Alpha Architect interprets Dmitriy Muravyev's 2026 working paper, which uses an in-house LLM replication pipeline to reconstruct the core results of 1,328 papers published in the Journal of Finance, Journal of Financial Economics, and Review of Financial Studies between 2000 and 2020, of which 1,005 were eligible for subsequent out-of-sample testing.
Read originalQlib v0.9.8 has been released, including security hardening completed at Microsoft's request, merging changes from #2339 and #2340. This version restricts the loading of executable recorder artifacts; loading artifacts from trusted storage requires explicitly setting trusted=True, and configuration-driven local .py imports also require explicitly enabling trusted: true. Before upgrading production workflows, please review the two migration guides. Source archives are available on GitHub, and PyPI packages can be queried separately.
Read originalQuantocracy has compiled recent quantitative research-related links as of October 5, 2026, covering 5 quantitative research pieces on different topics. The content includes a study on the century-long history of meme stocks, methods for constructing statistically valid backtests, market reactions following analyst downgrades, a comparison of 200-day moving average and volatility targeting strategies, and an introduction to the Orthogonal Clustering Risk Parity (OCRP) strategy.
Read originalThe SSRN paper "A Century of Meme Stocks and the Price of Coordination" proposes training machine learning models using market and company data to identify meme stocks, extending the traceable "memeness" indicator back to 1926.
Read originalQuantocracy compiled recent links related to quantitative research on October 3, 2026, covering five topics: a backtest of Warren Buffett's contrarian investment advice, the performance of a Nasdaq-100 polynomial regression band strategy, the causes of the asset growth anomaly, a test of the U.S. stock turn-of-the-month effect, and a method for calculating transaction costs in macro trading strategies.
Read originalQuantocracy has compiled the latest links related to quantitative research as of October 2, 2026, covering a range of quantitative research topics. These include 897,000 tests conducted on approximately 150 types of trading patterns, an analysis of the impact of 13 oil price surges on U.S. stocks since 1970, a study of asset performance during periods of rising bond yields from 1963 to 2026, and research on an attention factor based on the WallStreetBets herding effect and BERT sentiment.
Read originalQuantocracy released its latest roundup of quantitative research links on September 30, 2026, covering areas such as quantitative strategy replication, machine learning cross-sectional factors, quantitative trading podcast analysis, and correlation and volatility research. This includes the launch of the Quantpedia Awards 2027 competition, a replication of the intraday momentum strategy proposed by Gao et al. in 2018, and a systematic summary of the 335th episode of a quantitative trading podcast.
Read originalGoogle DeepMind announced the launch of the frontier large model Gemini 4 Argon on September 30, 2026. The model is designed for complex long-cycle workflows such as software engineering, legal and financial enterprise knowledge work, and cybersecurity defense, and is currently being phased in to trusted cyber defenders through the Fairwind Program.
Read originalThe article points out that data mining and AI-driven 'quant by feel' are identical in essence and philosophy, both attempting to skip the core step of understanding how markets work and profit directly from the market. A genuine trading edge requires identifying the counterparty being profited from, the reason they continue to pay, and the rationale for one's own ability to participate in the trade; such structural edge cannot be identified through backtesting optimization alone.
Read originalDrawing on two academic papers, the author identifies three structural limitations of using LLMs in trading research: First, training data is saturated with trading common sense and lacks mechanism-driven edge thinking. Second, across 35+ models, accuracy in time-series value tracking declines logarithmically as the number of historical updates increases, and neither prompt engineering nor expanding the context window can fix this. Third, outputs from 25+ models on open-ended questions show 71%–82% similarity, and models with different architectures converge to the same common answers.
Read originalThe author notes that AI has dramatically lowered the barrier to developing quantitative strategies, enabling fully coded and backtested strategies to be generated within minutes. However, cheap experimentation amplifies the multiple-testing problem, producing a flood of false backtest results that appear effective but are actually overfit to noise.
Read originalFinRL v0.3.8 adds a 2026 stock trading tutorial, accompanied by 3 scripts: FinRL_StockTrading_2026_1_data.py handles data downloading and preprocessing, and FinRL_StockTrading_2026_2_train.py trains A2C.
Read originalBridgewater, in collaboration with Eclipse, released the second part of its series on reviving U.S. manufacturing, pointing out that U.S. manufacturing lacks competitiveness due to high labor costs and a weak industrial base, and tariffs cannot bridge the cost gap. The report proposes leveraging mature technologies such as automation, computer vision, and additive manufacturing, as well as cutting-edge technologies like generative AI and embodied AI, to boost manufacturing productivity by 1 to 2 orders of magnitude. Scaling the application of these technologies is expected to take nearly a decade.
Read originalThe Bridgewater research team believes the AI bubble has not yet arrived, noting that the transformative technology and mature macro monetary environment needed to fuel a major bubble are already in place. This view addresses market concerns about AI valuations, commercialization, and slowing technological progress, while also highlighting risks of AI misuse and regulatory uncertainty. Bridgewater established AIA Labs in early 2023 with the goal of building a fully AI-driven investor that can outperform humans.
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