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20 OctTue
  1. arXiv q-fin67

    Conformal Kelly: Why Economic Performance Fails to Materialize When Marginal Coverage Targets Are Met

    The paper proposes using the half-width of conformal prediction intervals as a scaling measure for fractional Kelly position sizing with a leverage cap, and tests this rule under a pre-registered protocol. During the development phase, an autonomous LLM agent searched approximately 200 configurations on a 2016-2021 sample of 8 U.S. equity ETFs, with subsequent data sealed before the search, and the final configuration, 9 comparison portfolios, and conclusion mapping all registered before unsealing.

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  2. arXiv q-fin53

    A Study Based on a Sample of U.S. Stocks from 1999 to 2024: Disagreement and Compliance Classification Dynamics Across Different Islamic Finance Screening Rules

    This arXiv paper applies 7 sets of Islamic finance screening rules to a sample of 1.34 million monthly observations of U.S. securities from 1999 to 2024, and backtests and compares them on a unified threshold-anchored scale. The results show that the disagreement across the sets of rules reaches 35.9% across all compliant industry observations, and the disagreement in financial ratio screening alone reaches 47.3%. Under AAOIFI's market value denominator rule, changes in market value alone are sufficient to explain 65.6% of non-compliance events, and half of the denominator-driven non-compliances reverse within 3 months.

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  3. arXiv q-fin33

    On the Utility Problem in Markets with Instantaneous Price Impact

    For a discrete-time financial market model with trades having instantaneous price impact, the existence of a solution to the corresponding utility maximization problem is proven. This study removes the unnatural restrictions on market depth and resilience processes present in earlier work, with the non-standard feature of this problem being that the set of attainable portfolio values may not satisfy convexity.

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  4. arXiv q-fin37

    The Strategic Origins of Network Structure

    Researchers propose a game graph equilibrium framework that defines network structure as a solidified record of strategic interactions, proving that two types of games correspond to four network topologies, and K-class interactions correspond to K^K classes of topologies. This framework enables mechanism inference on low-dimensional game-theoretic manifolds through constrained block modeling, can reconstruct generative dynamics from observed networks, and performs well in real-world networks such as international trade.

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  5. arXiv q-fin52

    RegimeRank: An Interpretable Regime-Aware Ranking Model for Industry Rotation

    The paper proposes RegimeRank, an interpretable industry ranking model that frames industry rotation decisions as a task of ranking industry actions by future risk-adjusted active returns, using the forward-looking industry benchmark Sharpe ratio as the measurement metric. The model represents fundamental and technical signals through expert-defined time-series features, uses cross-attention to condition on learned macro factors, then models cross-industry dependencies through self-attention, while using expected gradient attribution to identify the contribution of individual signal-transformation pairs.

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  6. arXiv q-fin37

    The value of optimal trading filters and predictive signals

    This paper addresses the stationary quadratic trading problem with transient price impact, deriving optimal trading strategies using predictive theory tools, expressed as a combination of conditional predictions and explicit causal linear filters. The paper recovers the Gârleanu-Pedersen target portfolio under the exponential elasticity assumption, derives fractional trading filters under power-law impact decay, and clarifies the mechanisms determining the net realizable value of predictive signals under different trading cost scenarios.

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  7. arXiv q-fin54

    Evaluating the Substitution and Complementary Effects of TimeGAN Scenarios in Rachev Ratio Portfolio Optimization

    This arXiv paper evaluates the reliability and investment consequences of TimeGAN-generated scenarios in Rachev ratio portfolio allocation. Using 12 ETFs, it compares historical, substitution, and hybrid strategies under delayed execution and transaction costs. The study finds that generated scenarios underestimate volatility and tail losses and distort dependencies between assets; extending training only reduces reconstruction loss without improving cross-asset correlation accuracy. Substitution strategies increase turnover and are sensitive to the lower bound of the objective function denominator, and adjusting this lower bound does not consistently improve realized downside risk.

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  8. arXiv q-fin34

    Certifiable Neural Operators for Simultaneously Solving Infinitely Many Convex Programs: Logarithmic Rank and Linear Complexity

    This arXiv paper proposes proximal equilibrium operators (PEOs), constructed via finite-dimensional deep equilibrium layers, that can solve a family of convex optimization problems on separable Hilbert spaces. The paper proves that when input loss functions lie in a suitable infinite-dimensional set, PEOs can uniformly approximate corresponding solutions to arbitrary precision, with rank and width growing only logarithmically with the inverse of approximation error, and depth growing linearly. The related theory and trainability have been validated across three application domains: nonlinear PDEs, stochastic optimal control, and financial portfolio optimization and hedging.

