Dialogue on the Broad Path of Macroeconomics: What Problems Can Macroeconomic Research Truly Solve?

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

Hotspots 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; few are willing to delve deep and dissect those truly complex, tedious but crucial underlying logics.

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

They rarely chase hotspots and seldom participate in discussions on trending topics. Most of their time is spent on a seemingly "thankless" task—repeatedly deducing macro mechanisms, breaking down balance sheets, studying the transmission paths of liquidity across various markets.

This team is called TanTu Macro, led by Cheng Tan.

Interestingly, this "out-of-time" approach to research has not been overlooked by the market. On the contrary, mainstream institutional investors are increasingly favoring this "alternative" research team—over the past year, Cheng Tan has shared more than 300 research roadshows for over 100 institutions.

In an environment of growing informational noise, systematic frameworks themselves are becoming scarce.

"Small-town test taker" from Shandong

Cheng Tan is from Weihai, Shandong. He jokes that he is the "most typical small-town test taker."

After entering the Central University of Finance and Economics' experimental class in Mathematical Economics as an undergrad, he underwent an "extreme" mental training. According to Cheng Tan, their teaching system was "all-English textbooks + all-English teaching + numerous math courses," with the main characteristics being high difficulty and intensity.

Just how difficult? In their sophomore year, 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 matching the difficulty of Peking University's math department —the outcome of such extreme difficulty is that, except for students with a very strong mathematical foundation who could barely keep up, most others were simply muddling through graduation.

Fortunately, Cheng Tan persevered, graduated first in his major, and joined the Guanghua Finance Department at Peking University, continuing on to his doctoral studies.

This experience had a direct impact—later in work, he became used to approaching problems structurally, rather than starting from conclusions and seeking supporting evidence. When the market focused on short-term volatility, he was more concerned about the constraints between variables; when discussions centered on differences in viewpoints, he focused on whether the framework itself was consistent.

From model world to real-life games

After earning his PhD, Cheng Tan did not join the sell side or an investment bank, but instead joined the Central Foreign Exchange Business Center of the State Administration of Foreign Exchange.

This center once managed three trillion dollars in foreign exchange reserves and is one of the key players in global asset allocation.

It is commonly believed that the SAFE's investments are mainly passive, but Cheng Tan says the reality is quite different—active operations and tactical allocation actually account for a considerable proportion, with multi-asset, multi-market, and multi-tool operations requiring high judgment.

Cheng Tan's department was responsible for tactical allocation, and could operate both long and short positions in stocks, bonds, and FX. Any misjudgment would lead to very direct performance pressure.

The real challenge was not just market volatility, but a transformation in cognitive methodology.

In academic training, problems usually have an "optimal solution"; but in the actual market, it's more about trade-offs under constraints and repeated game-playing between policy objectives, market sentiment, and liquidity conditions.

His mentor at SAFE was Dr. Miao Yanliang, who later became Chief Strategist at CICC. Cheng Tan's greatest takeaway from that time was learning to place macro variables within real institutional environments and behavioral logic for understanding.

Many conclusions need to be repeatedly overturned and rebuilt.

What real problems can macro research solve?—Many people look at macros every day, but never understand how macro guides investment.

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

Grasp trends, judge turning points, filter noise.

It sounds simple, but a lot of verification work lies behind it.

A decade 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 climbed to 9%, and the Fed launched the fastest rate hike since the 1980s, with cumulative hikes exceeding 400 bps through the year. At that time, the US Treasury yield curve was deeply inverted, market recession expectations were strong, with the core argument being: only a large rise in unemployment could bring inflation to a reasonable level.

But Cheng Tan's judgment differed from the market.

He had two reasons. First, the enormous fiscal and monetary easing in 2020-2021 provided a thick "cushion," and second, US household and corporate debt was mainly fixed-rate, so the impact of rate hikes in the short run was limited. Cheng Tan thought the market might be overestimating recession risk. To verify this hypothesis, his team did three things.

First, they calculated in detail the balance sheet stress across households and companies of different income levels in a high-rate environment, finding that the financial pressure from rate hikes was much lower than in comparable historical cycles.

Second, they broke down the drivers of high US inflation, finding that over 50% was still from the supply side; as the US restarted post-pandemic, this disruption would likely subside gradually.

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

On these grounds, Cheng Tan's team revised their baseline outlook for the US economy to "soft landing" by mid-2022 and continued to emphasize a strategic positive view on US equities.

The second example is about Trump and the "TACO" trade—It was 2019. By then, China-US trade negotiators had held multiple rounds, but Trump still unilaterally escalated tariffs on China twice. At the time, pessimism was strong in both Chinese and US capital markets—S&P dropped 3% in a day, the market thought Trump’s actions were unpredictable, and there was almost no chance for a deal between China and the US. But Cheng Tan then published a report called "TRUMPUT"—"Trump" and "put" options.

