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.
154 documents
This brief coding question outlines a way to calculate fund performance statistics from a price series. It first derives periodic returns from price changes, then uses a performance-analysis library to compute cumulative return, annualized return, Sharpe…
The document summary highlights two applications of machine learning in quantitative investing. First, it describes forecasting volatility to inform how capital is allocated among strategies, based on the claim that many strategies’ profitability is closely…
This factor note defines a volume-weighted measure of a stock’s intraday relative price range. For each instrument and date, it calculates the high-low range divided by the opening price, weights that value by volume, and divides the summed weighted values…
This study examines how Chinese and US equity markets move together, with a focus on whether movements in one market help explain later movements in the other. It uses Granger causality tests on market returns and volatility, reporting evidence of two-way…
The document presents a SQL approach to estimating annualized variance for Chinese stocks. It first calculates daily close-to-close returns for each instrument, then applies a rolling 20-observation standard deviation, squares that value, and multiplies by…
This weekly market note links macro conditions, Bitcoin exchange-traded fund flows, spot momentum, and options positioning. It reports that diminishing outflows from one fund and inflows to other funds accompanied a rise in Bitcoin, and discusses the…
The document presents volatility of volatility (VoV) as a proxy for uncertainty about an asset’s probability distribution, distinct from ordinary risk. It argues that investors tend to avoid stocks with greater ambiguity and may favor stocks whose prospects…
This article explains risk parity as an allocation approach that assigns comparable risk contributions across assets or risk factors, unlike capital-weighted mixes such as a conventional stock and bond portfolio. It lays out assumptions behind the method,…
This overview surveys empirical research on pricing stock-index options, focusing on how systematic stochastic volatility and jump risk affect option values and returns. It describes the evolution from Black–Scholes–Merton assumptions, in which the…
This study develops two equity factors from the relationship between intraday volatility and the return-to-volatility ratio. The “rebuilding” factor uses the daily covariance between that ratio and volatility to represent cases where volatility rises without…
This brief market note reviews Chinese 50ETF options conditions for the week ending November 2. It reports that the ETF closed at 2.594 after gaining 3.1% for the week, while the trading-value put-call ratio fell from 0.754 on October 26 to 0.575. The note…
This page summarizes two research topics from an overseas literature review. The first concerns systematic value investing: it describes examining common claims about the value effect, considering how diversified value strategies might be implemented more…
This article describes a stock-selection screen based on three conditions: a daily high-low range above a stated threshold, an opening price near the ten-day moving average, and at least five years since listing. The intended interpretation is that elevated…
This article reviews global equity factor performance in the third quarter of 2021 and compares defensive factor positioning. It reports that momentum and low residual volatility led the pure factor results, while liquidity lagged. Regional index outcomes…
This research digest summarizes three studies. The first compares earnings announcement returns (EAR), which capture market reactions to unexpected information in company results, with standardized unexpected earnings (SUE). It reports annualized long-short…
This study asks whether corporate bonds become more vulnerable to price swings when held mainly by open-end funds with illiquid portfolios. It builds a bond-level fragility measure in two stages: first estimating each fund’s portfolio illiquidity from its…
This indicator description explains a ZigZag variant that calculates turning points from the high and low values of Heikin Ashi candles. The Heikin Ashi period is configurable, and users can choose whether the Heikin Ashi candles themselves are displayed.…
The report describes a framework that combines strategic asset allocation with tactical timing. Strategic weights favor assets that are relatively strong under different economic conditions, while tactical adjustments change overweights and underweights. Its…
This weekly report reviews style-factor signals across several Chinese equity universes and summarizes recent quantitative fund performance and market conditions. It reports that price-and-volume factors, especially beta, were relatively strong in the week…
This report evaluates the Shanghai 180 index and the Hua An Shanghai 180 ETF. It describes an index weighted heavily toward financial companies and food and beverage firms, with large, liquid constituents. The report characterizes its recent one-, three-,…
The document presents a ProBuilder implementation of the SuperTrend indicator. It builds upper and lower bands around median price using average true range over a configurable period and a multiplier. The trend state changes when closing price crosses the…
This paper compares machine learning methods for forecasting equity returns across the market time series and the cross section of stocks. It frames risk premium measurement as a prediction problem and describes how high-dimensional predictors,…
This research summary describes a study of retail investor attention and asymmetric volatility in China’s A-share market. The authors use the balance of positive, negative, and neutral posts on online stock message boards to construct a proxy for asymmetry…
This study compares ARIMA, a multilayer perceptron, an LSTM, and an ARIMA-GARCH combination for next-day NVIDIA share-price forecasts. It describes ARIMA as a model for linear time-series structure, neural networks as ways to capture nonlinear patterns, and…