Dialogue with Tantu Macro's Cheng Tan: What Problems Can Macro Research Solve?

In recent years, macro research has become increasingly crowded.

Hot topics rotate more quickly, viewpoints are updated increasingly frequently, and research reports are becoming shorter and more direct. Much of the content pursues emotional value and instant feedback; very few are willing to take the time to break down the genuinely complex, tedious, yet critical underlying logic.

However, recently, a team with a distinctly different style has emerged in the market.

They rarely chase hot topics, nor do they participate much in traffic-heavy discussions. Most of their time is spent on something that appears “thankless”—repeatedly deducing macro mechanisms, splitting up balance sheets, and researching the transmission pathways of liquidity across different markets.

This team is called Tantou Macro, and its central figure is Cheng Tan.

Interestingly, this somewhat “untimely” research approach has not been ignored by the market. On the contrary, mainstream institutional investors are starting to favor this “alternative” research team even more—over the past year, Cheng Tan has conducted more than 300 roadshow presentations for over one hundred institutions.

In an environment where informational noise is mounting, systematic frameworks themselves are becoming scarce.

On April 26, 2026, the masterclass "Decoding Dollar Liquidity" co-hosted by Wallstreet Insights and Cheng Tan sparked a strong reaction in the institutional market. Nearly a hundred institutional investors gathered in Shanghai just to fully grasp this hardcore and crucial advanced investment course. To meet users’ demands, we’re collaborating with Mr. Chen Tan once again to launch a brand-new annual column, "Cheng Tan Speaks," to help you see through the underlying logic of global asset pricing from macro trends to dollar liquidity.

“Small Town Exam Champion” from Shandong

Cheng Tan is from Weihai, Shandong. He jokingly refers to himself as the most typical “small town exam champion.”

After entering the Mathematics and Economics experimental class at the Central University of Finance and Economics as an undergraduate, he underwent an “extreme” form of thinking training. According to Cheng Tan’s recollection, they used “all-English textbooks + English lectures + extensive mathematics courses” as the core curriculum, the most prominent features being high difficulty and great intensity.

How difficult was it? By their sophomore year, they were studying Varian’s advanced economics (only offered at the graduate level at Peking University) and advanced macroeconomics. The level of math was even more intense, almost comparable to that of the Peking University math department—resulting in a situation where, apart from a few students with a strong foundation in mathematics, most students were essentially just muddling through.

Fortunately, Cheng Tan persevered and joined the Finance Department at Peking University’s Guanghua School as the top student in his major, going on to complete his PhD studies there.

The impact from this period was direct—in his later work, he became accustomed to understanding issues from a structural perspective, rather than searching for evidence to fit a conclusion. While the market focuses on short-term volatility, he pays more attention to the constraints between variables; when discussions concentrate on disagreements in opinion, he cares more about whether the framework is self-consistent.

From Model World to Real-World Game

After completing his PhD, Cheng Tan did not enter the sell side or investment banking, but instead joined the Central Foreign Exchange Business Center of the State Administration of Foreign Exchange.

The real challenge here was not just market volatility, but a transformation in ways of thinking.

In academic training, problems usually have an “optimal solution”; in the real market, it’s more about trade-offs under constraints—a repeated game between policy targets, market sentiment, and liquidity conditions.

While at SAFE, he was mentored by Dr. Miao Yanliang, who later became the chief strategist at CICC. The greatest gain for Cheng Tan during this time was learning to understand macro variables within the context of real institutional environments and behavioral logic.

Many conclusions need to be repeatedly overturned and rebuilt.

What problems can macro research truly solve?—Many people look at macro data every day, but always struggle to understand how macro guides investment.

Based on many years of practical experience at SAFE, Cheng Tan summarized the role of macro research in twelve characters:

Grasp the trend, judge inflection points, eliminate noise.

It sounds simple, but behind it lies a huge amount of validation work.

A Decade of Research Ups and Downs

Cheng Tan shared with Wallstreet Insights several interesting experiences:

The first example he calls “History Rhymes But Never Repeats”—In 2022, U.S. inflation once rose to 9%, and the Federal Reserve launched the fastest rate-hike cycle since the 1980s, accumulating over 400bps of hikes for the year. At the time, the U.S. Treasury yield curve was deeply inverted, recession expectations in the market were extremely strong; the main logic was: only a significant rise in unemployment could bring inflation down to a reasonable level.

However, Cheng Tan’s judgment at the time was different from the market consensus.

