companies in Nazi Germany. Purging Jewish managers from German companies reduced the aggregate market valuation of all companies listed on the Berlin Stock Exchange by approximately 5 percent between 1933 and 1943, or nearly 2 percent of the German gross national product, they find.

The researchers collected data on 30,000 managerial positions at German companies that had been listed on the Berlin Stock Exchange in 1932, when Hitler was on the path to becoming the leader of the country. At the time, Jews held about 15 percent of senior management positions in these companies.

After the Nazis took power in 1933, those managers either left or were forced out of their positions. The share prices of these companies then declined relative to companies that had never employed Jewish executives. The share prices of companies that lost Jewish managers started falling in 1933 and remained persistently 10 percent lower than the share prices of peer companies that had never had Jews in senior positions. more>

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How to develop a superstar strategy
By Ram Shivakumar – We live in an age of growing corporate inequality, with a few dominant companies and many underperformers.

The superstar archetype is Google, established in 1998 with the aim of rank-ordering web pages in what was then the nascent industry of search. By the beginning of the 21st century, Google had no revenues and no established business model. Fast-forward 18 years and a few hundred acquisitions, and Alphabet, Google’s parent company, has a market value in excess of US$750 billion.

In almost every industry, a small number of companies are capturing the lion’s share of profits. The top 10 percent of companies worldwide with more than $1 billion in revenues (when ranked by profit) earned 80 percent of all economic profits from 2014 to 2016, according to a recent study by the McKinsey Global Institute. The 40 biggest companies in the Fortune 500 captured 52 percent of the total profit earned by all the corporations on that list, according to an analysis of the 2019 ranking by Fortune.

This leaves less and less for the smaller fish to feed on. The middle 60 percent of businesses earned close to zero economic profit from 2014 to 2016, according to McKinsey, while each of those in the bottom 10 percent recorded economic losses of $1.5 billion on average.

Why do some companies succeed so categorically while the majority struggle? This question drives much of the management-consulting industry. It has also inspired a library’s worth of management books with varying explanations. Is it because successful companies have visionary and disciplined leaders, as management consultant Jim Collins argues in his best seller Good to Great? Is it because successful companies have superior management systems and organizational cultures? Is it because of positional advantages, as Harvard’s Michael Porter might argue? Or is it all down to timing and luck?

Concluding that luck is a big factor would be unlikely to sell many paperbacks in an airport bookstore; yet, undoubtedly, chance events have played an important role in many successes and failures. more>

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Want to pay less tax? Improve your firm’s internal reporting
By Marty Daks – When companies engage in the great American pastime known as tax avoidance, many parse the Internal Revenue Code for loopholes to reduce their effective tax rate. But research suggests they should also scrutinize the quality of their internal reporting.

Internal information quality (IIQ), a term coined by Chicago Booth’s John Gallemore and University of North Carolina’s Eva Labro, encompasses computer reporting systems and any other resources that a company devotes to ensuring the quality and ease of access of information within a firm. The elements that constitute IIQ have been largely overlooked in tax-avoidance literature—perhaps because they are usually not observable, and are difficult for academics to measure.

Gallemore and Labro argue companies should pay more attention to these issues, which they define in terms of the accessibility, usefulness, reliability, accuracy, quantity, and signal-to-noise ratio of the data and knowledge within an organization. Their findings suggest that firms with high IIQ tend to enjoy lower effective tax rates and, all else being equal, a smaller tax bite.

Gallemore and Labro employed four publicly available variables, using data from 1994 to 2010, to rate firms’ IIQ: the speed at which management released an earnings announcement after its fiscal year closed, the accuracy of management’s earnings forecasts, the absence of material weaknesses in internal controls, and the lack of restatements due to errors.

The researchers used these measures to identify companies that released earnings more rapidly and forecasted them more accurately, and had fewer Section 404 citations and restatements due to errors. They assigned these firms higher IIQ ratings.

High-IIQ firms, they find, tend to exhibit some positive traits, including centralized and standardized business transaction processing, more-efficient reporting practices, and the ability to share data across business units and geographical locations. more>

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Why the big banks aren’t safe yet
By Haresh Sapra – The next financial crisis will not come from the traditional banking sector. So goes conventional thinking among financial policy makers. The world’s biggest banks are now safer, according to the narrative, thanks to stricter capital requirements and frequent stress tests that have curbed the appetite for extreme risk and tightened up lax regulatory standards.

