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 practical questions about forecasting one financial series with information from another, using Microsoft and the S&P 500 ETF as an example. It considers jointly modeling both series as a vector time series, and asks how auxiliary…
This short exchange responds to a request for detailed introductions to Partial Least Squares Discriminant Analysis (PLS-DA), including intuition, methodology, and examples. The answers point readers toward introductory materials, a presentation with…
The document proposes estimating the expected log value of one geometric Brownian motion conditional on the other reaching a specified endpoint. It writes each process in closed form, solves for the second Brownian motion’s terminal value implied by the…
The document discusses implementing the Longstaff–Schwartz least-squares Monte Carlo method for valuing American options. The author reports matching prices in one volatility case from the paper’s first table but obtaining lower estimates in a higher…
The document explores ways to divide price histories into regimes with different trend and volatility characteristics, motivated by subjective segments in a short-interval EURUSD chart. It notes that a single large price bar may be an outlier rather than…
The document distinguishes automatic differentiation (AD) from finite differences and symbolic differentiation. AD propagates derivative information through the operations in a coded function, applying the chain rule as the function is evaluated. In…
The document describes a stock trading signal that combines multiple predictors with different forecast horizons. At each tick, the weighted indicator total is compared with a positive and negative threshold to determine whether to buy or sell. Differential…
The document concerns choosing a mathematical topic for stock price forecasting and discusses geometric Brownian motion, differential equations, neural networks, ARIMA models, adaptive filtering, and factor methods. Its answer argues that neural networks…
The document asks how to search OHLC data for shapes resembling a selected candlestick pattern, even when the matching sequence has a different number of candles. It describes two broad routes: use a deep neural network for chart images, or convert candles…
The discussion considers which prices to use when building and evaluating a daily stock trading model. It recommends adjusted closes over unadjusted prices because adjusted data account for dividends and avoid treating an ex-dividend price drop as a…
The document surveys possible approaches to valuing a loan prepayment option. One response frames prepayment as an option on a bond and recommends calibrating an interest rate model, with tree and simulation methods raised as candidate valuation techniques.…
The document describes a walk-forward approach to out-of-sample forecasting in R. At each step, a fixed-length moving training window drops its oldest observation and includes the latest one. A model is fitted on that window, used to produce a forecast, and…
The document considers ways to make a trend-following system’s equity curve less volatile after observing substantial drawdowns. Suggestions include studying whether returns show persistence or reversal, using a secondary trend or equity-based stop to adjust…
The document gathers suggested books and papers for learning quantitative alpha design and advanced quantitative finance. Its starting list includes texts on active and quantitative portfolio management, expected returns, financial machine learning,…
The document suggests market data to consider when forecasting a crypto asset, using Bitcoin as an example. For perpetual futures, it points to the contract’s pricing mechanism, including funding rules, funding intervals, and the related spot price. These…
The document investigates why the root mean squared error of an Euler simulation of geometric Brownian motion appeared to increase as the number of time steps rose. The simulation compared the Euler endpoint with an endpoint generated from the exact…
The document raises a modeling question about reinforcement learning for optimal execution. It asks whether the Markov property commonly assumed in this literature is justified, and whether empirical work compares non-Markovian models with fewer state…
The document addresses a software question about estimating a multivariate VECH GARCH(1,1) model in R. The question describes the model as representing residuals and a covariance matrix through a vectorized form of the matrix elements, and notes that the…
The document discusses whether using machine learning or other quantitative models in trading requires assuming the efficient market hypothesis (EMH). The responses distinguish the view that markets are broadly efficient from the possibility that temporary…
This discussion collects learning resources on applying hidden Markov models to financial markets. Suggested applications include detecting market regimes and forecasting changes in turbulence, inflation, and economic growth. The references describe…
The discussion explains why models can track a stock’s closing price closely while failing to predict its returns. Because consecutive prices tend to be similar, a regression using recent prices can produce predictions near the next close. That visual fit…
The document considers what expected return to assign a momentum strategy when using mean-variance portfolio optimization. It presents empirical estimation by backtesting the strategy, then updating the estimate with live trading results. Estimates may also…
The document raises a terminology question: quantitative finance programs are often labeled mathematical finance even though many practical tools appear statistical, including stochastic processes, probability, time series, multivariate analysis, and machine…
The document asks how to convert simulated uniform values from a fitted multivariate copula back into return observations. Its setup fits an AR(1)-GARCH(1,1) model to each return series, applies the probability integral transform to the standardized…