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19 OctMon
  1. arXiv q-fin37

    Merton Consumption-Investment Problem under Different Stochastic Integral Conventions

    This study examines the Merton consumption-investment problem under the Itô, Stratonovich, and Hänggi-Klimontovich conventions, corresponding to stochastic integral convention parameter α = 0, 1/2, and 1, respectively. The study finds that if the drift term is transformed synchronously, the optimal investment strategy is unaffected by the choice of convention; if the drift coefficient value is fixed, the investor's opportunity set will change. Under logarithmic utility, consumption decisions are unaffected, but the optimal portfolio weights shift, and the stochastic integral convention cannot be identified using return data alone.

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16 OctFri
15 OctThu
  1. Moontower36

    Two Risk Premiums That Work Well Together

    This article analyzes the combination of risk premiums for TIPS and crude oil. Based on data as of October 9, 2026, the 10-year TIPS real yield is 2.91%, up 116 basis points from the same period last year, and the 10-year breakeven inflation rate is 2.33%. This dual-asset portfolio can enhance expected returns with virtually no additional risk, while also offering educational value for dynamic position management.

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14 OctWed
  1. Moontower63

    Key Takeaways from the Interview with LTCM Co-Founder Victor Haghani: Position Sizing Matters More Than Instrument Selection

    Moontower host Kris Abdelmessih breaks down an interview with LTCM co-founder Victor Haghani, arguing that investment decisions consist of two parts: instrument selection and position sizing, and that LTCM's failure stemmed from flawed position sizing rather than poor instrument selection.

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13 OctTue
  1. Robot Wealth51

    Core Questions That Change Trading Mindset: Exploring the Advantages of Independent Traders and Starting Approaches

    Author Kris Longmore shares content from his 72-minute interview on The Algorithmic Advantage podcast, looking back on an early experience at a Sydney traders' meetup where he was unable to answer a proprietary trader's question about his own trading edge. He later joined that proprietary firm as a researcher and partner, and summarized relevant insights after leaving to trade independently.

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12 OctMon
  1. CFA Institute Research and Policy Center: Publications41

    Where Have All the Portfolio Managers' LLMs Gone?

    Most portfolio managers and analysts in the investment industry have not yet adopted large language models in their daily work, with only a few well-resourced large institutions having implemented them. The article notes that LLMs can improve investment decision hit rates and long-term productivity, and that current barriers to adoption stem primarily from non-technical factors such as organizational processes, incentive structures, and psychological safety.

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  2. AQR: Research38

    Misconceptions About Tax-Aware Long-Short Strategies: The Misleading Analogy to Direct Indexing (Part 3)

    AQR has released the third part of its research series on tax-aware long-short (TA LS) strategies, clarifying two common misconceptions. As an active investment strategy, TA LS deliberately retains tracking error to express alpha models and capture excess returns and tax deferral benefits, rather than minimizing tracking error like direct indexing. Portfolio construction for TA LS requires balancing expected returns, risk, costs, and taxes, rather than simply realizing capital losses or deferring capital gains.

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11 OctSun
  1. Quantocracy24

    Quantocracy's Latest Quantitative Research Link Roundup as of October 10, 2026

    Quantocracy has released its latest roundup of quantitative research links as of October 10, 2026, featuring four related research pieces. It notes that the 30-year U.S. Treasury yield closed at 5.60% in the first week of October, the highest level since 2002; the remaining content covers research discussions on momentum portfolio development, pre-market patterns, and market stop-loss hunting.

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6 OctTue
  1. Quantocracy24

    Quantocracy's Roundup of Quantitative Research Links for October 5, 2026

    Quantocracy 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.

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1 OctThu
  1. Quantocracy31

    Quantocracy Latest Quantitative Research Link Roundup for September 30, 2026

    Quantocracy 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.

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21 SeptMon
  1. Bridgewater: Research & Insights53

    Bridgewater Research: The Impact of Carbon Pricing on Global Industries is Quietly Expanding

    Bridgewater released a research report pointing out that current carbon pricing mechanisms cover approximately 30% of global emissions, mainly dominated by the emissions trading systems of the European Union and China. The EU Carbon Border Adjustment Mechanism (CBAM) is amplifying its impact and promoting the adoption of carbon pricing worldwide.

