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 explains how to align U.S. and Japanese market data when forecasting Japanese stock prices. Its central rule is to use only information available before the forecast target. Because the U.S. market closes while the next Japanese trading day has…
The document discusses combining forecasts from different trading methods, including sentiment measures, signal processing, and neural networks. One recommendation is to express forecasts in comparable units, such as expected returns or event probabilities,…
The document describes an olive plantation scenario-generation problem involving investment costs, delayed revenues, and temperature. The author proposes fitting a four-variable Gaussian copula to ranked observations of costs, revenues, temperature changes,…
The document addresses how a sequential financial series becomes usable in a supervised machine-learning model. The key idea is to form examples by pairing information available at a decision time with the later outcome being predicted. Each such…
The document discusses a three-class LSTM model for predicting financial trends from three time series. The question compares reported accuracy in a cited study with a lower result from an initial implementation and asks about model choices. The response…
The document examines how the sampling interval used to construct realized volatility relates to the estimated fractional differencing parameter d in an ARFIMA(0,d,0) model fitted to log realized volatility. The author reports that d appears to fall as bar…
This document asks what Lopez de Prado means by removing observations associated with rare labels in a machine learning workflow. It contrasts the idea with common financial labeling setups: binary go/no-go meta-labels and three-way short, flat, or long…
The document considers how to choose a parameter that divides observations into two groups, with the aim of making their empirical distribution functions as different as possible. It compares maximizing the largest pointwise absolute gap between the two…
The document describes a workflow for forecasting financial time series with technical indicators and machine learning: calculate indicators, remove highly correlated features, lag inputs, train with a rolling or expanding window, and evaluate on held-out…
The discussion frames hedge selection as choosing weights on available instruments to reduce the combined portfolio’s PnL variance. A simple example shows that when matching instruments exist, the method should recover offsetting positions; with imperfect…
The discussion considers missing daily prices in a futures portfolio, especially where spread legs become misaligned and create artificial profit and loss. It warns that imputing returns changes the observed distribution and can distort historical value at…
The document discusses how to feed time series with uneven observation density into a deep learning model, especially when each batch should retain historical context. It argues that the appropriate representation depends on the model and learning method.…
The document considers whether statistically simulated price histories can reduce the risk of selecting strategies that merely fit past winners. One reply warns that simulation adds a second assumption: the market model must be credible before a strategy’s…
The question describes applying principal component analysis to many correlated stock features while trying to predict the FTSE 100. It raises confusion about a result suggesting that no components explain 99% of variability, whether additional features such…
The document considers a proposed study path for an experienced engineer seeking a hedge fund role in statistical arbitrage. The plan focuses on real analysis, measure theory, stochastic differential equations, and finance-oriented Python, and asks whether…
The document surveys developments beyond the classic Black–Scholes and binomial option-pricing models. It describes extensions that add stochastic volatility, stochastic interest rates, credit risk, dividends, local volatility, jumps, or GARCH dynamics. It…
The document describes a replication of an equity statistical-arbitrage strategy in which a stock is hedged with a basket of ETFs. Regression residuals provide the trading signal, and regularized regression estimates the ETF hedge coefficients. The…
The document considers a mean-reverting process whose long-run level can shift after an unexpected event. It describes extending the process with a latent state variable, assigning a different mean-reversion level to each state and modeling the probabilities…
The discussion frames the task as explaining changes in a strategy’s daily profit and loss using candidate variables that were not used to generate its trading signals. It recommends first considering how the predictors relate to one another: dimensionality…
The document frames a modeling question about predicting the difference between a bond auction’s yield and its when-issued yield. The data are described as sparse: auctions occur monthly, and the author reports 850 results across maturities over a…
The document offers interview questions for candidates evaluating a quantitative trading group, especially when moving from academia into industry. Suggested topics include the markets traded, how research ideas become production models, constraints on…
The note discusses how to identify market periods that favor trend-following or mean-reversion strategies. One suggested approach is regime modeling with Hidden Markov Models, with two research papers offered as starting points. The answer also cautions that…
The document surveys several active research questions in quantitative finance. It highlights the transition from interbank offered rates to replacement overnight reference rates, which requires new or adapted pricing and risk models despite limited…
The discussion considers whether triple-barrier labeling should use a flexible vertical horizon alongside volatility-scaled profit and loss barriers. One response frames the vertical barrier as a practical limit on how long a trade event can remain tied to…