Friday, April 30, 2021
Inflation expectations term structure - What are forecasters thinking when we form an inflation curve?
Downside protection, tail risk and timing - Sorry, the path of equities will have huge impact on costs and benefits
Tracking the CBOE put protection index (PPUT) against the SPX provides useful information on the cost and timing of hedging. The PPUT index tracks a 5% out of the money one-month put strategy rolled monthly. There is a clear drag with performance versus the index which should be expected given the strong equity returns until the March 2020 crisis. At that time, the value of the put protected equity exposure downside. The five year total return through mid-April 2021 was superior to an unhedged portfolio.
Nonetheless, the five year performance does not tell the full story if an investor decided to employ a put hedge strategy starting on April 1, 2020. In that case, there is a strong performance drag.
Thursday, April 29, 2021
There are three broad means of creating and using proprietary data:
1. New data / unused data - Investors will often mean alternative, new, or unused data. If there is data that others don't have, there can be an edge. If previous unavailable data is used to answer a question more quickly, it can be viewed as alternative data. If there is data that is available but not well-known, it can be an alternative data source. Of course, the edge issue is whether the alternative data is actually correlated with future prices.
2. Manipulation of existing data - A second set of proprietary data is existing information that is manipulated in new and different ways. It could be z-scoring data or creating a ratio between two data sets. There can be value in looking at the same data differently than convention.
3. The application of data to markets - Once found, data have to be mapped or linked to market returns. There is an edge with the techniques used to relate data to a forecast. For example, the link between macro data and bond returns can be measured through linear regression or through some form of machine learning. A non-linear technique may find a relationship that may not be displayed with a linear model.
4. The use of data to make decision - Finally, there is an edge created when the manager acts on the data or converts information to action. For example, there can be two trend-followers who use similar models but generate different returns because the map between data, forecast, and action is different.
Managers should walk through their use of data to determine whether there is an information edge. Investors who select managers should try and determine what it is that the manager does with data that creates an edge.
Tuesday, April 27, 2021
Monday, April 26, 2021
“There can be few fields of human endeavor in which history counts for so little as in the world of finance. Past experience, to the extent that it is part of memory at all, is dismissed as the primitive refuge of those who do not have the insight to appreciate the incredible wonders of the present”. -
Yet, finance is a study of history. All theory and analysis are based on reviewing and manipulating the past to make judgments on the future. There are no financial experiments that can be run in a laboratory. There is no controlled environment. All we can do is study past data and look for generalization that may help us understand something about the future.
When we only look at a few years of data, we are closing our minds to history. Old data have to be reviewed in the context of the time; however, the past provides the fundamental insight that behavior often does not change. We repeat good behavior and bad mistakes.
Market practitioners or realists will often sneer at the academics who study market efficiency. It is not because the practitioners believe markets are completely inefficient or there is easy money to be made; rather they have a different mindset of how they look at markets. The theorist looks for generalization and simplification while the practitioner is likely a structuralist who is focused on the plumbing of markets. Market plumbing focuses on the uniqueness of institutions and not generalization. There is money to be made by focusing on the details that may offer return.
The practitioner out of necessity has to be an institutionalist that has to sweat the details of markets. The academic would like to avoid the uniqueness of markets and eliminate the small details that are market specific wherever possible. It is not that academics do not want to understand market details, but it does not often help with developing a general theory. Of course, there will be a conflict between these two views of the world given the difference in underlying assumptions.
The practitioner has to worry about:
- transaction and operational costs
- liquidity costs
- leverage, margin, and capital costs
- the changing effects of fundamentals
- the impact of surprises
- the impact of noise traders
- crowdedness and the behavior of other traders
Friday, April 23, 2021
As we examine the COVID economic rebound and reflation more closely, it has more characteristics of what some have called a K-shaped recovery rather than one that resembles the lifting of all boats. There are many lettered variation on recovery, but K-shaped seems to best fit the environment. We are not dealing with a classic inventory build recession or a banking crisis recession. A recession is always a time for dramatic economic change and technological upheaval as new methods for enhancing productivity are employed out of necessity; however, the relative impact of the COVID pandemic may make this recession more extreme.
The pandemic has changed both buying habits and the use technology and can be viewed as relative allocation adjustments associated with changing tastes. Taste and behavior adjustments will be more disruptive of the status quo which will create the divergent K-shaped recovery. Given this type of multi-dimensional recovery, it is unlikely that blunt instrument policies will work to help those on the bottom-end of the K and may lead to excessive on the top leg of the K.
A K-shaped recovery means that some industries and groups will see a strong rebound beyond pre-COVID growth while others will suffer from a slow rebound or be hit with the costs of economic failure and have no opportunity for renewal. There are long-term winners and losers, and the job of an investor is to find the winners and avoid those losers. Policy-makers may desire to contain the losses and temper these excesses while allowing the macro adjustments to realign resources in this new world, but policy-makers cannot change the tide of taste changes.