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15 SeptTue
  1. AQR: Research42

    Will Inflation Make a Comeback? AQR Updates Inflation Sensitivity and Inflation Hedging Analysis

    AQR has updated its previous analysis of inflation sensitivity and inflation hedging, based on over 50 years of market data. While global inflation has fallen rapidly from its 2022 peak, inflation uncertainty persists in major markets, stock-bond correlations are at multi-decade highs, and traditional stock-bond portfolios are vulnerable to simultaneous losses during inflation shocks. Commodities and trend-following strategies perform strongly during inflation upcycles and can serve as inflation diversification tools, with implementation methods significantly affecting long-term returns and macro diversification outcomes.

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8 SeptTue
4 SeptFri
  1. Rob Carver：This Blog is Systematic26

    Rob Carver Completes Final Edits on Fifth Trading Book "The Art And Science of Trading"

    Rob Carver has completed the final edits on his fifth trading book, "The Art And Science of Trading," scheduled for official publication on December 1 and now available for pre-order. Aimed at the mass market, this book serves as the unleveraged counterpart to his third book, "Leveraged Trading." It covers an introduction to trading delta-one instruments such as stocks, ETFs, and cryptocurrencies, explains trading science concepts like backtesting and the Kelly Criterion, and explores the integration of art and science within a semi-automated trading framework.

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1 SeptTue
  1. AQR: Research44

    AQR Research on Investing in a Warming World: Analysis of Differences Between Mainstream Portfolio Carbon Metrics and Financed Emissions

    AQR has released the research Investing in a Warming World, noting that mainstream portfolio carbon metrics such as carbon footprint and carbon intensity are easily affected by rising stock market valuations, and may show improvement even when corporate emissions change little, potentially overestimating decarbonization progress when used as time-series measures of decarbonization. Financed emissions are more directly linked to real-world emissions outcomes. The study also explores approaches to constructing climate-oriented portfolios, noting that combining multiple metrics can provide a more comprehensive assessment of climate-related investment progress.

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11 AugTue
  1. Aswath Damodaran：Musings on Markets56

    Damodaran on the Situational Awareness fund blowup: the collateral damage of investment conviction

    Aswath Damodaran, a valuation professor at New York University, uses the complete journey of Leopold Aschenbrenner's Situational Awareness hedge fund—from explosive growth to liquidation following a blowup—as a case study to systematically break down the definition, sources, and real-world impact of investment conviction.

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3 JulFri
  1. Rob Carver：This Blog is Systematic66

    Testing Return Pooling Schemes by Asset Class and Portfolio Weight Distance

    In the 10th installment of his 2026 portfolio optimization series, Rob Carver conducts rolling out-of-sample backtests to compare four return pooling schemes. The test sample consists of 50 instruments randomly selected from 214 available instruments, with in-sample lengths of 5, 10, and 20 years and out-of-sample lengths of 1 and 5 years. The optimization uses shrinkage methods with an SR shrinkage of 0.5 and a correlation shrinkage of 0.75, while also incorporating the reverse rule of each trading rule, with only positive SR rules retained for optimization.

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29 JunMon
  1. Rob Carver：This Blog is Systematic59

    Is Rolling Exponentially Weighted Estimation Better Than Full Sample: A Backtest Examination of Portfolio Optimization Parameters

    The authors use random subsample backtests to examine the effectiveness of exponentially weighted window estimates of the Sharpe ratio for portfolio optimization. They compare the out-of-sample Sharpe ratios of EWM spans of 5, 10, 20, and 30 years against full-sample estimates across various in-sample and out-of-sample periods.

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  2. Rob Carver：This Blog is Systematic64

    Estimating Profit and Loss for Cross-Instrument Combined Trading Rules: A Method Based on SR Vector Distance and Backtesting Results

    This article is the eighth installment in the Portfolio Optimization Series. The author proposes using the Euclidean distance of SR vectors across multiple trading rules for each instrument to determine whether to combine cross-instrument returns, calibrates 95% critical values for different historical lengths using random data, and then obtains pseudo-instruments through hierarchical merging. The author compares three approaches—full merging, algorithmic merging, and no merging—using in-sample backtests across 214 futures instruments. The results show inconsistent out-of-sample performance, with longer sample data favoring no merging. The author ultimately concludes that the algorithm is highly complex and the grouping results lack intuitive logic, so it will not be adopted for now, and future research will explore simpler merging rules.

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23 JunTue
  1. Rob Carver：This Blog is Systematic74

    How to Automatically Identify Structural Breakpoints in Momentum Strategy Parameter Estimation

    This article introduces a method for automatically identifying structural breakpoints in the Sharpe ratio of trading rules, using independent t-tests to compare mean differences across subsamples, recursively splitting the sample while requiring each subsample to retain at least 5 years of data. The authors provide complete Python implementation code, using the momentum4 rule for CORN futures as an example, with the algorithm automatically identifying August 26, 1982 as the breakpoint.

