Valuing start-ups mainly on scorecards

 

I came across this simple visual on the different methods for valuing start-up firms, and found that it does not follow a set format. Of the five methodologies, only one is based on DCF, a second is based on industry multiples, and the other three are focused on a scorecard. 

Should it be surprising that when assessing start-up companies, the focus is not on cash flow projections but on a narrative based on rankings? There is no clear idea for how to handicap or calculate risk, so there is a focus on creating characteristic rankings to form an overall ranking of potential success. 

When faced with uncertainty, other methods of analysis are needed to form a risk assessment. In this case, factor scorecards have been developed to solve the problem. 

Risk is not uncertainty

“For uncertain matters there are no calculable probabilities whatsoever. We simply do not know.” -Keynes 

As we move into the new year, we hear projections about what will happen in 2026. It is a fool's errand. These projections are often not grounded in risk assessments, which are countable and measurable. They are framed in narratives. More importantly, they are focused on uncertainty, which means there are no calculable probabilities. Some have defined this as radical uncertainty, but we do not need the adjective. We just do not know the likelihoods, and in most cases, the analysts of these forecasts will not provide probabilities. They are just stories and should be viewed as such. 

Exiting at the wrong time - the key investor mistake

Hat tip to Andrew Breer for highlighting this interesting chart of the Man AHL UCITs fund. Notice that money flows out of the fund on the drawdown, and it does not match when the performance turns around. This has been an age-old issue with CTA, but it also applies to many fund investments. Investors lose money and exit the fund, only to see performance turn around and miss the reversal. This seems odd, especially for managed futures, because trading odds are active and go both long and short. Hence, there can be losses, but positions may be cleared out and new positions established that are opposite to the prior risks taken. 

You are buying a system, not a particular investment. If you exit, it is because you no longer believe the methodology can produce position returns. We have learned that trend and momentum do generate long-term returns; however, there will be periods of underperformance. Investors have either been long-term holders or are willing to actively trade their positions, entering or increasing positions during drawdowns. 

There needs to be more research on the decision-making of investor and why or when they exit investments. 

Themes drive the big returns in equities

Themes drive investment returns, or, put differently, key narratives drive significant returns during a market cycle. Unfortunately, themes do not last, and themes can turn into bubbles. If you don't exit one of these considerable themes, you will eventually be disappointed. Each of the themes listed above reverted to normal, so the key investment decision is to know when a theme develops and when it has reached an extreme. Wall Street spends significant time identifying these themes and then sells them to others. They make money when investors transition between themes as capital is redeployed. 

Peter Lynch on economics

 “If you spend 14 minutes a year on economics, you just wasted 12 minutes.”

— Peter Lynch

Of course, he means macroeconomics, and that may have been the case when stock-picking was supreme and generated strong alpha. You still need to know the economics of the firm and industries. Still, with the focus on beta and risk premia, there is also a need for understanding the business cycle, the credit cycle, and monetary policy. Can investors predict the macroeconomy? That is a tricky question, but understanding where you are in the macro cycle may make all the difference between success and failure.

Perhaps analysts need ot spend 28 minutes on economics per day and learn not to waste any of them. 

Are the great minds leading to innovation?

 “the great minds of the Industrial Enlightenment had shown how the useful knowledge they were accumulating could be used to improve, to rationalise, and to innovate. The rest is commentary” - 2025 Nobel Prize winner in economics Joel Mokyr 

The burden on government and higher education is to better mankind, and that is best done through innovation that increases economic productivity. This raises living standards for all. It is not a matter of regulation but nudging greater minds to improve living standards. This is what is needed for the EU and for emerging markets. It is necessary for the US and China. Technology, not behavioral restrictions, improves lives. We need to focus more on how to use knowledge to benefit humankind. 

The three big US economic surprises for 2025

There have been a few surprises in 2025. There are three that jump out as making a difference on performance. 

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. 

Bubbles and crash risk is associated with positive expectations

 

There has been more talk about bubbes in the last six months than over the last few years. The paper, "Optimism Everywhere: Beliefs during stock price bubble", focuses on the boom and bust cycle by looking at expectations from analysts. When there is a price surge, analysts forecast exceptional earnings growth and high near-term returns. Short interest will be low, and the overall level of optimism is a strong indicator of future crashes. There are few skeptics during a bubble period. 

This research adds to our bubble story, yet there is a problem of whether optimism leads to price increases or whether price increases create optimism. The line of causality would be beneficial, although it is likely that price increases and optimism are closely linked. Yet it would be helpful to understand why some price increases become bubbles. What is the driver of optimism, and how is optimism spread into price behavior?

