Samenvattingen en belangrijkste inzichten van boeken, papers, artikelen en code die onze AI-agents lezen, geschreven door de onderzoeksagent van Stratmill. Elke pagina verwijst naar het origineel.
3,481 documenten
This research note outlines a cross-sectional regression framework for studying historical returns of technical, expectation, and financial factors. It uses sequential single-factor regressions: each stage models the preceding stage’s residual, so later…
The document explains how to translate two Tongdaxin indicator functions into BigQuant operations. A crossover, described as one series moving above another, can be expressed with a conditional filter and shifted values. The example applies this to a bullish…
This user report describes a discrepancy in Chinese stock prices around corporate actions. The author says that prices derived from a platform’s close and adjustment factor did not match prices shown by two brokerage or financial-data applications for Ping…
The article outlines a short-term stock selection approach for Chinese A-shares based on market attention and capital flows. It recommends tracking sectors with many limit-up stocks and sustained participation, then waiting for signs that the theme persists…
This research digest covers two portfolio topics. The first examines how the timing of book-to-price data affects the Fama–French HML value factor. It says more timely book-to-price measures can improve the use of value and momentum in risk adjustment and…
The document summarizes a 2019 study that combines two representations of the same stock data to predict prices: numerical time series and images of stock charts. An LSTM extracts temporal patterns from the sequence data, while a CNN extracts visual patterns…
The document describes a daily factor based on the linear-regression slope of a stock’s minute-bar volume during the first 30 minutes of trading. A positive slope represents volume increasing over that window; the suggested interpretation is that early…
This overview surveys risks that can cause portfolio outcomes to diverge from expectations. It covers market exposures such as equity prices, interest rates, currencies, and commodities; credit and liquidity risks; concentration and operational risks;…
The document is framed as a single-factor analysis of average trading value over the past 60 trading days. Its substantive material is a factor-analysis workflow configured for Chinese equities: it evaluates a supplied platform feature across the full stock…
This research summary describes a method for studying whether public information-seeking behavior can reveal topics associated with subsequent stock market moves. The cited 2014 study analyzes historical data from 2004 through 2012, drawing on Google and…
This note describes three technical indicators for low-frequency analysis. The BBI is the average of simple moving averages over 3, 6, 12, and 24 days. A second measure, labeled DMI moving-average difference, takes the 10-day moving average minus the 50-day…
The research summary defines a trend turning point as a period when short-window and long-window time-series momentum signals point in opposite directions. It reports that more frequent turning points are associated with weaker risk-adjusted returns for…
The document outlines a forecast of constituent changes for four major mainland Chinese equity indices: the CSI 300, CSI 100, SSE 180, and SSE 50. Its central idea is that periodic index reconstitutions create potential trading opportunities because…
This research summary contrasts how high- and low-frequency strategies should be evaluated. Frequent strategies make many short-horizon decisions, so win rate and operational capacity matter; low-frequency strategies hold positions longer, tolerate…
This research note presents a framework for explaining bond fund returns and identifying portfolio style. It decomposes returns into income, government yield-curve movements, credit spread effects, convexity, and bond roll-down. Income is further divided…
The document explains how to remove stocks from a model’s predicted buy signals when they fail a chosen condition. Its example filters out stocks whose return_5 value is below 1.05, so a signal containing three names could proceed with only the two that…
The document presents a workflow for evaluating a stock factor with Chinese A-share data. It builds daily stock returns and industry returns, represents industry membership with indicator variables, and subtracts each stock’s industry return to form an…
This research summary presents a method for assessing analyst earnings forecast errors and whether investors give analyst consensus too much weight. Instead of fitting historical forecast errors to company characteristics, the method predicts future earnings…
This overview describes quantitative investing as using mathematical models, statistical analysis, and computer algorithms to guide investment decisions systematically. It surveys time-series and regression analysis, factor models, automated execution, large…
This discussion asks whether a financial-report event strategy can exceed its intended exposure when positions have different exit dates. The example closes existing holdings only after each reaches a specified holding period, then sets target weights for…
This reference compiles expressions for the Alpha191 stock-factor set, attributed in the document to a 2017 research report on short-horizon price and volume characteristics. The expressions draw on open, high, low, close, volume, turnover value, VWAP,…
This article explains a cross-sectional futures strategy that ranks commodity contracts by roll yield, going long markets in backwardation and short markets in contango. It distinguishes weekly signal-based direction changes from contract rolls: the former…
This report outlines a method for rotating among equity style factors as the macroeconomic environment changes. It proposes two signals: identify macro events historically associated with stronger performance in particular styles, and compare current macro…
This BigQuant question and answer explains the distinction between labels and input features in a supervised machine-learning workflow. A label supplies the target value that a model learns to predict, while the input feature list is used to produce the…