Investing

Are You A Business, Man? The Surprising Benefits Of A Sole Prop and IRA

James Bailey1 Comment

I never thought of myself as a businessman- until 2015 when the IRS told me I was, and that I therefore needed to pay them more money to cover the self-employment tax. Naturally I was confused and angry about this at first, but in the long run it turns out they were doing me a favor.

If you make a tiny amount of 1099-MISC or 1099-NEC income on occasion, the IRS is probably* fine with characterizing this as ordinary income from a hobby. But if you earn 1099 income at all regularly, they will likely want to characterize you as a business, and want you to pay a self-employment tax similar to the payroll tax that W2 employees pay (though it will look higher to you, since you will pay both the employee and employer halves of the tax). If you make an intermediate amount of 1099 income, you might have the choice of whether to call this hobby income or business income; I had thought it would be better to avoid the complications and extra taxes of being a business, but it turns out that being a business unlocks new opportunities for deductions than can far outweigh the self-employment tax.

For example, a home office, business-related travel expenses, and advertising expenses can be deductible. For a writer, this could cover conferences, website expenses, computers, and much more. It also means you can start a SEP IRA– in addition to a personal IRA if you like. This alone could allow you to deduct thousands of dollars in income per year (technically up to $69k if you make at least $276,000 per year in business income, though if you make that much, you’re the one who should be giving me advice). The SEP IRA has the advantage over a personal IRA of a much higher income limit and, potentially a higher contribution limit, though again the beauty is that you don’t have to choose- you can just do both.

While this post is mainly about business, I also think regular IRAs might still be underrated. I didn’t start one until 2022, but I should have done it much earlier. First I thought I was too poor (low income, then higher income but with student loans to pay off first), then I thought I was too rich (above the income limits). It turns out though that you can still start a personal IRA even when you are above the income limits- it just means you only get one tax benefit instead of two, but that one tax benefit is still pretty good.

Every IRA has the benefit of investments growing tax-free; if you meet the income limits then IRAs get the additional benefit of avoiding income taxes either when you put the money in (for traditional) or when you pull it out (for Roth). But even if you “only” get the benefit of tax free growth, that can still be a huge monetary benefit depending on your investment strategy. It is also a big time benefit- every taxable brokerage account means at least one** extra tax form to deal with every year, while an IRA account avoids this.

Another great benefit to IRAs (SEP or regular) is that you can still start one now and make contributions for the 2024 tax year. I was just doing my taxes and kicking myself for not doing some things differently back in 2024 when it would have helped; but IRAs are like a form of time travel where you can still go back and fix things, at least until April 15th.

*Disclaimer- Not official tax advice, I’m not an accountant, I’m just a 37 year old guy with lifetime 1-1-1 record against the IRS. Three times they have told me I owed them more than I paid on a tax return. Once I won (I told them I owed nothing and explained why, and they agreed). Once I lost (I told them oh shit, you’re right and paid them). For the story I started this post with, I call it a draw (they told me I owed them X, I told them I owed nothing and explained why, then they told me I actually owed them 1/3X and I just paid it).

**More than one if like me you accidentally invest in a partnership and as a result get a K-1 on top of the usual 1099-DIV for that overall brokerage account

HT: Trinette McGoon

Perspective: This Stock Correction Fear, Too, Will Pass

Scott Buchanan1 Comment

For what it’s worth, I will pass along a couple of points from an optimistic take on the current stock market pullback, by Seeking Alpha author Dividend Sensei. The article is “History Says Shut Up And Buy: 12 Hyper-Growth Blue Chips To Buy Right Now”. His thesis is that corrections come and go as specific fears come and go, but tech stocks only keep going up, so now is a good time to buy.

History seems to be on his side. Below is a 25-year plot of the NASDAQ 100 fund QQQ. It is true that on a really long scale, any significant dip would have been a good buying opportunity. And the run-up since 2016 has been astonishing.  $10,000 invested then would be about $50,000 now. I find it sobering, however, that (just going by eyeball) it took about fourteen years for QQQ to regain its 2000 peak. That might be longer than most investors want to wait. And in the shorter term, these tech stocks lost some 80% of their market value between 2000 and 2002, and revisited that low in 2008. We can look back now from decades later and call this a “dip”, but at the time it felt like an endless investment nightmare.

