Author Archives: Max Haskin

On-Demand Economics Revised

Thesis: Q’s strategy is not setting a new standard, it’s simply using economics.

Katie Brenner wrote an article in Bloomberg View called “Happiness Can Be an On-Demand App,” where she discusses the hiring tactics of on-demand startups such as Uber. Brenner outlines two different approaches based on two gatherings she attended – one at Uber and another at Q (a New York startup that matches workers with companies that need office cleaning). “At the first meeting, last spring, drivers assembled in San Francisco to protest their treatment by Uber. They said the ride-hailing company was exploiting them. Uber staff members struggled to appease the disgruntled contractors, and the conversations were heated and tense.” The second was a gathering at Q, where “workers, called “operators” mingled with company founders, executives, engineers and sales staff at one of Q’s monthly operator assemblies. The conversations were loud at times, punctuated by hellos, hugs and high fives. They had gathered to express their concerns and problems and learn more about the company’s growth. Several operators told me they loved Q as well as the businesses they cleaned, which they referred to as their clients.”

The Uber protest signifies the first hiring tactic – one in which workers are regarded as contractors “who aren’t necessarily eligible for the minimum wage, benefits or compensation for on-the-job injuries and other claims.” This distinction is a key component of Uber’s competitive advantage and crucial for their long-term success. The reason is because Uber saves an incredible amount of money by avoiding insurance costs, administrative costs, and any legal liabilities.

The second tactic is the Q model, in which “operators are full-fledged employees who get benefits, including health care.” Now you might be wondering how this makes Q different than a regular cleaning service. The reason is that Q uses an algorithm to predict cleaning needs and schedules, and then uses an on-demand model to match workers with clients. With that in mind, we can now analyze Q’s hiring tactic from a strategic standpoint. Q has clearly made a calculated decision because as opposed to Uber, they have chosen to incur the associated costs. However, as evidenced by Brenner’s experience, Q has created a collaborative and friendly work environment that makes them preferable to many of their direct competitors.

Where Brenner goes wrong is that she incorrectly makes the assumption that Q is setting the new industry standard for on-demand workers. One of her arguments is that “when labor markets are competitive — which is increasingly the case in the on-demand sector — workers will take company culture into account. To attract the best talent, companies will need to show they value drivers, handymen and housekeepers in the same way as, say, tech workers who write code.”

Brenner’s point is a valid one, but I don’t see this as a huge revelation. I think this trend is simply an application of the Coase Theorem. We can think about this in terms of vertical integration. The difference between the Q tactic and the Uber tactic is that Q decided to vertically integrate by buying its suppliers as opposed to outsourcing. The Coase Theorem suggests that firms vertically integrate when it is less costly to perform an activity/transacting using a firm’s hierarchy rather than contracting with another firm than a market exchange.

In this case, Q believes that the potential cost of using contracted workers is greater than the cost of hiring them. This makes sense because in Q’s line of work, “Operators cross the threshold between public and private space, so there’s a greater need for the company to create a truly consistent experience no matter which operator cleans an office.” What this means to me is that the potential cost of a mishap using temporary workers is greater than the cost of creating the consistent experience by hiring them. Uber, on the other hand, is in a completely different line of work, where the cost of contracting is not as high, which is why they decided not to vertically integrate.

So in response to Ms. Brenner, I don’t necessarily think that Q “becoming the standard-bearer for a new type of on-demand service company,” I just think the strategy they chose was an application of economics.

 

Why the SEC Should Eliminate Blank Check Companies

In Bloomberg View, Matt Levine wrote an article about “blank-check” companies called “Pashminadepot.com Was Never Really About Pashminas.” First of all, I had never heard of a “blank-check” company until now. From my understanding, a “blank-check” company is a company that exists as a registered entity, but doesn’t do anything. According to investopedia, it’s “A company in a developmental stage that either doesn’t have an established business plan or has a business plan that revolves around a merger or acquisition with another firm.” My first question is how does such a company even exist in the first place? It seems incredibly fishy to begin with, and it doesn’t seem like the economy would lose much value by eliminating them entirely.

