Ken Rogoff's new book - A great way to understand international finance

 

This is a book that is a historical retrospective of the many key issues that Ken Rogoff researched over the last four decades. It is autobiographical as Rogoff walks through how he was engaged with many of the key topics of international finance. It was quite a run of problems: 

• The Cold War
• The Asian currency crisis
• The rise of China 
• The issue of Japan and currency moves
• The single currency in Europe 

All of these policy and economic issues required fundamental research to make the right choice and understand the crises being faced. Rogoff makes all of the issues approachable. The complexities are presented clearly, mixed with personal stories. If you want to understand the problems of international finance over the last few decades, you could not do better than read this book. 

Commodities march differently than gold

Gold is going higher, so it must be the case that commodities are also going higher? The data tells us otherwise. Commodities, on average, have been rangebound, but the gold versus commodity ratio tells a different story. The gains in gold are the result of something different. It is driven by the demand for a safe asset, not for its use in some production process or for consumption.  

Does this mean that commodity prices should see a gain in the future? There could be a general commodity rise, but it is not in the card based on a ratio. Sometimes a charge is interesting but not informative.

 

Opprtunity cost is the critical measure

 

The simple genius of Charlie Munger: 

“All intelligent people should think primarily in terms of opportunity cost. When deciding whether to do something compare it with the best opportunity you have.”

from farnanstreeetblog.com

Every finance decision, in fact every decision, has to be compared with the opportunity cost associated with the next best decision. Any use of time has to be compared with the next best alternative. Every decision has to be compared against other options every day. This is not supposed to drive a decision maker crazy because there are costs with changing decisions. Still, the idea of continuously measuring the opportunity cost is the basis for improving the use of time, energy, and money.

The loss of dollar dominance

I have written that the dollar decline is significant, but the fall only places the current dollar value near the long-term average. However, we need to acknowledge the extreme moves in the dollar over the last six months. The reason for the dollar decline is truly self-inflicted on the part of the US.

The dollar decline is not driven by systematic factors that would have been picked up by a quant model. The trend/momentum would have called for a short signal, but the exogenous factors have not been seen in past data. Growth in the US is not below the rest of the world. There is the threat of a recession, but the numbers do not suggest lower relative growth. Inflation is still higher than desired, but again the numbers do ot suggest a dollar decline. 

The three areas of concern are uncertainty, trade, and debt. Usually, higher uncertainty will lead to a flight toward safety, but in this case, the uncertainty is with the US. The trade and tariff issue is real, but we lack sufficient evidence of past tariff changes to accurately determine the correct dollar response. In the case of debt, there is clear evidence that large deficits will impact currency demand. Here is where the problem is centered, and there is no clear solution. The current deficits will not be solved with the budgets being suggested. There is a potential credit crisis with the dollar. 

Momentum and reversal related to turnover

 

Momentum is a key factor found across all asset classes. It is persistent and perhaps the most used of the major risk factors. A paper I had not read before provides more light on the topic by showing that you can have both reversal and momentum with 1-month returns if you sort on turnover. The low turnover decile shows short-term reversals, while the high turnover decile shows momentum. See "Short-term Momentum".

Reversal and momentum can coexist once you account for turnover. Large liquid stocks are less sensitive to price pressure effects. The price pressure effect is the main driver for short-term price moves and reversals, but turnover does matter. Don't fight short-term reversal unless you account for the turnover issue.

Dollar - Down but not out - Look at longer-run

 

The dollar has reversed two years of gains against advanced economies, but the moves are more muted compared to the broad dollar index and emerging markets. The speed of the decline is a concern, yet the dollar is still within a long-term range after a substantial gain. The question is whether the dollar is declining because there is less confidence in the US economy and financial system. It is a signal of US weakness, and that is a problem. 

The dollar remains the reserve currency, primarily due to its role as a medium of exchange; however, the store of value argument is problematic.

Conditional betas solve a classic problem

Beta is time-varying. There is no dispute about this. The traditional approach to addressing this problem is to utilize a rolling window to adjust beta over time; however, this method does not account for the changing environment. It just increases the use of new information.

