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 outlines a proposed historical equity backtest that starts with a total-market portfolio weighted by free-float market capitalization in a chosen year. It would then track that initial set of holdings over later years, reinvesting dividends…
The post asks how to maximize a portfolio return metric computed by a large R function with one bounded input parameter. The parameter is restricted to a range from 1.0 to 1.5, starts at 1.25, and only needs precision to increments of 0.05. The author…
The document considers why a portfolio VaR based on a covariance matrix and normally distributed market factors may exceed its stated confidence bounds more often than expected. It proposes decomposing portfolio risk into component VaR to identify which…
The document asks how to modify the closed-form Markowitz efficient-portfolio weights so that every asset weight is positive. The supplied formula uses expected returns and the inverse covariance matrix to describe the unconstrained efficient portfolio…
The document addresses which bond data to use when estimating a portfolio variance–covariance matrix. Its central recommendation is to model returns rather than covariance of raw prices. For longer-maturity bonds, historical returns from traded prices may be…
The discussion asks whether random-walk assumptions imply that dollar cost averaging must always underperform a lump-sum investment. The responses distinguish the broad idea of investing gradually from any particular schedule or stock-selection rule. A…
The document explains how cross-sectional regressions can help isolate an equity factor’s contribution when factors share exposures. To create a residualized factor, regress one factor component on other factors and use the residual scores. This removes the…
The document asks whether Kendall rank correlation can be used directly in place of Pearson correlation when constructing a covariance matrix by scaling correlations with return standard deviations. The response offers a conversion from Kendall’s tau to…
The discussion explains why an AA corporate bond yield can be used to discount corporate pension liabilities and how it relates to interest rates. A discount rate is an interest rate, but the appropriate rate depends on the liability and the purpose of…
The document recommends using a covariance matrix to identify whether a portfolio of trades is concentrated in the same factors or bets. The matrix can be estimated from a factor model or from the prediction signals produced by multiple strategies, giving an…
The document asks how computational cost scales for standard mean-variance portfolio optimization, whether minimum-variance portfolios are easier, and what runtimes or faster alternatives to expect. It characterizes MVO as a quadratic program and gives a…
The document asks how to define a stock’s beta relative to a market proxy such as SPY, and whether beta as a covariance-to-variance ratio equals the slope from a regression of stock returns on benchmark returns. It then considers hedging a long-short stock…
The document considers how much historical data to use when monthly portfolio rebalancing is based on Sharpe-ratio or mean-variance optimization. It does not identify a universally suitable window. One response emphasizes that expected returns and volatility…
The document compares covariance estimates from a pandas returns table and a NumPy array, where the results differ in shape and contain missing values. The core issue is that the two interfaces interpret the data layout differently: pandas computes…
The post considers whether regime-switching models can detect market shifts and support trading. It outlines a common approach: model market behavior using states with specified distributions, then estimate a Markov transition matrix. The intended benefit is…
The document raises questions about whether quantitative managers use the Black–Litterman framework, which combines a starting portfolio with investor views to form portfolio inputs. It observes that the method is often presented as a way for portfolio…
The discussion compares Bayesian and frequentist approaches in finance and argues that method choice should depend on the data, model, and question. Frequentist tools include least-squares estimation and hypothesis testing. Bayesian methods add prior…
The document concerns constructing a portfolio of individual bonds selected from a benchmark, with exposure to a chosen investment factor. The questioner seeks research on factor investing using actual bonds rather than exchange-traded funds or sovereign…
The document explains how to calculate a portfolio’s logarithmic return from its value at two consecutive times. With fixed holdings represented by weights, first calculate the weighted sum of component prices at each time, then take the logarithm of the…
The document seeks monthly market capitalization data for a set of equity, commodity, bond, and real estate indices. The intended use is to construct a value-weighted benchmark across asset classes for portfolio optimization and to estimate returns with…
The document outlines a framework for measuring corporate bond credit risk through CDS spreads. It describes deriving default and survival probabilities from CDS quotes, then pricing bond cash flows with risk-free discounting and a recovery component. Since…
The document considers how to assemble an ETF universe for historical portfolio-allocation backtests. One response suggests using a finance screener to sort ETFs by inception date, but cautions that requiring funds to exist by a chosen early date can sharply…
The discussion distinguishes bond valuation from portfolio weighting. A bond’s quoted clean or dirty price is a percentage of par, so multiplying that price by the notional held gives the market value of the position. For a portfolio’s dollar value,…
The discussion examines whether diversification can reduce portfolio risk when returns follow a Cauchy distribution. Unlike distributions with finite variance, averaging independent Cauchy variables does not narrow the distribution, so variance based on…