How much do we really know about corporate bond returns before the modern data era? Until recently, the answer was: not enough. Most empirical work in corporate bond pricing has relied on relatively short samples, especially the post-2002 TRACE period, leaving open the question of whether observed risk premia are robust over longer horizons. Ghaderi, Plante, Roussanov, and Seo (2026) Ghaderi, Plante, Roussanov, and Seo (2026) address this limitation by constructing a historical database of U.S. corporate bond returns from 1895 to 2022. Using hand-collected monthly bond quotes from sources such as the Commercial and Financial Chronicle, Standard & Poor’s Bond Guide, and Mergent/Moody’s Bond Record, they assemble a large panel of corporate bonds that allows for a much longer view of credit risk, return predictability, and factor pricing in fixed income.

If you’ve ever scrolled through Polymarket or Kalshi wondering whether the “wisdom of crowds” is actually wisdom—or just organized noise—you’re not alone. A new paper, “How Wise is the Crowd? Bias and Edge in Prediction Markets,” tears into the microstructure of modern prediction markets to ask a practical question: Who’s actually making money, and who’s just paying for the privilege of being loud? By engineering a high-frequency data pipeline that ingests tick-level order flow, on-chain wallet histories, and social commentary across decentralized finance and regulated venues, the authors expose structural inefficiencies that most traders overlook. The verdict? Market efficiency in Web3 betting isn’t dead—but it’s wearing a very clever disguise.

Quantitative investors usually start their research by analyzing individual trading strategies. They compare performance, risk, implementation complexity, market exposure, and the economic intuition behind each anomaly. However, once historical equity curves of individual strategies are available, a different research question becomes possible. Instead of asking only which individual strategy looks attractive, we can ask how to allocate capital across a broad universe of strategies.

The Quantpedia API makes this type of analysis practical. It provides access not only to strategy metadata, but also to historical strategy equity curves. Therefore, users can move from strategy discovery to systematic strategy portfolio construction.

Artificial intelligence is gradually changing the way quantitative researchers interact with financial data. Instead of manually browsing databases, comparing strategies one by one and filtering spreadsheets, modern research workflows increasingly rely on conversational systems capable of retrieving and summarizing structured information automatically.

One practical application is combining the Quantpedia API with an LLM such as ChatGPT, Claude or Cursor AI to create a lightweight quant research assistant. In this setup, Quantpedia API provides structured access to quantitative trading strategies, performance metrics, classifications, equity curves, trading codes, and related research metadata through the official Quantpedia API, while the LLM acts as a conv

Quantpedia Days 2026

– Celebrate with us the relentless pursuit of knowledge and ingenuity
– You can now subscribe to any of our services, be it 3-, 12- or 36-months Quantpedia Prime, Premium, or Pro subscription, and get the same 2nd subscription for your co-worker or fellow researcher for free – an offer valid between 28th May and 5th June 2026

Welcome to the Quantpedia Awards 2026 winners announcement. For the third time, we are proud to celebrate excellence in quantitative research and recognize the researchers behind innovative studies in quantitative trading. We are also pleased to see that the Quantpedia Awards have become an established and recognized brand within the quant community. This is the moment we have all been waiting for: who made it into the top five, and what will the authors of the winning papers receive?