(I should add that the 2000 peak pricing was not supported by appreciable cash earnings, but by breathless hype about this new thing called the “internet” that was going to change EVERYTHING. This past year has seen similar hyperventilation over AI, but in contrast to 2000, now the big tech firms make ginormous gobs of money, and gobs more each year. So maybe it really is different this time…)

QQQ total return since March 1, 1999; % scale. From Seeking Alpha.

The Psychology of Market Corrections

The author pointed out that every correction is based on some deep fear, and eventually that fear dissipates. I thought this table he showed of the fear factors involved in the 30 or so stock market pullbacks since the March 2009 low was interesting and instructive:

The type here may be hard to read, so I will repeat here the two most recent “fears” listed, both from 2024:

March 28-Apr 19 (5.9% drop): “Stubborn Inflation, Fed Pushing Back Rate Cuts, Iran/Israel Conflict”

July 16-Aug 8 (9.7% drop): “Disappointing earnings results, Recession Fears, Fed Behind Curve”

These are recent enough that any market-engaged reader here will resonate with these concerns which loomed so large at the time. And yet, the collective market shrugged them all off to post a robust 21% gain for all of 2024.

Where do we go from here? I have no idea. As of writing this Tuesday morning, we seem to be bumping along at a level 2-3% higher in QQQ than the lows last week, but still 10-11 % lower than a month ago. This has brought it to levels of about late September, 2024. If I look at a five-year log plot and draw an eyeball-fit straight line through it all, it seems like prices went above that line for Nov-early Feb, in a burst of post-election enthusiasm, but have now come back to the trendline. Barring some macro or geopolitical disaster, therefore, one might expect QQQ to trend 10-15 % higher in the next twelve months (with a standard deviation of another 10% or so around the trendline). But as old-time Yankees catcher Yogi Berra said, “It’s tough to make predictions, especially about the future.”

Disclaimer: Nothing here should be considered advice to buy or sell any security.

Michigan Consumer Surveys: Individual-Response Data

James BaileyLeave a comment

I’ve now posted individual-level responses to the 1978-2025 Michigan Consumer Surveys to Kaggle in CSV and Stata formats. The University of Michigan’s Consumer Surveys are a widely followed source for data on consumer confidence and inflation expectations:

Their official site is good if you just want summary tables or charts like this:

But what if you want detailed crosstabs to see how sentiment differs for different groups, or microdata so that you can run regressions? With enough clicks you can get this from what UMich calls their “cross-section archive“. But it is pretty hidden, my student looking into this thought they just didn’t offer individual-level data; and even once you get their data, it is in an unlabelled CSV file with hard-to-understand variable names and codes. So I wanted to make it clear that the full data with all responses for all years is available, and if you use my Stata version it is even reasonably easy to understand (the code I adapted for labelling it is on OSF). Then you can run your regressions, or make charts like this:

The College-Only Covid Recovery

If you’re new here, a reminder that you can find other cleaned-up versions of popular datasets on my data page.

Is This a Stock Market Correction or a Bear Market?

Scott BuchananLeave a comment

As of the market open today, tech stocks (e.g. the NASDAQ 100 fund QQQ) are down more than 10% from their recent highs. The broader S&P 500 fund SPY is down about 8%. Hands are wringing…what does it all mean?

By applying standard definitions, we can know exactly what it means:

A pullback is a market drop of 5-10% and is very short term.  It is a dip from a recent high during an ongoing bull market while upward momentum is still intact, and is a normal adjustment to a market cycle.

The market is in “correction phase” after a drop between 10-20% and can last a few months. These moves are typically met with higher volatility.  Corrections can be violent as investors’ fear levels rise and panic selling may hit the market.

Real time news and social media can intensify this fear as investors may follow the herd mentality.   The average market correction lasts anywhere between two and four months and is frequently accompanied by adverse market conditions.  However, corrections are often seen as ideal times to buy high-value stocks at discounted prices.

So, technically, the S&P has experienced a “pullback”, while the NASDAQ 100 has undergone a “correction”. Just to round out the infernal trinity of market moves with a definition of a “bear market”:

A bear market occurs after a drop of 20+% over at least a two-month time frame.  In a bear market, investor confidence has been shattered and many investors will sell their stocks for fear of further losses.  Trading activity tends to decrease as do dividend yields.

Bear markets tend to become vicious cycles when rallies are sold and not bought This happened in 2000 and 2007 and can typically be seen on charts as the market makes lower lows and lower highs.  Bear markets tend to occur in the contraction phase of the business cycle and last, on average, approximately 16 months.

