1. Financial conditions have improved for the year after a rocky start
2. Trade uncertainty has fallen after spiking in the spring.
3. Revision to global growth has come through US and not other parts of the world
Do not worry about the 2025 predictions. Think about the surprises of 2025 and whether they will create spillover in 2026.
Momentum can be displayed across different forms, and it seems as though each has different forms of persistence:
The factor and industry momentum are strongly persistent over the short and intermediate run. Stock-specific momentum is persistent over the intermediate but not the short run. Beta and country momentum do not show persistence. This has important implications for how we use the momentum factor. These issues are described in the paper, "Optimizing the persistence of price momentum: which trends are your friends." The critical takeaway from this work is that while momentum is pervasive, there are areas or pockets of momentum persistence that can be exploited. These points of persistence are extreme with factor and industry tilts. Industry behavior moves together as well as factor behavior. This is not found in the overall market or with individual stocks.
From the PowerPoint notes for the book Warren B. Powell, Reinforcement Learning and Stochastic Optimization: A unified framework for sequential decisions, John Wiley and Sons, Hoboken, 2022 (1100 pages).
This is a fascinating book that dives into the complex issues of stochastic optimization. At a high level, I found the description of many types of uncertainty beneficial. Only through dividing or classifying the many kinds of uncertainty can a researcher understand where the key risks are in a problem.
Is there data uncertainty or model uncertainty? Is the inference uncertainty or transitional uncertainty? If researchers can explore these differences, they can better define the risks faced and the sensitivity to a given answer.
It would be interesting to take a given strategy and decompose all the uncertainties faced.
I found this simple graph that matches how I use AI models. There is not one single provider that works best. The choice of AI model will differ by the task. Given these differences, I will sometimes use more than one model for he same task by asking the same questions or using the same prompts across several models and then comparing the results. Claude is easy to work with, but ChatGPT will sometimes provide more useful answers. Gemini and Co-Pilot are easy to use because of their integration with workflow, although their answers for more complex questions are not as clear.
While good at answering quick questions, their usage is harder to intergrate with normal quant work.
Bonds are still in a bear market, but valuations still look cheap. The problem is determining the current fair value and whether there will be a move toward a new equilibrium. The simplest bond valuation will be real growth plus expected inflation plus any term premium. Expected inflation is above 2% and is unlikely to fall to the target rate. The real GDP is above 2% for the Blue Chip forecast and above 3% for the Atlanta Fed GDPnow forecast. Even if there is no term premium, we are looking at something close to the current 30-year Treasury yield and slightly higher than the 10-year yield.
The only way to pick bonds is to assume a slowdown in GDP and a decline in inflation. This is possible for 2026, but this is not the current environment.
You are making a large macro bet if you think the bear market in bonds will reverse beyond current levels.
We have written about how hedge funds are trying to contain costs by trading more efficiently. We are also seeing cost containment and efficiencies through the use of AI. Similar to consulting, AI can make analysts more efficient at some of their core tasks through summarizing and sifting through data in reports. The use of AI through EDGAR filing is not new, but has become a core part of the work by both discretionary and quantitative researchers.
AI is being used as:
Hedge fund performance centers on alpha generation. Alpha can come in many forms, but one that is clearly dominating the attention of many firms is cost containment. The drive to cost containment is based on two key components. One, there is relentless pressure from institutional investors to cut fees. With fees always pushing downward, firms have to become more efficient. Second, as hedge funds increase their trading volume, transaction costs become an increasingly important area for potential value creation.
By cutting down the cost of trading, there is an immediate gain in return that flows through to the bottom line, reducing performance and incentive fees. Lowering the bid-ask spread improves returns. Executing with less slippage again enhances performance. The gain from cost containment is generally immediate and does not have to wait until ideas embedded in trades generate returns.
Cost containment is especially valuable to firms that are gaining scale. There can be specialized trading desks, centralized research, and risk management that can use economies of scale. All provide an edge that will squeeze out smaller firms that cannot gain economies of scale.
There is no question that this has been a great year for Communication Services and the Information Technology sector, which are up 34.88% and 24.36%, respectively, for the year through November. Still, we are seeing cracks in these sectors with differentiation across the Mag 7. What remains the key theme to watch is the rotation into global equities and emerging markets, which are up respectively by 29.84% and 22.40% through November. These indices are beating large, mid, and small-cap US stocks by a significant margin.
Buying a broad set of US stocks is not the direction for success in the equity markets. We are seeing low correlation across US stocks and high dispersion. Investors need to be selective with their stock choices. This is a global stock-pickers market. If you are not a stock picker, you can see it in the differentials across risk premia. High beta and momentum factors are showing strong returns, while low volatility and dividend stocks are underperforming, even amid the current talk of market bubbles.
Prices are the lifeblood of any quant model. If you have the wrong input, you will get incorrect predictions. In the case of an outlier, a single incorrect price may generate misleading signals about future opportunities, so any model should be thoroughly reviewed to assess its sensitivity to wayward signals.
If there are incorrect inputs, the model output should be adjusted to reflect the change in data when a replacement is made. Users have to be alerted to any changes. These are easy cases to deal with. There are also more difficult issues, such as data oddities or anomalies.
