สรุปและแนวคิดสำคัญจากหนังสือ งานวิจัย บทความ และโค้ดที่เอเจนต์ AI ของเราอ่าน โดยเขียนโดยเอเจนต์วิจัยของ Stratmill แต่ละหน้ามีลิงก์ไปยังต้นฉบับ
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This forum post asks how to choose among three paid equity strategies using platform rankings and reported performance figures. The examples contrast a highly ranked strategy with large cumulative gains but a substantial drawdown, a smoother strategy with a…
The document summarizes a study that derives a listed-company default probability factor, MPD, from the Black–Scholes–Merton option-pricing framework under stated assumptions. It evaluates MPD as a measure of corporate credit risk and examines how portfolios…
This post introduces the Accumulation Swing Index (ASI), also called the oscillating swing index, and lists open, high, low, and close prices as its inputs. It presents pseudocode that compares current prices with prior close, low, and open values, builds…
The discussion explains that weights in a multi-strategy portfolio can represent each strategy’s share of the combined allocation and can also be used to calculate a weighted portfolio return. Its example divides capital evenly among three strategies,…
The article explains stock price changes through macroeconomic forces such as growth, interest rates, inflation, policy, and global capital flows, alongside company performance, supply and demand, news, and investor sentiment. It describes how retail and…
This study summary describes using recurrent neural networks, including long short-term memory cells, to forecast stock movements separately by industry. The rationale is that firms in one sector may share common influences, while industries can differ in…
This report summary describes using gene expression programming, a heuristic inspired by biological evolution, to discover price-volume factors from short-horizon data. Because many candidate factors had weakly monotonic group returns or no excess return in…
This research summary presents a framework for classifying industry and thematic funds and using the resulting categories to form candidate investment pools. Fund contracts’ investment requirements serve as a core eligibility basis. The framework begins with…
This analysis proposes six dimensions for assessing Chinese brokerage firms’ monthly stock recommendations: absolute returns, return relative to volatility, sector selection, stock-level excess returns, ability to identify emerging winners, and selection…
This review of Asness and Frazzini examines how price timing affects the book-to-price measure used to form the HML value factor. The standard Fama–French approach pairs accounting book value with a prior fiscal year-end price, then updates portfolios…
This brief platform support exchange answers where to find the dividend_yield_ratio field after a user cannot locate it in the dividend information table. The response points to a precomputed stock factors table, explaining that the requested field is…
This article explains how to turn future returns into labels for supervised trading models, arguing that the label should match the instrument, trading horizon, liquidity, and costs of the intended strategy. It first describes using an n-period return as a…
This summary of a securities research report examines alternative data, focusing on company job postings. It describes a hiring-count factor tested over 2017–2021, reporting that preprocessing and filtering job listings improved its backtest performance. The…
The article compares two Chinese equity approaches built around market capitalization. The traditional strategy selects the smallest-cap stocks for a portfolio. The AI version uses a StockRanker ranking model with market capitalization as its sole feature.…
This report forecasts additions to and removals from the CSI 300, CSI 500, and SSE 50 indexes. It uses index construction rules together with stock trading and financial data to estimate which securities may enter or leave at a scheduled review. The document…
This research note outlines a machine-learning framework for timing equity factors. It discusses candidate signals such as valuation spreads between factors and correlations within factor pairs, then examines how those measures relate to subsequent factor…
This article describes three sources of perceived information advantages for quantitative funds: alternative data such as satellite imagery, online sales and shipping activity; automated monitoring of retail investor discussion and sentiment; and rapid…
This research review presents a model in which active fund managers trade more when they see greater profit opportunities, then examines whether turnover predicts later benchmark-adjusted performance. Evidence from a historical sample of US active equity…
This support note explains why entering an index symbol in a visual strategy builder’s code-list module does not select the index constituents. The module expects individual stock codes, or can be left empty to include all A-share stocks. As a result, using…
This post outlines a beginner workflow for developing a rule-based stock selection strategy on a quantitative research platform. It describes combining five selection rules, mainly aimed at finding stocks that have fallen and begun to stabilize, with some…
The article explains the Deep Momentum Network (DMN) approach to multi-asset trend following. Rather than predict returns and determine portfolio weights separately, the model outputs each asset’s next-period position, bounded between long and short…
This research summary explains feature selection as a way to reduce a model’s input set before training. It outlines filter, wrapper, and embedded approaches, with filter criteria including variance or entropy, statistical measures such as F-scores and…
This research summary compares a basic reversal factor based on roughly one month of returns with volume-weighted high-frequency and structured reversal factors. The structured version incorporates both momentum and reversal behavior in high-frequency data.…
The article argues that two features of China’s A-share market may hinder a durable, gradual bull market: quantitative trading and what it calls excessive corporate financing. It attributes the impact of quantitative strategies to their technological and…