Debiasing decisions - Some simple rules to follow

We all face biases or bring them to decision-making, so it is critical to develop strategies to reduce the risk of bias. The bias problem applies to both quantitative and discretionary decisions. Just because you are using a model does ot mean that you are immune to biases. There should be a checklist to review the potential impact of biased thinking. The paper “A user’s guide to debiasing” does a good job of exploring the basics of reducing biases and improving the quality of judgment in decision-making. Debiasing reduces logical inconsistencies and misperceptions or misjudgments of reality. Debiasing is not the same as gathering more factual informaiton. The objective is to improve decisions given a specific set of information.

Categorizing debiasing methods distinguishes between the person and the task. Improving the person requires training to help the decision-maker overcome their limitations. The second approach is to modify the environment to match the thinking required.

Many of the sources of our biases come from the confusion between system 1, fast and automatic responses, and system 2, which requires slower and more deliberate thinking. It is important to distinguish between narrow thinking and shallow thinking. Narrow thinking focuses attention on a single category of objectives and crowds out the ability to identify other alternative objectives. Shallow thinking devotes too little effort to the required task. For any decision, there has to be a level of readiness to perform better decision-making. 

The person can be modified through better education that generates more alternatives, tempers optimism, focuses on improving judgmental accuracy, and assesses uncertainty. Decision-makers can use defaults to get closer to making better decisions through regular processing, nudges to induce reflection through prompts or planned interruptions, and the formation of a set of active choices. If this can be done for the individual, a similar process can be applied to the organization. 

AI - all the time for business

 

You have to love some of these charts that bundle ideas that seem to be unrelated. In this case, we find that the word "AI" is used more often on earnings calss than the word "earnings". We should expect that the word earnings is fairly static on earnings calls, but the explosion of the word AI suggests that this is the what management and investors have as their main focus. For management, using the the word AI convesy what they are doing new to address issues of tehcnology and effciency. Investors want to know how firms are using new technology. The question is whether AI will actually lead to the efficiency gains that many expect.

Decision focused learning and portfolio selection

One of the more interesting papers on portfolio management links prediction with optimization. Rather than a two-step process, the authors focus on how optimization should be managed alongside prediction. The paper, “Return Prediction for Mean-Variance Portfolio Selection: How Decision-Focused Learning Shapes Forecasting Models”, provides insights on how decision-focused learning (DFL) can be used to improve overall portfolio returns. 

The usual process for building a portfolio is through mean-variance optimization. This process is two-staged. It is a predict-then-optimize method. In the first stage, a set of expected returns is generated, and in the second stage, the optimization selects the set of assets that maximizes return subject to a set of constraints. The problem with MVO has been studied extensively. The issue is that if the expected returns are poorly defined, the MVO will choose the “best" returns, yet the portfolio may be optimized on the forecast errors. The classic answer from Markowitz is that estimating expected returns is the investor’s job, not the optimizer’s.

The DFL framework will integrate the prediction and optimization to improve the outcomes. The issue is whether the MSE of forecasts for each asset is treated independently and equally or integrated with asset correlations. With DFL, the optimization accounts for prediction errors when finding the weights via a loss or regret function.

It is found that DFL identifies fewer assets than a standard MVO and exhibits a bias toward positively returning assets, given the optimization for a long-only portfolio. Still, it offers a better way to optimize a portfolio.

Pric e action by Rhetoric: Trump and Oil

 

The FT chart provides an interesting insight into the drivers of the current oil market. Of course, oil is driven by the events of the Iran War, yet the impact on oil prices is more subtle. There seems to be a direct connection between President Trump's comments and the surge or decline in oil prices.  When Trump makes comments that escalate tensions, usually before or on a weekend, there is a surge in prices, followed by comments that de-escalate tensions. The uncertainty in prices is associated with rhetoric rather than specific action surrounding Iran. The sample is small yet there may be a link between comments and oil price action. 

Dual role of prices - endogenous and exogenous events

 

...dual role of prices. By “dual role”, we mean that prices not only reflect the underlying economic fundamentals, they are also an imperative to action. That is, prices induce actions on the part of the economic agents. If some actions are the consequence of binding constraints and exert harmful spillover effects on others, then price changes can bring about amplifying spillover effects that disrupt the smooth working of the market, and sometimes shut down the market completely.

