The drivers of stock prices are time varying

 

Who would have thought that the drivers of stock prices are time-varying? This simple idea makes perfect sense and is presented in the paper "Time-varying Drivers of Stock Prices". 

The author finds that cash flow expectations are more important during period of financial stress and uncertainty. You worry about the earnings to be discounted during those periods. 

On the other hand, you will focus on the discount rate during periods of expansion. It is likely earnings will be trending, and the focus will be on when you get the cash flows. 

Inflation will be an important driver when inflation is high and less important during periods of low inflation. 

Factor returns will not track with earnings growth when there is low financial uncertainty, but track with earnings when uncertainty is high.

This a good reason to be macro focused. At times, the stock market will be cash flow focused and at other periods it will be discount, but the problem is that macro focus will change with the environment. These time-varying drivers will be very sensitive to the sector studied which also makes sense. Some sectors will be more sensitive to cash flows during swings in the business cycle. It is critical to focus on the right drivers at the right time.

Powerful paper - Analysts don't discount future earnings

 

Of course, we know how we price assets. You discount future cash flows. Every business school student, every MBA, every Wall Street analyst knows this. End of story, yet a new paper which is refreshing direct in it writing says that this is not the case. See "EXPECTED EPS × TRAILING P/E". 

Let's start with their abstract: 

"We study a sample of 513 reports and find that most analysts use a trailing P/E (price-to-earnings) ratio not a discount rate. Instead of computing the present value of a company’s future earnings, they ask: “How would a firm with similar earnings have been priced last year?” Even if other investors do things differently, it does not make sense to put discount rates at the center of every asset-pricing model if market participants do not always use one."

This paper looks at the actual way that analysts form their price targets. We know how they do it because they must report it. It is a FNRA requirement. Analysts do not discount future earnings but look to the past to make their forecasts. Almost everyone is doing it this way so there is not a lot of value with trying to do it the "right" way. The tables provided speak for themselves. The real question is why it is done this way given all the education of these analysts.

Gold reserves increasing off lows

 

Gold prices are at all-time highs, yet inflation is lower. There is geopolitical risk, yet these risks may not seem to warrant new highs. Perhaps thinking about gold should not be focused on the immediate but on the new economic regime. The post-COVID environment can be characterized by the following:

• The threat of inflation. We lived in a sub 2% world to now a world where higher than 2% is the norm.
• The polycrisis world where geopolitical risks are higher. We have not seen true downside event, but the risks are all higher.
• The sanction world of have and have-not states that may be cut from dollar financing.
• A fiscal debt threat world where safe assets may not be so safe. 

No one of these should push gold to highs but the combination is pushing even central banks to hold more gold. The conservative central banks that control inflation and capital flows is saying that they need gold.

If you believe the economic regime is different than the demand for gold is different. This is especially true for those outside the US who may be hurt more from this new economic regime. 

The 21st century has been defined by the GFC, the global pandemic, a war in Ukraine, the eastern edge of EU, Middle East tensions and rising tensions between China and the West. This is not your post-Cold War period, and the threat from nuclear weapons is higher than 30 years ago. Welcome to a new world and a new gold regime.

Ray Kroc on friendly business

 

“If any of my competitors were drowning, I’d stick a hose in their mouth and turn on the water. It is ridiculous to call this an industry. This is not. This is rat eat rat, dog eat dog. I’ll kill ’em, and I’m going to kill ’em before they kill me. You’re talking about the American way — of survival of the fittest.” -Ray Kroc 

We are not saying you should follow the advice of Ray Kroc, but he may have a more insightful handle on the describing what really happens in business. We often use words like creative destruction" or pure competition without providing description of what that means. There is no question that the hedge fund world is closer to the Ray Kroc vision. With trades often being crowded and alpha being finite, there is a fight for limited excess returns.

Predicting anomalies - It is possible

In our last post, "Anomalies offer opportunities after they are reported", we discussed the timing of anomalies. You can make money by fast reaction to new information anomalies Now, we want to discuss research that states that anomalies can be predicted, or you can act early on anomalies. 

This conclusion comes from the paper, "Predicting Anomalies" from the same set of authors. It is a nice companion piece. If we can make judgments on what happens after anomalies, we should be able to test whether there is some drift or systematic behavior prior to the anomaly. Well, what do you know but there is drift before these anomaly trading signals. You can make money acting before these information events; however, these signals are often centered around anomaly trading signals that are hard to forecast. 

