We give an explicit algorithm and source code for extracting expected returns for stocks from expected returns for alphas. Our algorithm altogether bypasses combining alphas with weights into "alpha combos". Simply put, we have developed a new method for trading alphas which does not involve combining them. This yields substantial cost savings as alpha combos cost hedge funds around 3% of the P&L, while alphas themselves cost around 10%. Also, the extra layer of alpha combos, which our new method avoids, adds noise and suboptimality. We also arrive at our algorithm independently by explicitly constructing alpha risk models based on position data.
Decoding Stock Market with Quant Alphas. (arXiv:1708.02984v1 [q-fin.PM])
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Wiley CIAexcel Exam Review Focus Notes 2017, Part 2: Internal Audit Practice
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Wiley CIAexcel Exam Review 2017, Part 1: Internal Audit Basics
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Wiley CIAexcel Exam Review 2017 Focus Notes, Part 3: Internal Audit Knowledge Elements
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Wiley CIAexcel Exam Review Focus Notes 2017, Part 1: Internal Audit Basics
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Wiley CIAexcel Exam Review 2017: Part 3, Internal Audit Knowledge Elements, 8th Edition
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August 11, 2017 - SS&C GlobeOp Hedge Fund Performance Index: July performance 1.24%; Capital Movement Index: August net flows advance 0.25%
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The Quant Fund Robot Takeover Has Been Postponed
The #quant fund robot takeover has been postponed https://t.co/RYakqC6OnZ â moneyscience (@moneyscience) August 10, 2017
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Why AI and machine learning researchers are beginning to embrace PyTorch - The Practical Quant's blog - MoneyScience
Blog Partner: Why AI and machine learning researchers are beginning to embrace PyTorch https://t.co/dx1FyZHY1b â Financial Technology (@fin_tech) August 3,â¦
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Hacking a computer using DNA is now a reality, researchers claim
Researchers say they have successfully hacked into a computer using custom strands of DNA. https://t.co/GuvAienZDZ â Financial Technology (@fin_tech) Augustâ¦
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A Tesla 2017 Update: A Disruptive Force with a Debt Problem!
These are certainly exciting times for Tesla. The first production version of the Tesla 3 was unveiled on July 28, with few surprises on the details and plenty of good reviews. Elon Musk was his usual self, alternating between celebrating success and warning investors in the stock that the company was approaching "manufacturing hell", as it ramped up its production schedule to meet its target of producing 10,000 cars a week. It is perhaps to cover the cash burn in manufacturing hell that Tesla also announced that it planned to raise $1.5 billion in a junk bond offering. Investors continued to be unfazed by the negative and lapped up the positive, as the stock price soared to $365 yesterday. With all of this happening, it is time for me to revisit my Tesla valuation, last updated in July 2016, and incorporate, as best as I can, what I have learned about the company since then.
Tesla: The Story Stock
I have been following Tesla for a few years and rather than revisit the entire history, let me go back to just my most recent post on the company in July 2016, where I called Tesla the ultimate story stock. I argued that wide differences between investors on what Tesla is worth can be traced to divergent story lines on the stock. I used the picture below to illustrate the story choices that you had when it came to Tesla and how those choices affected the inputs into the valuation.
In that post, I also traced out the effect of story choices on value, by estimating how the numbers vary, depending upon the business, focus and competitive edge that you saw Tesla having in the future:
With my base case story of Tesla being an auto/tech company with revenues pushing towards mass market levels and margins resembling those of tech companies, I estimated a value of about $151 a share for the company and my best case estimate of value was $316.46.
Tesla: Operating Update
If you are invested in or have been following Tesla for the last year, you are certainly aware that the market has blown through my best case scenario, with the stock trading this morning at $365 a share, completing a triumphant year in markets:
As Tesla's stock price rose, it broke through milestones that guaranteed it publicity along the way. It's market capitalization exceeded that of Ford and General Motors in April 2016, and in June 2016, Tesla leapfrogged BMW to become the fourth largest market cap automaker in the world, though it has dropped back to fifth since.
