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 compares drawdown objectives with minimum variance and expected shortfall in portfolio allocation. It presents several reasons drawdown may be less common: variance is a familiar target, drawdown optimization can be computationally demanding,…
The problem asks how to choose a fraction of current wealth to stake each day in an even-money game with a favorable win probability, so as to maximize the chance of reaching a target within a fixed horizon. One answer proposes a target-seeking sequence: bet…
The document proves that a constantly rebalanced long-only portfolio’s geometric average return is at least the weighted average of the constituent assets’ geometric average returns. It formalizes the claim for any number of assets and multiple holding…
The document asks how to choose portfolio weights that maximize expected return for a specified level of variance. The response identifies the problem as Markowitz mean-variance optimization and describes the efficient frontier using expected returns and the…
The discussion recommends books and supporting materials for learning quantitative finance workflows in R, especially portfolio selection and volatility forecasting. Suggested resources include a practical guide to financial risk modeling and portfolio…
The document collects ways to examine how a large set of financial series changes its dependence structure over time. Suggested views include animated eigenvalue plots, date-by-date tables of eigenvector changes, and sorted eigenvalues that show how variance…
The document explains consumption-based asset pricing as a framework linking an asset’s value to how its payoff covaries with consumption across economic states. It addresses the objection that active traders and wealthy investors may not make decisions…
The document explains how principal component analysis can be interpreted in portfolio risk modeling. For a covariance matrix of asset returns, the first eigenvector is a unit-length portfolio direction with the greatest variance; each subsequent eigenvector…
The discussion asks whether a client's past investing history can identify their risk aversion for portfolio optimization. Its central point is that realized returns are a poor basis for inference: they reflect outcomes that occurred after the allocation…
The document addresses whether a project’s discount rate should be set by its financing source. It argues that debt financing, equity financing, or a blend of the two does not by itself determine the rate: the rate should reflect the risk of the project’s…
The document explains a first-order condition for an investor maximizing expected utility by allocating wealth between a risk-free asset and risky assets. Writing terminal wealth as initial wealth grown at the risk-free rate plus positions in each asset’s…
The document compares portfolio objectives that penalize variance with objectives that penalize a power of portfolio risk, while retaining transaction costs and portfolio constraints. It explains that when the risk penalty is a convex increasing function,…
The note asks whether one theory can explain momentum across securities, sectors, and asset classes, and discusses why behavioral, informational, market-friction, rotation, and tax explanations may overlap. The proposed attribution question is whether…
The discussion asks whether an asset with rare, highly skewed returns should be valued by its individual return profile or as part of a diversified group. It introduces risk-neutral valuation: in an arbitrage-free, complete market, prices can be represented…
The document explains how to organize constituent returns when researching strategies on stocks that enter and leave an index. For a simulated portfolio that follows membership, include a stock’s returns only while the strategy has exposure: add it upon…
The document compares ways to allocate capital between two strategies when only their average daily P&L and Sharpe ratios are known. It infers daily volatility from those inputs, then distinguishes three objectives: maximize expected profit, minimize…
The discussion asks how to calculate the distributional statistics of the difference or sum of asset prices modeled as geometric Brownian motions, including the effects of drift, volatility, and correlation. It notes that the difference of two GBMs is not…
The document surveys several active research questions in quantitative finance. It highlights the transition from interbank offered rates to replacement overnight reference rates, which requires new or adapted pricing and risk models despite limited…
The document compares two ways to estimate one-day value at risk for a portfolio of funds: reconstruct and model the underlying assets, or treat each fund as a tradable asset. Modeling holdings directly can provide more information about constituent risk and…
The document describes a Black–Litterman portfolio calculation in which market weights and a covariance matrix are used to derive implied returns, then inverted to recover weights. The questioner reports implausibly large recovered weights for an ASX 200…
The document considers how to target a desired portfolio volatility when optimizing a dollar-neutral long-short portfolio with leverage and per-asset weight limits. Scaling optimized weights afterward by the ratio of target volatility to recent realized…
The document outlines several ways to assess whether a candidate signal adds useful return information. Suggested checks include risk-adjusted performance and tail risk, regression against established factor models to examine the intercept, trading costs and…
The document outlines the Daniel, Grinblatt, Titman, and Wermers approach to evaluating stock strategies or funds against characteristic-matched benchmarks. It describes forming 125 portfolios sorted by size, book-to-market, and momentum, then assigning…
The document seeks an accessible but mathematically rigorous introduction to investment concepts for teaching. It lists return measures, including logarithmic and geometric returns; arithmetic and geometric expected returns; volatility and annualization; the…