Levine goes on to explain how these companies are used to conduct fraud:

  1. You incorporate a company with a fake business plan.
  2. You register it with the SEC as a real company, not a blank-check one.
  3. Your SEC review is pretty simple: Your financial statements show no operations and almost no assets, because you’re claiming only to have a business plan, not an actual business.
  4. You go public, maybe sell a few shares to outsiders.
  5. You merge with another, private, company, possibly also with a fake business plan, so that that company can be public without SEC review.
  6. You make (possibly false) claims about the business successes of the merged company, sell a bunch of shares to dupes, pump, dump, etc.

There are even companies that specialize in creating fake business plans for blank check companies. The whole process screams fraud, and yet the SEC has made no significant changes to the registration process.

I see a couple of solutions to this problem. The first, as I previously mentioned, why not outlaw blank check companies entirely? By definition, they don’t actually do anything, so how could they be adding any value to the economy or contributing to GDP? The second solution, similar to the first, is why let blank check companies be public? The only purpose they seem to serve is providing an fraudulent companies with a shady way to go public by avoiding regulation, but if they weren’t public to begin with then they serve no purpose. A third option is to change the rules about being a public. If private company merges with a public company, it automatically assumes its public status with no further review by the SEC. Why not force those companies to go through the same process as IPOs?

 

 

Private Market Bubble

Revised Thesis: The private market is in a bubble.

A few weeks ago, I wrote a post about how the private equity market could be in a bubble. I wanted to revisit that post and refine my hypothesis: what I meant to say, is that the private market could be in a bubble, meaning the private companies/startups that private equity and venture capital firms purchase.

In my previous post, I focused on private equity, which is a form of investing where you buy a company by putting up some cash, but using mostly leverage. Then you take over management of the company for a period of time, using the cash flows to pay off the debt, with enough left over for fees and investor dividends. In most cases, the strategy used to generate cash flow is drastically cutting costs by closing divisions, cutting staff, scaling back marketing, R&D, etc. When the debt is repaid, you either take the company public or sell it to someone else.

The other player in private markets is venture capital. Venture capital is “Money provided by investors to startup firms and small businesses with perceived long-term growth potential. This is a very important source of funding for startups that do not have access to capital markets. It typically entails high risk for the investor, but it has the potential for above-average returns.”

Now that we have an understanding of private equity and venture capital, let’s get back to the thesis. First let’s talk about bubbles. In the Wall Street Journal article, “How to Spot a Market Bubble,” Joe Light provides three warning signs. First is rapidly rising asset prices. We’ve seen evidence for this with enormous valuations of tech startups – there are currently 82 startups that have been valued at over $1 billion. And as valuations increase, “Capital will still chase increasingly expensive deals. That won’t end well.”

Light’s second warning sign is when prices break sharply from an asset’s underlying value. We see evidence for this in the increasing multiples at which these startups are valued – many of which don’t even have earnings to report.

The third warning sign is the existence of an exciting technology or innovation to justify the prices. While that isn’t exactly the case here, the prices are being justified simply because private equity shops are willing to pay them. Finally, to tie in A Random Walk, private equity is inherently reliant on castles in the sky because the very nature of it relies on an exit strategy that either requires another buyer or an IPO, which means there needs to be a ‘greater fool’ to purchase the company next. Given the reasons provided, private equity could be heading for a major collapse.

Venture Capitalist Bill Gurley made another interesting point that doesn’t fall under any of Light’s three warning signs. His point is that “because investors are willing to give promising young tech companies so much money, those promising young companies are pushing too hard to grow fast enough to justify their valuations. It’s a feedback loop in which high asset values are driving value-destroying behavior.” This could lead to rapidly declining asset prices, another defining characteristic of bubbles.