A new paper, "Conditional Betas: A Non-Standard Approach," attempts to find a new method to account for changing beta. It compares the quality of beta forecasts with one of the leading alternatives of windsorizing the data for beta. The overall effect of a simple machine learning approach is very positive. Results are strong and only based on past price data. This is worth further exploration. 

I cannot tell you how frustrating it is to see a hedge balanced trade fall apart because the beta estimate is wrong. Market neutral is no longer market neutral. This may not seem like a significant issue for long-only managers, but for a long/short portfolio, it is a substantial problem.

Gold as a safe asset - an alternative to debt.

 

One of the topics that has received attention in microfinance research is the discussion of what constitutes a safe asset and whether it is in short supply. 

During a crisis, there is an increased demand for safe assets that are information-insensitive and serve as a means to protect wealth. A simple example of a safe asset is the US Treasury bill. When there is high uncertainty, investors tend to sell risky assets and shift to safer ones. However, if there is a shortage of these safe assets, the price will be bid up, placing downward pressure on interest rates. 

Nevertheless, there is the assumption that the supposed safe asset will really be safe. That is, the risk or market uncertainty cannot come from the producer of the safe asset. If there is an increase in risk from the safe asset, it will lose its convenience yield, and it will no longer be uncorrelated with risky assets. 

In this case, there will be a demand for alternative safe assets. One alternative is gold. Gold is often uncorrelated with risky assets during times of stress. It is negatively correlated with volatility and uncertainty, and it often protects against higher inflation that impacts the real value of debt-safe assets. It is information-insensitive, and it can be used as collateral. 

Many have suggested that gold is in a bubble, but that narrative shifts if you view gold as a safe asset substitute. If the US debt is less secure, then there will be a stronger demand for gold, which will push its value higher. If the relative safety shifts to gold and away from debt, then there will be stronger upward pressure on gold. The price increase has been significant, but it will be sustained if the safety feature continues to drive demand.

Performance at that the half year mark - nothing expected

We have lived through uncertainty, a war, trade battles, and various events that should have pushed markets lower; yet, we are at the highs in the core US market indices. This does not feel expected or normal.

One of my favorite exercises is to play out scenarios - what would you have expected to happen if a particular event occurred, and then examine the reality. No one would have expected the current outcome. This sharpens your intuition and also tests expected relationships.

We are now in a place with US stock indices touching all time highs and a widening of breath beyond the large cp tech sector. The markets have looked through uncertainty. Some of that uncertainty has been resolved, but that does not alter the underlying view that we are in an environment with a wide range of views. Of course, investors focus on downside uncertainty. There is also upside uncertainty, or a positive reaction to the unexpected., 

There is still a rotation effect toward international stocks; however, this bias is closing. High bet stocks and momentum have been the key drivers. In general, all of the worst case scenarios have proven to not be true. The overheated rhetoric has subsidies and the world seems. The doom reporting of the last six months may not be realized. 

The era of sudden shocks - we don't have an explanation

A recent FT article by Robin Wigglesworth highlights the thought that we are in a period of sudden shocks or short bursts of uncertainty. While economic volatility has declined, as described by the Great Moderation, even with the Global Financial Crisis (GFC) and COVID-19, there have been short-term shocks in financial volatility. The current volatility, as measured by the VIX, is close to the long-term average. Still, the volatility of vol is elevated, and there are these periods of volatility shocks. 

Is there an explanation for this vol shock environment? There is no easy answer. It could be related to the higher leverage in the marketplace, but that does not explain the short-term nature of these shocks. It could be quick policy responses, but there have been more short-term spikes than Fed responses. It could be what a friend has referred to as the "wall of money" that will invest when there is a short-term reversal. That could be a reasonable explanation, yet it does not explain why we have the shocks in the first place. 

The investment implications for this are worth reviewing. Currently, it states that investors should not be concerned about these shocks, even if they are frequent. For some strategies, such as trend-following, it is a negative outcome where managers get whipsawed by these spikes. Trading strategies require more activity, not less. 

 

Paul Slovic - there is a difference between risk as feeling and risk as analysis

Paul Slovic, one of the leading behavioralists in the field of decision-making, stated that there is a distinction between risk as a feeling and risk as an analysis. This is his way of thinking about the fast and slow thinking problem as described by Dan Kahneman. Risk, as feelings, is our natural reaction to danger, which could be called our jumpiness when faced with a risk. It can also be described as our experiential system. This is in contrast to risk analysis, which examines decisions under uncertainty as an analytical issue of measuring costs and benefits. 