Endogenous Extreme Events and the Dual Role of Prices

It is important to appreciate the dual role of prices, although this is a subtle concept. Prices first reflect exogenous events as markets reflect new information that impacts expectations. If inflation increases, for example, there will be a reaction in prices as investors adjust their expectations. However, investor will not just react to new information and adjust their portfolios. They will also adjust or take action when they see prices change. 

The reaction to price movements may be independent of the change in exogenous information. The reaction could be due to uncertainty about what information is being displayed in the price. A reaction to price behavior independent of exogenous information is an endogenous reaction. Investors react to prices rather than to any other information. This can lead to extremes and explain most of the price movement during the day. One of the most important tasks for an investor is understanding the difference between these price moves and appreciating the action that stems from the distinction between exogenous and endogenous moves. 

Neoliberals and the fight between dominium and imperium

 Quin Slobodian’s book Globalists: The End of Empire and the Birth of NeoLiberalism was written before the pandemic and before the uproar about Davos and the World Economic Forum. Yet it is truly relevant to anyone considering the current world order between some form of global government and nationalism. Globalist does a good job of describing the origins and rise of neoliberalism and helps readers understand the differences from national movements or a national focus. 

What I found most interesting was the focus on differences between dominium and imperium capitalism. Dominium refers to the rights of owners to control their property, which is a key feature of capitalist private property. The imperium or public power refers to the sovereign power of the state to rule, tax, and regulate. The intersection between these two forms of capitalism is the crux of how neoliberalism bears on the issue.  

For Slobodian, neopliberalism is not a simple version of laissez-faire capitalism, but a project to, as he terms it, “encase” the market within legal and institutional frameworks to ensure that dominium is protected over imperium. Global capital is protected from democratic pressures, nationalism, and social redistribution that seek to take property away from owners. Thus, dominium is given a higher priority over imperium, and the right to rule by nations is constrained by international institutions. Capitalism is not left to self-regulate but needs oversight and active management to protect the dominium. Neoliberals believe there is a need to constrain democracy in a pure form that will subvert property rights in an effort to redistribute wealth. Neoliberals want an active legal framework to protect property rights and allow the freedom of capital to move and be controlled by owners.  

Sorkin's 1929 - A tale of personality

 

I finally got around to reading Andrew Ross Sorkin’s 1929: Inside the Greatest Crash in Wall Street History - and How it Shattered a Nation. His narrative style is compelling for those who do not want to read dry financial history. The characters come alive with his writing, but I did not come away with any new insights into the stock market crash. There were signs before the October crash, so it was not a total surprise. Excess leverage, margins that were too low, excess greed, and herd mentality, but we already knew that. While I liked reading about the characters, I kept asking why we didn’t hear more about the central bankers. What about some of the surviving brokers? How about some of the businessmen outside of Wall Street? 

Do I better understand the psychology of bubbles and the 1929 crash? I don't think so. Perhaps I am jaded by all my reading on the topic, but did I learn anything new beyond the fact that the same tropes of greed and leverage are always with us?

What does 1929 tell us about markets today? Not much. There is significant leverage, crowd behavior, misinformation on policy choices, and fear of taking action. Perhaps that is the message. Things don't change.

Words have uncertainty - The enemy of precision for investors

 

With the evolution of LLM and natural language processing, there is a closer connection between the discretionary and quantitative world, yet the two are not perfectly linked. There is uncertainty in both worlds. For the quant, there is model and parameter uncertainty. For the discretionary trader or non-quant, the problem is the precision in words. What you say may not be precise by you as the sender and by the receiver. 

We have discussed this issue of precision in language in the past, yet most investors still seem to be at risk from word uncertainty. Just think of all the Twitter words and Substack posts driven by language. Are these words given any quality control? If you look at the research, the answer is no. If you look at the range of meaning for these words of estimative probability, you will have to agree that there is ambiguity concerning the words often used by any decision-maker. Ask for specifics. Ask for the actual probabilities. Close the range of uncertainty.

See "Variability in the interpretation of probability phrases used in Dutch news articles — a risk for miscommunication."

Words of Estimative Probability (WEP) - Words matter and poor precision leads to decision failures

Imprecise language - The big problem with investment analysis

Language, perception, and numbers - The translation problem

Sherman Kent - the godfather of precision in forecasting language

Sherman Kent - My guy for precision in forecasting language

Sherman Kent - The analyst's analyst - Be precise with your forecasts

Sherman Kent and the problem between warner and warnee

Risk management is in the preparation

Don’t panic. Yes, it is time to start to panic. But what is panic? It is a forced action in response to the threat of uncertainty. An investor does not know what to do. There is no precise plan of action because the environment and market behavior are unclear and highly dynamic. Market actions, as reflected in prices, are unclear, and players' actions within the market cannot be determined. 

