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 describes a proposed evaluation of mean-variance portfolios using daily S&P 500 returns across a three-year dataset. The researcher estimates optimal weights, including global minimum-variance and tangency portfolios, from the first two years,…
The document asks how to turn a stock’s historical open and close prices into returns for mean-variance analysis. Its answer points to the standard rate-of-return definition and gives an example of calculating a period return from the starting and ending…
The document asks how investors could trade exposure to the number of beds in purpose-built student accommodation, treating bed count as the underlying rather than seeking exposure to rental income or total returns. The response says a bespoke instrument…
The document raises an organizational question about what a financial risk team should provide to portfolio managers. It names common market-risk measures, including volatility, value at risk, expected shortfall, and coherent risk measures, then asks how…
The document considers an investor with exponential utility who allocates an amount to a risky payoff while keeping the remainder in a risk-free asset. Its central result is that the optimal amount invested can be independent of initial wealth. The answer…
The document asks how portfolio construction can limit reliance on expected-return estimates, which are subjective and can make mean-variance optimization highly sensitive to inputs. Minimum-variance optimization removes expected returns from its objective,…
The document considers a portfolio constraint under which each asset must either have zero weight or meet a positive minimum allocation. This creates a non-convex feasible set, so an ordinary continuous portfolio optimizer cannot represent the choice…
The document shows how the Euler condition in the Ramsey growth model follows from the first-order conditions of a discounted lifetime-utility problem. In the stated Lagrangian, differentiating with respect to consumption equates marginal utility to the…
The document explains why strict positivity of an optimal consumption vector matters in a proof connecting portfolio prices to state prices. Consumption is constrained to be nonnegative, so a strictly positive choice lies in the interior of the feasible set.…
The document describes implementing Konno’s mean absolute deviation portfolio optimization model as a linear program in a spreadsheet. It identifies the LP Simplex method as the solver approach, presenting MAD as an alternative risk measure for portfolio…
RAROC compares risk-adjusted return with economic capital, making it a risk-based measure of profitability. The document asks whether a target RAROC can be used to solve directly for the capital required, but the response does not provide a calculation…
The document describes a way to retrieve rolling Sharpe ratios for selected securities or portfolios from Bloomberg using BQL. Its example defines date iterations and requests rolling Sharpe calculations over several lookback windows, then returns the…
The document discusses why a mutual fund’s measured active return may fall as assets under management rise. One explanation is statistical: smaller funds have more variable outcomes, so they are more likely to appear among both unusually strong and unusually…
The document considers how to convert a private equity benchmark expressed as an equity index plus an annual premium into monthly benchmark returns. It contrasts adding a monthly equivalent spread with compounding the index return and premium, highlighting…
The document asks whether a newly listed stock with an expected return above its CAPM-required return should change an investor’s allocation. The investor targets maximum expected return subject to a volatility ceiling and initially holds a mix of the market…
The document discusses testing whether two funds have different Sharpe ratios, prompted by an R routine that offers HAC-based asymptotic inference and circular bootstrap inference. It raises a practical input question—whether to provide prices, returns, or…
The document explains why a quadratic-programming setup can mix two distinct mean-variance portfolio problems. Risk minimization finds the lowest-variance weights for a specified return, while risk-aversion optimization maximizes expected return minus a…
The document shows how to express a joint allocation across multiple portfolios as one quadratic program. It stacks each portfolio’s asset weights into a single decision vector, then writes constraints for distributing each asset’s total holdings, matching…
The document considers how to reallocate capital when a fund manager breaches a drawdown limit, but it does not provide a direct optimization rule for distributing the withdrawn money among managers whose allocations cannot be reduced. Instead, its answer…
The note examines whether investing an annual cash amount immediately or dividing it into monthly contributions produces a higher ending value under a positive annual return. The question arises from comparing geometric sums, converting the annual return to…
The note considers how to estimate the alpha of a portfolio split across US and German equities using Fama–French factors. It compares estimating separate country-level regressions and averaging their alphas, using global factors, or constructing a combined…
The document considers how to combine roughly ten signals across roughly one hundred assets before sending expected returns to a mean-variance portfolio optimizer with transaction costs and constraints. It suggests ridge regression or principal component…
The document asks how to adapt the traditional mean-variance portfolio objective when expected returns and covariance are specified for factors rather than directly for assets. It proposes combining factor returns, asset weights, factor exposures, and the…
The document describes constructing a portfolio from candidate asset returns to track a target return stream. It frames the task as index tracking through quadratic programming, using the covariance matrix of candidate returns and their covariances with the…