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 document asks how to combine trades from multiple stocks into a portfolio and track risk measures such as beta, conditional value at risk, and drawdown through time. Replies describe several software approaches, including using R packages for market data…
The document outlines a derivation connecting a benchmark's Sharpe ratio, a portfolio's Sharpe ratio, and the portfolio's information ratio. It begins with the identity that the squared portfolio Sharpe ratio is the sum of the squared benchmark Sharpe ratio…
The note explains how to incorporate skewness into portfolio optimization when a standard mean-variance objective does not capture the third central moment. It first expresses each portfolio return as a weighted sum of asset returns, then estimates portfolio…
The document asks how to estimate an investor’s stock portfolio weights from quarterly SEC 13F filings combined with historical prices. The practical issue is that some reported holdings have periods with zero trading volume. The author asks whether the…
The discussion outlines a project to build geometric-average indices for momentum and value stocks, then compare their behavior using correlations, volume and price-range measures, momentum, and moving averages. It weighs Python and R as tools for collecting…
The document asks why portfolio optimization commonly combines expected return with conditional value at risk (CVaR), rather than using median return as the performance input. The answer suggests that mean-CVaR optimization is familiar partly because…
The document considers a mean-variance portfolio objective that subtracts a square-root trading cost based on the change from current holdings. It asks how to handle that term with a quadratic programming solver. The answer explains that a quadratic solver…
The document outlines reasons a portfolio construction study may optimize only among risky assets. A method-focused paper may concentrate on solving a mean-CVaR problem efficiently and leave the choice of a risk-free rate outside its scope. In some markets,…
The document compares mean absolute deviation portfolio optimization with classical mean-variance optimization. It describes MAD as a linear-risk approach that can be formulated as a linear program, which may reduce calculation time for large portfolios. The…
The document asks why a portfolio optimization example fits a quadratic to the efficient frontier, derives a parameter from the fitted coefficients, and then uses that parameter in another quadratic program. The optimization itself balances expected return…
The discussion examines whether an unexpected rise in inflation should prompt investors to reduce a conventional stock-and-bond allocation. Its central point is that 60/40 is not inherently optimal: portfolio weights depend on investor objectives, risk…
This document describes an attempt to compute minimum-variance weights for a portfolio of three stocks using historical adjusted prices, return series, covariance matrices, and a quadratic programming solver. The proposed workflow first estimates weights…
This discussion presents principal component analysis (PCA) as a way to summarize variation in a dataset through orthogonal components. Its usefulness depends on which observations and variables are supplied: a matrix of asset prices can reveal broad…
The document shows how to calculate portfolio-level value at risk for different candidate allocations using component profit-and-loss observations. For each weight vector, scale each component’s P&L by its weight, sum the weighted values by observation, and…
The answer describes a Monte Carlo approach to value-at-risk for a bond portfolio. It recommends modeling historical yield changes through principal components, which represent curve movements such as level, slope, and curvature. Using these factors rather…
The document outlines a proposed workflow for estimating multivariate covariance under changing market regimes. It begins by fitting a GARCH(1,1) model to each series, removing estimated volatility, and fitting a hidden Markov model to classify observations…
The document asks how to translate a pairs-trading spread signal into positions in two assets. It defines a spread as one price minus beta times the other and considers entry signals when the spread moves above or below its mean by a standard deviation. The…
The document asks how to update the weight of a long or short swap after its value changes. It distinguishes a holding’s value relative to the portfolio’s original value from its weight in a portfolio whose total value has also changed. A long position with…
The document asks why adding assets reduces portfolio-specific risk in an arbitrage pricing theory setting. With equal weights and uncorrelated residuals, the portfolio’s residual variance falls as the number of holdings grows. For unequal weights, the…
The document addresses how an event-driven allocator can value free capital when future signals arrive unpredictably and positions tie up capacity. It frames the problem as approximate dynamic programming, with a compact state covering available capital,…
The document discusses symmetric orthogonalization as a way to turn correlated equity risk factors into uncorrelated components while seeking to preserve the original factors’ interpretability. It describes constructing a transformation from the inverse…
The discussion considers a portfolio allocated across stocks, high-yield bonds, and commodities, and asks how to estimate its volatility. One response notes that fixed-income returns need to account for bond characteristics such as duration and spread…
The document asks whether two-fund separation still applies when a risk-free asset is added to a portfolio of risky assets. The answer says it does: adding the risk-free asset changes the efficient frontier into the capital market line, and investors can…
The discussion examines why investors may prefer positively skewed returns and challenges the idea that skewness alone determines which distribution is better. It explains that rare extreme losses in a negatively skewed distribution can reduce expected log…