It is not clear that all businesses should be supported, or all job protected, but policies should attempt to focus on the bottom leg of the K to manage this transition. First, anything that supports stemming COVID infections supports the bottom leg of the K in order to minimize the cost of underproduction and underuse of labor. Second, lowering rates to support speculation without affecting the credit channels for lending to small businesses only helps the upper half of the K. Additionally, excessively low rates will also lead to over-valuation and excessive demand for the "good" firms versus "bad" firms. The result is a wider dispersion in valuation as seen in the graph below. There is no value judgment here. It is a current reality.
Thursday, April 22, 2021
Crowdsourced forecasting methods are being tested and assessed by the intelligence agencies and the US government to help forecast some thorny issues in geopolitical analysis. This work is still in its infancy and is not accepted by everyone, but the results from testing suggest that this can be an important alternative tool for forecasting under uncertainty.
There has been a growing interest in prediction polls, forecasting competitions, prediction markets, and different forms of crowdsourcing to help with forecasts, but these tools have often been ad hoc and have not been integrated as part of any normal forecasting process with organizations. The premise for crowdsourced forecasts, however, is simple and based on the concept that there is "wisdom of crowds". Now the potential for groupthink when crowds are used in present; nevertheless, significant evidence exists that a set of diverse independent opinions creates better forecasting outcomes from crowd aggregation.
Think of the averaging of individuals from a crowd as a form of ensemble modeling. Along with value from the averaging of diverse opinions, individuals can be tracked and compared to others in order to find individuals that have forecasting skill. Crowdsourced forecast can complement existing methods while also uncovering additional useful information on specific topics.
Crowdsourced forecasting methods can uncover unique information if it is conducted through precise methods that ask for likelihood to specific events. The forecasts have to be falsifiable. For example, will the 10-year yield be above 2% by the end of 2021? Will the YOY CPI inflation be below 2% at the end of the year? The answer can be in a form of a probability that can be updated. This is the preferred method for posing questions over broad questions.
There is limited value from asking general opinions on a topic. Asking question in the form of a probability of an event occurring can provide precision in thinking on uncertain events. Output from precise crowdsourcing forecasts leads to tracking over time as well as a means of assessing the skill of predicting specific events.
Forecasts that are falsifiable have been around for the longest time on Wall Street when analysts are asked about such things as the level of the stock market, interest rates, or GDP. This is the bread and butter of Wall Street forecasters, yet many tasks could use better crowdsourced precision. This precision is especially the case within investment committee structures. It is not just important to assess outside forecasters. Rather it is the inside forecasts where there is more value. For example, what is the likelihood that hedge fund manager X will exceed a 10% total return? Or, what is the likelihood that volatility (VIX) will exceed 25% at the end of the third quarter. Crowdsourcing techniques and analysis increase accountability to those involved in an investment process.
See the recent report: "Keeping Score: A New Approach to Geopolitical Forecasting" from Perry world House (University of Pennsylvania)
Tuesday, April 20, 2021
Monday, April 19, 2021
Boom and Bust - A Global History of Financial Bubbles by William Quinn and John Turner should be added to the reading list of anyone who is interested in the history of financial bubbles and wants to have a framework to think about bubbles today. It is deeply researched, adds new insights about booms that have not been discussed in other books and has an easily applied framework for describing all of the historic episodes presented.
The authors use the metaphor of a fire triangle has to describe the three key components of a bubble: speculation that provides heat, fuel that comes from cheap credit, and oxygen which comes from marketability or the ability to allow a bubble investment to get in the hands of investors. Every bubble will be driven by these three components, speculation from those looking for quick gain, easy credit from low interest rates, and the marketability that creates the opportunity for further speculation by allowing new market participants to play the game.
The researchers start with some of the oldest and well-known bubbles, yet also explain why tulipmania did not meet their criteria for a significant boom and bust event. They present some interesting cases not often discussed in other bubble books like the LATAM boom and railroad mania in the first half of the 19th century. They also review the Australian land boom and bicycle mania in the latter half of the 1800's. Of course, Quinn and Turner cover the usual suspects of the 20th century: the roaring 20's, Japan bull market, the dot-com bust, the sub-prime mess, and the Chinese stock excesses.
They do a particularly good job of discussing how government while not always a cause, can abet booms and bust from actions that may in some cases seem well-intended but actually support or encourage the bubble fire triangle. Greed, supported by self-interest from a number of different parties often leads to unintended consequences which usually ends with well-known results.
I now use the fire triangle metaphor to looks at current booms and it helps as a framework to show that the present is once again just a repeat of the past. The markets and situations may change but the end will still be the same.
