Research Directions
Methods for asset allocation, risk parity, rebalancing, and portfolio optimization.
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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.
Read originalAQR has published a research article noting that the market often incorrectly analogizes tax-aware long-short investing (TA LS) to direct indexing strategies, clarifying two core misconceptions.
Read originalAQR 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.
Read originalAQR’s latest research, "Liquidity without Liquidation", shows that tax-sensitive long-short strategies can extract large amounts of cash without fully liquidating the portfolio, while maintaining pre-tax returns, target tracking error, and leverage levels.
Read originalRob 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.
Read originalAQR 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.
Read originalAswath 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.
Read originalIn 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.
Read originalThe 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.
Read originalThis 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.
Read originalThis 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.
Read originalThis 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.
Read originalThe 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.
Read originalIn 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.
Read originalThis 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.
Read originalThis 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.
Read originalPyPortfolioOpt 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.
Read originalBridgewater 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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