You don’t know if you are really in a bear market until things get really bad, at which point it is probably too late to sell. (Amateurs get discouraged and sell AFTER stocks have dropped, which is why the average investor does appreciably worse than the accounts of dead people where stocks just sit there without being traded). When stocks recover at least 20% following a bear market over at least a two-month period, that is defined as the start of a new bull market regime.

Having a correction (i.e. 10-20% dip) in the middle of a bull market year is pretty normal. Although whole-year market returns have been positive for 34 out of the past 45 years, the typical year experiences a correction averaging 14%.

None of this vocabulary clarification answers the practical question of how bad will the current pullback/correction get? As usual, I read argument on both sides. The bears are saying (a) what they have been saying since 2018 or so, that the market is unrealistically overvalued, and (b) the macroeconomic world is about to fall apart, which they have also been saying for years. This time may be different, with the new administration’s erratic policies, but history shows that so far, the market is not much correlated to who is in the West Wing.

The bulls are saying (a) the market values did get run up unrealistically after the election and with AI hype, so the current pullback is just a healthy reset to a level for resuming further market growth, and (b) despite negative talking, the actual numbers show decent employment and GDP, so macro is OK (and it is very rare to have an actual bear market absent a serious bad macroeconomic driver).

If I really knew the answer here, I would be writing this from my private Caribbean island. But I’ll share how I am playing it. For the past 15 years or so, it has nearly always worked well to buy in after a say 10% correction. What seemed so gut-wrenching and scary at the time almost always turns into just a blip on the endlessly rising market charts in hindsight.

I had set aside some “dry powder” funds specifically to take advantage of buying opportunities like now. So, I am manfully mastering my fears and buying small amounts every couple days of 2X levered funds like SSO and QLD. (See here for discussion of such funds, they go up or down $2 for every $1 the underlying S&P or NASDAQ go up or down, so it’s kind of like being able to buy twice as much stock for the same dollar amount. But as usual, caveat emptor).

But I am not going all-in on any particular day. It is always frustrating to miss buying right at the bottom, but nobody rings a bell there, either. I have searing memories of March 2020 and of 2008 when just when you thought the bottom was in, it dropped out the next day or week.

Disclaimer: Nothing here should be considered advice to buy or sell any security.

Trump’s Economic Policy Uncertainty

Zachary Bartsch3 Comments

I was on a panel of economists last night at an event titled “The Economic Consequences of President Trump”. We each gave a 5-minute summary from our area of expertise and then opened up the floor for questions.  This is a truncated summary of my talk. Since the panel included an investor, two industry economists, and another macro economist, I wanted to discuss something that was distinct from their topics. I’ve published a paper and refereed many articles concerning economic policy uncertainty (EPU) and asset volatility. I wanted to look at the data concerning President Trump – especially in contrast to Presidents Obama and Biden.

EPU matters because uncertainty can cause firms and individuals to delay investment and hiring decisions. Greater uncertainty can also cause divergent views concerning forecasted firm profitability. The result is that asset prices tend to become more volatile when EPU rises. One difficulty is that uncertainty occurs in our heads and concerns our beliefs, making it hard to measure. We try to get at it by measuring how often news media articles include the terms related to uncertainty, policy, and the economy. Since news content tends to report what is interesting, relevant, or salient to customers, there’s good reason to think that the EPU index is a decent proxy.

Using the Obama years as a baseline, the figure below simply charts out EPU. It was relatively low during Trump’s first term and then it was higher during Biden’s term – even after accounting for the Covid spike. The sharp increase toward the end is after Trump won the 2024 election. The EPU series conflicts with my perception of social media and media generally. My experience was that the media was far more attentive to the uncertainty that Trump caused. But, it may just be that the media outlets had plenty to report on rather than it being particularly indicative of EPU. After all, if the president exercises his power, then there is a certain swift decisiveness to it.

But if we look at a couple of particular policy areas, Trump’s administration faired worse. Specifically, Trump caused a ruckus concerning trade policy and immigration. Remember when Biden continued the aggressive trade policy that Trump had adopted? That’s consistent with lower EPU. Similarly, Biden made the immigration process much easier and faster while Trump’s deportation haranguing results in a somewhat stochastic means by which people are deported.  Again, that spike at the end is after Trump won the 2024 election.

