Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
4,194 documents
The document considers whether investors with long horizons can trade liquidity and drawdown advantages for greater long-term returns. Its answer identifies potential benefits of less frequent trading: lower brokerage costs, possible tax advantages for…
The document seeks a broad inventory of reported market anomalies and points to research catalogs covering many proposed return factors. It highlights a study that frames the large number of factor discoveries as a multiple-testing problem, arguing that…
The document examines a linear factor model in which an observed vector is represented by an intercept, factor exposures, and residual terms. It asks how to interpret the condition that factors are uncorrelated with residuals, and whether mutually…
The document works through a derivation associated with Jegadeesh and Titman’s explanation of momentum: the expected cross-sectional covariance between a stock’s return and its prior return. It clarifies that the bar denotes a cross-sectional average, then…
The document distinguishes the regression meaning of alpha from the broader way traders use the term. In a single-benchmark model, portfolio returns are regressed on benchmark excess returns; beta captures market exposure, while alpha is the intercept…
The document examines whether the single-index model determines the covariance between two stocks from their market betas and the market variance. It explains the textbook formula by expressing each stock’s excess return as a market-linked component plus an…
The discussion addresses how to obtain historical Eurozone fixed-income index data for a multi-factor study of European bond funds when Bloomberg is unavailable and replacement ETFs lack sufficiently long histories. The response names Yieldbook index…
The document addresses whether factor betas that are imprecisely estimated in time-series regressions can still produce a statistically precise risk premium in a cross-sectional regression. The answer is yes in principle, but beta measurement error works…
The document raises questions about Merton’s derivation of the security market line from the tangency portfolio. Its focus is the covariance between an individual asset and the market portfolio: the reader asks why that covariance can be written as a…
This question describes fitting a time-varying multifactor return model with a Kalman filter in R. The example uses a hedge fund return series and two benchmark factors, estimates observation and state variances by maximum likelihood, then filters and…
The document examines a portfolio problem that seeks to maximize variance while requiring zero exposure to each listed risk factor. It explains why those constraints alone do not produce a finite maximum: if a feasible portfolio exists, scaling all its…
The document explores whether a high price-to-earnings multiple can benefit shareholders when a company’s earnings rise. It reasons that, if the multiple stays unchanged, each additional unit of earnings per share translates into a larger price change at a…
The document asks whether residuals from regressing equity returns on principal component factors can support a mean-reversion strategy, given the common regression assumptions of normally distributed, independent errors. The response clarifies that fitting…
The document explains how to read eigenvalues from a correlation matrix of stock returns. Each eigenvalue measures the variance associated with a corresponding mode of co-movement, while its eigenvector describes the combination of stocks that moves together…
The document contrasts quantitative investing with quantitative trading, emphasizing the larger capital base and capacity requirements of asset management. Strategies that exploit small arbitrage opportunities may not scale to the size of investment…
The document raises a methodological issue when comparing the goodness of fit of Fama–MacBeth regressions with that of a pooled panel regression. The author argues that the procedures do not estimate one shared regression: Fama–MacBeth runs separate…
The document addresses how to calculate average monthly percent excess returns for portfolios formed while replicating the Fama–French five-factor model. Its suggested workflow is to obtain historical portfolio prices or returns and a Treasury bill rate or…
The document raises a model-interpretation problem in an autoregressive distributed lag analysis of GDP and foreign direct investment. The author reports that selected lag coefficients have signs that change across quarters and asks whether this pattern…
The note surveys ways to approximate a hedge fund’s return stream using a different, potentially passive strategy. It groups prior research into linear factor models, nonlinear or less transparent models, and work emphasizing the difficulty of reliable…
The document asks how to choose the number of factors in an equity risk model when adding factors can explain more return variance and reduce specific variance. It distinguishes describing risk from explaining average returns: a factor may improve fit…
The document explains that Fama–MacBeth estimation does not require a special way to aggregate period-specific R-squared values. After estimating coefficients with the procedure, use the chosen coefficient estimate and the relevant observations to calculate…
The document asks how to measure monthly returns for two S&P 500 equity portfolios formed from the highest- and lowest-scoring ESG companies. The proposed portfolios are reconstituted annually using the prior year’s ESG scores, and the author wants to…
The document introduces approaches for judging whether a stock may be undervalued, including comparing price-to-earnings and price-to-book ratios with industry peers. Its example treats a utility company with a higher P/E than its sector as potentially…
The document addresses how to estimate correlations between company revenues across industries. Its practical approach is to obtain company-level revenue data together with industry classifications, then calculate correlations from those observations. It…