Saturday, April 17, 2021
Friday, April 16, 2021
Crowded futures market trade measurement and momentum, value and basis factor strategies - Watch speculators who are adding positions
No one wants to be in a crowded trade. You want to get in early, but when others rush in, you want to head to the exits. Yet, measuring crowdedness is not easy. Crowdedness, the excess capital or interest in specific trades, can vary through time and create time varying returns.
Recent research has focused on the potential crowdedness of factor strategies for momentum, carry, and value in futures markets through tracking net trade flows for specific reporting groups. This offers a direct way of measuring speculative interest through the information collected in the CFTC's Commitment of Traders reports. See "Crowding and Factor Returns".
The researchers find that when speculative non-commercial interest is high there is a negative predictive impact on expected factor strategy returns. Your speculative factor returns are better when there is less competitive from other speculators.
The speculative positions are measured as the net non-commercial positions relative to open interest against the average net speculative positions over the last year. Higher crowding leads to lower subsequent excess futures returns.
Variation in crowding can also be measured and is related to past returns, performance chasing, higher volatility, and the cost of arbitrage capital as measured by increases in repo or the TED spread. Speculators follow performance and will change positions with capital costs.
Factor strategies can be created by ranking momentum returns for 26 commodity markets, value based on deviations from long-term mean prices, and carry (basis) constructed from the difference between the first and second nearby futures. Once factor returns have been calculated, the crowdedness measure across all markets can be used to sort factors between periods of high and low crowdedness. From this sort, a measure of conditional crowdedness can be constructed. Excess returns are higher during periods of low crowding. We show the momentum results below. Similar relationships are found for value and the basis.
Thursday, April 15, 2021
The Fed's TPLT framework, Temporary Price-level Targeting - Love or hate it, you need to understand it
According to Clarida, who has mentioned this three times in the last few months, we have an TPLT framework, temporary price-level targeting. The markets are anchoring inflation expectations below the 2% target because of ELB, an effective lower bound, so there has to be an overshoot to break the ELB problem.
"As I highlighted in speeches at the Brookings Institution in November and the Hoover Institution in January, I believe that a useful way to summarize the framework defined by these five features is temporary price-level targeting (TPLT, at the ELB) that reverts to flexible inflation targeting (once the conditions for liftoff have been reached)."
We have to start thinking about monetary policy using the language and guidance that has been provided by the Fed. We can disagree with the success of this policy, and we may also be willing to trade against this view, but we have to accept that this is the framework that will be driving their internal discussions. The following are the five features that represent current Fed thinking.
Wednesday, April 14, 2021
"There is no other proposition in economics which has more solid empirical evidence supporting it than the Efficient Market Hypothesis" - Michael Jensen in 1978.
I came across this old quote from one of the leaders in finance research at the time - how prophetically wrong. Of course, making money is not easy because markets are generally efficient, but there is enough evidence to tell us that the world is more complex than our simple 70's view.
There has been an explosion of research on behavioral finance, the limits of arbitrage, ideas of adaptive market efficiency, the ascent of risk premia modeling, new testing of efficiency, data science, high frequency trading, and market microstructure. All have helped refine our thinking of efficiency.
There is no settled science in investments where market behavior changes and adapts. Empirical evidence changes. Theories adapt. New tests are developed. The investment challenge is learning how to adapt to a dynamic non-linear world. We are often dealing with the same question but with different answers. The conclusion here is simple, take nothing for granted and continually questions conventional wisdom.
Some thinking on market efficiency over the years:
Tuesday, April 13, 2021
Carry strategies are core to cross-sectional currency trading; buy the high-yielding currencies and short the low-yielding currencies. This strategy works but has been subject to negative skew and underperforms during crises or periods of high volatility. For researchers, the concept of currency carry is in conflict with uncovered interest rate parity and has been a puzzle which is only deepened without good pricing models to explain the risk premia. This puzzle has only gotten murkier with a paper called "Good Carry, Bad Carry".
There are prototypical G10 carry trade currencies (AUD, CHF, and JPY) with JPY and CHF usually used as funding currencies with low rates and AUD often serving as a high yield currency. For the longest time, the sort of carry currencies would have showed same exposures for decades. A carry portfolio will always hold the rate differential extremes.
However, it was found that if you exclude these three currencies from a G10 currency carry sort, you will get portfolios with higher Sharpe ratios and lower skew. Limiting the set currencies to exclude specific currencies will actually improve your portfolio efficiency. This is not supposed to be how a cross-sectional carry sort should work. Placing restrictions on currencies included in a portfolio will create good and bad currency mixes with the bad portfolios having lower Sharpe ratios and negative skew.
The authors tested a number of variations for good and bad and get the same results. Hypotheses based on current carry thinking were tested for this anomaly but were not able to draw any definitive conclusions. Good carry portfolios reduce tail risk and have better return to risk. Investors need to think beyond simple sorts or look at risk through different criteria.
