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.
5,018 documents
The document frames the search for stock portfolio signals as an optimization problem. An expression combines market or fundamental variables with unary and binary operators and parameter choices; evaluating it across stocks produces weights, which are…
The document examines an artificial-economy paper that uses quantum-mechanics terminology but includes a business-cycle update with a normally distributed random variable. The answer argues that the described economic model is not quantum in the physical…
The document addresses how to incorporate technical indicators, such as RSI, ATR, and moving averages, alongside historical returns when assigning portfolio weights. Its central suggestion is to use a model that converts selected inputs into expected returns…
The document outlines a basic approach to forecasting realized variance (RV) from an existing RV series. It constructs daily, weekly, and monthly lagged features: the previous observation and rolling averages over five and twenty-two observations, each…
The document discusses using nonnegative matrix factorization (NMF) to identify latent factors in stock data. It describes decomposing a positive input matrix into loadings and factors, and recounts attempts using stock prices and returns shifted to positive…
The document raises questions about the present and future roles of stochastic modeling in finance as machine learning tools become more capable. It considers whether the wider use of Black–Scholes may make it harder to find profitable opportunities by…
The document asks for an open source C++ Kalman filter implementation that can calculate likelihood, with functionality comparable to a named R package. A respondent recommends KFilter and points to its documentation. The exchange is brief and does not…
The document compares two methods from robotics and computer vision and cautions that they address different tasks. A Hough transform detects shapes or objects from observations; it can support tracking, but the answer describes it as lacking a prediction…
The document asks how to explore whether a time series component modeled as a Lévy process may instead have stochastic volatility. The described observations are uncorrelated increments, slight correlation in squared increments, and tails somewhat heavier…
The document describes a backtesting question in which repeated runs of the same classification model and data produce sharply different returns. It reports that Random Forest and other scikit-learn models produced a gain in one run and a loss in another,…
The document offers a starting point for a novice interested in algorithmic trading. It recommends studying Ernest Chan’s books and blog to learn how strategies are structured, how trading signals are generated, and how to backtest and interpret results. It…
The document shows how to derive moments of geometric Brownian motion from the moment generating function of its underlying Brownian motion with drift. For a normally distributed variable, the moment generating function has an exponential form involving its…
The document compares two neural-network asset pricing models that use firm characteristics and macroeconomic variables to estimate time-varying stochastic discount factors and tangency portfolios. It reports differing out-of-sample performance, portfolio…
The note considers whether to use overlapping or non-overlapping returns as the dependent variable in a predictive regression. Overlapping samples can create concurrency: observations may share return intervals and therefore are not independent in the same…
The document outlines several broad ways to use slow-moving fundamental information alongside more frequent market data in a quantitative trading model. One option is to combine both sources in a single model and carry each fundamental observation forward…
This exchange clarifies why properties such as unbiasedness and minimum variance matter even when a researcher observes only one historical sample. The accepted answer explains that estimator bias and variance are defined across repeated samples from a…
The document discusses a preprocessing choice in a pairs trading workflow that applies principal component analysis (PCA) to ETF returns and clusters the resulting factor loadings. The question compares a paper’s stated procedure, which standardizes returns…
The document considers whether machine learning can identify firm characteristics that sort stocks into portfolios with predictive returns, and whether it can estimate asset returns in a CAPM-style setting. Its answer focuses on factor discovery: regress…
The document asks whether RSI should be converted from continuous values into directional labels for a machine learning model. It describes the familiar interpretation of readings above 70 as overbought and below 30 as oversold, and considers assigning…
The document asks how stochastic-volatility models, particularly SABR, can be calibrated under the historical measure for long-horizon exposure simulations. It focuses on counterparty credit risk and economic scenario generation, where simulations may need…
The document compares rolling historical standard deviation with a GARCH(1,1) volatility estimate for use as a regressor in a cross-sectional study. GARCH models conditional variance as a function of a baseline level, recent squared residuals, and prior…
The document poses a parameter-estimation problem for simulating bond-index prices with a one-factor Hull–White stochastic differential equation whose drift, mean-reversion, and volatility terms may vary over time. The author proposes estimating the…
The discussion challenges claims about the best stock prediction accuracy by emphasizing that standalone accuracy figures do not establish a profitable, durable trading strategy. Machine-learning evaluations often assume data points are independently drawn…
The document explains how to estimate the chance that stock B moves up when stock A moves up. If the joint distribution of their prices or returns is known, the conditional probability follows from the joint probability divided by the probability of A’s…