 

 

 

No, Don’t Worry About The Stock Market

In today’s Bloomberg View, Mark Gilbert wrote an editorial titled “Yes, Worry About The Stock Market,” which outlines Stanley Druckenmiller’s pessimistic outlook of the economy. Druckenmiller is cautiously citing the impressive 175% gain in the world equity market since 2009 as the source of his skepticism. “[His] message is that the economic backdrop doesn’t justify the Federal Reserve keeping borrowing costs near zero, and that its policies are forcing investors to take on extra risk to boost returns.” According to Druckenmiller, the market conditions feel eerily similar to 2004, just before the financial crisis, and fears that an asset bubble is forming.

As evidence, “Druckenmiller cited a thought experiment from a decade ago, when the Fed held its interest rate at 1 percent for most of 2003 and 2004 even though the quarterly growth rate for those two years averaged 3.8 percent and had surged to 6.9 percent in the third quarter of 2003. By keeping borrowing costs so low for so long, the Fed helped finance a buying spree that saw investors loading up on toxic assets.”

My problem with Druckenmiller’s argument here is that you can’t just extrapolate market conditions from one time period and apply them to another, where the overall economic backdrop is completely different. While the Fed’s loose monetary policy at that time was perhaps a contributing factor, there were also a plethora of other factors that ultimately led to the sub-prime mortgage crisis including weak regulatory oversight and a lack of understanding of new, complex financial instruments.

In Druckenmiller’s mind, in order for the Fed to justify near-zero interest rate policy, “the central bank would have to believe the economy is in its worst shape in more than a century.” In order to prove that the economy is in fact healthy, Druckenmiller cites the rally in U.S. household net worth since 2007:

 

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I find this to be a massive flaw in Druckenmiller’s argument because he is U.S. household net worth is not an accurate measure of the economy as a whole. My problem with it is that it doesn’t tell us where the wealth is coming from. Would you consider the economy to be healthy of the majority of those gains were accrued by the 1%? If I were to guess, the graph of U.S. household net worth probably looks very similar to a graph of the stock market, which makes sense because as the value of stocks rise, people who own them become wealthier. But most of the money in the stock market comes from people who already have a lot of money, which means if the rise in stock prices is causing the rise in wealth then they disproportionately represent the wealthier households.

In response to Druckenmiller, I would ask what he has to say about inflation and wage growth. In 2004, inflation was around 3% compared to near 0 today, which is the main reason the Fed has kept rates where they are. While the stock market might be rallying in a similar fashion to that of 2004, that is simply not enough evidence to suggest that the Fed should tighten its policy.

On-Demand Economics

Thesis: Q’s strategy is not setting a new standard, it’s simply using economics.

Katie Brenner wrote an article in Bloomberg View called “Happiness Can Be an On-Demand App,” where she discusses the hiring tactics of on-demand startups such as Uber. Brenner outlines two different approaches based on two gatherings she attended – one at Uber and another at Q (a New York startup that matches workers with companies that need office cleaning). “At the first meeting, last spring, drivers assembled in San Francisco to protest their treatment by Uber. They said the ride-hailing company was exploiting them. Uber staff members struggled to appease the disgruntled contractors, and the conversations were heated and tense.” The second was a gathering at Q, where “workers, called “operators” mingled with company founders, executives, engineers and sales staff at one of Q’s monthly operator assemblies. The conversations were loud at times, punctuated by hellos, hugs and high fives. They had gathered to express their concerns and problems and learn more about the company’s growth. Several operators told me they loved Q as well as the businesses they cleaned, which they referred to as their clients.”

The Uber protest signifies the first hiring tactic – one in which workers are regarded as contractors “who aren’t necessarily eligible for the minimum wage, benefits or compensation for on-the-job injuries and other claims.” The second is the Q tactic, in which “operators are full-fledged employees who get benefits, including health care.”

Brenner goes on to explain how Q is setting the new industry standard for on-demand workers. One of her arguments is that “when labor markets are competitive — which is increasingly the case in the on-demand sector — workers will take company culture into account. To attract the best talent, companies will need to show they value drivers, handymen and housekeepers in the same way as, say, tech workers who write code.”

