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By John Butler Andrew Simon 20-08-2020

Could US Tech Companies Start Buying Gold And Bitcoin?

(5 min read)


US corporate cash holdings have risen to record highs in recent years, both outright and as a percentage of net worth. A majority of that excess cash is on the balance sheets of the large tech companies. With the Covid-19 shock having dampened near-term investment prospects, it is unlikely that this cash is deployed soon. Undoubtedly some of the cash will be used for buybacks, but for the cash remaining, will firms continue to accept close to zero percent returns and expose themselves to a further decline in the USD? In most cases, we would expect them to seek out some alternatives.

 

Corp Cash Holdings at Record (FoF)

US Corporations Hoarding Cash

The growing cash pile is certain to be a hot topic in corporate boardrooms. Some large corporations, such as Apple, have been running what amount to internal investment funds for years. Other tech giants are well-known financiers of start-ups, either directly or via investments in venture capital firms.

For the more risk-averse corporate boards, however, investing excess cash in other companies’ securities may not seem the best idea. In such cases, cash is likely to be held in some combination of bank deposits, money-market funds, or government securities. The problem here, however, is that with rates on cash holdings effectively zero and possibly going negative at some point, cash holdings become an automatic drag on earnings.

Corporate treasurers are now looking at under 1% annual potential returns – fixed income price appreciation returns should be minimal with the Fed wanting to avoid negative rates – combined with a potentially further depreciating USD (see Macro Hive’s recent podcast with renowned economist Stephen Roach) for companies that have large international businesses. If you are one of those corporate treasurers, why not look now to diversify into other assets with better potential long-term returns, albeit with somewhat higher volatility?

 

Enter Bitcoin

It is against this backdrop that MicroStrategy (MSTR:US) recently announced that it is diversifying its corporate cash pile into Bitcoin. As Michael Saylor, CEO of MicroStrategy Incorporated, stated in a recent press release:

‘Our investment in Bitcoin is part of our new capital allocation strategy, which seeks to maximize long-term value for our shareholders…This investment reflects our belief that Bitcoin, as the world’s most widely-adopted cryptocurrency, is a dependable store of value and an attractive investment asset with more long-term appreciation potential than holding cash… MicroStrategy has recognized Bitcoin as a legitimate investment asset that can be superior to cash and accordingly has made Bitcoin the principal holding in its treasury reserve strategy.

…Our decision to invest in Bitcoin at this time was driven in part by a confluence of macro factors affecting the economic and business landscape that we believe is creating long-term risks for our corporate treasury program—risks that should be addressed proactively…We believe that, together, these and other factors may well have a significant depreciating effect on the long-term real value of fiat currencies and many other conventional asset types, including many of the assets traditionally held as part of corporate treasury operations.…MicroStrategy believes digital transformation has quickened amid rapidly shifting market requirements. These dynamics have many corporations rethinking their offerings, operations, and systems, as well as their balance sheets and financial strategies.’

MicroStrategy is not the first listed US company to announce publicly a strategic allocation to bitcoin. Back in 2014, erstwhile Overstock CEO Patrick Byrne announced that the company was not only accepting bitcoin for online purchases but allocating $200mn to Medici Ventures, a bitcoin and blockchain investment firm. While not exactly the same as holding bitcoin directly, the move was clearly intended to gain exposure to the leading cryptocurrency. Overstock (OSTK:US) remains heavily involved in bitcoin and blockchain development projects today, with the current CEO recently stating that ‘blockchain is the future’.

The largest corporate cash balances are primarily in the biggest tech companies. Many of the earliest adopters of investing in Bitcoin and other cryptocurrencies have been developers at some of these firms. Much of the senior management is already well versed in the crypto universe, including Facebook, where they have been trying to launch their own coin. It does not seem too much of a stretch to see Facebook management deciding to take a part of their $50bn+ cash reserves and diversify into cryptocurrencies, among other alternatives.

