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 addresses whether standard deviation satisfies the subadditivity condition: the risk of a combined position should not exceed the sum of the risks of its parts. It derives the volatility of the sum of two returns from their individual…
The document raises a portfolio construction problem: a daily or weekly rebalanced equity portfolio should target a level of idiosyncratic risk while controlling market and sector exposures and retaining some style exposure. It asks whether correlated…
The document outlines a risk-neutral pricing argument for a European derivative on a dividend-paying stock. Starting from a geometric Brownian motion model, it describes changing to a risk-neutral measure, expressing discounted stock and derivative prices as…
The document discusses building an affinity matrix to cluster stocks, with the aim of studying cohesion within and between ETFs and possibly supporting arbitrage research or risk modeling. It compares clustering on summary statistics such as average return…
The document examines why a weak predictor can be hard to estimate in a noisy return series, even when the assumed relationship is linear and observations are independent. It highlights unstable coefficient estimates and the risk of mistaking noise for a…
The document considers how to compare two funds designed to track the same index. It contrasts the standard deviation of return differences with regression fit statistics such as R-squared, and argues that a tracking metric should reflect the criterion being…
The document describes a researcher’s method for constructing monthly market excess returns while replicating Fama–French factors for German equities. The proposed calculation weights each company’s monthly simple return by its market value at the prior…
The document considers how to turn return-based factors into expected returns for portfolio optimization. The question proposes forming a long-short portfolio, perhaps from the highest and lowest ranked assets, and combining standardized returns with…
The document asks how to simulate future stock returns from an ARMA(1,0)-GARCH(1,1) model when the conditional mean and volatility are forecast. Its proposed one-step approach draws standard normal shocks and scales them by the forecast conditional standard…
The document points readers toward literature on how many stocks may be needed for diversification and portfolio optimization. It names Fisher and Lorie’s early study of return variability, identifies Statman’s work on the number of stocks in a diversified…
The document addresses how component value at risk (VaR) should be combined across assets and accounts. It confirms that, when the component VaRs are calculated consistently for the full portfolio, their sum equals the portfolio VaR. Accordingly, the listed…
The passage describes a cautious investment climate during the 2010s, linking investor concern about market declines to central-bank actions that supported asset prices and suppressed yields. The explanation identifies low-cost bank funding and bond…
Tracking error is the standard deviation of the difference between portfolio and index returns. Because standard deviation does not measure the mean of that difference, a portfolio can have zero tracking error while consistently underperforming or…
The document explains why combining two perfectly negatively correlated assets can eliminate portfolio volatility without forcing the portfolio's expected return to zero. Expected return is the weighted average of the component returns; correlation affects…
The document discusses how to calculate returns for a long–short equity portfolio. One response recommends tracking long positions, short positions, and cash as separate components. Cash can capture borrowing costs, other expenses, and flows; alternatively,…
The document asks how to assess a monthly equity portfolio’s estimated market beta and hedge ratio against the beta observed after the hedge period. The portfolio estimates beta from a two-year covariance and volatility window, then hedges its benchmark…
The document describes a question about simulating correlated prices for a portfolio in Excel. It starts with independent standard normal draws, combines them using rows of a Cholesky factor to produce correlated random variables, then uses those variables…
The document asks how to estimate the future price distribution of a basket of stocks. It presents a portfolio variance expression built from constituent weights, volatilities, and pairwise correlations of log returns, then raises a modeling concern: a…
The discussion considers how an allocation algorithm can flag input data that may be prices rather than returns. A simple check for negative observations can identify some return series, since prices are generally nonnegative, but it misses price paths whose…
The document distinguishes choosing a portfolio of risky assets from deciding how much to invest in that portfolio versus a risk-free asset. Asset allocation forms an optimal risky portfolio by weighing expected returns against covariance risk in a…
The question asks how to obtain the numerator in a market index dividend yield calculation, using Australian listed shares as its setting. The proposed formula divides the index’s indicated annual dividend rate by its current level. The response says to…
The document addresses how to translate an annual target excess return stated in basis points into an equivalent daily target over a 252-day year. It rejects both taking the annual number to a fractional power directly and simply dividing the basis-point…
The document raises how to allocate portfolio Value at Risk when returns are non-normal and modeled with a g-and-h distribution. It summarizes a 2013 result attributed to Doowoo Nam: after estimating the distribution’s four parameters, an individual asset’s…
The document challenges the idea that frequent turnover makes portfolio optimization irrelevant. It describes an integrated approach in which changing return forecasts feed a portfolio optimizer, and the optimizer itself produces trades. Active strategies…