Dialogue on Macro Perspective: What Problems Can Macro Research Truly Solve?

In the past two years, macro research has become increasingly crowded.

Hot-spot rotation has accelerated, views are being updated more and more frequently, and research reports are becoming increasingly “short, flat, and fast.” A lot of content chases emotional value and instant feedback, and few people are willing to dig in and break down those truly complex, tedious, yet crucial underlying logic.

But recently, a team with a very different style has appeared in the market.

They rarely chase hot spots and don’t really participate in discussions about traffic topics. More of their time is spent doing something that looks like it’s “hard to justify”—repeatedly running scenarios of macro mechanisms, breaking down balance sheets, and studying the transmission paths of liquidity across different markets.

This team is called Tantu Macro, and its key figure is Cheng Tan.

What’s interesting is that this kind of “out of step with the times” research approach hasn’t been ignored by the market. On the contrary, mainstream institutional investors have started to favor this “alternative” research team more and more—over the past year alone, Cheng Tan has delivered more than 300 investment research roadshow presentations for over a hundred institutions.

In an environment where information noise is getting louder, systematic frameworks themselves are starting to become scarce.

A “small-town problem-solver” from Shandong

Cheng Tan is from Weihai, Shandong. He jokingly calls himself the “most typical small-town problem-solver.”

After entering the Experimental Program in Mathematical Economics at Central University of Finance and Economics for his undergraduate studies, he received a set of “extreme” thinking training. As Cheng Tan recalls, at the time they used an instructional system of “all-English textbooks + all-English instruction + a large number of math courses.” The biggest feature was that the courses’ difficulty was high, and the intensity was high.

How difficult was it, exactly? In their second year of undergrad, they had to study Varian’s advanced economics (Peking University only offered it at the graduate level) and advanced macroeconomics. In math, it was even more extreme—almost on par with the difficulty of the Peking University math department. The result of such extreme course difficulty was that, besides a small number of students with very strong math foundations, everyone else was basically listening to “things they couldn’t understand” and barely getting through to graduation.

Fortunately, Cheng Tan managed to push through. With a top major-average score, he joined Peking University’s Guanghua School of Management’s finance program and continued his doctoral studies.

The impact of this experience was very direct. In his later work, he became accustomed to understanding problems starting from structure rather than starting from conclusions to look for supporting evidence. When the market focused on short-term fluctuations, he cared more about the constraint relationships among variables. When discussions centered on differences in viewpoints, he cared more about whether the framework was internally consistent.

Stepping out of the model world into real-game dynamics

After graduating from his PhD, Cheng Tan didn’t go into the sell-side or investment bank. Instead, he joined the Central Foreign Exchange Business Center of the State Administration of Foreign Exchange.

Back then, it managed foreign exchange reserves of three trillion US dollars, making it one of the important participants in global capital allocation.

People outside often assume that the SAFE’s investments are mainly passive allocation. But Cheng Tan said that wasn’t the case—active operations and tactical allocation also accounted for a fairly high proportion. With multiple asset classes, multiple markets, and multiple instruments running in parallel, it requires very strong judgment ability.

The department Cheng Tan belonged to was responsible for tactical allocation, and assets in equities, bonds, and foreign exchange could be traded in both directions. Once the judgment is wrong, performance pressure would be very direct.

The real challenge wasn’t just market volatility, but the shift in how one thinks.

In academic training, a problem usually has an “optimal solution.” But in real markets, it’s often a trade-off under constraints—repeated games among policy objectives, market sentiment, and liquidity conditions.

At the time, the person who led Cheng Tan at the SAFE was Dr. Miao Yanliang, who later became the Chief Strategist at CICC. Cheng Tan’s biggest takeaway from that period was learning to understand macro variables within real institutional environments and behavioral logic.

Many conclusions need to be torn down and rebuilt again and again.

What problems can macro research truly solve?—Many people watch macro every day, yet still can’t figure out how macro actually guides investing.