The researchers find that a simple martingale model will work best. Acting on the signal from the prior quarter is a good indicator of what will happen next year based on the current quarter. We can say there is trend in anomalies. There are predictable patterns prior to the release of annual financial statements. Accounting-based anomaly signals based on financial accounting events are predictable with even the worst prediction models have F1 scores above 60%. Of course, the signal strength will vary with the anomaly. For example, earnings and revenue show less persistence. This should be expected since those are the accounting numbers that investors spend the most time studying. Some financial accounting data are very persistence and predictable while others are not.

This is an important paper that requires careful review for any equity quant manager.

BCG growth share matrix and investing - more dynamic betas

The BCG growth share matrix has been used in business to rationalize where to invest and what to divest within a business portfolio. The general observation is that the switching between boxes in the matrix have increased. Businesses move more quickly to drop pets and move cash from the cash cows to stars or question marks. Investments in question marks have shorter time to prove themselves.

Finance and quants should be aware of these changes because switching within the growth share matrix will change the overall risk of the firm and thus the market beta in general and within an industry. A firm that has a high cash cow share that decides to take risks in the question mark or move to the star share will see an increase in beta. Those firms that cut the pet projected will see downside risk decline. 

If these changes occur slowly, then the switch in beta will be slow; however, if there are speedier changes in the growth share matrix, the firms market beta will be more uncertain and there will be an adjustment in the cost of capital. For short-term traders, this impact will be small but for long-only longer-term investors, a changing beta that is not accounted for will have am impact on portfolio risk.

Go global or local - An issue of transfer learning

Go global or go local with pricing models? This is an important question when thinking about pricing assets outside of the US. Do you estimate models only using local information or do you take relationships that exist in the US as the basis for empirical analysis. If prices in local markets are driven by a global model with US coefficients, then the development of pricing models for local markets can be made much easier. The focus can be on building strong global models and then just applying to the diverse of markets around the world. Quant life would be much easier. 

This a problem in transfer learning and a recent paper suggests that using pricing models in the US as a basis for pricing in other countries makes sense.  See "How Global is Predictability? The Power of Financial Transfer Learning" 

While we focus on a simple global model, the researchers include a local component based on the deviation for the global component which they refer to as a generalized elastic net which can adjust to the global model. Again, this may be an easy way to build pricing models across the global by starting with a universal model. Easy may not be the right word for describing this approach but it has merit and places a focus on universal sensitivity of key variables.

 

Contagion measure important in a crisis

Contagion is an important problem in finance. It does not happen often, but if you are not prepared for it, the cost can be high. Foremost, returns can swing quickly during a contagion event, but perhaps as important there can be a change in correlation between markets. The classic view is that correlations will move to one during a crisis. Risk will, of course, also increase with this higher correlation. Finally, the direction of causality will change as the contagion spreads. Causality will move from a primary driver to secondary markets. The spread can be measured which will help with nay portfolio decisions. 

A recent paper focuses on measuring contagion in the currency markets, see "A new way of measuring effects of financial crisis on contagion in currency markets", but more importantly, the authors look at the causal relationship that are generated during a crisis. The linking of markets during a contagion crisis is a risk management problem, but determining how the contagion spreads allows for better portfolio responses and the opportunity to generate profits. 

Contagions can be mapped as a network with the strength of the causal relationship measured by estimating the autoregressive properties of an asset with the autoregression betas of cross-assets, the network contagion part. The network contagion factor (NECOF) is the volatility explained by the cross-asset divided by the autoregressive volatility, the network contagion volatility and the error volatility.

 

Playing offense and defense with trend-following

There has been a significant focus on trend following as a defensive alternative investment. You hold the trend-follower because the strategy is expected to do well during periods of poor equity returns. The name "crisis alpha" has stuck to trending following as the shorthand descriptor. However, trend followers can make money when there are strong uptrends in markets. What trend-followers need are market divergences. If the equity market diverges to the upside, a trend-follower should make money, but the returns will, of course, be less than the returns of a pure equity investment. So, the results are very simple. Possible positive returns in all divergent environments, but excess returns versus stocks when equity markets fall and underperforms when the stock market rises.

Aspect Capital has come up with a new turn of the phrase called "unpredictability alpha" to explain the fact that trend-following can do well in both up and down market divergences. It is trying to cover more than crisis alpha, but I don't think it hits the mark. Let's stick with the basics from years ago, trend-following is divergent trading as opposed to convergent trading. Divergent trading makes money when prices move away from equilibrium level and are likely to trend. 