Brenner’s point is a valid one, but I don’t see this as a huge revelation. I think this trend is simply an application of the Coase Theorem. We can think about this in terms of vertical integration. The difference between the Q tactic and the Uber tactic is that Q decided to vertically integrate by buying its suppliers as opposed to outsourcing. The Coase Theorem suggests that firms vertically integrate when it is less costly to perform an activity/transacting using a firm’s hierarchy rather than contracting with another firm than a market exchange.

In this case, Q believes that the potential cost of using contracted workers is greater than the cost of hiring them. This makes sense because in Q’s line of work, “Operators cross the threshold between public and private space, so there’s a greater need for the company to create a truly consistent experience no matter which operator cleans an office.” What this means to me is that the potential cost of a mishap using temporary workers is greater than the cost of creating the consistent experience by hiring them. Uber, on the other hand, is in a completely different line of work, where the cost of contracting is not as high, which is why they decided not to vertically integrate.

So in response to Ms. Brenner, I don’t necessarily think that Q “becoming the standard-bearer for a new type of on-demand service company,” I just think the strategy they chose was an application of economics.

 

 

 

 

 

Robo Investing

Thesis: Nash and Schwab are both right because they capture different segments of the market.

Noah Smith recently wrote a piece in Bloomberg View entitled “Would You Trust Your Nest Egg to a Robot?” The article is about the cross-road in the asset management industry between human managed funds and robot managed funds. Smith writes, “A fairly spectacular battle just took place in the asset-management world. The fight was between Adam Nash, chief executive officer of Wealthfront, and Charles Schwab Corp. Wealthfront is an automated investor service, or robo-adviser — a type of service that has soared to prominence in the last few years. Schwab, an old-line discount brokerage, is rolling out its own robo-adviser service.”

On one side of the debate, Nash accuses Schwab of being a bad deal for investors. He claims that Schwab holds a significant portion of the investors’ assets in cash, as opposed to riskier investments that would earn higher returns. He also claimed that Schwab’s fees are too high, which we learned in class today significantly cuts into investor surplus.

On the other side of the debate, Schwab defended its position on cash holdings by claiming that their clients are risk averse to the point they are willing to sacrifice some of their investor surplus. We also learned in class that risk aversion is a significant factor that determines investor surplus – the lower the risk aversion, the higher the return.

Using the Capital Asset Pricing Model, we learned that there are a few factors that affect investor surplus: mean excess market returns, risk aversion, and the variance of the portfolio’s return. Armed with that knowledge, we can now weigh in on Schwab and Nash’s debate. My question is, why can’t both of these guys be correct? On the matter of fees, we know that they directly cut into average return, which decreases investor surplus. Both of these services charge fees, and it’s really just a matter the structure of the fees that investors need to pay attention to. The more interesting topic is about risk aversion. On this matter, I think both Nash and Schwab have a point. Wealthfront is a very young, and rapidly growing firm that uses newer technology. Because of this, they might be attracting a younger clientele, which would most likely mean they are attracting less risk averse customers. Schwab on the other hand, is an older, more traditional financial advisory firm that attracts older, more traditional clientele, which would mean they are more risk averse. I believe that both of these companies can coexist because they capture different segments of the market.

Efficient Market Theory and Crashes

Thesis: Donier and Bouchaud’s theory doesn’t predict crashes, it just facilitates them.

In Bloomberg View, Mark Buchanan wrote an article called “Bitcoin and Market Crashes,” in which he discusses a new theory for predicting market crashes that was developed by two French physicists.

“Jonathan Donier and Jean-Philippe Bouchaud, both of whom work at Paris-based hedge fund Capital Fund Management, started from an obvious idea: It would be easy to foresee big crashes if you could monitor the actual thoughts and expectations of all investors. With that kind of superhuman knowledge, you could get an early warning of emerging imbalances between pessimists and optimists, between likely sellers and buyers. Such imbalances set the groundwork for a crash — specifically, when the number of potential buyers gets very small.” Here is the link to their full paper.