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| Largest Auto Companies (Market Capitalization) on August 9, 2017 |
While Tesla's market cap has caught up with those larger auto makers, its production and revenues are a fraction of theirs, leading some to use metrics like enterprise value per car sold to conclude that Tesla is massively over valued. I don't have faith in these pricing metrics since they can be misleading, especially when comparing a company with massive potential to companies that are in decline, as I think many of the conventional auto companies in this table are currently.
As I noted at the start of the post, it has been an eventful year for Tesla, with the completion of the Solar City acquisition and the imminence of the Tesla 3 dominating news, and its financial results reflect its changes as a company. In the twelve months ended June 30, 2017, Tesla's revenues hit $10.07 billion, up from $7 billion in its most recent fiscal year, which ended on December 31, 3016; on an annualized basis, that translates into a revenue growth rate of 107%. That positive news, though, has to be offset at least partially with the bad news, which is that the company continued to lose money, reporting an operating loss of $638 million in the most recent 12 months, with R&D expensed, and a loss of $103 million, with capitalized R&D. The growth in the company can be seen graphically by looking at how quickly its operations have scaled up, over the last few years:
Tesla's growth has not just been in the operating numbers but in its influence on the automobile sector. While it was dismissed by the other automobile companies as a newcomer that would learn the facts of life in the sector, as it aged, the reverse has occurred. It is the conventional automobile companies that are, slowly but surely, coming to the recognition that Tesla has changed their long-standing business. Volvo, a Swedish automaker not known for its flair, announced recently that all of its cars would be either electric or hybrid by 2019, and Ford's CEO was displaced for not being more future oriented. A little more than a decade after it burst on to the scene, it is a testimonial to Elon Musk that he has started the disruption of one of the most tradition-bound sectors in business.
Tesla: Valuation Update
The production hiccups notwithstanding, the company continues to move towards production of the Tesla 3, with the delivery of the handful to start the process. There is much that needs to be done, but I consider it a good sign that the company sees a manufacturing crunch approaching, since I would be concerned if they were to claim that they could ramp up from 94,000 to 500,000 cars effortlessly. My updated story for Tesla then is close to the story that I was telling in July 2016, with two minor changes. The first is that the production models of the Tesla 3 indicate that the company is capable of delivering a car that can appeal to a much broader market than prior models, putting it on a pathway to higher revenues. My expected revenues for Tesla in ten years are close to $93 billion, a nine-fold increase from last year's revenues and a higher target than the $81 billion that I projected in my July 2016 valuation. Second, the operating margins, while still negative, have become less so, reducing reinvestment needs for funding growth. The free cash flows are still negative for the next seven years, a cash burn that will require about $15.5 billion in new capital infusions over that period. With those changes, the value per share that I estimate is about $192/share, about 20% higher than my $151 estimate a year ago, but well below the current price per share of $365.
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| Download spreadsheet |
As with every Tesla valuation that I have done, I am sure (and I hope) that you will disagree with me, with some finding me way too pessimistic about Tesla's future, and others, much too optimistic. As always, rather than tell me what you think I am getting wrong, I would encourage you to download the spreadsheet and replace my assumption with yours. I think I am being clear eyed about the challenges that Tesla will face along the way and here are the top three:
- Can Tesla sell millions of cars? One of Tesla's accomplishments has been exposing the potential of the hybrid/electric car market, even in an era of restrained fuel prices. That is good news for Tesla but it has woken up the established automobile companies as well, as is evidenced by not only the news from Volvo and Ford, but also in increased activity on this front at the other automobile companies. In my valuation, the revenues that I project in 2010 will require Tesla to sell close to 2 million cars, in the face of increased competition.
- Can it make millions of cars? Tesla's current production capacity is constrained and there are two production tests that Tesla has to meet. The first is timing, since the deliveries have been promised for the middle of 2018 and the assembly lines have to be humming by then. The second is cost, since a subtext of the Tesla story, reinforced by Elon Musk, is that the company has found new and innovative ways of scaling up production at much lower costs than conventional automobile companies.
- Can it generate double digit margins? In my valuation, I assume an operating margin of 12% for Tesla, almost double the average of 6.33% for global auto companies. For Tesla to generate this higher margin, it has to be able to keep production costs low at its existing and new assembly plants and to be able to charge a premium price for its automobiles, perhaps because of its brand name.
