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This equity research summary describes high-frequency factors built from combinations of price and trading-volume information. It presents two examples: an illiquidity factor adjusted using changes in the price path, and a trading-behavior factor derived…
This short learner response defines quantitative investing as using scientific inputs, such as mathematics, public opinion data, or financial information, together with software to make investment decisions partly or fully automatically. It identifies…
This research note proposes a way to study the microstructure behind equity reversal. For each stock, it examines the prior twenty trading days, calculates average transaction value per trade for each day, and separates days with relatively high and low…
This preliminary example applies a functional LSTM network to Chinese equity data, using the CSI 300 as its prediction target. The sequence input contains six market features—closing, opening, high and low prices, amount, and volume—across 30 time steps. The…
This podcast recap explains how Pyth Network supplies blockchain applications with financial market prices. Its publisher-driven design obtains first-party data from exchanges, market makers, and trading firms, then aggregates publisher updates for use by…
A BigQuant user reports that a group of order-size factors was missing during a scheduled factor task and remained unavailable during the first two dependent simulated-trading runs. The factors appeared in a later run that evening. The user says similar…
This research roundup summarizes two studies. The first revisits the equity size effect, the long-standing claim that smaller-capitalization stocks earn higher average returns than larger ones. It describes the study as examining competing claims about the…
This factor combines intraday price direction with trading volume concentration to estimate how effectively trading activity is associated with price movement. It separates volume-weighted returns during rising and falling minutes, then uses the balance…
This factor proposal estimates intraday active buying from minute bars: when a bar closes above its open, its traded amount is counted as active buying; bars closing below their open are assigned to passive buying. The daily active-buy ratio divides the…
The report summary presents a Campisi-style performance attribution model for bond funds using net asset value returns, an approach intended for cases where timely holdings data are scarce, especially for private funds. It describes five explanatory factors:…
This short forum entry concerns an error in a data-merging function used for rolling backtests. The shown approach reads a DataFrame from each datasource, selects rows whose date is on or after a supplied cutoff, resets the index, and then combines the…
This research summary describes a model for estimating the probability that large-cap stocks will outperform small caps over the coming month. It uses changes in short-term interest rates and broad-market volatility as leading inputs, with the aim of…
This research summary describes using market microstructure data to estimate the share of individual stocks held by public mutual funds. It reports that a model trained across the full stock universe has some out-of-sample predictive ability, and that…
This report compares ways to construct and standardize equity factors from analyst consensus forecasts for expected return on equity, net profit, profit growth, and two-year compound growth. It recommends time-series standardization to create…
The document explains how genetic programming can evolve formulas from stock price and volume inputs to discover candidate equity selection factors. It outlines the evolutionary cycle of selection, crossover, and mutations, and describes adapting a genetic…
The report describes a stock-selection approach that forecasts the coming information coefficient of seven style factors, then adjusts their weights according to expected effectiveness. Its features include each factor’s historical information coefficient…
This example describes an event-driven stock selection strategy that evaluates companies on the day their financial reports are announced. It selects stocks with year-over-year net profit growth below 1 and ranks candidates by that growth measure. The screen…
This document explains a simple trend-following rule using five-day and ten-day moving averages of closing prices. It buys at the next session's open when the shorter average is above the longer average, then sells at the next open when the shorter average…
This report describes a Probit model for predicting Chinese listed companies likely to announce high stock dividends or share splits. It groups predictors into company characteristics such as share price, total share capital, and listing age; growth measures…
This article compares active and index approaches to high-yield bonds, focusing on costs, liquidity, market-value weighting, and pricing inefficiencies. Its central case for active management is that thin trading can make index rebalancing costly and…
This brief BigQuant troubleshooting note addresses an HDF5 error encountered when using the Chinese stock filter component. Its practical guidance is to avoid placing the filter directly after a code-list component; instead, connect the data first and apply…
This support thread describes a BigQuant stock-ranking workflow that backtests successfully but fails when launched as simulated trading. The logs show that feature extraction runs, but later extraction steps return empty datasets; the ranker then receives…
This event description outlines a defensive quantitative fund of funds approach built around diversification across asset classes. It proposes combining macroeconomic conditions—growth, interest rates, inflation, and credit—with market-level measures such as…
This analysis studies the predicted 2018 midyear constituent changes for the SSE 50, CSI 300, and CSI 500 indices. It estimates potential price pressure with an impact coefficient and reviews historical rebalancing effects. The report finds that removal…