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.
5,922 documents
The discussion considers how to optimize a portfolio of currency exposures when a forecast sometimes points in the same direction against the U.S. dollar for every currency. Replies distinguish currency positions from currency pairs and emphasize defining…
The document explains portfolio terminology through examples involving dividend reinvestment, depositing cash, and selling one asset to buy another. It presents a portfolio as a collection of asset positions whose composition can change over time. In…
This discussion compares ways to summarize the price-to-earnings ratios of stocks in an index. For a free-float market-capitalization-weighted index used to represent an index fund or a portfolio holding stocks at index weights, it recommends the weighted…
This discussion explains how to calculate a portfolio’s expected return and volatility, then outlines how to find the global minimum variance allocation between two stocks. Expected portfolio return is the weighted sum of constituent expected returns.…
The document considers a mean-variance portfolio optimization with short positions allowed, linear equality constraints, and a bound on the absolute size of each position. It asks whether an auxiliary variable and a large penalty are needed to express the…
The document frames covariance forecasting as a trade-off between estimation error and model misspecification. A sample covariance matrix makes few structural assumptions but can be noisy when many assets or factors are estimated. A highly structured model,…
The document asks how to interpret the volatility term for a portfolio formed from two functions of a stochastic process. The process has drift terms and a Brownian component with coefficient B(t, X_t). Applying Itô’s formula to each portfolio component…
The document asks which platforms can support quantitative equity research and portfolio management while reducing time spent preparing data and handling foundational analysis. Responses name services for data, factor modeling, portfolio analytics,…
The document raises a conceptual question about the connection between portfolio theory and the Capital Asset Pricing Model. It describes portfolio theory as a framework for comparing portfolios by risk and expected return, accounting for diversification and…
The document considers an investor whose primary concern is whether portfolio value ever falls below the initial investment, even if the strategy later earns a positive return. It explains why variance, Sharpe ratio, maximum drawdown, and tail-loss measures…
The document compares active portfolio optimization framed around residual return and residual risk with formulations that maximize active return relative to tracking error. It focuses on whether a manager should constrain portfolio beta to one to separate…
The document examines formulas for two portfolios defined using a return vector, a covariance matrix, and a budget vector. One portfolio is described as maximizing the Sharpe ratio, while the other is described as minimizing variance. The author asks how…
The document asks how to solve a constrained portfolio optimization problem in R. The proposed objective minimizes portfolio variance while requiring zero covariance with a representative market index, fully invested weights, and an expected return above a…
The note explains how a portfolio turnover formula separates weight changes caused by trading from changes caused by asset returns. It first estimates each stock’s end-of-month weight under a no-trading assumption: its starting weight is adjusted by its…
The discussion addresses how to build a long history of monthly prices for the changing constituents of an equity index. It warns that authoritative historical membership data may require a paid data source, and that current-constituent price histories alone…
The document describes a factor-research workflow: build long-short strategy returns from cross-sectional z-scores of predictive factors, run principal component analysis on the time series of those returns, and retain a subset of components. The researcher…
The discussion considers how to select liquid, low-cost ETFs spanning broad asset classes and regions for testing a portfolio selection algorithm. It emphasizes choosing funds that closely track indexes with long histories, so older index data can extend a…
The document discusses why investors and fund managers compare performance with an index rather than judging returns only in absolute terms. Its answers offer several explanations: benchmarks help assess whether an active manager adds value over a passive…
The document asks whether single-period Markowitz mean-variance optimization is theoretically suitable for portfolios containing assets with different cash-flow and maturity structures. It highlights that bonds pay coupons, mature, and respond to…
The document asks how a portfolio Sharpe-ratio rule can be understood through marginal risk contribution. The rule under discussion says to consider adding an asset when its Sharpe ratio exceeds the portfolio Sharpe ratio multiplied by the asset’s…
The document interprets a formula for the weight of one asset in a two-asset portfolio optimized for risk-adjusted return. It examines how the allocation depends on both assets’ expected returns, their variances, and their covariance, with the question…
The document asks how to find funds or exchange-traded funds by asset class and determine how far back their historical data extends. The replies point to free historical price data from Yahoo Finance and mention libraries that can retrieve it. They also…
The document describes a replication attempt for a competitive stock trading-volume model with informed and uninformed investors. The author organizes the solution into steady-state Kalman filtering, solving for symmetric coefficients in investors’ quadratic…
The document presents an attempted minimum-variance portfolio calculation for stock returns. It estimates each asset’s market beta and residuals, forms a covariance estimate from a market factor and residual variances, applies covariance shrinkage, then…