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
This report examines whether mainland China’s northbound investor flows can inform A-share trading. It tests flow thresholds for market timing, ranks sectors and stocks by net inflows, and combines sector selection with stock selection. The timing approach…
The code builds a portfolio of instruments through a greedy selection process. It first scores each eligible instrument individually, then repeatedly adds the candidate that gives the highest estimated portfolio Sharpe ratio. Correlations enter through a…
The document explains simple investment return and annualized return. It defines return using beginning and ending investment value, with cash distributions such as dividends included in the ending value. For periods other than one year, it describes…
This Qlib configuration defines a Chinese equity workflow using the CSI 300 universe and its associated benchmark. It prepares Alpha360 features with robust score normalization and missing-value filling, while labels are rank-normalized after missing labels…
The document presents a fixed-income portfolio optimization problem implemented with a convex optimization package. The setup uses expected returns, a covariance matrix, a benchmark or reference rate, and constraints related to nonnegative positions and…
The document presents a portfolio optimization problem involving integer long or short futures positions, transaction costs, expected returns, covariance, position bounds, and a target volatility range. The proposed objective subtracts transaction costs from…
The document asks how to estimate the distributions of drawdown duration and depth under specified portfolio dynamics. It notes that the arcsine law for Brownian motion suggests extended periods below a prior high, and asks how the analysis might change for…
The document distinguishes portfolio returns across securities at one point in time from cumulative returns across time. In its example, the daily portfolio return comes from comparing the combined closing value of one share of each holding with their…
The document addresses why a PyPortfolioOpt workflow can return NaN weights, expected returns, volatility, and Sharpe ratio when optimizing a portfolio from historical data. Its example estimates expected returns and sample covariance from a table of asset…
The document discusses a student’s portfolio-management exercise involving three risky assets, expected returns, standard deviations, a tangency portfolio, and a mix of that portfolio with a risk-free asset. The response focuses on clarifying how to generate…
The document outlines ways to construct an equity portfolio using dividend characteristics. One example selects stocks using forecast dividend yield, calculated from expected next dividends relative to price, then weights selected holdings by market…
The document derives the first-order condition for an investor maximizing expected terminal wealth minus a risk penalty based on terminal wealth variance. With terminal wealth expressed as initial wealth multiplied by the risk-free growth and risky-asset…
The document raises the problem of exchanging trading signals between collaborators who want to preserve some confidentiality. A signal should not be judged by its standalone predictive strength alone: its value depends on how it overlaps with the…
The discussion asks whether mean-variance optimizers produce the same portfolio when one accepts expected returns, volatilities, and correlations while another estimates a covariance matrix from historical prices. The answer explains that correlation…
The discussion examines a claim that portfolio variance is always below the weighted average of the component variances. For two assets, it writes portfolio variance in terms of each asset’s variance and their correlation, then uses the fact that correlation…
The document addresses how to identify the portfolio fraction that maximizes expected log wealth in a continuous-time Samuelson model with a zero safe rate. A risky allocation generates drift proportional to its expected return and volatility proportional to…
The document considers whether daily strategy returns for a Sharpe ratio should use realized profit and loss from cash alone or include the market value of open positions. The answer favors marking the account to market each day, so gains and losses accrue…
This question considers how to estimate a company’s weighted average cost of capital in a market organized around Islamic finance. The author distinguishes transaction-based financing, where returns are tied to an asset purchase, from profit-and-loss-sharing…
The document considers how an investor might choose between a company’s equity and debt rather than allocating to each solely according to benchmark weights. Holding both securities can offset some of the company’s financing leverage, while giving the…
The document lists statistical and structural features proposed as checks on whether generated financial correlation matrices resemble empirical ones. These include a pairwise-correlation distribution shifted toward positive values, an eigenvalue spectrum…
The document explains how to include a zero factor-exposure requirement in a mean-variance portfolio optimization. For asset weights collected in a vector and asset betas in another vector, portfolio beta is their dot product. Setting that product to zero…
The document discusses how to interpret regression coefficients from Fama-French three-factor and Carhart four-factor models applied to a portfolio of large DJIA companies. The portfolio has a statistically significant market loading, a negative and…
The document asks whether a maximum-Sharpe portfolio built from a short rolling history can underperform equal weighting when the asset universe is small. Its response says this can happen and points to research on the difficulty of estimating portfolio…
The document explains why a single equity risk model may not serve every portfolio task. Commercial models bring substantial research and data-cleaning expertise, but their design may prioritize objectives that differ from a user's needs. The choice depends…