While most investors will always focus on fast and slow thinking, the Slovic approach is a nice addition or contrast to what we already know about decision-making.

From Risk as Analysis and Risk as Feelings: Some Thoughts about Affect, Reason, Risk, and Rationality

The difference between certainty and uncertainty

Certainty - firm conviction, with no doubts, that something is the case

Uncertainty - the conscious awareness of ignorance 

-from The Art of Uncertainty by David Spiegelhalter 

All investors deal with issues of certainty and uncertainty. Importantly, there is no certainty. Get that out of your head. We live in a world of probabilities from what is countable and a world of uncertainty based on ignorance. Uncertainty is foremost what we do not know, so the job of any analyst is to reduce uncertainty from ignorance. There is some uncertainty that we will not be able to fully learn our way out. In those cases, we have to make some subjective probabilistic judgments. 

All investment research is about reducing uncertainty and increase the precision of our probabilistic estimates. 

What makes a good forecaster from Dan Gardner

Dan Gardner,  an essential writer on forecasting, states that there are three key components to being a good forecaster. 

Aggregation - Good forecasters are great aggregators of information and other opinions. They will utilize all available information, even if it contradicts their current views. They will seek out alternatives. They are open to small ideas and not a single unifying thesis. 

Meta-cognition - They are good probabilistic thinkers who also take into account their own biases. They are fully aware of decision bias and reflect on whether they are engaging in these biases. Hence, they never rush to judgment.

Humility -  Good forecasters will admit when they are wrong. Hence, they show a significant amount of humility in their work. They do not have to prove they are right. They can accept error and then try to adjust. 

Use these three components as a checklist to ask whether you are making good forecasts.

Hedge fund start-ups - a thing of the past

 

A CAIA post shows that private capital firms now outnumber hedge funds. In fact, the number of hedge funds has declined over the past decade. Hedge funds are not a growth business. This can be associated with the rise of multistrategy firms that will pick up talent in pods, as opposed to investing in individual firms. This has not happened in other areas of alternative investing. The performance across hedge funds is tighter than other alternatives, even though there are significant differences in strategies. 

The hedge fund industry is undergoing consolidation, with higher start-up costs and increased operating expenses. You have to ask whether someone wanting to start a hedge fund is a little crazy. 

The Fed always to the rescue?

There has been considerable discussion about the need for the Fed to lower interest rates. I do not want to get involved in that battle at this time. Instead, I have been shocked by the chart below concerning the "lender of last resort" Fed behavior. The numbers are outstanding. We thought that the Fed was active during the GFC; yet, if we look at the post-2020 period, we will see the Fed going to almost any length to support stability. The COVID response was overkill, but the First Republic response was on a different level. This may be internalized in the minds of investors, yet we cannot know with certainty what the Fed will do next time. There is moral hazard uncertainty, and this can be a problem. 

 from @greg_martis 

Innovation and change impacts quant results

I found this table one of the most interesting for the month. Corporate behavior is all about change, innovation, and adapting to market demands. This is a constant, yet many of the models we build to describe equities are based on assumptions of stability. The first products offered by many firms are long gone, and most investors do not remember or have never encountered these products. Lego - wooden toys? McDonalds - hot dogs? Who would have guessed?

We believe that betas are stable; however, if you examine this list, firm betas will be pretty dynamic. Now it may be stable over a short-term three-year period, but if a firm is transitioning strategy, these short-term betas will also be under attack and changing radically. Modelers cannot escape the news about firms.

from @stats_feed 

 

Corporate bond stress falling - what will it take to change spreads?

Despite all of the noise in politics, policy uncertainty, and geopolitical tensions, corporate bond spreads have fallen to levels seen at the beginning of the year. While there was a significant increase in stress in April, overall stress levels remain below the peak during the COVID-19 pandemic. Indeed, the market is sensitive to changes in policies that will impact earnings; yet, corporate buyers have overlooked these risk and uncertainty issues. The next test will be softness in the real economy, yet the link between economic uncertainty and spread risk does not seem strong.

