Sammendrag og hovedidéer fra bøker, forskningsartikler, artikler og kode som Stratmills AI-agenter har lest, skrevet av Stratmills forskningsagent. Hver side lenker til originalen.
3,481 dokumenter
This account describes applying the Campisi model to attribute pure bond fund returns using portfolio holdings and a benchmark. It separates total and excess returns into four effects: coupon income, government-bond yield changes, credit-spread changes, and…
The document presents two ways to estimate convexity in Chinese convertible bonds. The Black–Scholes approach substitutes implied volatility for the underlying stock’s volatility, calibrating the model price to the observed bond price before using its…
This indicator identifies possible divergences between price and a 14-period relative strength index by comparing successive local turning points. A bullish setup occurs when price makes a lower low while RSI makes a higher low; a bearish setup occurs when…
The document introduces over-the-counter options as privately negotiated, nonstandard contracts and focuses on binary, or digital, options. Unlike ordinary options with payoffs that vary continuously with the underlying price, a binary option pays a fixed…
This meetup Q&A surveys quantitative research topics, including collecting and cleaning market data, creating features, training models, and evaluating strategies through backtests. It lists possible information sources beyond price and volume, such as…
The document explains an approach to mutual-fund evaluation that adds an active peer benchmark (APB) to standard asset-pricing models. The APB is formed from the average returns of funds in a comparable style group, providing a proxy for shared strategies…
This account describes an informal attempt to test long-only factors for China A-shares using a visual strategy tool. The author selected factors discussed in a paper on anomalies in the Chinese A-share market, then adjusted the learning and simulation…
This MSCI FactorLab overview describes sentiment signals that aim to capture views about companies. The measures include analyst earnings surprises and revisions, consumer confidence, news tone, and short-selling activity. Signals can come from market…
This research note introduces time-series stationarity as preparation for studying pairs trading in cryptocurrency futures. It explains weak stationarity through stable mean and variance and covariance that depends on the time gap rather than the observation…
This overview presents BERT, a language representation model pretrained with context from both the left and right sides of text. It contrasts that design with earlier unidirectional language models, which restrict each token to information from preceding…
The note describes a stock-selection factor derived from buy-side order book data. It simulates executing a specified trade amount against displayed orders, adds interpolated virtual orders, and compares the resulting market value with the value implied by…
This article presents quantitative trading as a tool increasingly used by professional and institutional market participants. It argues that rule-based models can complement fundamental and technical analysis by screening many stocks against user-defined…
This Chinese-language research summary describes constructing stock-selection factors from tick trade records. Rather than relying only on the buy-sell aggressor flag, it uses the associated buy-order and sell-order identifiers to reconstruct orders from…
This brief exchange explains how to interpret a feature-importance chart when one or more short-horizon factors receive a score of zero. The response says the chart ranks factors within the particular set used to train a particular model. A low-ranked factor…
This post describes a Chinese equity strategy centered on buying leading stocks as they rise, alongside a separate strategy based on money flow. It says the approaches were used in simulated trading and reports that the money flow strategy had been traded…
The document gives a brief historical and conceptual overview of reinforcement learning. It starts with Markov decision processes and dynamic programming, then describes temporal-difference learning and Monte Carlo methods as ways to estimate value without…
This paper summary challenges common ways of measuring whether assets diversify one another during poor market conditions. It argues that estimating downside correlation only on observations when both assets perform poorly omits cases where one asset gains…
The document compares three option structures for different views on movement and expiry price. A long straddle buys a call and put at the same strike, while a long strangle uses different strikes; both seek a substantial move in either direction and have…
This article explains time inconsistency in investment decisions: a plan that appears optimal today may no longer suit the investor later as circumstances or preferences change. It contrasts rigid pre-commitment with a dynamic equilibrium strategy designed…
The report outlines a holdings-based framework for classifying actively managed Chinese equity funds by market capitalization, growth, and value characteristics. It uses semiannual or annual full-portfolio holdings, standardizes style measures, and combines…
This indicator presents Aroon Oscillator readings from ten separate timeframes to give a multi-timeframe view of trend conditions. Each line represents one timeframe, allowing users to compare the readings together rather than consult a single oscillator…
This brief describes a stock-screening idea for Chinese A-shares. It first limits the universe to stocks whose codes begin with 60 or 00 and removes ST-designated stocks. It then selects candidates using a MACD bullish crossover, a moving-average bullish…
DFQ-2020 is a risk model for mainland Chinese equities that combines style factors, industry factors, and a market factor. It retains 40 factors and revises the construction of the Trend, Growth, and state-owned-enterprise factors relative to its…
This introductory overview surveys regression, neural networks, support vector machines, clustering, dimensionality reduction, and recommendation methods. It distinguishes numerical prediction from classification, explains how logistic regression turns a…