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 note explains a quotation describing mean-variance optimization as an estimation-error maximizer. It separates the optimizer’s response to estimated inputs from the sampling uncertainty of those inputs: all else equal, higher estimated returns, negative…
The document asks how to reproduce the profit and loss of a USDCHF position with a stated size of 1,000 by trading USDEUR and EURCHF instead. It notes the triangular exchange-rate relationship: USDCHF is the product of USDEUR and EURCHF. The practical…
The question considers how to apply the Kelly criterion when capital is allocated across several trading rules that generate multiple trades. It asks whether to assign capital to each rule in advance, size each trade according to its changing outlook, or…
The document presents an attempt to maximize expected portfolio return minus a risk-aversion-weighted conditional value at risk (CVaR) penalty. It uses the Rockafellar-style scenario formulation, with a threshold variable for VaR and auxiliary variables…
The document asks how to adjust individual asset returns so that their compounded cumulative return matches a chosen target, and whether there is a formal name for distributing the discrepancy. It contrasts multiplying each return by a common factor—an…
The document asks whether a global tactical equity allocation model should compare country equity returns in local currency, currency-hedged form, or unhedged form. The model may already include currency positions, so the choice concerns which return series…
The answer explains why Alphalens factor analysis can represent a long-short portfolio after factor values are demeaned. Each value is centered by subtracting the cross-sectional mean, then scaled by the sum of the absolute demeaned values. These transformed…
The answer presents Omega, Sortino, and Kappa as alternatives to the Sharpe ratio when return distributions are non-normal or asymmetric. Omega compares probability-weighted gains and losses around a chosen minimum acceptable return, using the full return…
The document outlines a proposed way to estimate a covariance matrix with a statistical factor model. It begins with a sample or robust covariance estimate, decomposes that matrix into eigenvalues and eigenvectors, rotates the factors, and retains only the…
The document distinguishes valuing a dividend-paying stock from valuing an entire corporation. The question supplies projected free cash flow to the firm, a constant perpetual growth assumption, and the weighted average cost of capital. The answer explains…
The document examines whether a portfolio of stocks with beta near two should return twice as much as the S&P 500. Under CAPM, a fully diversified portfolio with beta two is expected to earn twice the market risk premium, rather than necessarily twice the…
The document describes a way to represent industry neutrality as a constraint on equity portfolio positions. First, classify stocks using an established industry taxonomy, which may have broad categories as well as more detailed levels. Then create a…
The document asks how to interpret the angle between a vector of expected returns and the portfolio weights produced by mean-variance optimization. It draws on a working paper that uses vector geometry to study Markowitz portfolios and describes robust…
The document considers how fund-of-funds investors can distinguish correlation during favorable periods from co-movement during losses. Two candidate funds may have similar overall correlations with an existing holding, yet differ in whether their…
The document explains why Value at Risk can satisfy subadditivity when portfolio profits are jointly normally distributed. It expresses VaR at a confidence level below one-half using the position’s mean and standard deviation, then compares the VaR of two…
The discussion introduces quantization as a way to approximate a continuous space with a finite set of representative points. In the response, this idea is associated with centroidal Voronoi methods: each generator represents a neighborhood, allowing a…
The document explains state variables as the information needed to determine a value function and the decisions available in a dynamic problem. Examples mentioned include wealth, capital, dividends, physical position, speed, and fuel. The central practical…
To update portfolio weights after a period of asset returns, multiply each starting weight by one plus that asset’s return. These products represent the assets’ ending values relative to the portfolio’s initial total value. Sum the products to find the…
The document considers Monte Carlo simulation of portfolio utility when both expected returns and covariance are uncertain. Expected returns are modeled with a Gaussian shock, while covariance is scaled by a random factor centered on one. The central…
The document proposes a weekly, market-neutral equity strategy for a filtered Nordic stock universe of roughly one hundred names. It combines three equally weighted signals: changes in analyst recommendations, changes in forward earnings estimates, and…
The document explains why a monthly return computed from the first and last account values can differ from the sum of the daily percentage returns. The monthly holding-period return is found by dividing the ending value by the starting value and subtracting…
The note considers mean–variance portfolio optimization when assets trade during different hours, leaving many hourly return observations unavailable or recorded as zero. The questioner worries that treating closed-market periods as zero returns can…
The document asks how widely institutions use MSCI Barra equity risk models, ideally measured by the fraction of assets under management relying on Barra rather than competing providers. It reports a single published estimate from a 2018 paper on portfolio…
The document asks how to interpret a shrinkage estimator that blends sample average returns with a prior proportional to each asset’s volatility. Its central expression is a scalar coefficient multiplied element-wise by the vector of asset standard…