 

Don’t Forget Gold

It might seem natural for a tech company to see Bitcoin and other cryptocurrencies as an alternative to cash, but it would certainly be a stretch for a more traditional corporation. However, the basic reasoning, that ‘fiat currencies and many other conventional asset types’ could be at risk from the most aggressive peacetime fiscal and monetary policy mix in history (and not only in the US but in most major economies) is entirely sound in our opinion.

So what if more traditional corporations nevertheless begin to look for a low-risk way to diversify out of cash? If not bitcoin, what might they consider instead?

Here, too, we have a recent example: last week, Warren Buffett’s Berkshire Hathaway, known for investing almost exclusively in traditional industries, released its quarterly 13-K filing, which showed that they have taken a stake in Barrick, the second US-listed gold miner. Owning a gold miner is not quite the same thing as owning the metal itself, but as a major producer with proven resources in safe jurisdictions, owning Barrick comes awfully close.

Given that Buffett is hailed the world over as one of the greatest investors of his or any other modern generation, this decision has made theheadlines. But as corporate boards are often slow to revise or otherwise change their Treasury policies, it could be months before one or more major corporations choose to follow Buffett’s lead and diversify some of their cash holdings into gold or gold-related securities in some way, as a hedge against dollar or general fiat currency depreciation.

Looking beyond the boardroom, however, Buffet’s decision to move into gold is almost certain to have a general impact on institutional investors, in particular among the value crowd. Indeed, with the price of gold now over $2000/oz, many gold mining companies offer the sort of deep value that Buffet has always sought out for Berkshire’s portfolio.

Finally, unlike cash, and indeed unlike all financial assets, the supply of precious metals is finite and highly inelastic. Central banks create cash reserves as desired, which are subsequently transmitted through to the economy by the banking and financial system. Corporations issue debt and equities as required to grow and expand their operations. Metals are therefore a natural diversifier vis-à-vis that which is elastic in supply, and in recent years, extremely so.

 

Bottom Line

US corporations are sitting on a record amount of cash and securities, unless they are preparing to allocate all to buybacks or dividends, it makes logical sense to allocate some to metals and possibly Bitcoin. The Federal Reserve’s recent move of putting short-term interest rates near zero and the beginning of a possible long-term depreciation trend in the USD should combine to pressure corporate treasurer’s to consider diversifying out of cash holdings into higher-returning assets like gold and Bitcoin.

The interesting dynamic for both is the high inelasticity of supply, even a small shift in the overall investment demand function for precious metals implies large increases in price. By some estimates, investors only hold about 1-2% of their assets in precious metals. Were that to increase to 5-10%, then precious metal prices would need to increase by five-fold or more. Where Buffet sees deep value, others might see a speculative play. In either case, notwithstanding a strong recent run, the bull market in precious metals may still be in the early stages, with highly asymmetric upside potential.

 

 

John Butler has 25 years experience in international finance. He has served as a Managing Director for bulge-bracket investment banks on both sides of the Atlantic in research, strategy, asset allocation and product development roles, including at Deutsche Bank and Lehman Brothers.

 

Andrew Simon has spent over 25 years in finance on both the buy side and sell side. He co-founded Eschaton Opportunities Fund, a $100 mn+ hedge fund focused on global thematic value investing.

 

 

 

(The commentary contained in the above article does not constitute an offer or a solicitation, or a recommendation to implement or liquidate an investment or to carry out any other transaction. It should not be used as a basis for any investment decision or other decision. Any investment decision should be based on appropriate professional advice specific to your needs.)

 

 

By Sam van de Schootbrugge 19-08-2020
In: deep-dives | Investment Strategy Rates

How Hedge Funds And Asset Managers Make Money In Bonds

(9 min read)


The Study in a Nutshell

There has always been a question of how hedge funds and asset managers make money. By exploiting a comprehensive regulatory database, a new Bank of England working paper finds that these investors have made abnormal returns in bond markets since 2011. They have done so by obtaining a competitive edge from observing other investors, responding quickly to macroeconomic news and correctly forecasting macroeconomic fundamentals.

