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Research-backed market ideas, separated from the usual noise.
Alpha in Academia is for readers who want to know what financial research actually shows—and what survives when you try to use it.
We do not begin with a market opinion and search for evidence. We begin with the paper, examine the data and method, and separate the result from the story built around it.
Two editions, two jobs
Every Saturday, the free Recent Academic Research edition maps the most useful new work across finance, economics, and quantitative research.
Every Thursday, paid readers get an implementation, replication, or deep-dive issue. These pieces ask harder questions: Does the result survive different assumptions? What happens after costs? Can the method be reproduced? Where does it break?
Choose your way in
1. Start with the research
Browse the Recent Academic Research collection for concise summaries of new papers and why their findings matter.
A good first edition is How machine learning finds a private company’s public twin, why uncertain forecasts make long-term rates overreact, what the VIX quietly leaves out, and why bond indexing only works at large scale.
2. Start with implementation
Why Knowing When the Market Trades Matters More Than You Think takes an intraday-volume forecasting problem from research idea to an implementable model. This is a paid issue with code.
3. Start with model risk
Beyond the Expected Value looks past a single option payoff estimate and works with the full probability distribution. This is a paid issue with code.
You can also browse the full archive, but starting with one of these paths is usually more useful than reading chronologically.
What paid membership includes
The Thursday implementation or deep-dive issue
The Saturday research roundup
Access to the paid archive
Code, notebooks, and data when they are part of the published analysis
Not every paid issue has code. We are rebuilding the historical code-and-data index so that files no longer have to be requested one email at a time. Until that work is complete, reply to the relevant issue if a promised artifact is missing.
If you are new, start with the research collection. If you already know the literature and care about implementation, start with one of the two paid examples above.