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Why Low Returns Are Predicted for Stocks Over the Next Decade

Scott Buchanan2 Comments

I saw this scary-looking graphic of S&P 500 returns versus price/earnings (P/E) ratios a couple of days ago:

JPMorgan

The left-hand side shows that there is very little correlation between the current forward P/E ratio and the returns in the next year; as we have seen in the past few years, and canonically in say 1995-1999, market euphoria can commonly carry over from one year to the next. (See here for discussion of momentum effect in stock prices). So, on this basis, the current sky-high P/E should give us no concern about returns in the next year.

However, the right-hand side is sobering. It shows a very strong tendency for poor ten-year returns if the current P/E is high. In fact, this chart suggests a ten-year return of near zero, starting with the current market pricing. Various financial institutions are likewise forecasting a decade of muted returns [1].

The classic optimistic-but-naïve response to unwelcome facts like these is to argue, “But this time it’s different.” I am old enough to remember those claims circa 1999-2000 as P/E’s soared to ridiculous heights. Back then, it was “The internet will change EVERYTHING!”.  By that, the optimists meant that within a very few years, tech companies would find ways to make huge and ever-growing profits from the internet. Although the internet steadily became a more important part of life, the rapid, huge monetization did not happen, and so the stock market crashed in 2000 and took around ten years to recover.

A big reason for the lack of early monetization was the lack of exclusive “moats” around the early internet businesses. Pets.com was doomed from the start, because anyone could also slap together a competing site to sell dog food over the internet. The companies that are now reaping huge profits from the internet are those like Google and Meta (Facebook) and Amazon that have established quasi-monopolies in their niches.

The current mantra is, “Artificial intelligence will change EVERYTHING!” It is interesting to note that the same challenge to monetization is evident. ChatGPT cannot make a profit because customers are not willing to pay big for its chatbot, when there are multiple competing chatbots giving away their services for practically free. Again, no moat, at least at this level of AI. (If Zuck succeeds in developing agentic AI that can displace expensive software engineers, companies may pay Meta bigly for the glorious ability to lay off their employees).

My reaction to this dire ten-year prognostication is two-fold. First, I have a relatively high fraction of my portfolio in securities which simply pump out cash. I have written about these here and here. With these investments, I don’t much care what stock prices do, since I am not relying on some greater fool to pay me a higher price for my shares than I paid. All I care is that those dividends keep rolling in.

My other reaction is…this time it may be different (!), for the following reason: a huge fraction of the S&P 500 valuation is now occupied by the big tech companies. Unlike in 2000, these companies are actually making money, gobs of money, and more money every year. It is common, and indeed rational, to value (on a P/E basis) firms with growing profits more highly than firms with stagnant earnings. Yes, Nvidia has a really high P/E of 43, but its price to earnings-growth (PEG) ratio is about 1.2, which is actually pretty low for a growth company.

So, with a reasonable chunk of my portfolio, I will continue to party like it’s 1999.

[1] Here is a blurb from the Llama 3.1 chatbot offered for free in my Brave browser, summarizing the muted market outlook:

Financial institutions are forecasting lower stock market returns over the next decade compared to recent historical performance. According to Schwab’s 2025 Long-Term Capital Market Expectations, U.S. large cap equities are expected to deliver annualized returns of 6% over the next decade, while international developed market equities are projected to slightly outperform at 7.1%.1 However, Goldman Sachs predicts a more modest outlook, with the S&P 500 expected to return around 3% annually over the next decade, within a range of –1% and 7%.42 Vanguard’s forecasts also indicate a decline in expected returns, with U.S. equities falling to a range of 2.8% to 4.8% annually. These forecasts suggest that investors may face a period of lower returns compared to the past decade’s 13% annualized total return.

After the Fall: What Next for Nvidia and AI, In the Light of DeepSeek

Scott Buchanan1 Comment

Anyone not living under a rock the last two weeks has heard of DeepSeek, the cheap Chinese knock-off of ChatGPT that was supposedly trained using much lower resources that most American Artificial Intelligence efforts have been using. The bearish narrative flowing from this is that AI users will be able to get along with far fewer of Nvidia’s expensive, powerful chips, and so Nvidia sales and profit margins will sag.

The stock market seems to be agreeing with this story. The Nvidia share price crashed with a mighty crash last Monday, and it has continued to trend downward since then, with plenty of zig-zags.

I am not an expert in this area, but have done a bit of reading. There seems to be an emerging consensus that DeepSeek got to where it got to largely by using what was already developed by ChatGPT and similar prior models. For this and other reasons, the claim for fantastic savings in model training has been largely discounted. DeepSeek did do a nice job making use of limited chip resources, but those advances will be incorporated into everyone else’s models now.

