Dialogue with Tantu Macro Chengtan: What Problems Can Macroeconomic Research Solve?
```
In the past two years, macro research has become increasingly crowded.
Hot topics rotate faster, viewpoints are updated more frequently, and research reports are increasingly "short, concise, and quick". Much of the content pursues emotional value and instant feedback, and few are willing to go deep to dissect those truly complex, boring, yet critical underlying logics.
But recently, a team with a very different style has appeared in the market.
They rarely chase hot topics, nor do they participate much in discussions about trending issues. Most of their time is spent on something seemingly "hard work with little reward"—repeatedly deducing macro mechanisms, breaking down balance sheets, and studying the transmission paths of liquidity across different markets.
This team is called Tantu Macro, with Cheng Tan as its core figure.
Interestingly, this "untimely" research approach has not been ignored by the market. On the contrary, mainstream institutional investors are increasingly favoring this "alternative" research team—Cheng Tan has conducted over 300 investment research roadshow presentations for more than a hundred institutions in the past year.
In an environment with increasing informational noise, systemic frameworks themselves are becoming scarce.
On April 26, 2026, the Wallstreetcn master class "Decoding Dollar Liquidity" in collaboration with Cheng Tan sparked intense reactions in the institutional market. Nearly a hundred institutional investors gathered in Shanghai solely to understand this hardcore and crucial investment advanced course. To meet the needs of our vast user base, we have once again teamed up with Professor Cheng Tan to launch a brand new annual column "Cheng Tan Speaks", helping you see through global asset pricing logics from macro trends to dollar liquidity.
The "Small-town Exam Student" from Shandong
Cheng Tan is from Weihai, Shandong. He jokingly calls himself "the most typical small-town exam kid".
After entering the Mathematical Economics Experimental Class at Central University of Finance and Economics for his undergraduate studies, he underwent an "extreme" mental training regime. According to Cheng Tan, they used "all English textbooks + all English lectures + a large number of math courses." The biggest feature was high difficulty and intensity.
How difficult? As sophomores they studied Varian’s Advanced Economics (which Peking University only offers at the graduate level) and Advanced Macroeconomics. The math was even more intense, almost on par with the difficulty of Peking University's mathematics department—such extreme course difficulty meant that, except for a few students with strong mathematics backgrounds who could barely keep up, most students just muddled through graduation.
Fortunately, Cheng Tan persevered and, graduating first in his major, joined Peking University's Guanghua Finance Department and continued to finish his doctoral studies.
The impact of this experience was direct—in his later work, he got used to understanding problems from the structure, rather than searching for arguments from the conclusion. When the market focuses on short-term volatility, he pays more attention to the constraints between variables; when discussions centered around differing opinions, he cares more whether the framework is internally consistent.
From the World of Models to Real Competition
After completing his doctorate, Cheng Tan did not join the sell-side or an investment bank, but instead joined the Central Forex Business Center of the State Administration of Foreign Exchange.
The real challenge here was not merely market volatility, but a change in cognitive approach.
In academic training, problems typically have an "optimal solution"; but in the real market, it is more about trade-offs under constraints, repeated wrestling between policy goals, market sentiment, and liquidity conditions.
His mentor at SAFE at the time was Dr. Miao Yanliang, who later became the chief strategist at CICC. The biggest takeaway for Cheng Tan during that period was learning to understand macro variables within the real institutional environment and behavioral logic.
Many conclusions needed to be repeatedly overturned and rebuilt.
What can macro research really solve?—Many people watch macro every day but never understand how it guides investing.
Combining years of practical experience at SAFE, Cheng Tan summarized the role of macro research in twelve characters:
Grasp trends, judge turning points, eliminate noise.
Sounds simple, but a lot of validation work is needed behind it.
Ten years of Research Ups and Downs
Cheng Tan shared several interesting experiences with Wallstreetcn:
The first example he calls "History rhymes but never repeats"—In 2022, US inflation once climbed to 9%, and the Federal Reserve began its fastest rate hikes since the 1980s, with over 400bps total hikes that year. The US Treasury yield curve was deeply inverted, market recession expectations were very strong. The core logic: Only a significant rise in unemployment could bring inflation down to reasonable levels.
But Cheng Tan’s judgment differed from the market.