I wish I were completely reassured. But as an accountant, I know that the headline capital numbers result from a subjective calculation. Banking regulators typically spend too little time digging into how those figures are calculated. I also know that when the US financial system is healthy, as it is now, we should strive to do better at accounting for potential losses, because that might cushion the blow when the inevitable downturn arrives.

To be sure, the big banks have all passed the Federal Reserve’s stress tests with flying colors. And this reflects substantial increases in capital buffers: the 35 banks that underwent 2018’s stress test have added about $800 billion in the highest quality type of capital over the past decade, according to the Fed. The central bank has deemed that the banks would therefore be strong enough to continue lending if the economy were to plunge into another severe downturn.

But I am not the only observer who remains concerned. In a speech to Americans for Financial Reform in May, Georgetown’s Daniel Tarullo, who was a Fed governor from 2009 to 2017, questioned the robustness of the stress tests. Banks know what regulators are looking for, Tarullo observed, enabling them to “find clever ways to reshape their assets,” thereby reducing their capital levels without reducing their risk exposures. And he also cast doubt on a Fed proposal to create a “stress capital buffer” to stop banks from running down their capital cushions by using dividend payments. Such a buffer, Tarullo argued, could actually prompt banks to take on even more risk. more>

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Three strategy lessons from GE’s decline
By James E. Schrager -It may be too early to write an obituary for General Electric, but only just. In the past few years, the company has gone from iconic American corporate titan and darling of Wall Street to a humbled, awkward, oversized giant. In June 2018, GE was kicked out of the Dow Jones Industrial Average, the blue-chip club of the United States’ largest public companies. It had been a member since the stock gauge was launched in 1896. Some analysts have GE on bankruptcy watch.

To those who have been paying attention, this has been a long, slow decline. In fact, GE never had much of a chance once Jack Welch retired as chairman and CEO in 2001. That wasn’t because of bad luck or lackluster management. Instead, Welch’s perfectly brilliant growth strategy had simply run its course.

Welch’s great mistake was to fail to plan for the “end of history”—what happens when the golden goose stops laying. The story is worth revisiting not just because it explains the deterioration of GE. It also holds three powerful lessons about corporate strategy:

  1. All growth from any single market or technology will end. Companies that endure are those that plan for this reality.
  2. If you are successful, many will copy your success. Companies that continue to prosper update and adapt their strategies.
  3. Smart corporate strategies are flexible and nimble, enabling action rather than constraining it.

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Startup founders, put on your sales hat
Selling skills are essential for entrepreneurs and need to be taken seriously
By Michael D. Alter – I know an entrepreneur who built a hugely successful mobile-marketing company, starting out in the early 2000s until he sold it for a good deal of money in the mid-’00s.

The first few years of his business were in that dark era before smartphones were invented, but the idea was for advertisers to use his platform to get their messages on to people’s phones. He spent a lot of his energy trying to persuade big brands and big advertising agencies to sign up for his service.

One of his board members happened to know the executive chairman of a large US media group, and was able to help the entrepreneur secure a meeting with him to pitch his startup. This was a huge opportunity, but our entrepreneur was confident rather than nervous.

First, this was not his first startup. He already had a few successful new ventures behind him. He knew his skills: he was a good salesperson, and a good entrepreneur. Secondly, he knew he had to do his homework. He prepared more diligently than he had for any other meeting. He had the most impressive slide deck known to PowerPoint, with every imaginable chart and data point for whatever questions the executive might ask.

In the startup world, we talk about the importance of creating strong business plans, building a team with industry experience, and tapping into big markets. All of these are important, but we undervalue what is really the most important thing: selling the product. As a result, we do a disservice to ?<the entrepreneurial world by not giving startups the right priorities for success.

Entrepreneurs often think that if they have a truly great product, sales will take care of itself—as they say: build a better mousetrap, and they will beat a path to your door. I think of this as the first great myth of entrepreneurial selling. In reality, if people don’t know about it, they can’t and won’t buy it.

Selling helps to bridge that information gap. more>

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Are investors chronically pessimistic?
No—but that doesn’t mean they adhere to rational expectations
By Dwyer Gunn – The assumption that investors hold rational expectations of market returns is central to many asset pricing models. However, in recent years, surveys of investors have revealed that market participants’ reported expectations often deviate from the objective predictions of financial models working with large pools of data. One theory is that these deviations are the result of persistent pessimism on the part of investors: survey respondents, according to this hypothesis, are discounting the rationally expected rate of return to reflect the risk of investing in stocks.