He had two reasons. First, the massive fiscal and monetary easing in 2020-2021 in the U.S. created a fairly thick “buffer,” and second, debt for U.S. households and corporates was mostly at fixed rates, so the short-term impact of rate hikes was limited. Therefore, Cheng Tan believed the market might be overestimating the risk of recession. To verify this hypothesis, Cheng Tan’s team did three things.

First, a detailed calculation of the balance sheet stress on U.S. households and corporations at different income levels under a high-interest-rate environment revealed that the financial stress brought by rate hikes was much lower than in historical comparable cycles.

Second, by breaking down the drivers of U.S. high inflation, they found that over 50% of inflation still came from the supply side, with disturbances likely to dissipate as the U.S. economy reopened after the pandemic.

Third, by reviewing the 1970s–80s, they found that anchored inflation expectations and increased labor market flexibility helped avoid stagflation.

For these reasons, Cheng Tan’s team adjusted their baseline outlook for the U.S. economy to a soft landing by mid-2022 and continued to emphasize their constructive view on U.S. equities.

The second example concerns Trump and the “TACO” trade—It was 2019, and at that time the China–U.S. trade negotiation teams had conducted several rounds of talks. Yet, Trump defied the consensus and twice escalated tariffs against China. Both the domestic and U.S. capital markets were deeply pessimistic, with the S&P 500 plunging 3% in a single day. The market believed Trump's actions to be unpredictable and that a deal was almost impossible. Yet at the time, Cheng Tan released a report called "TRUMPUT"—Trump plus put option.

Because Cheng Tan had already keenly observed that, whether in terms of motivation, support, or historical patterns before elections, Trump was highly unlikely to escalate indefinitely; on the contrary, a trade deal was more likely. The market's linear extrapolation and pessimism provided a good entry point. As it turned out, Trump as expected made a TACO by the end of August, and in December, the first-phase trade agreement with China was reached.

The third example is about Silicon Valley Bank—On March 10, 2023, Silicon Valley Bank suddenly collapsed, the 10-year U.S. Treasury yield fell by more than 20bps in one session, and the S&P 500 fell 3.3% within two days, raising market fears of a new financial crisis.

But interestingly, on March 9 (one day before SVB's collapse), Cheng Tan had already written a report on SVB’s bankruptcy. The core of his analysis was that although SVB itself might fail, since its problems were idiosyncratic (a severe asset-liability mismatch), and the problematic assets were U.S. Treasuries that had dropped below par due to rate hikes, the central bank and Treasury’s ability to rescue was strong; there was no issue of "wanting to rescue but lacking authority" as in 2008.

That report concluded that SVB's collapse would not evolve into a systemic financial crisis, nor would it alter the path toward a global economic soft landing—as later events confirmed Tan’s judgment.

However, there is no “perpetual winner” in the market. Even Cheng Tan, with his Peking University PhD in finance, honed his skills through repeated failures. In conversation, he admitted that several misjudgments left a deep impact, and even altered his research framework:

For example, in September 2019, a sudden liquidity crisis erupted in the U.S. repo market, with repo rates surging 300 basis points in one day. In fact, by mid-2019, Cheng Tan’s team had already predicted the Fed's balance sheet reduction was near its end, and the onset of a cash crunch was the direct signal. Their view at the time—sharp rate jumps would significantly impact equity markets.

But in hindsight, abrupt short-end rate swings did not ripple through to the stock market. This case made Cheng Tan realize: the U.S. dollar liquidity market is actually highly segmented. Tightness in one sub-market does not necessarily spread to others.

Another example: in March 2020, the Fed released a series of unprecedented new liquidity support tools. However, Cheng Tan’s team, based on fundamentals, predicted the U.S. economy would enter a prolonged recession and thus remained cautious on U.S. equities.

In retrospect, it was precisely the immense injections of fiscal and monetary liquidity during “covid lockdown” that fully protected the U.S. economy. People isolating at home had more time and funding to buy financial assets online, ultimately triggering a liquidity-driven bull run in U.S. stocks and other risk assets.

It was from 2020 onwards that Cheng Tan’s team began tracking fiscal incomes and household balance sheets.

This market miscall profoundly "stimulated" Cheng Tan, revealing that traditional macro frameworks fell short in fully explaining the market phenomena at that time.

Thus, Cheng Tan was compelled to construct a more comprehensive research perspective: macro policy, the financial system, and household sector balance sheets are different facets of the same system. Neglecting financial intermediary structures and capital flows, by looking only at aggregate indicators, can easily lead to misjudgments.