 

Gold and central banks - Do what we do not what we say

 

I thought at one time that central banks believed that gold was a "barbarous relic". Those times are long gone. The latest survey on central bank behavior and gold shows that 3/4ths will increase their gold portion of total reserves. It is higher simply because the price of gold is higher; yet, we are seeing more central banks increasing their gold exposure. Where is it coming from? Survey results suggest that dollar exposure is expected to decline. Reduce dollar exposure and hold more gold. 

43% of central banks expect to increase their gold exposure in the next 12 months. This is up from 8% in 2019. Gold, the hard asset, is back with central banks. No, this comes even as inflation has fallen, so central banks may be arriving late to the table, although central bank gold holdings have been on the rise for years. 

The explosive increase in gold prices is simply. There is a shortage of this "safe asset" relative to supply. There have been pockets of lower demand, but central banks are price-insensitive 800-lb gorillas in the gold marketplace. This is more than an inflation hedge, but for many EM central banks, a sanction hedge. 

It is hard to see a substantial price reversal in gold when central banks are key buyers. Gold is not at attractive prices for accumulation but central banks are telling the market you should hold this diversifier. 

Market macrostructure matters

 

I found a recent paper, "Market Macrostructure: Institutions and Asset Prices", an interesting research piece that opens up new thinking about markets. The concept is simple. The market macrostructure, or the combination of key players in the marketplace with different objectives, will impact the return-generating process; however, further work is needed in this area to develop empirical tests for changes in macrostructure.

The premise for examining market macrostructure is straightforward. In market microstructure, the focus is on the dynamics of transacting, whereas macrostructure states that asset returns are influenced by changes in the behavior of key traders in the marketplace. The change in the behavior of central banks, pensions, and other financial intermediaries will translate into changes in return patterns. For example, changes in the behavior of central banks through quantitative easing (QE) or quantitative tightening (QT) policies will impact the return pattern of markets. Their size and influence will impact how returns are generated. For example, a central bank's asset purchase program based on policy considerations will differ from the behavior of profit-maximizing traders. Hence, the macrostructure will change. The macrostructure will change again when the central bank becomes a net seller or refrains from engaging in active buying and selling.  

The authors do not explain how they plan to thoroughly test this modeling. Regime changes focus on changes in return patterns through observing time series. Still, these return patterns are influenced by the market's macrostructure, which encompasses policy changes, regulatory changes, and financial innovations. The cause of regime shifts is shifts in the market macrostructure. If you can identify the shifts in macrostructure

Causal discovery and trading

 

Causal discovery techniques can help any quantitative hedge fund, but may be especially helpful for enhancements to trend-following through finding causal links with other markets. The basic structure for a trend-following model is to use past values of a variable to extrapolate ot the future. Look for the trend, yet it would add significant value if you could learn whether other markets may have some causal impact on another variable. 

The standard approach to time series causality is to use Granger causality tests, which simply determine whether some time series Y causes or has an impact on the prediction of X. However, a growing number of alternative techniques are available to aid in causal discovery, thereby improving trading, such as time series data causal inference, vector autoregressive linear non-Gaussian acyclic models, and time-varying interactions models for nonlinear observations. The code for these algorithms is already written, so it is relatively easy to implement for a set of assets.

We are not planning to explore all of these techniques, but there are ways to support better causal discovery that can be used to improve the inputs in investment strategy. See "Trading with Time Series Causal Discovery: An Empirical Study" for a simple application of causal discovery for long-short equity portfolios. Now, these algorithms are not easy to implement due to the time required for computation; however, this seems to be a fruitful area for further research, especially given the growing interest in causal reasoning in finance.

Choose your correlation carefully - Kendall's Tau

Portfolio construction is fundamentally based on the correlation between assets. The lack of correlation creates diversification, yet limited work has been conducted in testing alternative forms of correlation. Most construction work is based on Pearson correlation, which looks at linear dependence across assets. There are limitations with Pearson correlation, so sometimes an alternative is used, Spearman's rho correlation, which is based on rank ordering. Spearman's correlation can adjust for non-normality and outliers, but there is a problem with the assumption of monotonicity. The third alternative, Kendall's Tau is based on measuring the concordance between asset pairs through counting the sign of movements across assets. 