This new crowdedness factor seems to be independent of other key equity risk factors. The collective wisdom or information edge of hedge fund managers along with their buying power seems to find stocks that will generate good positive relative returns. But there is a catch.
Monday, April 12, 2021
Geopolitical threats create risk and uncertainty. This seems intuitive and can actually be measured through tracking geopolitical risk indices. These threats can be linked to market reactions, so investors can measure and act on these evolving risks. Below is the widely used Geopolitical threat index developed and updated by Matteo Iacoviello. It uses key word searches from leading newspapers around the world to measures geopolitical threats and acts. In many cases, elevated threats will impact financial markets.
A common theme of my investing thesis has been to focus on the nexus between risk, uncertainty and pricing. If uncertainty increases, it will carry over to market risk as measured by volatility. This increase in market risk will add to market dispersion, change correlations, affect risk aversion and sentiment, and change risk premia. Even if market prices don't move significantly, there will be a change in the wings of return distributions.
Markets that engage in global trade in sensitive geopolitical area or have been perceived as a place of safety should be more sensitive to changes in these threats. Threats go up and there should be a flight to safety and a movement out of risky assets.
A causal link from geopolitical threats spillover to oil price volatility and gold moves has been found with recent research. Similarly, threats influence the capital investment decisions of companies. This alternative data index can help with global macro decisions.
See recent research:
“Are geopolitical threats powerful enough to predict global oil
Environmental Science and Pollution Research https://doi.org/10.1007/s11356-021-12653-y
“Geopolitical Risk and Corporate Investment” Ruchith Dissanayake, Vikas Mehrotra, and Yanhui Wu
Sunday, April 11, 2021
Saturday, April 10, 2021
Remember, all Sell-Side Research contains at least 1 of the following 3 elements
1.Trades that 40-Act Funds are running after serious traders/HFs stopped out.
2.Death Trap Trades where the bank’s desk needs to take the other side.
3. An honest opinion of an analyst.
Never forget, in life, even lies are intriguing and useful, they reveal where someone's interests are
Thursday, April 8, 2021
The ladder of causality, as presented in the Book of Why, is a simple way of looking at how inference increases in complexity. Using the ladder is a good way for walking through how reasoning can progress from what is easy to more difficult.
On the first rung, there is the activity of seeing. This reasoning is the power of finding association. Association is not always an easy activity but seeing a relationship may not tell us anything about causality. Association can be simple or may use complex statistical analysis.
On the second rung, there is the activity of doing or what is called intervention. If I take a specific action, what will happen as a response? There can be a testable link from action, the doing, to effect, the result. This doing can be basis of experimentation.
The third rung on the ladder of reasoning is associated with counterfactuals or the use of imagination. It involves imagining what would happen under different circumstances. Can I imagine a situation or world that does not exist? This is the realm of theory and provides understanding through generating a narrative that can be generalized. Imagining of counterfactuals can provide hypotheses of what may happen.
A quant modeler will move up and down the ladder of causality as he looks at new data, makes predictions and finally develops a model to describe what can happen in the future. Progress on an investment research project will be based on climbing the ladder of causality.
Monday, April 5, 2021
One of the key concepts that separate good from great investors is better decision-making in an uncertain environment with limited information. Some refer to this special skill through the broad term of intuition, yet the concept of intuition is often fuzzy and imprecise. A negative view of intuition is a misconception. Intuition can and does have structure and can be well explained within the context of naturalistic decision-making.
Too often the focus of quick decision-making under uncertainty has been on biases and irrationality. In reality, there is much to learn from realistic approaches to decision-making that are based on practical rules or heuristics that cut time delays and focuses on the information available not what would be needed in a perfect world.
There has been significant work on modeling the idea of intuition through the pioneering with Gary Klein in his many studies of decision-makers under the stress of time and information constraints. One of the core approaches he developed was the recognition primed decision model (RPDM).
The recognition primed decision model starts with experience or base knowledge of the decision-maker. This experiential knowledge allows the decision-maker to assess many different situations and variation from core situations. The first question the decision-makers has to ask is whether the decision is typical. If the situation is typical, then the decision maker can focus on four by-products of recognition: expectancies, the relevant cues associated with a typical situation, plausible goals, and a set of possible actions.
From recognition, there needs to be an evaluation of the action plan. If the answer is that the plan will not work, the action has to be reassessed. If it can work, with some exceptions, then the plan has to be modified. Once a plan is accepted, the course of action should be implemented.
If the situation is not typical, the decision-maker will have to diagnose what makes this situation unique and look for more data. If the situation is recognized but has an anomaly, then there needs to be clarification with more data or a restructuring of the diagnosis. There is a feedback loop between recognition of similar situations and action against unique situations that require deeper thinking or more information and then an evaluation. Experience and intuition can be process driven.