To examine whether investors have a pessimistic bias, Oxford’s Klaus Adam, the Bank of Canada’s Dmitry Matveev, and Chicago Booth’s Stefan Nagel examined existing evidence—including surveys of individual investors, professional investors, and CFOs going back to the 1980s—to compare expected returns with realized returns.

The research suggests that, contrary to the pessimism hypothesis, investors are just as likely to be optimistic.

Investor expectations closely matched realized market returns over the full length of time the researchers examined. But at any given time, expectations tended to be procyclical: investors expected higher returns during boom times in the stock market and lower returns during market contractions, even though many asset pricing models work in precisely the opposite direction.

Thus, the apparent conformity of investor expectations to market returns on average over time actually reflected investors’ biases—alternately optimistic and pessimistic, with the two balancing each other out. more>

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How machine learning can improve money management<
By Michael Maiello – Two disciplines familiar to econometricians, factor analysis of equities returns and machine learning, have grown up alongside each other. Used in tandem, these fields of study can build effective investment-management tools, according to City University of Hong Kong’s Guanho Feng (a graduate of Chicago Booth’s PhD Program), Booth’s Nicholas Polson, and Booth PhD candidate Jianeng Xu.

The researchers set out to determine whether they could create a deep-learning model to automate the management of a portfolio built on buying stocks that are expected to rise and short selling those that are expected to fall, known as a long-short strategy. They created a machine-learning algorithm that built a long-short equity portfolio from the top and bottom 20 percent of a 3,000-stock universe.

They ranked the equities using the five-factor model of Chicago Booth’s Eugene F. Fama and Dartmouth’s Kenneth R. French. Fama and French break down the components of stock returns over time into five factors: market risk, in which stocks with less risk relative to their benchmark outperform those with more risk; size, in which companies with small market capitalizations outperform larger companies; value, where a low price-to-book ratio outperforms high; profitability, where higher operating profits outperform; and reinvestment, in which companies that reinvest outperform those that don’t. more>

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Free markets for free men
By Milton Friedman – Do free markets make free men, or do free men make the free markets?

That might seem like a play on words or a purely semantic question, but it is not. It is a very real and very important question, and I think it contributes a great deal to understanding the kind of world we live in, and might live in.

One’s offhand impression is to say, “Well it must be free men who make free markets.” There’s an element of truth in that, but I think to a far greater extent, free markets make free men and not the other way around.

It’s true that there have been free men who have made free markets. The founders of the United States were free men who believed in individual and personal freedom, and they set up a constitution that was designed to preserve free markets.

But many people who regarded themselves as free men have produced totalitarian societies. The intellectual creators of the Soviet Union would have called themselves free men and would have said that they believed in individual and personal freedom. Yet they created not free markets but controlled markets. more>

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How bookies can outwit smart bettors
By Michael Maiello – Sports-betting markets are based entirely on predictions. A bettor has to pick a winning contestant, and a market maker―a bookie―bets on the opponent. The predictions rely on available information about both sides as well as conditions that might affect the outcome. As bookies have to take the other side of every bet, they have to know what they’re doing. But a bookie can be manipulated by a skillful bettor.

Chicago Booth’s John R. Birge, Booth PhD candidate Yifan Feng, Duke’s N. Bora Keskin, and Uber’s Adam Schultz explore the dynamics of how a bookie can keep from being manipulated. Because the sports-betting market shares features with financial markets that rely on spreads―including credit default swaps and options―the implications of the research could apply far beyond the $4.9 billion-a-year Nevada sports-betting industry. The findings may interest sports bettors and hedge-fund managers alike.

The researchers identify a key problem for bookies and financial market makers: they are vulnerable to being bluffed by knowledgeable bettors or bettors with inside information, such as whether a star is able to play. The bets being placed are the bookie’s best source of information—by analyzing betting patterns, a bookie can effectively crowdsource information about the expected outcome of an event.

However, a clever bettor might place some phony bets to throw the bookie off. Through the application of a theoretical model, the researchers identify a set of policies, which they call inertial policies, that enable bookies to strike a balance between learning from market participants and bluff proofing their business. more>

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