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.
4,510 documents
This stock-selection screen combines three conditions: membership in the metaverse theme, a prior-day close above the 250-day moving average, and an appearance on the previous day’s trading-rank list. The stated rationale is to focus on a popular sector,…
This stock-screening example selects shares with a daily high-to-low range above one percent, observations from 2021, and positive institutional net flow. It explains the range threshold as a way to find more volatile names and treats positive institutional…
This market snapshot connects Bitcoin’s price swings between $60,000 and $70,000 with policy headlines, spot demand, ETF holdings, and Mt. Gox repayments. It cautions that a large change in reported ETF holdings can reflect a transfer between investment…
The document describes an indicator that identifies round-number price levels across the historical range of a selected currency pair. Its premise is that traders may pay attention to psychologically salient prices, such as levels ending in several zeros,…
This Chinese-language article presents an equity screening rule that selects stocks with turnover between 3% and 12%, first-level bid volume greater than ask volume, and positive net main-fund buying on the prior day. It proposes taking up to 50 names from…
The document summarizes research on whether company managers’ tone in financial communications predicts equity returns. It describes building a sentiment index by applying text analysis to corporate reports and earnings-call transcripts, then comparing its…
This note outlines an equity screen requiring a 14-period RSI below 65, displayed buying volume at the best bid greater than volume at the best ask, and positive net buying attributed to major participants during the opening auction. It combines a technical…
This note describes a Chinese equity screen that excludes Beijing-listed shares, selects stocks with a daily high-low range above one percent, and seeks evidence of institutional buying. It frames institutional accumulation as a possible sign of…
The article explains how market data can support leveraged crypto strategies, focusing on margin trading, short selling, and futures and options. It describes how exchange price differences, varying settlement practices, and changing collateral or…
This Chinese-market stock screen selects shares with an intraday range above one percent, a session low between four and five percent below the prior close, and first-level bid volume greater than first-level ask volume. The stated rationale combines price…
This stock-selection method combines an RSI below 65 with first-level bid volume greater than ask volume, then filters by large-order net flow. The accompanying examples calculate RSI and compare buy-side and sell-side volume, while defining net large-order…
This document proposes selecting stocks whose turnover lies between 3% and 12%, whose best bid volume exceeds best ask volume, and whose price bottoms are rising. The rising-bottom pattern is treated as a sign of improving market confidence, while the…
This document outlines a stock screen combining three conditions: RSI below 65, first-level bid volume greater than first-level ask volume, and circulating market capitalization above 10 billion yuan. It interprets RSI as a price condition, the…
This Chinese equity-screening proposal selects stocks with a stated price-amplitude threshold and a ratio combining the previous day's turnover rate with current auction volume relative to prior volume. It keeps values within a specified range, then ranks…
The Investor Preference Index is presented as a long-term stock-market sentiment measure originally described by Cyril V. Smith Jr. in a 1997 magazine article. Its stated premise is that investor preferences can be assessed by comparing the performance of…
The question asks how to include exchange rates in a monthly investor sentiment index spanning the United States, the United Kingdom, China, Canada, Japan, and Australia. It considers using each country’s currency against the U.S. dollar, and asks what rate…
The document explores how economic indicators relate to sector ETF prices, focusing on surprising correlations between consumer sentiment and financial or consumer staples funds, and between housing starts and technology or staples funds. It describes…
The document explores whether behavioral preferences could shape asset pricing models and help explain differences in expected returns beyond conventional risk factors. It asks whether ideas from Prospect Theory, including loss aversion and sensitivity to…
The document outlines ways to study whether investor sentiment influences stock returns or returns influence sentiment. It distinguishes lexicon-based sentiment measures from methods that label text polarity using stock returns. The latter can create…
The document describes a challenge in training a model to classify stock news as a long, short, or no-bet opportunity while producing usable probabilities. Expected-value optimization can favor taking bets even when the estimated edge is tiny, because…
The document challenges a news explanation that linked a small oil price recovery with a risk-on move in the Australian dollar. Its answer argues that labels such as risk-on, risk-off, risk appetite, and market sentiment lack precise definitions in common…
The document compares two common ways to calculate a put-call ratio from options open interest: puts divided by calls, and puts divided by the combined put and call total. It also notes that the ratio can instead use trading volume or a selected subset of an…
The document introduces fixed effects and random effects as alternative ways to model differences across companies in panel data. Its example concerns testing whether Google search volume predicts stock returns, trading volume, or volatility for companies in…
The document considers whether a long history of earnings announcement timestamps can be used to predict future dates. Its answer argues that timing is not merely a calendar forecasting problem: firms may advance or delay announcements, and the timing itself…