Based on his years of practical experience at the SAFE, Cheng Tan summarized the role of macro research into 12 Chinese characters:

Grasp the trend, identify turning points, and eliminate noise.

It sounds simple, but behind it requires a great deal of verification work.

A decade of study through wind and rain

Cheng Tan shared a few very interesting experiences with Wall Street Insights:

The first example, he calls “History may rhyme but never repeat.” In 2022, US inflation once rose to 9%. The Fed began the fastest pace of rate hikes since the 1980s, with total hikes of more than 400 bps for the full year. At that time, the US Treasury yield curve was deeply inverted, and recession expectations were very strong. The core basis was that only a sharp rise in unemployment could bring inflation down to a reasonable level.

But Cheng Tan’s judgment then was different from the market’s.

At the time, he had two reasons. First, in 2020–2021, the US had extremely large-scale dual easing in fiscal and monetary policy, which provided a relatively thick “cushion.” Second, US household and corporate debt was mainly at fixed interest rates, so the short-term impact of rate hikes was limited. Therefore, Cheng Tan believed the market was overestimating recession risk. To verify his hypothesis, Cheng Tan’s team did three things.

First, they conducted detailed calculations of the balance-sheet pressure on US households and businesses at different income levels in a high interest rate environment. They found that the financial stress caused by rate hikes was far smaller than in historically comparable cycles.

Second, they decomposed the drivers of US high inflation and discovered that more than 50% of inflation still came from the supply side. After the reopening following the pandemic, this disturbance would likely gradually subside.

Third, they reviewed cases from the 1970s–80s and found that anchoring inflation expectations and rising labor-market flexibility help avoid stagflation.

Based on the reasons above, by mid-2022, Cheng Tan’s team had already revised the baseline outlook for the US economy to a soft landing, and continued to emphasize their judgment that they were strategically bullish on US stocks.

The second example is about Trump and “TACO**” trades**—At the time it was 2019. The US-China trade negotiation delegation had already gone through multiple rounds of talks, but Trump still persisted in unilaterally escalating tariffs on China twice. At that time, both domestic and US capital-market sentiment was deeply pessimistic. The S&P 500 plunged 3% in a single day. The market believed Trump’s actions were unpredictable, and that there was almost no possibility of reaching an agreement between the US and China. But at that time, Cheng Tan published a report titled 《TRUMPUT》—Trump and the bearish put options.

Because Cheng Tan had already keenly realized that, whether looking at motives, approval ratings, or historical patterns ahead of the election, Trump could not infinitely escalate the friction. Instead, he was more likely to reach a trade agreement. The market’s linear extrapolation and pessimistic sentiment formed a decent buy point. As a result, by late August, Trump executed TACO as expected, and by December, the US and China reached the Phase One trade agreement.

The third example is about Silicon Valley Bank— On March 10, 2023, Silicon Valley Bank suddenly failed. US 10-year Treasury yields fell by more than 20 bps in a single day. The S&P 500 dropped 3.3% within two days. The market worried that a new financial crisis might be repeating itself.

But interestingly, Cheng Tan wrote a report about SVB’s failure on March 9 (one day before the SVB collapse). The key logic was that although SVB itself might fail, because its issues were heterogeneous in nature (SVB was naturally facing severe asset-liability mismatches), and the problematic assets were US Treasuries that had fallen below par due to the rate hikes, the central bank and the Ministry of Finance had strong rescue capabilities. So there was no situation like in 2008 where they “wanted to save it but didn’t have the authority.”

The conclusion of that report was that SVB’s failure was not possible to evolve into a systemic financial crisis, nor would it be able to alter the global economy’s trajectory toward a soft landing. And what happened afterward followed Cheng Tan’s expectations as well.

But where is there really a “never-lose general”? Even Cheng Tan, who was trained in finance with a PhD background from Peking University, had to “climb through failures” continuously. During the course of exchanges with Cheng Tan, he also admitted that several times when his judgment was wrong deeply moved him and even affected his entire research framework.