Tesla has shown a capacity to attract and keep customers and I think it is more than capable of meeting the first challenge, i.e., sell millions of cars. It is the production challenge that is the more daunting one, simply because this has always been Tesla's weakest link. Over the last few years, Tesla has consistently had trouble meeting logistical and delivery targets it has set for itself and those targets will only get more daunting in the years to come. Furthermore, if its production costs run above expectations, it will be unable to deliver on higher margins. To succeed, Tesla will require vision, focus and operating discipline. With Elon Musk at its helm, the company will never lack vision, but as I argued in my July 2016 post, Mr. Musk may need a chief operating officer at his side to take care of delivery deadlines and supply chains.
Financing Cash Burn: Tesla's Odd Choice
There is much to admire in the Tesla story but there is one aspect of the story that I find puzzling, and if I were an equity investor, troubling. It is the way in which Tesla has chosen to, and continues to, finance itself. Over the last decade, as Tesla has grown, it has needed substantial capital to finance its growth. That is neither surprising nor unexpected, since cash burn is part of the pathway to glory for companies like Tesla. However, Tesla has chosen to fund its growth with large debt issues, as can be seen in the graph below:
That debt load, already high, given Teslaâs operating cash flows is likely to get even bigger if Tesla succeeds in its newest debt issue of $1.5 billion, which it is hoping to place with an interest rate of 5.25%, trying to woo bond buyers with the same pitch of growth and hope that has been so attractive to equity markets. That suggests that those making the pitch either do not understand how bonds work (that bondholders don't get to share much in upside but share fully in the downside) or are convinced that there are enough naive bond buyers out there, who think that interest payments can be made with potential and promise.
But setting aside concerns about bondholders, the debt issuance makes even less sense from Tesla's perspective. Unlike some, I donât have a kneejerk opposition to the use of debt. In fact, given that the tax code is tilted to benefit debt, it does make sense for many companies to use debt instead of equity. The trade off, though, is a simple one:
If you look at the trade off, you can see quickly that Tesla is singularly unsuited to using debt. It is a company that is not only still losing money but has carried forward losses of close to $4.3 billion, effectively nullifying any tax benefits from debt for the near future (by my estimates, at least seven years). With Elon Musk, the largest stockholder at the company, at the helm, there is no basis for the argument that debt will make managers more disciplined in their investment decisions. While the benefits from debt are low to non-existent, the costs are immense. The company is still young and losing money, and adding a contractual commitment to make interest payments on top of all of the other capital needs that the company has, strikes me as imprudent, with the possibility that one bad year could its promise at risk. Finally, in a company like Tesla, making large and risky bets in new businesses, the chasm between lenders and equity investors is wide, and lenders will either impose restrictions on the company or price in their fears (as higher interest rates). So, why is Tesla borrowing money? I can think of two reasons and neither reflects well on the finance group at Tesla or the bankers who are providing it with advice.
- The Dilution Bogeyman: The first is that the company or its investment bankers are so terrified of dilution, that a stock issue is not even on the table. Once the dilution bogeyman enters the decision process, any increase in share count for a company is viewed as bad, and you will do everything in your power to prevent that from happening, even if it means driving the company into bankruptcy.
- Inertia: Auto companies have generally borrowed money to fund assembly plants and the bankers may be reading the capital raising recipe from that same cookbook for Tesla. That is incongruent with Elon Muskâs own story of Tesla as a company that is more technology than automobile and one that plans to change the way the auto business is run.
Teslaâs strengths are vision and potential and while equity investors will accept these as down payments for cash flows in the future, lenders will not and should not. In fact, I cannot think of a better case of a company that is positioned to raise fresh equity to fund growth than Tesla, a company that equity investors love and have shown that love by pushing stock prices to record highs. Issuing shares to fund investment needs will increase the share count at Tesla by about 3-4% (which is what you would expect to see with a $1.5 billion equity issue) but that is a far better choice than borrowing the money and binding yourself to make interest payments. There will be a time and a place for Tesla to borrow money, later in its life cycle, but that time and place is not now. If Tesla is dead set on not raising its share count, there is perhaps one way in which Tesla may be able to eat its cake and have it too, and that is to exploit the dilution bogeyman's blind spot, which is a willingness to overlook potential dilution (from the issuance of convertibles and options). In fact, why not issue long term, really low coupon convertible bonds, very similar to this one from 2014, a bond only in name since almost all of its value came from the conversion option (which is equity with delayed dilution)?