The important point is that trend-following can be both a defensive and offensive strategy. It can protect when key markets fall and it can make money when prices move to the upside. There is value with holding a strategy that can do both. 

Inflaton expectations - it is about the things you buy all of the time

Inflation has been the number one economic issue for both the Fed and consumers, yet many policymakers have focused on the inflation of the last few years as a supply shock from the pandemic that was abnormal. Inflation is returning to normal levels; however, the inflation of the last few years was anything but normal. There has been a focus on core inflation, but the headline and anti-core is what may drive expectations. 

The first chart shows what has been called anti-core inflation. This is the inflation associated with food and fuel costs. This is the inflation that consumers face every time they go to the store. Anti-core inflation was not just high but extraordinary. The spike hit 40% which was just below the highs in Great Inflation. Headline CPI was the highest in over 40 years which has driven expected inflation highs for the next five-year period. Given the experience of the last three years, inflation expectations will not just return to 2%. The Fed has a consumer expectation problem and lowering rates at this time will not solve this problem. 

Anomalies offer opportunities after they are reported

The paper "Anomaly Time" provides insights on very interesting methodological issue as well as further information on efficient market behavior.  The authors look at the timing of returns around the publication of news or anomalous information signals. Most academic publication form portfolios around a specific calendar data and not when data are produced. Think about this process. Portfolios, for academic work, are usually structure around June regardless of when the company data is really reported. If you look at the actual publicly reporting data, you will get a very different answer on many of the key anomalies that are reported in the academic testing. Who would have thought?

Now getting firm information on 10K was actually not that easy and was a very time consuming and costly process before EDGAR. Getting data in a timely fashion was hard work. It is still hard work to get it arranged properly This paper makes two predictions: 1. Return predictability should be strongest immediately following the release of this key information and then decay. 2. As the cost of information gathering decreases, the markets will become more efficient with less return predictability and more active trading activity after the information release.  These results should not be surprising. What took academics so long to realize what traders knew a long-time ago, yet this provides another good story for how market efficiency works. This study looks at how changes in the EDGAR database reporting and dissemination has made a difference on how research is conducted and thus information is discounted. 

What is volatility telling us at this time?

Equity volatility, as measured by the VIX index, is reaching highs or is at least beyond the normal range. The VIX not at the level of the yen carry trade unwind, but it is still high and says something about the uncertainty and risk with the US presidential election. If there is max uncertainty with an election, then volatility should be at high levels. However, the MOVE index of bond volatility is telling us a different story. It is below average and suggesting that nothing will change with inflation, growth, or debt regardless of the election. This is an interesting comparison. 

 

Financial history pre-CRSP - key factors still worked

 

A recent paper looks at a new dataset that includes stock information before the well-known CRSP data set. We now have information back before the 20th century. This was a mammoth project, but it provides us the ability to check factor analysis for a period of 150 plus years. See "The cross-section of stock returns before CRSP". 

Here are some of the highlights from the data:

The beta of the CAPM does not seem to work. There is no relationship between return and risk. This data just adds another nail in the failure of the CAPM model 

There does not seem to be a small cap effect. This again seems to tell us that size is not a separate factor.

Value investing does seem to work. You will be rewarded for buying cheap stocks. 

The momentum also seems to work. Buy high performers and sell losers. There is strong consistency with this factor. 

The low-risk effect of being compensated for holding low risk or low beta stocks also seems to hold further back in time.

Macro risks do not seem explain what is going with the risk and return trade-off with stocks. Delegated management may be an explanation for these factors, but these results do not seem to fit this story for the pre-CRSP dataset. Market crashes suggest that momentum investors are being compensate for the risk of large market down moves. Again, it does not seem as though this data fits this narrative.

Machine learning was applied to this data set, and it finds that momentum, value, and low risk are the key factors extracted from the data. These strategies may be associated with behavioral finance explanations. For example, the endowment effect can explain the value factor. Herd behavior (FOMO) can explain the momentum effect. Low risk investing is associated with the aversion to loss. Investors want to hold low beta stocks.  These theories seem to offer go explanations for these older data.

There is no evidence of significant out of sample decay, so value, momentum, and low risk continue to work over the long-term.  

Who grows our crops around the world by country?