A relatively simple paper looks at the portfolio construction of daily foreign exchange pairs using all of the same parameters except for the correlation matrix. See "Beyond Correlation: Enhancing currency portfolio construction through Kendall's Tau and Correspondence Analysis". I was surprised by the results. Yes, volatility is higher, but the overall portfolio performs better. There are clear benefits from using Kendall's Tau. The numbers are compelling enough to ask a simple question: "Why not try this alternative?"

 

The impact of narrative: The power of Fed speak

 

We expect that the Fed speeches have an impact; however, the measure of their effect on equity and bonds has not been precise. A paper, "Mind your language: Market responses to central bank speeches," shows that from the speeches, there are forecast revisions that can then explain volatility and tail risk in major asset  classes. Fed chairman speeches will have more impact than others through larger forecast revisions, but the Fed chairman can also calm markets with the right speech. 

The paper utilizes NLP, or natural language processing, to aid in identifying information that causes changes in macroeconomic forecasts. The critical point is analyzing the continuous flow of central bank communication, not just an isolated speech. There are clear regimes in Fed speak, and it is good to identify these trends. 

Quants focus on what is countable, yet the non-countable, like we see in speeches, is essential. If you can turn the non-countable from narrative into something measurable, there is an opportunity to form probabilities and make better trades.

I have taken the view that you want to avoid FOMC and major Fed speeches because there is too much uncertainty; however, if we decompose what is said, investors may be able to tilt their positions to their advantage. 

The power and gravitation pull of doing nothing in asset management

There is value in doing nothing in asset management. One, doing nothing reduces transaction costs. Two, doing nothing allows for the power of compounding. Three, doing nothing reduces the emotional biases associated with trying to take action. Four, doing nothing helps to clarify the distinction between effort and work. Showing that you are doing something is not the same as doing work. 

By having a long-term view, there should be less trading. Long-term investing is a do-nothing management approach because the long-term decisions should not be swayed by short-term changes in markets. There is more value in doing less. 

These are all good reasons, but there is also a pull to doing nothing that has a negative effect. Nevertheless, no action at the wrong time will be costly. Fear of making mistakes and emotional regret may stop an investor from taking needed action. Aversion to regret will reduce action. You cannot regret the action not taken. You could, but generally, regret is about what was done, not what was missed. Lack of knowledge or ignorance.

So, there needs to be a checklist for action. Do I have a valid reason for action? Do I have the right time perspective? Have I accounted for the cost of trading? Will I regret this decision if the market direction changes?  Be careful with action. From a model perspective, what is the action beyond a prediction of noise. 

Sophisticated investors and market efficiency

 

Market efficiency will vary by the type of investor. There are different levels of efficiency based on your structural advantage. Market efficiency is based on the behavior of a given market and not on the profitability of a given trader. Hence, you can declare a market as efficient, yet there could still be profitable investors. Similarly, market efficiency could be rejected, yet that does not ensure an investor can make money in that market. 

For retail investors, the market is very efficient. You cannot get an edge if you are slow to react, have less information than other investors, process the information poorly, and have high transaction costs. If you are an institutional trader, your sense of efficiency is different. You may have a slight edge on reaction time, trading efficiency, and information processing. If you are a hedge fund, you may have an even greater edge; however, being declared a hedge fund does not necessarily confer a lower efficiency level. 

The old argument by Friedman on the efficiency of speculation is that reasonable speculation will drive out poor speculators and thus make the market efficient. The counterargument is that noise traders are more prevalent than shrewd speculators and can keep the markets inefficient. A corollary to the Friedman argument is that there are different classes of investors with varying levels of capital that can exploit opportunities, so while efficiency may exist on average, that is not the same as saying the markets are efficient for everyone.

A sophisticated investor has an edge and creates an opportunity to exploit inefficiencies. Hence, the job of any due diligence is to identify sophistication and the chance for the edge that can be exploited. 

Stan Fischer - A great influencer on macroeconomics

 

Stan Fischer is one of the great macroeconomist of the last 50 years, and as shown by the figure above he influenced many of the other leading economists of this time. He was one of the key pillars of MIT macroeconomics and was clearly one of the strong influencers of monetary policy choices around the globe. You cannot talk about macroeconomics or international macrofinance without looking at some of his papers. I cannot say that I always agree with his research work, but that does not change his substantial impact on macro thinking. 

