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 study applies a factor-timing framework from a 2019 paper to China A-share data. It parameterizes factor weights using external variables and compares two approaches based on where the information enters: a time-series model and a cross-sectional model.…
This short forum post asks how to export a backtest’s daily returns to a CSV file and where to place a suggested calculation in the BigQuant workflow. The proposed approach reads a performance DataFrame, adds one to the cumulative algorithm return, computes…
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-,…
This strategy ranks the full Chinese A-share universe using two cross-sectional signals: circulating market capitalization and five-day price momentum. It ranks each factor, adds the scores, and selects the highest-ranked 50 stocks. The portfolio assigns…
The document summarizes a study of deep value episodes, defined as periods when the valuation gap between cheap and expensive securities is unusually wide relative to its history. The study examines individual stocks across global markets, equity index…
This article surveys three uses of unsupervised learning in investment research: manifold learning, clustering, and matrix factorization. For visualization, it applies t-SNE to fund returns, projecting high-dimensional observations into two dimensions so…
This screening rule selects Chinese stocks when daily MACD is positive, the 20-day simple moving average is above the 120-day average, and amplitude exceeds the stated threshold of one. The post describes applying the conditions before the open to produce a…
A BigQuant user reports that a simulated run fails at a missing-data cleanup step, even though the backtest sample contains two rows. The discussion distinguishes the historical backtest data from the prediction data used during simulation, which may be…
This research summary tests whether hedge funds can time sector-specific returns and whether that ability predicts subsequent performance, flows, and survival. The study first removes market exposure from industry returns, then estimates fund-level timing…
This report summary presents a method for setting equity allocations across a retirement investment horizon when the investor has a specified terminal wealth goal. It works backward from that goal, optimizing allocations while controlling risk. Two objective…
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 article summarizes a study of machine-learning methods for fundamental equity valuation across 17 European countries. It estimates monthly fair values from 21 accounting variables, then defines a mispricing signal as the gap between model-implied value…
The article presents five claimed strengths of quantitative investing: building models from processed data and backtests, using machine learning to handle information, applying systematic rules to reduce emotional decisions, analyzing broad datasets, and…
The article describes five intraday equity patterns and offers rules for interpreting price relative to the opening price, the intraday average-price line, volume, and recent swing highs and lows. It treats an early spike and pullback that holds above the…
This article explains why individual investors may find it difficult to compete with quantitative trading firms. It highlights four claimed advantages: automated rules can reduce emotional decision-making; computational systems can monitor many securities…
This research note explains how genetic programming can evolve mathematical formulas from market data to discover equity selection factors. Starting with randomly generated expressions, the algorithm evaluates their fitness against a target and applies…
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,…
The paper combines return prediction and portfolio construction in a single neural network, aiming to reduce the decision errors that can arise when forecasts are optimized separately. It compares a model-free network, which learns allocations directly, with…
This article describes a market-level risk control for an equity strategy. It calculates the five-day cumulative return of the Shanghai Composite Index and uses that reading as a benchmark risk signal. If the index return falls below negative four percent,…
The post asks how to add an industry-strength ranking to a short-term stock momentum selection process. The proposed workflow first selects five stocks using a momentum strategy, then ranks them by the speed of gains in their primary or secondary industry,…
This Chinese-language research summary explains how to estimate consensus net profit from analyst forecasts and turn that estimate into equity-selection signals. It proposes weighting forecasts by report recency and by each analyst’s prior adjusted forecast…
This literature digest summarizes two research topics. The first examines whether approval of new IPOs affects existing Chinese stocks. Treating the IPO approval system as a natural experiment, the summarized study reports negative price effects on existing…
This weekly research summary compares fundamental and price-related equity factors across Chinese index constituent universes. It reports that valuation, profitability, and financial-quality factors were relatively strong during the week, while growth was…
A multi-factor analysis reports factor coverage below the required threshold, even though basic descriptive statistics and a null check do not reveal an obvious problem. The suggested diagnosis is that a feature extraction step removes rows containing…