This a very interesting chart on who is growing the major crops around the world. The US is still the breadbasket for the world, but it may surprise many what is happening around the rest of the world. Note, we are not incorporating rice production. First, the production in the US has been falling albeit slightly, yet the world still needs food. That food production is coming from one major supplier, Brazil. The explosion of soybeans from Brazil is truly amazing. It outstrips the US and every other country. When you add the rest of Latin America, the EM south is the bean capital for production. The amount of corn from this region is also quite large. It may surprise many how much corn and wheat production comes from China. This is for domestic production, but corn is king in China. The world trade in grains is mainly about the US, in the north, Brazil in the south, and extra needs from China.

That crazy yield message - overreaction in bonds

A quick look at the yield curve will show interest rate expectation in a state of flux. With lower inflation, fears about the labor market, and a Fed that pointed to a change in policy, there was a clear drop in interest rates especially around two years. You get a higher than expected print in the employment number and there is a wholesale revision in rates with more than a third of the drop being reversed out the curve. So much for recession fears, let's get back to inflation fears. So much for 50 bps cuts, let's start to worry about debt issues. 

Does this make sense? The reversal is an adjustment to an overreaction to the Fed in September. This goes back to the basics of forward guidance. With a data dependent Fed unable to articulate how to look at data in context, you get misdirection and mistakes by investors. How do investors play this environment. Assume that rapid changes represent overreaction and expect that actual rates should show smoother moves.

The secret of trading - being right when other are wrong

"Now, I'm going to tell you a secret about trading. Making money trading is not about being right. It's about being right when everyone's wrong ... When people are wrong, their predictions are wrong. When people's predictions are wrong, their prices are wrong. And when prices are wrong we make millions." 

- from The Trading Game by Gary Stevenson 

Yes, this is the central paradox of trading. Being right is not enough. You must be right when everyone else is wrong. This is different from holding an index when you want everyone to be right. If you are a buyer, you need to have sellers. There may be a lot of reasons for those sellers, yet it seems the key is to have sellers with opposite opinions. Even with trend-following there needs to be others who have opposite opinions or opinions that come later than your own.

While the book is a sometimes good read, it may not be completely true. See Gary Stevenson claims to have been the best trader in the world. His old colleagues disagree

John Bull cannot stand 2% - what happens later

 

It is a fact of experience that when the interest of money is two per cent, capital habitually emigrates, or what is here the same thing, is wasted on foolish speculations, which never yield any adequate return.

- Walter Bagehot 1848

We faced the John Bull period of lower interest rates and the chase or reach for yield. That period ended with the rise in interest rates from the Fed. The impact on many financial sectors was severe, yet the pain was not as widespread as expected. 

Now we are again headed to lower rates. The Fed solution to balance sheet risks is to lower rates so leverage can remain high and balance sheet can be repaired through lowering the cost of capital, yet the quality of cash flows from these projects has not been discussed. 

This cash flow shortfall is the problem in China. It has been an ongoing problem in Europe. It is a problem in emerging markets. Slower growth has meant that the expected cash flows have not been forthcoming. It has been less of a problem in the US because of aggressive fiscal policy, yet the underlying issue still exists. Lowering rates will solve the problems of the past but it may create new problems in the future

When the abnormal becomes normal with macroeconomics

"The highly abnormal is becoming uncomfortably normal." 

- Claudio Borio, 2014 

Furthering budget deficits without any austerity to bring government expenses back to normal, that is normal.

Driving monetary policy back to lower levels even with inflation still above target and price levels too high, that is normal.

Not having clear policies other than that there is a focus on data, that is normal

Using monetary and fiscal policy to solve structural problems, that is normal.

Changing the rules of antitrust policies, that is normal.

Adding more regulation to control prices that are the result of government inflation, that is normal

Using tariffs as a form of industrial policy, that is normal.

Using sanctions to punish countries but not enforcing them, that is normal.

The list can go one, but the abnormal cannot go on forever. 

The policy that will never happen - Andrew Mellon

 

"Liquidate labor, liquidate stocks, liquidate the farmers, liquidate real estate... It will purge the rottenness out of the system. High costs of living and high living will come down .. enterprising people will pick up the wrecks from less competent people." 

- Andrew Mellon, 1932 

There is a price of anxiety or the price of insurance and that is embedded in interest rates. This was discussed by Ferdinando Galiani in his book On Money (1751). Of course, the rate of interest should be associate with the price of anxiety, but that price has been quite low because no one expects that the Andrew Mellon world will exist. There has been a limited term premium because there is no risk that we will have a liquidation event. The China policies is just the latest example.