In the Bitcoin market, buyers and sellers place there orders early, so the data of buyers and sellers is easily available. Because of the transparency, Donier and Philippe were able to use the Bitcoin market to test their theory. “Using this method, they were able to predict the size of the biggest 14 single-day drops in bitcoin value between January and April 2013 to a high degree of accuracy.”

Here is my first question: how is this any different from the efficient market theory? The article states that Donier and Bouchaud use publicly available data. According to the efficient market theory, asset prices should reflect all available information. According to investopedia, EMH is an investment theory that states it is impossible to “beat the market” because stock market efficiency causes existing share prices to always incorporate and reflect all relevant information. According to the EMH, stocks always trade at their fair value on stock exchanges, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices.” So either Donier and Bouchaud disproved the efficient market theory, or there is something else going on.

My follow up question is what kind of predictive power does this theory really have? The theory tells us when the number of sellers significantly outweighs the number of buyers. But isn’t that already too late? If everyone knows that there are no buyers, then prices have to drop to reflect demand, which is what causes a crash. So isn’t the theory just a self-fulfilling prophecy? By telling people there is no demand, the theory itself would cause prices to drop.

 

 

March Madness Investing (Revised)

Thesis: Predicting the future is not impossible.

In his article “The March Madness Theory of Investing,” Barry Ritholtz makes some really interesting connections between filling out a March Madness bracket and investing. He actually touches upon a number of topics that we have discussed in class. For example, his first point is that it is impossible to predict the future – “The defeat of several favorites, most notably Kansas and Maryland, remind us that predicting the future is a fool’s errand. We simply never know what will happen next. It is as true for sports as it is for politics, investing or economics.” This is essentially exactly what we learned from A Random Walk. Another connection to A Random Walk is where he says that “Expert forecasts are about as good as those of nonexperts.” Essentially what he is saying is that all of the (fundamental/technical) analysis done by ESPN analysts really don’t mean much.

For the most part, I agree with a lot of what Ritholtz has to say, and I think the comparisons he draws between March Madness and investing are fair. However, I want to address his claim that predicting the future is impossible. While it is true that no one has a crystal ball, and it is extremely difficult to predict specific outcomes, I don’t think it is impossible. In a follow up article called “March Madness and the Perils of Predicting,” Ritholtz defines a prediction as “ a forecast of a future event, specific in time and numerical value.” Because there are others who share my view, he addresses us by clarifying the distinction between predictions and probability/mean reversion. In doing so, he is addressing those who say that predicting the future isn’t impossible because you can predict with a reasonable amount of certainty what will happen in the long run. For instance, “stocks will tend to become more valuable over long periods of time because stocks reflect the value of some portion of our overall output.” According to Ritholz’s definition, that is not a prediction. However, it is my view that you can use probability to make predictions even under the Ritholtz definition.

We also need to address rational expectations. Rational expectations is the “assumption that what economic agents psychologically expect a random variable to be in the future is equal to the statistical expectation of that variable based on all available information.” Let’s use March Madness as an example. I do not consider myself an expert by any means in college basketball, but based on information and some personal assessment, I chose Kentucky to reach the Final 4. According to fivethirtyeight, Kentucky had a 72% chance to reach the Final Four, so with that rational expectation I predicted that specific outcome.

Now there is also the issue of rational expectation surprise. “In discussing rational expectations, it is useful to use to represent the expectational error discovered when the actual value of a variable is revealed—that is, is the “surprise” an agent experiences when the truth becomes known with the passage of time.” So now our prediction is equal to the expected value plus some random error term. The error, or “surprise” is essentially the “madness” in March Madness, that random factor that defies expectations. In my example, now we know Kentucky made the Final Four, the surprise revealed itself to be 0, which means my prediction was true. Obviously that strategy doesn’t work for picking every game, but it does prove that predicting the future is not always impossible.

 

 

 

 

 

 

 

March Madness and Investing

Thesis: Predicting the future is not impossible.