He will be missed, yet we must ask what would have happened to macroeconomic thinking if Fischer had not existed. Would we be better or worse off with our thinking?  Would someone else have filled the void? More so than any one piece of research, Fischer was a teacher, whether at MIT, the IMF, the World Bank, or the Fed, who set the agenda for many other researchers. In this case, he could not be replicated.

Riding bubbles is a strategy - but more than one way to do it

Jarrow and Kwok, in their new paper "Riding A Bubble: A Study of Market-timing Trading Strategies," identify when there are Q-bubbles based on local martingale properties. The idea is that a bubble will exhibit extreme values over different time horizons, and an investor should hold a significant bubble move until it reaches a set barrier. At this time, the investor should exit. Ride the bubble until the returns reach an extreme and then walk away. The basic story seems easy enough, yet the key is to determine the bubble component, which is based on the tail probabilities. Bubbles have a fat-tailed Pareto distribution. If an investor sets an upper bound on the price of the asset and it is reached before a certain time, then exit. 

The idea is relatively simple, yet despite the simulations run in the paper, this point of exit is harder to find in practice. It will encourage getting out of positions early, even if there is an optimization and an accounting for risk aversion. 

The trend-follower will generally not follow this type of strategy. The trend-follower will always hold the position until there is a reversal and a stop is hit. You will sacrifice some of the return in exchange for carrying any position as long as possible. Yes, there will be losses in the end when the market turns, but the ability to maintain a position in a bubble will generally be worth the added risk and the likelihood of some give-back. 

Financial innovation is a virus!

"Financial innovation is like a virus, finding weaknesses in existing inventive schemes and regulations. When something is growing very fast, that suggests they have found a weakness." - Jeremy Stein Harvard University. 

This is one way to think about financial innovation, but it is not very appealing. It argues that innovation is just an attempt to evade regulation. There is no doubt that some goals of innovation are evasion, but there are also other reasons, such as market efficiency. Nonetheless, one can argue that regulation reduces efficiency, and innovation attempts to address the problem. If the problem is corrected, there will be more growth in innovation. Securitization, derivatives, and ETFs are all significant innovations that make the markets more efficient, while also addressing regulatory concerns.

Knowledge and wisdom for picking your financial facts

 

"Knowledge is a process of picking up facts, wisdom lies in their simplification" - Martin H. Fischer 

from story on Jane Street's traders:

Jane Street software engineer Ian Henry said the firm's traders all need "fighter pilot eyes" to deal with "extremely high information density" while making trading decisions. Henry said that, when making tools for these traders, he has to fine tune their size by a matter of pixels, in order for traders to maximize what's on the screen.

Henry says one of two main categories of applications built at Jane Street is focused on "managing traders' attention," ensuring they're alerted to interesting things amid that sea of information. He says the challenge for engineers is around "balancing noisiness" and stopping those tools from annoying traders with unnecessary information. 

Is the problem for Jane Street the acquisition of knowledge or its simplification? I want more information because I never know what will be helpful, but then I have to be selective to focus my attention. 

The trend trader will say that I focus my attention on only a limited number of issues—the trend in price. All other information is unimportant. The discretionary trader will argue that all information is essential, and I don't want to be constrained by limits on what I can review. 

Where is the trade-off, and how much information is enough, is one of the key issues for any investor

Bond and equity expectations are different

 

A recent paper by AQR, "Why are bond investors contrarian while equity investors extrapolate," makes an interesting observation. I have always thought that bond investors were mena reverting based on their conservative nature. There are limits to where yields can go. Equity investors are optimists, which means that returns can always move higher based on unlimited possibilities. Overoptimism will lead to the extrapolation of good news. Of course, this does not explain what happens to markets when they start to move negatively. The pessimism of bond investors forms beliefs about limitations and the notion that good news cannot last.

AQR states that the cause is information salience, the attention -grabbing qualities of certain information. This, however, does not focus on why there is salience that is different across markets and why it may persist. Nonetheless, it is essential to think about differences in how expectations are formed in major asset classes.