For example, the CME outage during the Thanksgiving period is a market anomaly that has to be addressed, especially given that it occurred at month-end. To provide context, there was an 11-hour system outage ending at 1335 GMT. It was during the Thanksgiving break, which is associated with low trading volume in the US, but it impacted all global markets on a Friday, a month-end. An outage will lead to a change in trading and a surge upon reopening. Hence, the inputs for open, high, low, and close will be distorted from what they would be in the absence of an outage. This will lead to slightly different signals generated from any model.
So what should a modeler do about this? One response is that the price is the price and to do nothing. It is reasonable and defensible, yet it may seem odd not to account for some distortion; however, there is no way to determine the impact of any outage. What would be the right price? Another option is to drop the price to the last close. This can be defended, but replacing data seems somewhat arbitrary.
The best response is to focus on the output and look at the marginal trade signals generated. Does it matter? An output sensitivity analysis can be conducted to see what happens with the new prices, rather than looking at what would have happened if no change had been made. If there are small marginal changes, then keep the latest prices. If there is a large set of new signals, investigate further on why and flag the changes. The prices can be kept, but the flagged trades can be ignored. However, this creates another set of problems if the trade is closing an existing position. When do you exit the old position?
These real-life problems tell the user that there is no such thing as a fully automated system.
Still spending time on the 6/40 stock bond mix. The reason is simple - it is the benchmark for asset allocation. Yes, it works; however, investors should be aware that there are extended periods of flat returns. You can have lost decades of no performance. The challenge is finding alternatives during these flat periods, especially when real yields are negative. Can alternative investments do the trick? The core solution is to find positive real returns with low correlation to equities. Trend-following can do it, but the core returns do not match the periods of lost decades. The challenge is finding the right mix at the right time, with clear rules for triggering a switch in asset allocation.
In the paper "The Power of Narrative Attention: Linking Geopolitical and Economic Storylines to Currency Risk and Return Predictability", the authors find that narrative shocks are not fully priced into FX markets and will impact returns over a period of weeks. Even after accounting for major factors, fluctuations in currency markets show time-varying exposure to economic narratives such as recessions, trade wars, and inflation. Narrative sentiment can vary depending on the tone and volume of coverage.
Even if you are a quant, you should read the news and stay aware of current market narratives. However, these narratives can be systematically measured and incorporated into a model to increase explanatory power. We always knew this was the case, but the key finding is that narrative sentiment persists and is independent of other factors.
Equity liquidity is still below average, though higher than during the spring period associated with tariff shocks. Treasury liquidity has increased substantially since the second quarter, but liquidity in the 2-year Treasury remains low.
Liquidity is not a concern until you need it, so risk management should consider the exit costs before a crisis.
I am trying to understand the relatively new concept of the Total Portfolio Approach (TPA), which is being embraced by many large pension funds and endowments. Now, we know the foundations of SAA (Strategic Asset Allocation) and TAA (Tactical Asset Allocation), but there does not seem to be a clear definition of TPA.
SAA is structured around an investment committee allocating capital to major asset classes based on expected returns for each class. This is a long-term perspective, and in the short run, it will be applied for the year. TAA is a short-term. Both of these involve a top-down process in which the focus is on identifying asset classes and then making assumptions about future returns, volatility, and correlations to determine a set of portfolio weights. The allocations are then given to specific investment groups that aim to beat the benchmarks for each asset class. The investment committee monitors the performance and the weight for each asset class. The foundations of this approach are modern portfolio theory, with a focus on diversification.
The reality is that many endowments have not been able to beat their SAA portfolio benchmarks, and the benchmarks themselves have not been effective because the underlying return and risk assumptions have proved ineffective. The process does not seem to work, so there is room for an alternative approach.
The new approach is the total portfolio approach, which still embraces diversification and modern portfolio theory. It also embraces the idea that allocations should be more flexible, drawing on the principles of tactical asset allocation. Still, there is no new theory behind the concept.
There is a belief that allocations should be more flexible and that endowment portfolios should not be siloed into fixed asset-class weights. There is a shift in investment staff to compete for capital across all asset classes. It argues that siloed thinking will not be effective for generating alpha or for adjusting to a dynamic market environment. TPA is more of an approach to managing the portfolio and not setting goals. The objective is to maximize surplus (assets - liabilities), subject to a downside or volatility constraint on surplus risk.
This all sounds good, but it is not clear that there is a single acceptable definition of the concept. Hence, TPA may be in the eyes of the beholder.
We take for granted the process of receiving a credit score and the distribution of our credit history across banks and retailers, yet both are recent phenomena. We also assume that retailers' credit extensions are relatively simple. Pre-Civil War, most retail purchases were made in cash or with simple extended terms from a local grocer or retailer. Credit amounts or ratings were held by an single institutions and only extended to those that were know.
As cities grew and consumers, this was not a workable solution. Even in a city, there began sharing arrangements regarding customers' credit histories. Runners would move from store to store to gain information on a new customer. The transaction costs were high, but not knowing the consumer was costly.
Suppliers placed pressure on retailers to pay their bills so retailers had to better know who they were extrending credit to. Lists were made, and the process started to be centralized through local credit bureaus. As more credit was extended, there was a need for more credit professionals and further automation to track and rate consumers. This was furthered through the use of comutuers and the ascent of credit cards.
There is not much to apply to trading with this history of credit information. Still, Creditworthy: A History of Consumer Surveillance and Financial Identity in America by Josh Lauer is a fascinating history that makes you think about the fundamentals of credit information and ratings.