Conclusion
The Tesla story continues to evolve, and there is much in the story that I like. It is changing the automobile business, a feat in itself, and it is starting to deliver on its production promises. The next year may be manufacturing hell, but if the company can make its through that hell and find ways to deliver the tens of thousands of Tesla 3s that it has committed to delivering, it will be well on its way. I still find the stock to be too richly priced, even given its promise and potential, for my liking, but I understand that many of you may disagree. That said, though, I do think that the company's decision to use debt to fund its operations makes no sense, given where it is in the life cycle.
YouTube Video
Previous Blog Posts
Spreadsheet Attachments
YouTube Video
Previous Blog Posts
- Tesla: It's a story stock, but what's the story? (July 2016)
Spreadsheet Attachments
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On the overestimation of the largest eigenvalue of a covariance matrix. (arXiv:1708.03551v1 [math.PR])
In this paper, we use a new approach to prove that the largest eigenvalue of the sample covariance matrix of a normally distributed vector is bigger than the true largest eigenvalue with probability 1 when the dimension is infinite. We prove a similar result for the smallest eigenvalue.
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Oil economy phase plot: a physical analogy. (arXiv:1708.03533v1 [q-fin.GN])
A phase plot of the oil economy is built using the literature data of world oil production, price, and EROEI (Energy Returned on Energy Invested). An analogy between the oil economy and the Benard convection is proposed; some methods of interpretation and forecast of the system behavior are also shown based on "phase portrait" using as main variables the price, production and EROEI values. A scenery is proposed on this basis.
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Technology networks: the autocatalytic origins of innovation. (arXiv:1708.03511v1 [q-fin.EC])
We search an autocatalytic structure in networks of technological fields and evaluate its significance for technological change. To this aim we define a technology network based on the International Patents Classification, and we study if autocatalytic structures in the network foster innovation as measured by the rate of production of patents. The network is identified through patenting activity of geographical regions in different technology fields. Through our analysis we show how the technological landscape of the patents database evolves as a self-organising autocatalytic structure that grows in size, and arrives to cover the most part of the technology network. Technology classes in the core of the autocatalytic structure perform better in terms of their innovativeness, as measured by the rate of growth of the number of patents. Finally, the links between classes that define the autocatalytic structure of the technology network break the hierarchical structure of the database, and indicate that recombinant innovation and its autocatalytic patterns are an important stylised fact of technological change.
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Simulating Business Cash Flow Taxation: An Illustration Based on the "Better Way" Corporate Tax Reform -- by Seth G Benzell, Laurence J Kotlikoff, Guillermo LaGarda
The U.S., according to some measures, has one of the highest marginal effective corporate tax rates (METRs) of any developed country. Yet the tax collects less than 2 percent of GDP. This paper studies the impact of replacing the U.S. corporate tax with a Business Cash Flow Tax (BCFT). Our paper studies BCFT reform with reference to a particular, but reasonably generic, proposal, namely the House Republican "Better Way" tax plan. We use the Global Gaidar Model - a 17-region, global, overlapping-generations model, calibrated to U.N. demographic and IMF fiscal data - to simulate the dynamic, general equilibrium impact of this reform. In the short run, the U.S. capital stock, pre-tax wage rates, and GDP rise by roughly 25 percent, 8 percent, and 9 percent, respectively. Over time, the capital stock and wage rates remain significantly above their baseline values. There is a smaller long-run increase in GDP as workers spend some of their higher wages on additional leisure. The tax reform produces enough additional revenues to permit a reduction in personal income tax rates while maintaining the economy's initial debt-to-GDP ratio. The beneficiaries of the House plan are today's and tomorrow's workers. We also simulate a matching METR cut by the rest of the world, which raises the world interest rate. The short-run increases in the capital stock, pre-tax wage rates, and GDP are smaller. However, along the transition path, all U.S. agents experience slightly higher welfare than under the House plan. This reflects the combination of a higher post-corporate tax world interest rate and Americans' disproportionately large holdings of global assets
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On the Dynamics of Community Development -- by Levon Barseghyan, Stephen Coate
This paper presents a dynamic political economy model of community development. In each period, a community invests in a local public good. The community can grow, with new housing supplied by competitive developers. To finance investment, the community can tax residents and issue debt. In each period, fiscal decisions are made by current residents. The community's initial wealth (the value of its stock of public good less its debt) determines how it develops. High initial wealth leads to rapid development. Low initial wealth leads to gradual development that is fueled by community wealth accumulation. Wealth accumulation arises from the desire to attract more households to share the costs of the public good. The long run size of the community can be too large or too small and development may proceed too slowly. Nonetheless, some development occurs and, at all times, public good provision is efficient.