The chart below captures the predictive power of fund managers concisely. It shows the event-time cumulative returns of the long-short portfolios (long on the top tercile and short on the bottom tercile of government bonds) sorted by daily order flows of the two investor types. We can see that hedge fund trading positively forecasts bond returns in the short run followed by a strong reversal in the subsequent month. Mutual fund order flows, on the other hand, positively forecast bond returns in the subsequent two months.

 

Charts 1 & 2: Fund Managers Are Good at Forecasting Bond Returns

Source: Page 44 of ‘Informed Trading in Government Bond Markets’

 

The paper also shows that hedge funds and mutual funds have a significant advantage over other market participants in collecting, processing, and trading on information that is relevant for future gilt returns. In particular, the findings highlight the differences in the two groups’ approaches to earning abnormal returns in the government bond market.

A. HFs gain from both trading ahead of other investors and quick responses to the arrival of macroeconomic news.

B. MFs profit from their ability to understand and forecast macroeconomic fundamentals.

Through their active trading, these professional managers help to impound value-relevant information into gilt yields and expedite the price discovery process in one of the world’s most important financial markets.

 

The Theory of Bond Yield Variations

There are two competing theories the literature puts forwards for variations in bond yields:

I. The traditional view: Monetary policy announcements and the arrival of public information drive the main source of variation in the terms structure of interest rates. According to this view, trading in government bond markets is mostly due to rebalancing and hedging needs and is unlikely to have a large, persistent effect on bond yields.

II. The alternative view: Heterogeneity in investor beliefs generate variations. This stems from differences in investors’ access to information and their ability to relate publicly available economic fundamentals to the term structure of government bond yields. An immediate prediction of this view is that as long as learning is imperfect, trading of the better informed should persistently outperform that of the less informed.

The BoE working paper focuses on the second channel. A large empirical literature on institutional trading has so far found little evidence that professional money managers are able to earn significant abnormal returns in stock and corporate bond markets. Instead, this research asks whether a subset of non-dealer institutions have superior knowledge about future government bond returns.

On this front, the authors find:

‘Daily hedge fund trading positively forecasts gilt returns in the following one to five days, which is then fully reversed in the following month.’

‘Mutual fund trading also positively predicts gilt returns, but over a longer horizon of one to two months. This return pattern does not revert in the following year.’

The work then goes on to address why fund trading forecasts government bond returns, and for this they build on recent theoretical work by Farboodi and Veldkamp (2019). They postulate that arbitrageurs can engage in two types of activities: (i) to predict and trade ahead of other investors’ demand, and (ii) to learn about future asset values in an accurate and efficient manner (more so than the average investor in the market).

Both mechanisms are examined, and the authors find:

‘Part of short-term return predictability is due to hedge funds’ ability to anticipate future demand of other investors’.

‘It is partly due to mutual funds’ ability to forecast changes in short-term interest rates’.

 

The Data

The study uses the ZEN database, a comprehensive regulatory dataset maintained by FCA containing all secondary market trades in UK government bonds (gilts) by all FCA-regulated financial institutions. Given that all gilt dealers are UK-domiciled and hence FCA-regulated institutions, the ZEN database effectively covers the entire trading activity in the UK government bond market.

The database offers three main advantages:

  1. It provides detailed information on all individual transactions (the date and time stamp, transaction price, transaction amount, etc.).
  2. One can observe the identities of both counterparties in each transaction (for example, a transaction between a dealer bank and a bond fund).
  3. It covers nearly all investors and transactions; more precisely, the buy and sell transactions in our sample sum up to the total trading volume in the gilt market.

The sample period spans August 2011 to December 2017. They only keep bonds with a time-to-maturity longer than one year and exclude inflation-indexed gilts from the sample. The granularity and completeness of the data enable them to systematically analyse the extent to which any investors have a competitive advantage in this market and, furthermore, are able to profit from their information edge.

