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 frames required return as a threshold expected return that makes an investor indifferent between holding a market portfolio alone and adding a risky asset. It starts with a probability model for the asset and market returns, derives the…
The document answers whether returns in Kenneth French’s data library are logarithmic. It states that the library’s returns are not log returns. Instead, Fama–French factors are built from portfolio returns, with the cited factor construction using…
The document explains how a factor model can produce a time series of returns for a selected characteristic, such as dividend yield. The key idea is to run a cross-sectional regression at each date: asset returns are related to the characteristic of interest…
The document explains why factor research often compares stocks with high and low values of a characteristic. A typical method sorts stocks at a point in time, then compares the subsequent returns of the high-characteristic group with those of the…
The document asks how to attribute the risk of a broad equity portfolio when its constituent sector and regional weights are unavailable. It proposes risk-based performance attribution: regress the portfolio’s returns on sector index returns and use the…
The document asks whether researchers can apply published Fama–French factors when their stock universe or screening rules differ from those used to construct the factors. One response recommends matching factor exposures to the investable universe, while…
The document explains why a portfolio’s estimated loading on the Fama–French size factor need not rank in the same order as its average company market capitalization. A factor regression measures how portfolio returns behave relative to the factor; it does…
The document clarifies how security-specific fundamental ratios such as price-to-earnings fit into factor modeling. In a cross-sectional regression, a ratio can serve as a security’s explanatory variable or loading, while the model estimates its relationship…
The document describes a two-stage Fama–MacBeth analysis of stock returns using market, size, and value factors. The first stage estimates each stock’s factor betas from time-series regressions; the second runs cross-sectional regressions at each date and…
The document compares two ways to estimate factor betas in equity risk models. A time-series method defines factor returns in advance and estimates each security’s exposure from its historical returns. A cross-sectional method uses current, normalized…
The document asks how to compare CAPM regressions for two portfolios when their intercept estimates and adjusted R-squared values point in different directions. For P1, the intercept is reported as statistically insignificant and adjusted R-squared as 29%.…
The document discusses speeding up rolling time-series regressions for a large stock panel. The motivating workflow estimates each stock’s factor exposures over rolling 60-month windows, requiring enough nonmissing returns in each window, and repeats the…
The document considers how to assess whether short-horizon equity strategies earn alpha or merely load on common sources of return. It frames alpha and beta through regression of strategy returns on benchmarks, then suggests intraday analogues: a local…
The document asks why coefficients from separate regressions can match those from a regression containing several binary explanatory variables alongside fixed effects. The author reports that the dummy variables are uncorrelated by construction and observes…
The document describes a simple way to calculate and inspect stock momentum in R as part of a factor-investing exercise. The contributor uses adjusted price histories and momentum or rate-of-change functions from a technical-analysis package, then plots the…
The document derives the covariance matrix for asset returns from a linear factor model. Each asset return is represented by its factor exposures applied to factor returns plus an asset-specific residual. Assuming factor returns have a covariance matrix and…
The document considers how to weight stocks after sorting an equity universe into portfolios by market-to-book ratio and then comparing excess-return regression alphas. Equal weighting gives each company the same portfolio influence, which can increase the…
The document explains idiosyncratic risk as the portion of a stock’s return variability left after accounting for systematic market movements. In a market model, beta scales the market’s excess return to estimate the firm’s market-related return; subtracting…
The document explains how an expected return estimated with CAPM can serve as a risky discount rate in equity valuation. It describes estimating an asset’s market sensitivity from its excess returns relative to a market index, then combining that sensitivity…
The document asks whether the second stage of a Fama–MacBeth procedure remains valid when the first-stage asset returns use characteristics specific to each firm, such as size and book-to-market. It outlines the familiar two-stage setup: estimate asset…
The discussion explains how to use Fama-French factor returns in regressions on a portfolio’s returns. It recommends aligning the time series and fitting a multiple regression with an intercept, then interpreting the factor loadings, intercept, goodness of…
The document raises a comparison of CAPM, Fama–French three- and five-factor models, and Carhart’s four-factor model using time-series regressions on size and book-to-market portfolios. The proposed comparison examines estimated market, size, value,…
The document discusses what belongs on the left-hand side of a fundamental factor model regression. It contrasts gross stock returns with returns in excess of the risk-free rate, then asks whether the average excess market return should also be removed…
The document clarifies the meaning of pricing errors in the Fama–MacBeth asset-pricing procedure. At each time period, a cross-sectional regression relates assets’ excess returns to their factor exposures and estimates period-specific factor premia. The…