Concerns remain regarding built-in bias and censorship to support the Chinese communist government’s point of view, and regarding the safety of user data kept on servers in China. Even apart from nefarious purposes for collecting user data, ChatGPT has apparently been very sloppy in protecting user information:

Wiz Research has identified a publicly accessible ClickHouse database belonging to DeepSeek, which allows full control over database operations, including the ability to access internal data. The exposure includes over a million lines of log streams containing chat history, secret keys, backend details, and other highly sensitive information.

Shifting focus to Nvidia – – my take is that DeepSeek will have little impact on its sales. The bullish narrative is that the more efficient algos developed by DeepSeek will enable more players to enter the AI arena.

The big power users like Meta and Amazon and Google have moved beyond limited chatbots like ChatGPT or DeepSeek. They are aiming beyond “AI” to “AGI” (Artificial General Intelligence), that matches or surpasses human cognitive capabilities across a wide range of cognitive tasks. Zuck plans to replace mid-level software engineers at Meta with code-bots before the year is out.

For AGI they will still need gobs of high-end chips, and these companies show no signs of throttling back their efforts. Nvidia remains sold out through the end of 2025. I suspect that when the company reports earnings on Feb 26, it will continue to demonstrate high profits and project high earnings growth.

Its price to earnings is higher than its peers, but that appears to be justified by its earnings growth. For a growth stock, a key metric is price/earnings-growth (PEG), and by that standard, Nvidia looks downright cheap:

Source: Marc Gerstein on Seeking Alpha

How the fickle market will react to these realities, I have no idea.

The high volatility in the stock makes for high options premiums. I have been selling puts and covered calls to capture roughly 20% yields, at the expense of missing out on any rise in share price from here.

Disclaimer: Nothing here should be considered as advice to buy or sell any security.

DeepSeek vs. ChatGPT: Has China Suddenly Caught or Surpassed the U.S. in AI?

Scott Buchanan1 Comment

The biggest single-day decline in stock market history occurred yesterday, as Nvidia plunged 17% to shave $589 billion off the AI chipmaker’s market cap. The cause of the panic was the surprisingly good performance of DeepSeek, a new Chinese AI application similar to ChatGPT.

Those who have tested DeepSeek find it to perform about as well as the best American AI models, with lower consumption of computer resources. It is also available much cheaper. What really stunned the tech world is that the developers claimed to have trained the model for only about six million dollars, which is way, way less than the billions that a large U.S. firm like OpenAI, Google, or Meta would spend on a leading AI model. All this despite the attempts by the U.S. to deny China the most advanced Nvidia chips. The developers of DeepSeek claim they worked with a modest number of chips, models with deliberately curtailed capacities which met U.S. export allowances.

One conclusion, drawn by the Nvidia bears, is that this shows you *don’t* need ever more of the most powerful and expensive chips to get good development done. The U.S. AI development model has been to build more, huge, power-hungry data centers and fill them up with the latest Nvidia chips. That has allowed Nvidia to charge huge profit premiums, as Google and other big tech companies slurp up all the chips that Nvidia can produce. If that supply/demand paradigm breaks, Nvidia’s profits could easily drop in half, e.g., from 60+% gross margins to a more normal (but still great) 30% margin.

The Nvidia bulls, on the other hand, claim that more efficient models will lead to even more usage of AI, and thus increase the demand for computing hardware – – a cyber instance of Jevons’ Paradox (where the increase in the efficiency of steam engines in burning coal led to more, not less, coal consumption, because it made steam engines more ubiquitous).

I read a bunch of articles to try to sort out hype from fact here. Folks who have tested DeepSeek find it to be as good as ChatGPT, and occasionally better. It can explain its reasoning explicitly, which can be helpful. It is open source, which I think means the code or at least the “weights” have been published. It does seem to be unusually efficient. Westerners have downloaded it onto (powerful) PCs and have run it there successfully, if a bit slowly. This means you can embed it in your own specialized code, or do your AI apart from the prying eyes of ChatGPT or other U.S. AI providers. In contrast, ChatGPT I think can only be run on a powerful remote server.

Unsurprisingly, in the past two weeks DeepSeek has been the most-uploaded free app, surpassing ChatGPT.

It turns out that being starved of computing power led the Chinese team to think their way to several important innovations that make much better use of computing. See here and here for gentle technical discussions of how they did that. Some of it involved hardware-ish things like improved memory management. Another key factor is they figured out a way to only do training on data which is relevant to the training query, instead of training each time on the entire universe of text.