The central irony for solving a financial crisis

"The central irony of [a] financial crisis is that while it is caused by too much confidence, too much borrowing and lending and too much spending, it can only be resolved with more confidence, more borrowing and lending, and more spending." 

- Larry Summers 2011 

Will they [central bankers] not consider the possibility that ultra-low nominal yield might actually reduce aggregate demand while breeding financial instability, bank failure, 'zombification' and reduced economic dynamism?  

- Larry Summers 2019 

Larry Summers can be insufferable, but he is also one of the most insightful macroeconomists. He has been talking about poor monetary policy choices for well over a decade, and with the current thinking from central banks, they have not learned much. We have 3% growth, 4.051% unemployment, and inflation still above target (don't forget the average inflation rate idea), and there is still talk about a need for rate cuts. What is the case? Yes, there are those who are hurting in the current economy, but that is still associated with inflation. Asset prices are inflated and the talk is always about a need for policies that will ensure that those prices stay high.

There is risk in every trade, you just have to find it

 "And now I'm gonna tell you something even more important, right? Don't you ever f***ing tell me, in your whole f***ing life, that there's a trade that doesn't have a f***ing risk in it, OK? That's what those **** over in Credit thought and look at what the f*** happened to them.... And you should probably go back to your seat now and have a good long think about what all of that means. And you make sure its the last f***ing time you ever do a trade that you don't know the risks of"  - from The Trading Game by Gary Stevenson 

Well, it could have been said more politely, but the thought is correct. Every trade, every asset has risks. Return does not come for free. It is your job to know what are the risks. 

While the book is a sometimes good read, it may not be completely true. See Gary Stevenson claims to have been the best trader in the world. His old colleagues disagree

The difficulty with testing and the mundane

In the words of P.J. O’Rourke, “Everybody wants to save the Earth; nobody wants to help mom do the dishes”. This is an excerpt from Guiseppe Paleologo's new book draft on portfolio management. Everyone wants to be the portfolio manager or the trader. Nobody wants to do the important work of backtesting and the careful analysis associated with building models correctly. Research takes time and is tedious. It is often boring, but the rewards are strong. For a large upfront investment, a good model will allow for significant time savings in the future and the ability to make consistent decisions. 

Sweat the details. There are no big issues to solve but the consistent work of building the models correctly.

Perfect information may not help you with investing

What if you had perfect information, could you make money? A new study suggests that even if you had the headlines of new announcements before the event, you would still have a problem. The study was quite simple. Assume you have perfect knowledge of some news, but you don't know what is the market reaction. Could you still make money? The answer is no. This has been tested in the paper, "When a crystal ball isn't enough to make you rich."

This study is based on the Nassim Taleb conjecture that if you gave an investor the next day's news 24 hours in advance, he would go bust in less than a year. Markets are complex. They will not behave as expected even when you know in advance what will happen in the news. For the study, the researchers found that of the 1500 participants of this game, half lost money and one in six went bust. Does this make any sense? They guessed the direction of stocks with a 51.5% accuracy and bonds with 56% accuracy. In general, the investors who played the game over-levered. They did not size their bets correctly by using some form of risk management. 

This is a variation of the efficient market hypothesis. On average, it is hard to make money in the markets even if you have clarity on the news. There will be some winners - investors who know how to size bets and be careful with their decisions. There will also be players who do not know how to size bets and make good decisions - they will go bust. What decision you make and how you make them matters. Knowing is one thing; doing is something else. 

Anxiety about downside risk not the same as uncertainty

Investor market behavior is often filled with anxiety - the negative emotion from the anticipation of future risk, specifically downside risk. Anxiety will be displayed with the behavior of investors through their exposure to negative news. "Anxiety is the response to future uncertainty about payoffs in a risky asset, resulting in strategic uncertainty driven by multiple narratives", see "Anxiety, Investment Behavior and Asset Market Volatility".

Anxiety focuses on downside risks and its increase in magnitude and can be measured through news stories and survey information. These measures of risk, a focus on the downside, are different from economic policy uncertainty or uncertainty indices in general which serve as proxies for subjective uncertainty and not downside risks. 

This research finds that there is a countercyclical relationship between anxiety and volatility. An increase in anxiety which driven by news of downside will be linked to lower volatility. Volatility and anxiety are not the same thing.