The final sample consists of 55 gilts covering nearly all gilt transactions. The majority of guilt trades take place in the inter-dealer market. The following chart shows the market share in the UK government bond market.

 

Charts 3 & 4: Hedge Funds and Mutual Funds are Big Players in Bond Markets

Source: Page 43 of ‘Informed Trading in Government Bond Markets’

 

For the investigation of whether funds are able to use value-relevant public information efficiently, the authors focus on announcements of UK inflation and labour statistics, and the Monetary Policy Committee (MPC) meetings. MPC meeting dates are collected from the Bank of England, and the UK Office for National Statistics publishes the other macro-announcement dates.

Finally, to calculate risk-adjusted bond returns, they construct three tradable factors mimicking the level, slope, and curvature factors of the term structure of government bond yields. For the level factor, they use the value-weighted average return of all available gilts. For the slope factor, they use the return differential between the twenty-year gilt and the one-year gilt. The curvature factor is the average return of the twenty-year and one-year gilts, minus that of the ten-year gilt.

 

The Results in Detail

This section is divisible into three parts. One, results on the predictability of HF/MF trading on gilt returns. Two, the ability of HFs/MFs to predict other investors’ returns. Three, the ability of HFs/MFs to learn from value-relevant information and respond to it more efficiently than other market participants.

  1. On predictability of bond yield returns, the authors sort gilts (with different maturities and vintages) into terciles based on the previous-day net purchases of HFs/MFs. This is because the market does not immediately and fully respond to the order flows of HFs and MFs, so one would expect to see a price drift in the same direction in subsequent periods.

They find that both HFs and MFs have significant information advantages in the gilt market. There is a strong positive correlation between HF/MF trading and contemporaneous gild returns – gilts heavily collectively bought by hedge funds and mutual funds on a particular day outperform those heavily sold by 1.82 bps. Specifically:

Hedge Funds

  • The tercile of gilts heavily bought outperform the tercile heavily sold by 1.28 bps on the following day, and 2.88 bps in the following week, with an annualized Sharpe Ratio of 1.2.
  • The return spread then becomes a statistically insignificant 1.32 bps (t-statistic = 0.73) by the end of month one, and -1.28 bps (t-statistic = -0.31) by the end of month two.
  • This return predictive pattern is virtually unchanged after controlling for known risk factors (i.e. the level, slope, and curvature factors). For example, the five-day three-factor alpha of the long-short bond portfolio remains economically and statistically significant at 2.94 bps.
  • Consistent with these results based on daily order flows, monthly hedge fund order flows have no predictive power for bond returns in the subsequent months.
  • Finally, these results also hold in Fama-MacBeth regressions (used to control for omitted variables, such as lagged bond returns and known predictors of government bond returns) and exhibit strong persistence in the cross-section of hedge funds.

Mutual Funds

  • The return spread between the top and bottom terciles of gilts (long-short portfolio), sorted by the previous-day mutual fund order flow, is a statistically insignificant 0.45 bps on the following day, and an insignificant 1.75 bps in the following week.
  • The return spread then grows to 6.47 bps by the end of month one, and to 15.61 bps by the end of month two. There is no evidence of reversal over the following twelve months; the cumulative return of the long-short gilt portfolio by the end of month twelve is nearly 1.3%.
  • Sorting gilts into quintiles based on the previous-month mutual fund order flow, they find the return spread between the two extreme quintiles in the following month is 27.52 bps, with an annualized Sharpe Ratio of 1.5.
  • After controlling for known risk factors, the three-factor alpha is only modestly reduced to 17.98 bps per month. This return pattern again exhibits strong persistence in the cross-section of mutual funds.