A number of experts scoff at the claimed six million dollar figure for training, noting that if you include all the costs that were surely involved in the development cycle, it can’t be less than hundreds of millions of dollars. That said, it was still appreciably cheaper than the usual American way. Furthermore, it seems quite likely that making use of answers generated by ChatGPT helped DeepSeek to rapidly emulate ChatGPT’s performance. It is one thing to catch up to ChatGPT; it may be tougher to surpass it. Also, presumably the compute-efficient tricks devised by the DeepSeek team will now be applied in the West, as well. And there is speculation that DeepSeek actually has use of thousands of the advanced Nvidia chips, but they hide that fact since it involved end-running U.S. export restrictions. If so, then their accomplishment would be less amazing.

What happens now? I wish I knew. (I sold some Nvidia stock today, only to buy it back when it started to recover in after-hours trading). DeepSeek has Chinese censorship built into it. If you use DeepSeek, your information gets stored on servers in China, the better to serve the purposes of the government there.

Ironically, before this DeepSeek story broke, I was planning to write a post here this week pondering the business case for AI. For all the breathless hype about how AI will transform everything, it seems little money has been made except for Nvidia. Nvidia has been selling picks and shovels to the gold miners, but the gold miners themselves seem to have little to show for the billions and billions of dollars they are pouring into AI. A problem may be that there is not much of a moat here – – if lots of different tech groups can readily cobble together decent AI models, who will pay money to use them? Already, it is being given away for free in many cases. We shall see…

Buying on Margin is Like an Option

Zachary Bartsch4 Comments

Over the winter break I was able to catch up on a lot of podcasts. I also began listening to the Marginal Revolution podcast (which is phenomenal). I especially enjoyed the final episode of season 1 about options and how many transactions can be characterized as giving someone an option. Here, the term option echoes a financial option. You pay today for the ability to do something in the future. In financial markets, you can purchase the right to buy or sell at a particular price in the future.

But lots of things count as options. Staying in the financial context, purchasing a stock gives you the option to sell that stock at the future spot price. So, in this way, something can be characterized as an option even though we are not accustomed to describing as such explicitly. More mundane transactions can also be interpreted as options. Assume that you buy a can opener. You are buying the option to have that tool on hand in the future and to open some shelf-stable food. You can choose to exercise the option simply by opening your kitchen drawer.

But financial options often include the possibility of losing money. It may be that your grocery purchases never include canned items and that you never have occasion to use your can opener. Maybe that’s a bad investment. You sunk your money into something that you never used. Except… You did in fact have the option to use the can opener. Maybe you had peace of mind that you were well prepared just in case a guest arrived with a can of something. Buying a can opener is like buying an option.

Returning to the realm of finance, let’s discuss buying on margin. Buying an asset on margin is when you borrow from your broker in order to purchase a financial asset. It’s not entirely free money. They have rules about the amount you can borrow and, of course, you must pay back the loan with interest.

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Forecasting 2025

James Bailey2 Comments

WSJ’s survey of economists reports that inflation expectations for 2025 were around 2% before the election, but are closer to 3% now. Their economists expect GDP growth slowing to 2%, unemployment ticking up slightly but staying in the low 4% range, with no recession. The basic message that 2025 will be a typical year for the US macroeconomy, but with inflation being slightly elevated, perhaps due to tariffs.

Kalshi has a lot of good markets up that give more detailed predictions for 2025:

For those who hope for DOGE to eliminate trillions in waste, or those who fear brutal austerity, the message from markets is that the huge deficits will continue, with the federal debt likely climbing to over $38 trillion by the end of the year. This is one reason markets see a 40% chance that the US credit rating gets downgraded this year.

While the US has only a 22% chance of a recession, China is currently at 48%, Britain at 80%, and Germany at 91%. The Fed probably cuts rates twice to around 4.0%.

Will wage growth keep pace with inflation? It’s a tossup. Corporate tax cuts are also a tossup. The top individual rate probably won’t fall below it’s current 37%.

If you want to make your own predictions for the year, but don’t want to risk money betting on Kalshi, there are several forecasting contests open that offer prizes with no risk:

ACX Forecasting Contest: $10,000 prize pool, 36 questions, must submit predictions by Jan 31st

Bridgewater Forecasting Contest: $25,000 prize pool, half of prizes are reserved for undergraduates. Register now to make predictions between Feb 3rd and March 31st. Doing well could get you a job interview at Bridgewater.