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18 JunThu
  1. Rob Carver：This Blog is Systematic58

    Does Portfolio Optimization Need Clustering? A Backtest Examination Across Different Asset Sizes and Sample Lengths

    This is the sixth article in a portfolio optimization series. The author conducts hundreds of random backtests to examine the impact of different numbers of clusters, correlation shrinkage, SR shrinkage, and in-sample vs. out-of-sample lengths on out-of-sample Sharpe ratios across asset sizes of 20, 30, and 40. The results show that most parameter combinations have no significant differences, with only weak evidence that moderate clustering outperforms no clustering, and that the number of clusters should not be too large. The author ultimately recommends using an empirical value of 6 clusters when clear statistical evidence is lacking, while noting that clustering significantly increases optimization time.

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  2. Rob Carver：This Blog is Systematic60

    Equal-Weighted Averaging of Portfolio Weights Generated by Different Shrinkage Methods Fails to Outperform Single Shrinkage Approaches Across Multiple In-Sample and Out-of-Sample Backtests

    The author examines the effect of equal-weighted averaging of portfolio weights generated by different shrinkage methods in the Portfolio Optimization Series, comparing 9 shrinkage parameter combinations with 6 weight averaging combinations, totaling 15 approaches. The backtests use randomly selected 9 trading rules and random instruments, with 1-, 5-, and 10-year in-sample periods and 1- and 5-year out-of-sample periods, each group containing 4,000 random portfolios.

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15 JunMon
  1. Rob Carver：This Blog is Systematic62

    Comparing Portfolio Optimization Shrinkage Methods Using Real Return Data: Backtesting Conclusions for Monte Carlo, Bootstrap, and Double Shrinkage

    In the fourth installment of the Summer 2026 Portfolio Optimization Series, the author backtests Monte Carlo, bootstrap, double shrinkage, naive mean-variance, equal weight, maximum diversification, and EPO using real trading rule and instrument return data. Each test randomly selects 9 trading rules and one instrument, comparing portfolios over 1-, 5-, and 10-year in-sample periods and 1- and 5-year out-of-sample periods. The results show that under real data, correlation estimates are less stable than Sharpe ratios, requiring more correlation shrinkage rather than more Sharpe ratio shrinkage.

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1 JunMon
  1. Robot Wealth54

    Triangulated statistical arbitrage: how to identify genuine pricing deviations rather than mispricing

    This article is the fifth in a statistical arbitrage series for independent traders, focusing on the triangulated statistical arbitrage method. Using the energy sector spread network as an example, the author explains how to cross-validate multiple overlapping spreads to identify genuinely mispriced individual stocks, rather than treating deviations in a single spread as a direct trading signal.

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22 MayFri
  1. Robot Wealth53

    From Pairs to Portfolios, Part 4: A Triangulated Statistical Arbitrage Approach for Independent Traders

    This article introduces a triangulated statistical arbitrage approach. Using 8 spread groups constructed from 6 energy stocks as an example, the z-score signal of each spread is decomposed into opposite votes for the two individual stocks, which are then aggregated into long-short signals at the individual stock level. This allows direct trading of undervalued or overvalued stocks, replacing the traditional pairs-trading model of hedging both legs simultaneously. The approach can improve capital efficiency, reduce transaction costs, and increase signal utilization, while achieving variance control similar to pairs trading through a long-short basket portfolio. The article will further explain how to evaluate the quality of aggregated signals.

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27 FebFri
  1. PyPortfolioOpt：GitHub Releases20

    PyPortfolioOpt v1.6.0 released

    PyPortfolioOpt v1.6.0 has been officially released, adding support for Python 3.14 and pandas 3.0, while dropping support for Python 3.8 and 3.9. This version reduces dependency size; existing dependencies can be used as optional dependencies via the newly added all_extras dependency set. It also optimizes CLA calculation speed and includes multiple maintenance and documentation updates.

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22 SeptMon
  1. Bridgewater: Research & Insights52

    Bridgewater: Inflation Risks Enter a New Era of Structural Uptrend

    Bridgewater released a research report, arguing that the inflation center in the post-pandemic era has risen compared with that before the pandemic, 2% is more like a bottom than an upper limit, and the distribution of inflation outcomes is wider and skewed upward. The report compares the current environment with that in the 2010s, pointing out that restraining factors such as China's export of low-priced goods, shale oil production, and post-crisis deleveraging have weakened; while the medium-term expansion deficit of about 7% of US GDP, the compromised independence of the Federal Reserve, tariffs and trade restructuring, potential depreciation of the US dollar, and AI power and commodity demand constitute structural inflationary pressures.

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