In his article “The March Madness Theory of Investing,” Barry Ritholtz makes some really interesting connections between filling out a March Madness bracket and investing. He actually touches upon a number of topics that we have discussed in class. For example, his first point is that it is impossible to predict the future – “The defeat of several favorites, most notably Kansas and Maryland, remind us that predicting the future is a fool’s errand. We simply never know what will happen next. It is as true for sports as it is for politics, investing or economics.” This is essentially exactly what we learned from A Random Walk. Another connection to A Random Walk is where he says that “Expert forecasts are about as good as those of nonexperts.” Essentially what he is saying is that all of the (fundamental/technical) analysis done by ESPN analysts really don’t mean much.

For the most part, I agree with a lot of what Ritholtz has to say, and I think the comparisons he draws between March Madness and investing are fair. However, I want to address his claim that predicting the future is impossible. While it is true that no one has a crystal ball, and it is extremely difficult to predict specific outcomes, I don’t think it is impossible. In a follow up article called “March Madness and the Perils of Predicting,” Ritholtz defines a prediction as “ a forecast of a future event, specific in time and numerical value.” Because there are others who share my view, he addresses us by clarifying the distinction between predictions and probability/mean reversion. In doing so, he is addressing those who say that predicting the future isn’t impossible because you can predict with a reasonable amount of certainty what will happen in the long-run. For instance, “stocks will tend to become more valuable over long periods of time because stocks reflect the value of some portion of our overall output.” According to Ritholz’s definition, that is not a prediction. However, it is my view that you can use probability to make predictions even under the Ritholtz definition.

Let’s use March Madness as an example. I do not consider myself an expert by any means in college basketball, but based on information and some personal assessment, I chose Kentucky to reach the Final 4. According to fivethirtyeight, Kentucky had a 72% chance to reach the Final Four, so using that knowledge I predicted that specific outcome. Obviously that strategy doesn’t work for picking every game, but it does prove that predicting the future is not always impossible.

 

 

 

 

 

Private Equity Bubble

Thesis: Private equity could be a bubble.

Today’s Wall Street Journal featured an opinion piece written by Andy Kessler about the status and outlook of the private equity market. Here are Kessler’s opening remarks:

“Private equity is done. Stick a fork in it. With Kraft singles and Heinz ketchup as toppings, there are many signs that private equity has peaked as an asset class.”

Just to clarify, private equity is a form of investing where you buy a company by putting up some cash, but using mostly leverage. Then you take over management of the company for a period of time, using the cash flows to pay off the debt, with enough left over for fees and investor dividends. In most cases, the strategy used to generate cash flow is drastically cutting costs by closing divisions, cutting staff, scaling back marketing, R&D, etc. When the debt is repaid, you either take the company public or sell it to someone else. Private equity has been extremely profitable, with 2014 posting record returns: “annual U.S. private equity exit volume increased 35% year over year from $191 billion in 2013 to $257 billion in 2014 — pushing the 2014 exit volume to the “highest level ever,” according to the Private Equity Growth Capital Council.

Now that we have an understanding of private equity, let’s get back to the thesis. I want to take Kessler’s argument a step further, and say that not only is private equity done, but a private equity bubble could be forming. First let’s talk about bubbles. In the Wall Street Journal article, “How to Spot a Market Bubble,” Joe Light provides three warning signs. First is rapidly rising asset prices. As private equity continues to post impressive returns, more and more funds want to take part in the action, which increases the purchase prices of the target companies. As Kessler states, “Capital will still chase increasingly expensive deals. That won’t end well.” Which leads us to Light’s second warning sign – when prices break sharply from an asset’s underlying value. The reason Kessler says the deals are so expensive is because companies are being valued at increasingly large multiples. The third warning sign is the existence of an exciting technology or innovation to justify the prices. While that isn’t exactly the case here, the prices are being justified simply because private equity shops are willing to pay them. Finally, to tie in A Random Walk, private equity is inherently reliant on castles in the sky because the very nature of it relies on an exit strategy that either requires another buyer or an IPO, which means there needs to be a ‘greater fool’ to purchase the company next. Given the reasons provided, private equity could be heading for a major collapse.