The only way to deal with a panic is through careful planning. But how can you plan for what may not have been anticipated? The only solution is to run scenarios or thought experiments on possible reasons for panic and then work through possible responses. This is not easy, and there is no reason you will get the drivers of panic right, but by conducting these exercises, you will identify common themes for addressing the unknown. 

Facts, information, and knowledge - Not all the same

 

There are no facts, only interpretations - Friedrich Nietzsche. 

What is a fact? A snippet of information. Something you did not know. A fact is unprocessed information. It does not have any context. Information is processed facts. There is some meaning or context given to the fact. Knowledge will be the applied understanding of that information or fact. Anyone can spout facts with no meaning but facts are usually used in an argument. Facts are used to persuade. Facts, when used as a tool of persuasion, need to be turned into information through knowledge.

Are facts reality? Yes. Facts do not have feelings. Yet knowing facts is not useful without processing and some knowledge; that is where most get into trouble. The disagreement is not with the facts but with the interpretation.

More on the Sharpe ratio - Look for its stability

 

A recent paper introduces a new concept to help investors assess managers and trading strategies: the Sharpe Stability Ratio (SSR). See “The Sharpe Stability Ratio:Temporal Consistency of Risk-Adjusted Performance”. This performance metric accounts for the temporal consistency of risk-adjusted returns. An investor, if given two Sharpe ratios with the same value, should choose the one that has more stable characteristics. You should like the persistent Sharpe ratio. This paper treats the Sharpe ratio as a rolling performance measure. It defines stability as the ratio of the mean rolling performance to the heteroskedasticity- and autocorrelation-consistent (HAC) standard deviation. 

Using the time-series approach can help analyze point-in-time SR or the probabilistic Sharpe ratio (PSR). Given strong serial correlation in the Sharpe ratio, arising from the rolling average and return consistency, the HAC correction provides a better measure than simply scaling by the standard deviation.

The important issue for investors is to look at persistence and consistency with strategies. This may be the true hallmark of skill.

Uncertainty about signals and price impact

Every model will have signal noise or uncertainty, and every model will have imprecise impact uncertainty from trading. This is a certainty because any training set will have to be imperfect. Hence, we should expect performance problems due to parameter uncertainty. Modelers should take this into account in their work. 

First, the model’s Sharpe ratio will be lower than expected from what is generated from the training set. There will be estimation error and model misspecification. Second, the model will affect transaction costs, with the impact being greater for smaller and less liquid stocks. Notably, weak signals will have less impact on trading costs, while strong signals will have a greater impact on trading costs. See “Trading with Uncertainty about signals and price impact”.

How do you solve these problems? One way to solve the problem is to define a reference model and then assess the costs associated with variations from it. The uncertainty can be managed to lock in a reasonable Sharpe around the reference level. The math in this paper is not easy, but the key is to be aware of the problem and to place bounds on what is possible.  

Beta is sensitive to the level of risk aversion

We have just written about the impact of changing beta. See There is no one beta - It changes across regimes. This is not new information, but researchers have taken a deeper look at the issue and have found some interesting relationships. Another paper that has found an interesting beta relationship is “Risk Appetite and (Mis)Pricing”. It has examined some beta portfolios conditional on high and low risk aversion. The risk aversion index was developed in prior research and is not new, but it has not been used in this type of study.

The results are strong and very thought-provoking. In a high-risk aversion environment, the researchers find a positive relationship between beta and returns. In contrast, in a low-risk aversion environment, there is a slight negative relationship between beta and returns. This can help explain why we do not find the usual CAPM relationship in the data. When risk aversion is high, the market risk premium idominatesother factors that may create distortions, such as sentiment. When risk aversion is low, mispricing becomes more important, and there is a positive relationship with a positive intercept. The study carefully examines the evidence and finds that risk aversion plays a key role in determining whether the CAPM holds or fails. When aversion to risk is low, sentiment-driven mispricing will be the key driver of returns and will offset the market risk premium effect.