He had two reasons. First, the massive fiscal and monetary easing in 2020-2021 left a thick "buffer;" second, US household and corporate debt was mainly fixed-rate, so the short-term impact of rate hikes was limited. Therefore, Cheng Tan believed the market might have overestimated recession risk. To test this idea, his team did three things.
First, they calculated asset-liability pressures for US households and corporates at different income levels under high interest rates, and found the financial pressure from rate hikes was much lower than during comparable historical cycles.
Second, they broke down the drivers of US high inflation and found that over 50% of inflation still came from the supply side, and this disturbance was likely to fade as the US reopened after the pandemic.
Third, they reviewed the cases of the 1970s-80s and found anchored inflation expectations and rising job market flexibility helped avoid stagflation.
For these reasons, Cheng Tan’s team revised the US economic baseline outlook to a soft landing by mid-2022 and continued to emphasize their strategic bullish view on US stocks.
The second example is about Trump and the "TACO" trade—It was 2019. The China-US trade negotiation teams had conducted many rounds, but Trump still unilaterally escalated tariffs against China twice. At the time, pessimism was strong in both Chinese and US capital markets, with the S&P plunging 3% in a single day. The market considered Trump’s moves unpredictable and thought a China-US deal was nearly impossible. But Cheng Tan published a report called "TRUMPUT"—Trump and put options.
Because Cheng Tan had already keenly observed that, judging by motivation, approval ratings, and election timing, Trump could not infinitely escalate friction and instead was more likely to reach a trade deal. The market's linear extrapolation and pessimism provided a good buying opportunity. The result: Trump performed TACO as scheduled at the end of August and reached a Phase 1 trade deal with China in December.
The third example is about Silicon Valley Bank—On March 10, 2023, Silicon Valley Bank suddenly collapsed. The US 10-year Treasury yield fell more than 20bps in a day, and the S&P 500 dropped 3.3% within two days. The market feared a new round of financial crisis.
Interestingly, Cheng Tan wrote a report about SVB’s imminent collapse on March 9 (one day before SVB went bankrupt). His key logic was that while SVB itself might go under, its problems were idiosyncratic (severe asset-liability mismatch), and the problem assets were US Treasuries devalued by rate hikes, so the central bank and Treasury had strong rescue capability. There was no "want to rescue but no authority" situation like in 2008.
His report concluded SVB’s collapse could not trigger a systemic financial crisis nor would it derail the global economy's soft landing—subsequent events unfolded as he predicted.
But there are no "invincible generals" in the market. Even Cheng Tan, a Peking University finance Ph.D., "struggled through constant failures." In conversations with Cheng Tan, he admitted a few misjudgments deeply affected him and even shaped his entire research framework:
For example, in September 2019, the US repo market suddenly exploded with a liquidity crisis, repo rates jumped 300bps in one day. Actually, Cheng Tan’s team had already judged in mid-2019 that the Fed’s balance sheet reduction was nearing its end, with the money shortage as the strongest signal. At the time, their view was— sharp rate spikes would obviously negatively impact equity markets.
But it was later shown that severe short-end rate swings did not spread to the stock market. This case made Cheng Tan realize: the dollar liquidity market is highly segmented. Liquidity stress in one submarket doesn't necessarily spill over to others.
Another example is March 2020, when the Fed rolled out unprecedented new liquidity support tools. But Cheng Tan’s team, based on fundamentals, judged that the US economy would fall into prolonged recession and remained cautious on US stocks.
Looking back, it was actually the vast liquidity injection from fiscal and monetary policy that fully protected the US economy during the pandemic lockdown. People isolated at home ended up with more time and money to buy financial assets online, and this fueled a liquidity-driven rally in US stocks and other risk assets.
Starting in 2020, Cheng Tan’s team began tracking household fiscal income and retail investor asset-liability conditions.
This wrong call on the market deeply "stimulated" Cheng Tan. He realized that the traditional macro analysis framework couldn't fully explain what was happening.
This also forced Cheng Tan to build a more integrated research perspective: macro policy, the financial system, and household balance sheets are different faces of the same system. If you overlook financial intermediaries and capital flows and only look at aggregate indicators, it is easy to make wrong judgments.
Risk Warning and DisclaimerThe market has risks, and investment must be cautious. This article does not constitute personal investment advice and has not taken into account individual users’ special investment objectives, financial situations, or needs. Users should consider whether any opinions, views, or conclusions in this article fit their particular circumstances. Invest accordingly and be responsible for your own decisions. ```