The Japanese Index jumped over 1,500 points this week.
The catalyst? Sanae Takaichi's ascent as Japan's likely next Prime Minister, signaling a return to Abenomics-style fiscal stimulus and aggressive growth policies.
Most of the move is likely reacting to headlines. Leaving opportunity for price dislocation on real market impacts.
Which sectors actually benefit from Takaichi's policy mix? Which subsectors within those sectors have the best risk/reward? Which specific names have government contract exposure, export leverage, or margin expansion potential under a weak yen regime?
This is where AI comes in.
We can now "read" Japanese primary sources leveraging AI!
I used ChatGPT's deep research mode to map the entire "Takaichi trade" from policy regime → sector exposure → subsector dynamics → individual tickers.
The goal: build a targeted long/short shortlist of names anchored in policy transmission mechanics, not just momentum chasing.
Here's the plan:
- Map Takaichi's policy levers (fiscal stimulus, strategic subsidies, BOJ pressure) to economic pathways
- Translate those pathways into sector-level exposures (who benefits, who suffers)
- Screen for individual names with the best fundamental setups and catalyst timing
- Build a "loser watchlist" for potential shorts or underweights
The Prompt
We are going to use our chain of prompt technique we like to use for deep research. One to prepare a prompt. The second to run the big targeted prompt.
Prompt 1: Prepare the prompt alongside ChatGPT
I need a deep research prompt to help me capitalize on the "Takaichi trade". I want to understand the implications of Takachi for equity markets, the sectors and subsectors most likely to benefit, and a list of tickers within each sector and subsector. For context, I am a sophisticated hedge fund investor looking to place long or short bets on this market change in Japan. I need you to be as objective as possible. I would prefer the deep research to cover primary sources particularly in Japanese, and can cite sources including not just primary but also secondary and blogs. Before you begin, please ask me any questions you may have so we can do a 10/10 job.
Prompt 2: Run the prompt in Deep Research
12 page prompt here: TakaichiTrade_Prompt_Dave_Wang.pdf
The Result
Full output here: Link
PDF output here: The “Takaichi Trade” Output.pdf
AI delivered a comprehensive policy-to-portfolio framework in under 30 minutes while I multitasked on other stuff.
ChatGPT identified these longs:
Semiconductors → Tokyo Electron (8035), Advantest (6857), Shin-Etsu Chemical (4063). Thesis: Explicit government support for chip fabs plus weak yen boosts tech exporters' earnings. Catalysts: subsidy announcements, new fab projects.
Defense → Mitsubishi Heavy (7011), Mitsubishi Electric (6503), IHI (7013). Thesis: National security spending set to soar with boosted procurement of military hardware and dual-use tech. Catalysts: defense budget releases, contract awards.
Infrastructure → Kajima (1812), Obayashi (1802), Taisei (1801). Thesis: Classic stimulus targets—fiscal spending on public works boosts revenues. Under Abe's 2013 stimulus, construction was a big winner. Catalysts: supplementary budget details, project bidding results.
Automotive Exporters → Toyota (7203), Denso (6902). Thesis: Yen at 150 adds tens of billions to operating profit given export volumes.
The loser watchlist:
Import-Dependent Retailers → Fast Retailing (9983), ABC-Mart (2670). Thesis: Low margins, can't absorb weak yen cost impacts. Costs rising faster than revenues.
Airlines → ANA (9202), JAL (9201). Thesis: Fuel costs priced in USD, heavy post-pandemic debt.
We've now been able to use AI to narrow down the ocean of "all Japanese stocks" to a highly targeted list for us to start due diligence!