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 raises identifiability questions for linear Gaussian state-space models, where latent states evolve through a transition matrix and noise process and observations depend on those states. It notes that orthonormal changes of state coordinates can…
The note clarifies the timing and roles of variables in an OLS model built from principal component projections of asset returns. The dependent variable is the accumulated log return over the next H periods, from t+1 through t+H. The explanatory variables…
The note considers how to forecast currency exchange-rate volatility and compares broad approaches, including simple purchasing-power-parity ideas, factor models, univariate GARCH, and multivariate dynamic conditional correlation models. The author is…
The document explains how to interpret a correlation heatmap associated with many strategy backtests in a financial research paper. The questioner is unsure how thousands of index-versus-strategy data points are ordered in the exhibit. The accepted…
The document considers how to adapt an exponential smoothing parameter online, distinguishing a univariate estimator such as a mean or variance from multivariate regression. It argues that choosing a smoothing window is meaningful only when the data may…
The document explains market dynamics as the conditions and behaviors that influence a trading model’s parameters and the patterns it seeks to exploit. A model estimated on historical data assumes that relationships such as a market beta remain sufficiently…
The document asks how to interpret the Longstaff–Schwartz least-squares Monte Carlo method for pricing American options, especially how its regression target relates to the backward recursion described by Tsitsiklis and Van Roy. The central distinction is…
Option open-interest figures published by financial websites are generally daily snapshots, which limits their direct use in intraday analysis. The post raises the question of whether intraday estimates or suitable vendor data are available, motivated by the…
The document addresses obtaining downloadable, multi-year data for European companies to build a credit or default-risk model. Desired fields include company identifiers, country, industry classification, and year, alongside financial ratios, ESG measures,…
The document considers how to identify which candidate factor returns help explain mutual fund returns. It presents factor selection as a feature-selection problem and lists several approaches: rank factors using individual correlations, use recursive…
The document discusses preparing financial data for an LSTM intended to predict index direction or returns. It compares using price levels with returns or differences, and explains that the concern about values exceeding a scaling range reflects both…
The document presents possible master’s thesis directions in option pricing, aimed at a beginner seeking a manageable empirical or model-comparison project. One suggestion is to fit a GARCH volatility model to market data, use it in Monte Carlo option…
The document considers how to test whether an arbitrary daily time series predicts a stock’s future movement. The proposed workflow is to split observations into training and test sets, difference both series to work with changes, and examine correlations…
The discussion explains that a machine learning alpha signal can enter a stochastic control problem if it is given a temporal model that supports planning. One simple approach is to represent known time variation in trading opportunities and incorporate it…
The document connects weight of evidence (WOE) and information value (IV) with logistic regression and Kullback-Leibler divergence in binary credit-risk modeling. Under an independence assumption for predictors, class-conditional log likelihood ratios add to…
The document defines a supervised-learning target for limit order book data using the mid-price, calculated from the best ask and best bid. Each book update is treated as an observation, and the target records whether the mid-price rises, falls, or stays…
The document considers whether machine learning can produce several plausible future price paths from historical closing-price behavior, for planning trade adjustments. The answer describes the task as supervised learning: define and label outcome classes…
The document considers how to train a model to estimate broad US market sentiment from headlines, social media posts, and economic indicators. The proposed output is an ordinal sentiment signal with negative, neutral, and positive classes, potentially…
The document raises a question about estimating the constant, ARCH, and GARCH parameters of a GARCH(1,1) model with Markov chain Monte Carlo. It asks why a cited approach samples parameters using an auxiliary proposal distribution instead of a normal…
The discussion compares R with C++ and Python for quantitative research, data analysis, and production software. Contributors describe R as productive for exploratory analysis, statistical modeling, visualization, and working with established packages. They…
The document describes a researcher’s search for continuous-time stochastic volatility models suitable for simulation and estimation through indirect inference. The stated preferences are models that are reasonably easy to simulate and have relatively few…
The document surveys several ways to detect whether a systematic strategy’s edge is weakening. One approach tracks the information coefficient, the correlation between predicted returns and subsequent benchmark returns, and tests whether it remains…
The document considers how to model monthly prepayments for a mortgage-backed securities sector when the dataset contains millions of loans over several years. It asks whether to aggregate loan observations by month using weighted averages of explanatory…
The document raises a model-evaluation problem: a recurrent neural network appears to forecast familiar nonfinancial datasets well, yet forecasts of macroeconomic series and stock-index measures do not beat the persistence forecast that carries the latest…