Saturday, June 24, 2023
From Angela Shi
It used to be that analysts would get a set of data, form hypotheses, and then use regression as the model workhorse. Those days are gone and that is a good thing. One of the major advancements from the ML revolution is a providing a broader set of tools to solve data problem. A quick look at the set of supervised learning tools shows the growing complexity of choices. These enhanced tools are especially important as data sets get larger and more complex.
Are more tools always better? No. The new challenge is learning the unique features of these tools and determining when is the right time to use them. Now, quants must set up models, find data, pick the right tools, and then build the portfolio. The choice set is more complex and requires new skills. A manager's comparative advantage will be associated with making the right tool choice.
Thursday, June 22, 2023
There will be talk about "smart" money and "dumb" money. Investors will talk about crowded trades. There will often be stories about groups of traders driving markets. The flows will matter. The dynamics of the market have been given a lot of press but there has been limited thinking about who profits from all this trading and the behaviors of different investors and how they process information.
A recent paper tries to solve this problem. See "Which Investors Drive Factor Returns?" by Morad Elsaify. In this work, the author focuses on the processing of information as imbedded in the risk factors. The paper looks at a large set of risk factors and then measures the behavior of different trading groups around these factors. Different trading groups will show different portfolios selection around risk factors.
The processing of market information can come in two forms or choices: the persistent of fundamental risk factors or choice of risk factors, and the timing of idiosyncratic risk around a risk factor or factor timing.
The fundamental trading is associated with the selection of risk factors while changes in risk factors is associated with idiosyncratic risk or factor timing. Smarter money will be able to factor time and focus on idiosyncratic risk while less sophisticated traders at processing information will focus on factor selection. Hedge funds seems to take more factor timing risk while more passive investors will focus on factor selection. Hedge funds will make markets efficient and act like arbitrageurs for factor returns. They will buy cheap factors and sell rich factors which will be idiosyncratic differences away from long-term averages. While those with less skill will just focus on factor selection or the fundamentals and not the transitory shocks in risk factors.
Traders need to use information about the risk factors to solve two types of uncertainty, the average pay-off of a risk factor and the transitory or idiosyncratic portion of a risk factor. The attention or detail to processing information about any risk factor will determine whether a trader will be a timer or factor selector.
This not an easy paper to read, but it is highly suggestive of how information is processed by different trader types and provides a good explanation for what hedge funds do and how they behave.
Momentum is one of the bets know and consistent risk premium in the finance factor zoo. The strategy us simple. Sort the returns of some stock universe and buy the top winners and sell the losers. It is not considered part of the five factor "holy grail", yet there may still exist some puzzling results. Foremost the times series of returns from this factor are highly variable. While consistent in the long-term, there are periods of poor performance.
An interesting piece of research has been published that tries and explain the time variation in momentum based on the dispersion or gap between the winners and losers. It has been called the momentum gap. See "The Momentum Gap and Return Predictability" by Simon Huang.
The momentum gap negatively predicts momentum profits. This applies to both the US and international stock markets. If there is a one standard deviation increase in the gap, there will be predicted a 125 bps fall in monthly momentum returns even after controlling for other factor predictors.
Sunday, June 18, 2023
I just heard this quote on a podcast with Jim Grant, one of the best financial commentators/journalists we have on markets. Old school can always be fresh.
The biggest problem with forecasting is not getting the idea or concept right. It is getting the timing right. Whether it is inflation, a banking crisis, a recession or a market reversal, the signs are often early. The signs may be right in front of us, yet the time frame can be maddening slow.
The variation on this theme is from Ruddy Dornbusch:
“In economics, things take longer to happen than you think they will, and then they happen faster than you thought they could.”
Saturday, June 17, 2023
There is political cost with being a reserve currency - Investors love to hate on you and look for a fall. When you are on top, everyone is looking for a fall because you cannot go any higher. The dollar has gone through some rough patches but still has shown the ability to bounce back from negative stories.