 

"Look at your fish" - same with market prices

There is importance in looking closely at the things we study. There is an old parable from Samuel Scudder called "Look at your fish". The story can best be described in "The Student, the Fish, and Agassiz," a 19th-century educational parable in which Harvard professor Louis Agassiz forces a student to study a dead fish for days, using only observation and sketching, to teach that true knowledge comes from intense, firsthand examination. It emphasizes finding "general laws" through detail.

The same process can be applied to markets. We can start with price charts. Look at your charts. Look at the prices, but just don't look once; look deeply into what the markets may be saying. Then, after looking at the prices, look at the news surrounding the prices. This does not mean that everything has to have a pattern, but for any analysis, the first order of business is looking at the data. 

See before starting to analyze. 

There is no one beta - It changes across regimes

 

An interesting but simple paper, “Your Beta Is Wrong Regime-Dependent Alpha & Beta for Major Asset Classes”, explores the issue of regime-dependent beta. Your beta is not stationary, so alpha will not be stable but will move with the regime. This does not mean that you should calculate beta on a rolling basis; assume that beta is regime-dependent, and when the regime changes, so does the beta. Below are two examples of significant changes in beta. One shows silver, and the other is for Alphabet, one of the classic Mag 7 stocks. 

Do not assume there is one beta for any asset. This may seem obvious, but when seen in a distribution, the numbers are stark.

Think of global equity markets as a network

 We have been spending more time thinking about markets as networks or clusters. Don’t think about asset returns in isolation, but through the connections across markets and regions. Some of the latest work in this is presented in the paper, “Clustred Network Connectedness: A New Measurment Framework with Applicaitons to Global Eqiuty Markets” by Buchwalter, Diebold, and Tilmaz. 

These authors have been working on the network process for asset returns through variance decomposition of VAR models. From these models, the authors have been able to distinguish causality from and to markets across a wide set of markets. Their latest work on global equity markets seeks to address econometric issues arising from the decomposition method. This process of decomposition will provide a different narrative but will also answer questions about whether there is contagion or just co-movement across the network. The graph above shows the traditional method for forming the clustered identification. The graph below looks at the same data, accounting for groupings within the network after accounting for generalized identification.

Note that in the clustered identification, the US equity market serve as the center of netwrok behavior while the generalized idienificaiton which accoutns for correlation within groupinsg of the 16 equity markets studied, shows the high connection that is the focus of the EU cluster. Both provide interesting interpretations for how equity markets are connected. 

 

Good News - Bad News - overreaction to the bad news

There is good news and bad news that comes to the markets through new information that impacts expectations. News will have a differential impact, and investors should always be ready for it. Good news will drive prices higher, and of course, bad news will have the opposite effect, yet news will have a differential impact. Good news will usually be met with underreaction, while bad news, at the extreme, will be met with overreaction. To put it simply, the bad news forces investors to sell, and there has to be a buyer on the other side of the trade. To find the buyer, the markets will have to overreact to provide the buyer with a premium for considering the higher risk posed by the bad news. In the case of good news, there is no forced selling that requires new buyers. There will be a reaction, and there may be some extreme buying, but the buying excess is driven by a supply shortage, not by a need for a premium to induce buyers. 

Oil shocks and war - This could be different

UBS provides an interesting chart on the impact of war on oil prices. As expected, there will be a positive price shock, but it usually returns to normal after 4-5 months. Call this a fear factor. There will be some hoarding at the beginning of the war to protect inventories. Once the worst is over and the disruption is viewed as manageable, the price increase will be reversed. Yet, you cannot extrapolate from this evidence that the current situation will be normal. First, there is no reason to expect a return to normalcy. We only know that after the fact. Second, a disruption ot the Middle East is different from a war in other regions. If infrastructure is lost due to the destruction of refining capacity, there cannot be a quick adjustment. Oil can be pumped, but without refining, a "soft target" there will not be any easy way to create the end product needed by consumers. Capital expenditure for refining is costly and long-term, unlike the sinking of a tanker or the closing of a strait. The focus for any oil shock should be centered on what is happening to infrastructure. 

 

Retail investors love hard to value stiocks

 

How do retail investors behave? I wish I knew. It seems that they have an increasing impact on some markets, but where is the focus? A paper titled "The Retail Habitat" seeks to answer this question and finds that retail investors prefer to trade hard-to-value stocks. Stocks with a lot of retail trading have more intangible capital, longer-duration cash flows, and are more likely to be mispriced. So why do retail investors focus on the harder-to-value names? The authors do not fully explore this critical issue. They just identify the stocks that seem to have more retail focus. I would suggets that that retail traders focus on big bets, the lottery tickets. The lottery ticket names, of course, will be stocks that can possibly produce large gains.