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Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data -- by Serena Ng
This paper seeks to better understand what makes big data analysis different, what we can and cannot do with existing econometric tools, and what issues need to be dealt with in order to work with the data efficiently. As a case study, I set out to extract any business cycle information that might exist in four terabytes of weekly scanner data. The main challenge is to handle the volume, variety, and characteristics of the data within the constraints of our computing environment. Scalable and efficient algorithms are available to ease the computation burden, but they often have unknown statistical properties and are not designed for the purpose of efficient estimation or optimal inference. As well, economic data have unique characteristics that generic algorithms may not accommodate. There is a need for computationally efficient econometric methods as big data is likely here to stay.
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Level and Volatility Factors in Macroeconomic Data -- by Yuriy Gorodnichenko, Serena Ng
The conventional wisdom in macroeconomic modeling is to attribute business cycle fluctuations to innovations in the level of the fundamentals. Though volatility shocks could be important too, their propagating mechanism is still not well understood partly because modeling the latent volatilities can be quite demanding. This paper suggests a simply methodology that can separate the level factors from the volatility factors and assess their relative importance without directly estimating the volatility processes. This is made possible by exploiting features in the second order approximation of equilibrium models and information in a large panel of data. Our largest volatility factor V1 is strongly counter-cyclical, persistent, and loads heavily on housing sector variables. When augmented to a VAR in housing starts, industrial production, the fed-funds rate, and inflation, the innovations to V1 can account for a non-negligible share of the variations at horizons of four to five years. However, V1 is only weakly correlated with the volatility of our real activity factor and does not displace various measures of uncertainty. This suggests that there are second-moment shocks and non-linearities with cyclical implications beyond the ones we studied. More theorizing is needed to understand the interaction between the level and second-moment dynamics.
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Globalization and the Increasing Correlation between Capital Inflows and Outflows -- by J. Scott Davis, Eric van Wincoop
We document that the correlation between capital inflows and outflows has increased substantially over time in a sample of 128 advanced and developing countries. We provide evidence that this is a result of an increase in financial globalization (stock of external assets and liabilities). This dominates the effect of an increase in trade globalization (exports plus imports), which reduces the correlation between capital inflows and outflows. In the context of a two-country model with 14 shocks we show that the theoretical impact of financial and trade globalization on the correlation between capital inflows and outflows is consistent with the data.
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Portfolio Liquidity and Diversification: Theory and Evidence -- by Lubos Pastor, Robert F. Stambaugh, Lucian A. Taylor
A portfolio's liquidity depends not only on the liquidity of its holdings but also on its diversification. We propose simple, theoretically motivated measures of portfolio liquidity and diversification. We also develop an equilibrium model relating portfolio liquidity to fund size, expense ratio, and turnover. As the model predicts, mutual funds with less liquid portfolios have smaller size, higher expense ratios, and lower turnover. The model also yields additional predictions that we verify empirically: larger funds are cheaper, funds that trade less are larger and cheaper, and funds that are too big perform worse. We also find that mutual fund portfolios have become more liquid because both components of diversification, coverage and balance, have trended upward.
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