An interesting story that has been given a lot of attention is dollar hatred expressed in Money: Inside and Out by Jens Nordvig in "A Brief History of Dollar Hatred". Nordvig makes the point that we have gone through several dollar hatred periods since 2000. During these periods of dollar hate, there is a strong narrative for why the dollar should decline only to see the dollar bounce back. I agree with the fact that we have gone through periods of dollar negativity but the reality, the dollar has shown resilience and moved beyond the hatred associated with a given time or story.
The dollar hatred periods can be broken into three themes:
- 2004-08 - the great current account scare - The period when economists focused on the large US current account deficits which could only be solved through a dollar decline.
- 2009-13 - The QE infinity story - Excessive monetary policy will lead to a dollar fall and the currency is debased from too much money in the financial system. Other central banks increased their money so on a relative basis the dollar was not debased as expected.
- 2021-present - Dedollarization by major countries who do not want to be hurt by potential sanctions. The dollar hatred is associated with US sanction hegemony.
The dollar has risen and fallen with changes in the macro environment. That should not be surprising. However, disequilibrium in the international finance environment will be solved through several channels. The price of the dollar is just one of those channels.
In the case of the current account deficit, the rebirth of oil industry changed the current account environment. The QE infinity story was offset by higher growth in the US and the impact of QE in other countries. The dedollarization story is still playing out, but the limited convertibility of currencies for countries who want out of the dollar system is a major sticking point.
There is dollar hatred and there are issues which will drive the dollar lower, but dollar hatred cannot be looked at in isolation. The dollar has strengthened at key times because it is a place of safety. The GFC saw dollar gains. The pandemic saw flight to the dollar. EU problems after the GFC again made the dollar attractive.
A negative narrative is just a story until there is a major price correction that is not being offset by other policies or price action.
What makes a good hedge fund? Can you replicate hedge fund returns? These questions are not easy to answer when you think about replication strategies. You can create through bundling ideas a good hedge fund with a high Sharpe ratio, but there are hidden risks that may not seem obvious on the surface. We can think about the classic examples provided by Andy Lo with his humorous firms, Capital Decimation Partners (CDP) and Capital Multiplication Partners (CMP) as a case study on the drivers of hedge fund returns and replication.
The returns of his fictitious hedge fund CDP are a combination of holding the stock index with selling out of the money puts for protection. You do well because you pick up premium every month until the time there is a large market downturn and then the pain begins. The hedge fund manager hopes that the size and frequency of a market decline just does not occur. Hope is not a strategy. The high Sharpe ratio comes because the tail event is not accounted for in the return to risk ratio. Increase carry in exchange for tail risk and you improve the Sharpe but create a different risk profile.
So, let's look at (CMP). In this case, the returns are generated through a switching model between the SPX and one-month Treasury bills assuming that you have perfect foresight on what will do better. This timing model is like buying the SPX and with a put option struck at the price of the index plus the one-month bill return. Unfortunately, you cannot get those returns given the cost of the option and the issue not having perfect timing. We can only receive something less than the perfect forecast. Nevertheless, we can use the thinking behind forming a hedge fund in theory to develop replication strategies through linear approximation. These will not be perfect, but it can be an alternative to buying hedge funds.
Replication of a hedge fund can be tried through using several factors to find a linear fit, regression, between the hedge funds returns and a model. The alpha from the manager is the constant and residual or returns not associated with the linear regression. The results suggest that it is possible to clone the average return for hedge funds within a style category. Unfortunately, getting average returns is not what many investors want. Additionally, there is an error term with replication so you will not get something that will be close in the short run.
Looking at hedge funds as either an option program or a linear combination of factors is a good start to describe the risks when investing with these alternatives.
Friday, June 16, 2023
We are strong believers in language precision and have often talked about Sherman Kent and WEP, words of estimative probability. When using words to describe likelihoods and probabilities, there is not always agreement on meaning. This is what makes discretionary decision-making so difficult when placed in a committee structure. Someone can say an event is "likely", but there may not be agreement on what is the translated probability of the word.
Recently, a friend sent the above chart on what words mean in other cultures for a given point on the normal distribution. It is humorous, but like most humor there is a grain of truth in the joke. Ask a person in a different culture and you will get some very imprecise meanings or translations. In Britain or Australia, one word covers everything. Think of this when you ask someone about recent returns or their monthly performance. "Not bad" or "It's fine" could mean anything.