For example, in September 2019, liquidity stress suddenly broke out in the US repo market. The repo rate surged by 300 bps in a single day. In fact, Cheng Tan’s team had already judged by mid-2019 that the Fed’s balance-sheet reduction was approaching its endpoint, and the appearance of a cash shortage was the most direct signal. At the time, their view was that—when rates jump sharply, it would cause a clear negative impact on the equity market.

But after the fact proved that the violent volatility in short-end rates did not transmit to the stock market. This case made Cheng Tan realize that the US dollar liquidity markets were actually highly segmented. Tight liquidity in one sub-market doesn’t necessarily spread to other markets.

Another example is March 2020. The Fed rolled out a series of unprecedented new liquidity support tools. But at that time, Cheng Tan’s team still made judgments based on fundamentals and believed the US economy would face a sustained recession, so they stayed cautious about US equities.

Looking back, it was precisely the huge liquidity injection from fiscal and monetary policy that fully protected the US economy during the “pandemic lockdown.” People isolated at home, but they actually had more time and funds to buy financial assets via the internet. Ultimately, this drove a strong, liquidity-driven rally in US stocks and other risk assets.

Starting in 2020, Cheng Tan’s team began incorporating the tracking system for households’ fiscal income and the balance-sheet conditions of retail investors.

This misjudgment of the market deeply “stimulated” Cheng Tan. He found that the traditional macro analysis framework was difficult to fully explain the market phenomena at that time.

This also forced Cheng Tan to build a more comprehensive research perspective: macro policy, the financial system, and households’ balance sheets are different facets of the same system. If you ignore the financial intermediation structure and the direction of capital flows, and only look at aggregate indicators, it’s easy to produce misjudgments.

Explain complex problems clearly

For years, Cheng Tan served as a training lecturer within the SAFE. One of his long-standing principles is: if a framework can’t be understood by new entrants, then it likely hasn’t truly been absorbed and mastered yet.

In 2025, Cheng Tan chose to leave the SAFE after working there for a decade and founded the research institution Tantu Macro, starting to export its research system to a wider range of institutional investors.

In just one short year, he delivered more than 300 roadshows. He directly served over 100 institutional clients. His high-frequency and hard-core output even surpassed that of many top brokerage chief strategists. He hoped to transform that top-tier analytical framework that used to belong to the “state team” into a cognitive weapon that every professional investor can get started with.

Cheng Tan firmly believes: A good researcher must be able to grasp major macro trends from the top down, and also verify every interwoven detail from the bottom up.

At a global macro crossroads where the environment is becoming increasingly complex, top-level macro thinking is no longer a nice-to-have. It has become an entry ticket to investing.

To this end, Wall Street Insights has specially invited Cheng Tan on April 25, 2026, to give a master class in Shanghai titled 《Reading Through Global Asset Pricing’s Underlying Logic from Dollar Liquidity》. The master class will condense his investment research system—tested in ten years of real practice, and used to deliver roadshows for many institutional clients late into the night—into this 3-5** hour hard-core master class**.

In this course, Cheng Tan will help you completely escape the predicament of “feeling around like blind men trying to describe an elephant”:

Top-down grasp of the trend: show you the logic behind shifts in global macro themes, and help you see the big picture.

Bottom-up verification of details: pierce the fog of the money markets and liquidity, and verify the real resilience of fundamentals.

This is not only a course about the US dollar and macro. It’s also an opportunity for a cognitive “mind cleansing.” It will help you break down those tedious yet real details and reveal the operational truths behind the global financial “black box.”

Risk warning and disclaimer

        There are risks in the market; investing requires caution. This article does not constitute personal investment advice, nor does it take into account the specific investment objectives, financial conditions, or needs of individual users. Users should consider whether any opinions, viewpoints, or conclusions in this article are consistent with their specific circumstances. Invest at your own risk based on this.