He had already noticed astutely that based on motivations, approval ratings, and patterns before elections, Trump could not infinitely escalate friction. Instead, it's more likely a trade deal would be reached. The market’s linear extrapolation and pessimism constituted a good buying point. The result? In late August, Trump executed "TACO" as expected, and in December signed the Phase I deal with China.

The third example is about Silicon Valley Bank—On March 10, 2023, SVB suddenly went bankrupt; the 10-year Treasury yield fell more than 20bps in a day, the S&P 500 dropped 3.3% in two days, and the market feared a new round of financial crisis.

Interestingly, Cheng Tan had written a report on SVB’s bankruptcy a day before it happened (March 9). His judgment was that while SVB was likely to fail, its issues were unique (serious asset-liability mismatch), and the problem assets were US Treasuries falling below par due to rate hikes. The Fed and Treasury had strong rescue ability and there was no “can’t rescue due to lack of authority” as in 2008.

The report concluded SVB’s bankruptcy would not evolve into a systemic financial crisis or change the global soft landing trajectory—and subsequent developments matched Cheng Tan’s forecast.

But there is no "perennial winner" in the market—even Cheng Tan, with a Peking University finance PhD, rolled and struggled through repeated failures. In conversations, he admitted that several misjudgments deeply affected him and even changed his whole research framework:

For example, in September 2019, the US repo market suddenly experienced a liquidity crisis and repo rates spiked 300bps in one day. Actually, Cheng Tan’s team had already predicted in mid-2019 that the Fed’s balance sheet reduction would soon hit its limit, and the money crunch would be the direct signal. At the time, they believed—surging rates would have a clear negative impact on equity markets.

But afterwards, it turned out that the extreme short-term rate volatility did not transmit to the stock market. This made Cheng Tan realize: the USD liquidity market is highly segmented. A liquidity squeeze in one sub-market does not necessarily spill over to others.

Another example: In March 2020, the Fed launched a series of unprecedented new liquidity support tools. But Cheng Tan’s team still relied on fundamental analysis and believed the US economy would fall into prolonged recession, so remained cautious on US stocks.

Looking back, it was actually the massive liquidity injections from fiscal and monetary policy during “COVID lockdown” that completely shielded the US economy. People were stuck at home, but had more time and money to buy financial assets online, and this ultimately drove a liquidity-fueled rally in US stocks and other risk assets.

Starting in 2020, Cheng Tan’s team began to track household fiscal income and retail investors’ balance sheets as part of their monitoring system.

This misjudgment deeply "stimulated" Cheng Tan, and made him realize the traditional macro framework was hard to fully explain the market phenomena back then.

This forced Cheng Tan to develop a more comprehensive research perspective: Macro policy, the financial system, and household sector balance sheets are different facets of the same system. If you ignore financial intermediary structure and capital flows, and only look at aggregate indicators, you may easily misjudge the market.

Explaining complex problems clearly

For years, Cheng Tan has served as an internal training instructor at SAFE. One principle he holds firmly: If a framework can't be understood by newcomers, it's probably not fully mastered yet.

In 2025, Cheng Tan chose to leave SAFE after ten years and founded TanTu Macro, starting to deliver research frameworks to a wider institutional investor audience.

Within just one year, he has held over 300 roadshows, served over 100 institutional clients directly, and his frequent and in-depth output even surpasses many top broker chief analysts. He hopes to transform the top-tier analysis framework once reserved for the "national team" into a cognitive weapon every professional investor can use.

Cheng Tan firmly believes: A good researcher must be able to grasp grand trends top-down and validate every intricate detail bottom-up.

Standing at the crossroads of increasingly complex global macro environments, macro thinking at the top level is no longer just a nice-to-have, but the ticket to entering the investment arena.

For this reason, WallstreetCN has specially invited Cheng Tan to give a masterclass in Shanghai on April 25, 2026: "Understanding the Underlying Logic of Global Asset Pricing through Dollar Liquidity"— distilling his ten-years-proven, late-night roadshow research framework for institutional clients into this 3-5 hour hard-core masterclass.

In this course, Cheng Tan will help you escape the "blind men touching the elephant" predicament:

Top-down grasp of trends: Insight into global macro theme switching logic, seeing the big picture.

Bottom-up validation of details: Penetrate the fog of money markets and liquidity, test the true resilience of fundamentals.

This is not only a course about the dollar and macro, it is also a cognitive "marrow cleansing." It will help you break down those tedious yet real details, revealing the operational truths behind the global financial "black box."

Risk warning and disclaimerMarket risk exists and investment requires caution. This article does not constitute personal investment advice, nor does it take into account individual users' particular investment objectives, financial circumstances, or needs. Users should consider whether any opinions, viewpoints, or conclusions presented here are suitable for their specific situation. Invest accordingly, at your own risk.