Asset allocation is always about the stock-bond correlation

The primary asset allocation decision is based on the relationship between stocks and bonds. If the correlation is negative, there is a significant benefit to holding bonds. If the correlation is rising and positive, the bond diversification benefit is limited, and it is time to consider other alternatives. The trend is not favorable to bonds. The long-term trend is higher, though the recent trend has returned to negative territory.

Meaaurement uncertainty is real

 

Macro investors have to deal with measurement error within government data. This is the second year in which we have seen major revisions to labor data. Last year, a major seasonal adjustment affected employment numbers. This year, there has been a significant change due to benchmark revisions. The two charts below provide evidence of what has happened to the labor data. The first chart shows that nonfarm payrolls have been revised down by approximately 1 million jobs. In the second chart, you can see the monthly change.

This new data provides further evidence of the K-shaped economy. Some of the revisions are based on demographics, so they do not translate into higher unemployment, but they do create a more confusing picture of the macroeconomic environment, which will impact bond returns in particular. 

I use macro signals to improve my trend- and price-based signals, but when macro data are noisier, the value added by macro analysis diminishes.

Innovation, industrial policy and controlling the fate of nations

 

How Progress Ends: Technology, Innovation, and the Fate of Nations, by Carl Benedikt Frey, is one of the most important economic and policy books of the last year. It is especially relevant given the discussion following the Draghi Report that has influenced European thinking about the need for change and innovation. Frey makes an important argument that economic progress is a combination of innovation, which often occurs in a decentralized environment, and the implementation of scale through bureaucracy. He develops this argument through close observation of 1,000 years of history across different economic systems around the globe. Technological progress and economic growth are inevitable. There needs to be a special combination between technology and bureaucracy.

Frey draws a distinction between technological innovation that often occurs in a decentralized environment, where experimentation and exploration of new ideas take place. The technology then has to be put to use, which requires a bureaucracy or centralization to effectively employ it. Some countries did not get the technology right because bureaucracy stifled innovation. In contrast, other countries lacked technological advances but were able to grow by harnessing their bureaucracy to build on others’ innovations. 

The EU needs new technology, but that is not enough. There also needs to be a bureaucracy that not only gets out of the way but also uses its power to allow for economies of scale. 

The complexity of trade - not always simple

 

Those imports are taking away good jobs. We have to impose tariffs to stop the invasion of foreign goods to our shores. These comments all sound good, but the reality is more complex. It is usually always that way. One chart that caught my attention showed that many good imports are intermediate goods rather than final products. We take in components and assemble the final product. As an intermediary good, a tariff will increase the price of the final good. The answer could be to produce these intermediate goods in the US, but they are often specialized and may be hard to produce at low cost. Some industries are very dependent on these imports, and they do not fit the usual trade story being used to justify tariffs. Think about complexities and realize most problems are not easy yes or no answers. 

The relative US - EM inflation story

If I asked the simple question, "Is EM inflation higher than US inflation?", most would say that EM inflation is higher. It has been, and always will be, yet the reality is different. US inflation has been higher than EM for almost 5 years. That is right. The Fed manages an inflation regime that is worse than that of the combined EM economies, and the gap is widening. Do you have to wonder why the dollar is falling? 

What drives performance of machine learning

 

A recent paper, "What drives the performance of machine learning factor strategies?" seeks to disentangle two key ingredients in modeling: expanding the dataset and allowing for flexible functional forms. Now, as expected, as you move closer to a realistic setting, you find that the value of both deteriorates. However, this research finds that the value of an expanded dataset is more persistent than the functional form employed. While many may think that machine learning is a form of holy grail for investing, the reality is that real-world constraints and transaction costs are key drivers of performance. Reality indicates that adding nonlinear complexity does not add value, whereas being non-sparse is beneficial. 

Risk appetite is always worth following - Currently, normal

We have been following the Wilmot Risk Appetite Index for decades, when it was first called the Credit Suisse RAI, as developed by Jonathan Wilmot, who is now in private practice. It will be provided to investors through HedgeIndex LLC in the coming weeks, along with their broad set of alternative indexes. The basic construction is provided below. 

The current reading indicates that risk appetite is within the normal range, though it is rising. There may be individual assets with extreme values, but the RAI does not indicate a general market extreme.