The frequentist approach forms an expectation from a sample of data. The Bayesian approach uses some knowledge to form a prior. In the case of the frequentist, a sample of data is taken or observed and from that sample conclusions are drawn. In the Bayesian approach, there is a start with some knowledge, and data are used to update the prior knowledge. Conflicting evidence will lead to an update of priors.
The frequentist assumes events are based on frequencies, the count, while Bayesian inference will draw on prior knowledge. The frequentist does not calculate the probability of a hypothesis. He accepts or rejects and is an absolutist. The Bayesian always thinks in terms of probabilities and reasons in relative differences.
The frequentist believes a parameter is not a random variable. The Bayesian says that a parameter is a random variable and measure likelihoods.
The frequentist will talk about a confidence interval, p-value, power, and significance. The Bayesian will use the term creditable interval, prior, and posterior. The frequentist will think about action to take, accept or reject hypotheses, and getting a right answer. The Bayesian will discuss opinions or prior beliefs and how they may be updated. There are no right answers only more or less likely answers.
If you are a trader, you are more likely to be a Bayesian and think about probabilities and priors.
"I'm a pessimist, but that is no reason to be gloomy!"
- the late Cormac McCarthy
Cormac could have been a fixed income guy, but he was a physicist by training. This phrase should be placed on the wall of every risk manager's office. Yes, you should look for risks, but that does not mean that the world is always going to come crashing down.
Tuesday, June 13, 2023
There is a problem with replication of finance studies on risk premia and trading strategies. Additionally, there is a problem of out-of-sample results not matching in-sample returns. You don't get what you think with most financial studies. There are a couple of reasons for this disconnect. One, the construction of the test was poor. Two, the articles published are only extremes from data mining. Three, risk premia, once observed are arbitraged away. Four, market behavior and structures change. Overall, the buyer needs to beware.
Investors are aware of research and exploit it so that future returns are lower. There is a life expectancy for strategies and once the cat is out of the bag and the general trading public knows the strategy, excess returns are quickly gone. The out-of-sample results also show that there are strong performance declines.
So, what is the slippage that you should expect from models? Studies have found that portfolio returns for a strategy may fall about 25% for out-of-sample work. The drop can be over 50% during the five years after publication.
Quantpedia did a study of out-of-sample results and found that the return decline is universal across strategies. Testing a large set of strategies, they found an average drop of 33% out-of-sample and with the median drop over 40%.
Nevertheless, there is a significant performance difference between in and out-of-sample results. There is a lot of variation. However, a careful analysis suggests that since there is factor momentum, there can be ways to reduce the out-of-sample problem. Hold the factor premium or strategy that is trending higher.
Monday, June 12, 2023
The noise bottleneck is really a paradox. We think the more information we consume, the more signal we’ll consume. Only the mind doesn’t work like that. When the volume of information increases, our ability to comprehend the relevant from the irrelevant becomes compromised. We place too much emphasis on irrelevant data and lose sight of what’s really important.
Friday, June 9, 2023
We have cost-push, demand-pull, headline, and core inflation to name just a few descriptors. We have also been exposed to shrinkflation, products at the same price but have less. Now we are hearing about greedflation from the chief economist of the OECD.
We have heard this term before, but it now shameful to try and keep your margins the same during an inflationary period. If your costs are going up, shouldn't firms try and pass those costs onto customers? Customer demand may fall and the pass-through may not be possible, but it is in the interests of shareholders for managers to make the attempt to pass on costs. The customers will decide whether the margins can be maintained. Is this process greedy or just the normal behavior of businesses doing their job?
Tuesday, June 6, 2023
Hat tip to Saul Dobilas for the great picture
How do you choose the right technique for the right problem? This is a growing issue with machine learning because there are so many approaches to problem-solving. The first thing to do is classify techniques and the color wheel does a good job of providing a first pass. It is a good way to start to solve the technique choice problem.
Saturday, June 3, 2023