 

2. The authors regress order flows of hedge funds in the same bond in the previous week onto aggregate order flows of an investor type (mutual funds, non-dealer banks, and ICPFs) in a bond in the next five days. They find:

Hedge Funds

  • Daily trading is a strong predictor of future mutual fund trading; a one-standard-deviation increase in hedge funds’ net buying in a week forecasts an increase in mutual fund net purchases in the following week by more than 1%.
  • Hedge fund trading is largely unrelated to future order flows of non-dealer banks and ICPFs; and, importantly, aggregate order flows of other investor types (aside from hedge funds) do not predict future order flows of hedge funds.
  • Isolating the part of mutual fund trading that can be relatively easily predicted (capital-flow-induced trading), they find that hedge fund order flows significantly and positively predict mutual funds’ flow-induced trading in the following week.
  • This means hedge fund trading should be more profitable in periods of relatively large mutual fund flow-induced trading in absolute terms – they are. The long-short gilt portfolio sorted by hedge funds’ order flows earns significant abnormal returns only in periods with high aggregate absolute FIT.

Mutual Funds

  • MF trading (measured at the daily or monthly frequency) has no predictive power for future order flows of other investors, consistent with the view that mutual funds are usually not specialised in forecasting the demand of other investors.
  • Instead, the authors link the trading activity of mutual funds to future movements in the term structure to identify whether mutual funds are able to forecast variations in certain parts of the yield curve – they can. Shifts in the weighted-average portfolio duration significantly and negatively forecast changes in short-term interest rates (the one-year rate) one to three months in the future.
  • For example, at the three-month horizon, the coefficient on changes in mutual funds’ average duration is a statistically significant -1.73. This estimate implies that a one-standard-deviation reduction in the average portfolio duration of mutual funds forecasts a 4.49 bps increase in the one-year interest rate.

 

3. Finally, the paper repeats the return predictability test of HF/MF trading separately for macro-announcement days and non-announcement days. Again, they sort all gilts into terciles based on hedge fund order flows on the day prior to the announcement. They then track the performance of the long-short portfolio on the announcement day. In a time-series regression setting, controlling for known predictors of future interest rates, they find for:

Hedge Funds

  • The long-short portfolio sorted by hedge fund daily trading earns substantially higher returns on macro-announcement days. Hedge funds earn nearly twice as much on announcement days (2.50 bps) than on non-announcement days (1.28 bps).
  • Interestingly, hedge funds seem to earn higher abnormal returns on labour/inflation statistics announcement days than on monetary policy announcement days: the long-short gilt portfolio sorted by hedge fund trading earns an abnormal return of 1.22 bps on MPC announcement days vs 3.53 bps on inflation/labour statistics announcement days.
  • On this, the authors write: ‘A potential explanation for this result is that labour/inflation announcements contain less forward-looking information than monetary policy announcements, consistent with the short-lived outperformance of hedge funds’.

Mutual Funds

  • Out of the 17.98 bps monthly alpha earned by mutual funds, 7.24 bps are earned on just two days: one with monetary policy announcements and the other with inflation and labour statistics announcements.
  • Put differently, mutual funds earn 3.62 bps/day on macro-announcement days and only 0.5 bps/day on other days. These results suggest that about 40% of the total monthly alpha (7.24 bps out of 17.98 bps) are realized on just two macro-announcement days.

 

Bottom Line

Both hedge funds and mutual funds are informed investors in the gilt market. The former have short-term predictive power which can be attributed to their trading ahead of other investors’ predictable order flow. Mutual funds also positively predict bond returns, but over a horizon longer than one to two months. The superior performance of mutual funds is partly due to their ability to forecast future movements in short-term interest rates.

The punchline: nimble hedge funds are good at trading ahead of other investors’ future demand; mutual funds are instead more concerned with economic fundamentals.

To view the full paper – click here

Sam van de Schootbrugge is a macro research economist taking a one year industrial break from his Ph.D. in Economics. He has 2 years of experience working in government and has an MPhil degree in Economic Research from the University of Cambridge. His research expertise are in international finance, macroeconomics and fiscal policy.

 

 

(The commentary contained in the above article does not constitute an offer or a solicitation, or a recommendation to implement or liquidate an investment or to carry out any other transaction. It should not be used as a basis for any investment decision or other decision. Any investment decision should be based on appropriate professional advice specific to your needs.)

 

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