 

China growth coming in lower

 

ChinaNow is a real-time, alternative measure of Chinese real GDP designed to capture business cycle dynamics in China that are not readily observable in official GDP data.  The authors employ a dynamic factor model (DFM) that draws on a broad set of high-frequency indicators informative about the Chinese economy and its business cycle.

Many are skeptical of the official stats coming out of China. In addition to methodological issues, these GDP data are politically sensitive; therefore, it is important to identify alternative growth indicators. The ChinaNow index appears to closely match official figures but provides more timely and higher-quality data, particularly during periods of slowdown. 

If there is a slowdown in China, there are stronger incentives to push exports at lower prices to keep factories humming. Hence, China's domestic growth has a strong impact on the rest of the world. 

Asset allocation of university endowments

 

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A recent survey from NACUBO-Commonfund Study of Endowments provides interesting insights into the asset allocations for university endowments. Clearly, the larger funds have allocated more to alternatives and private assets and less to bonds, while the smaller funds appear to follow a more traditional allocation.

Nevertheless, the performance of endowments is not markedly different from that of a 60/40 portfolio. In fact, over three years, the endowments underperformed the classic mix, and over ten years, only the largest endowments seem to have beaten the simple benchmark. Does that mean 60/40 is better? No, but it does indicate that adding alternatives should be done carefully, with an eye to how they may compare to a simple approach.

King dollar - can it be toppled

 

I was expecting a standard book on the history of the dollar’s rise, and the reasons it should be a dominant currency, as well as why it will fall. I have read many articles on this topic and thought I would get more of the same with King Dollar: The past and future of the world’s dominant currency. I was surprised by something different. While I don’t always like the breezy approach of new reporters to complex economic topics, I found this an interesting read. 

Blustein takes the reader on a different ride, focusing on the plumbing of banking through SWIFT messages and CHIPS. It provides a unique look at the history of clearing and the ascent of the dollar that many monetary theorists avoid. More importantly, the author focuses on how the anti-money laundering efforts of the US Treasury have an important impact on banking and the use of the dollar. There is a vast amount of behind-the-scenes efforts to control the flow of dollars for the benefit of the world economy, yet this interference has a dark side that leads some state actors to avoid surveillance. The attempt to restrict Russia from global banking and trade shows how regulation and oversight can affect the flow of money. The author also reviews the work to develop CBDCs, central bank digital currencies.

As alluded to in the final chapter, for the "king currency" having a throne comes with great responsibility.

 

Buffered ETF - the product of 2026?

 

For better or worse, the top ETF product for 2026 may be the buffered ETF. At a very general level, a buffered ETF, is one that provides downside protection versus some referenced index. That is, there is a buffer on the losses associated with the ETF. However, in exchange for this protection, the investor wil give-up some of the upside. The market has grown from approximately $5 billion in 2020 to a $90 billion AUM as of the end of 2025.

This type of protection can be achieved by managing the portfolio. There is a cost with having the ETF provide you protection, yet there always seems to be a need for these products. First, the mechanism for offering this protection is systematic and removes emotion from any allocation decision. Second, the timing of the protection is well-defined. Yet the payoff structure is complex and comprises multiple option positions. There are parts of the distribution that are protected or buffered, parts that are exposed to risk, and an upside portion that is capped. The folks at Alpha Architect provide a good overview of the problem. 

Realize that if you want protection from downside risk, there is no free lunch. If you move to cash, you lose the upside. If you buy derivatives, there is a cost. If you allocate to alternatives, you are at the mercy of their return profile. Choose your protection wisely.

What are the big risks of 2026?

Each year, the World Economic Forum provides a global risk perception survey. This is one of the most comprehensive risk surveys, and it provides useful context on what business leaders are thinking about potential disruptions to the global economy. The benefit of this survey is that it provides overall rankings for each year and measures change from the prior year.

By far, the current global risk landscape identifies geoeconomic confrontation as the top risk, followed by state-based conflict. Now, for anyone reading the news, this seems very logical, but it is sobering to see it in print. It has moved up from the eighth spot last year to number one. Along with confrontation, economic downturns, inflation, and bubbles pose key risks. These risks are important in both the short and long term, although climate change is still considered a long-term risk.

What does this mean? First, it is hard to believe that risky assets will continue on the current path with these views. Second, as noted in a recent post, Bonds are not always a safe asset; holding bonds may not provide the desired safety during an armed conflict or war.