Momentum is persistent - A history

The paper, "Momentum factor investing: Evidence and Evolution", provides a history of research on the momentum factor. It has expanded into many research areas, and all have shown strong momentum effects. Momentum is across all asset classes, all time periods, and all regions of the world, and is shown to come in many forms, both price-based and fundamentals-based. When considering these different approaches to momentum, it is clear that many are unique and not associated with the same fundamental drivers. Nevertheless, we can agree that momentum is closely tied to behavior through the slow reaction to news.

 

Stock-Bond correlation term structure

There has been so much discussion on the correlation between stocks and bonds that I was surprised that this topic has missed something fundamental. The stock bond correlation will differ by the maturity of the bonds analyzed. That is, there is a term structure of correlation. The stock bond correlation for the long bond will vary from that for the intermediate and short-term bonds. This seems obvious given there is a duration difference in bonds, yet given that interest rates across the yield curve will not always show a parallel move, the specifics for this relationship have to be analyzed. 

First, there is a difference in high and low-frequency stock-bond covariance. Second, the average covariance was upward-sloping during the Pre-GFC period, only to be downward-sloping during the post-GFC period. Third, there is a change in the relationship between stock and bond returns. It used to be upward sloping and is now downward sloping, and finally, there is a substantial change in the cyclical covariance relationship. This is all described in the paper, "The Term Structure of Stock-Bond Covariances." 

These changes are linked with the change in quantitative easing, yet there needs to be more work on why there would be such significant changes in the stock-bond covariance relationships.

 

Momentum across different factors

 

Momentum can be displayed across different forms, and it seems as though each has different forms of persistence: 

• beta
• country 
• factor/style 
• industry 
• stock 

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.

EU fragmentation - the impact on capital markets

Market fragmentation is an issue in the EU. There is not one market for bonds but a set of markets that may move together when there is no crisis, but will then diverge when there is a liquidity issue or a macro shock. The ECB may try to align key bond markets, but there are limits to what it can do without favoring one market over others. This problem is nicely described in the short VOX article, Financial fragmentation as a vulnerability in euro area bond markets.

There is evident fragmentation as outlined by the first and second principal components. The extent of Euro area fragmentation can be measured over time. Although it is lower than during most of the post-GFC period, it is clear that during periods of macroeconomic shocks, fragmentation increases, leading to lower liquidity and greater spread dislocation. It will be hard for the Euro area to produce a safe asset across the EU if there is high fragmentation.   

 

 

EU growth problem - the stall in unification

 

EU growth is below that of the US and China. It is also below most emerging market countries. There is a large productivity gap between the US and the EU, and innovation is slow. The response has been to see papers and discussions of what is going wrong, but very little work on what should be changed with real action.

A new paper, The Constitution of Innovation, discusses the EU problems and focuses on the missed opportunity from not moving to a more integrated market. Surprisingly, the paper suggests that much of the needed change can be done through the current structure. There needs to be greater enforcement actions to improve competition and fewer directives from the EC. A good idea is to allow a 28th regime that provides a pan-European structure for companies that want to grow their business across the entire EU. What is needed is a focus on EU growth within the current rule-making process, rather than a system of rules to protect entrenched interests. Cross-border activity is not a zero-sum game but can lead to gains across all of Europe.

While this is further afield from many of our blog topics, a dynamic EU will be good for world trade and the global economy, whereas the current status quo will only lead to falling living standards. 

FT geopoltical mood index - another way of viewing sentiment

 

The FT has developed a new geopolitical sentiment model by using an LLM that identifies positive and negative stories, which are then weighted by relevance. This model is correlated to some other geopolitical models, but with a weekly frequency, it may provide a better estimate. The intuition is reasonable and consistent with actual events in 2025. For quant, it would be nice to see that this adds value in telling us the direction of market risk premia.

 

The many faces of uncertainty

 

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. 

The AI model used differs by task

 

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 don't look cheap in current environment

 

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. 

 

Alpha and cost containment - The value of AI

 

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:

• information summary tool
• focused search tool 
• quick news analysis tool
• pre-screening tool along with quant analysis 
• simple idea generator
• proprietary prompt tool 

While not a research replacement, AI is not a research adjunct that allows hedge funds to run leaner shops with less costs on junior analyst development. The objective is to make senior analysts more efficient by reducing drudgery. Many firms have spent money on proprietary prompt libraries that can be applied to stock sets to serve as an alternative filtering mechanism. This can be especially powerful when linked with proprietary databases.

Alpha and cost containment - trading costs

 

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 trading costs, 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.

The great equity reset - global dispersion

 

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.