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This report describes a Chinese equity-selection process built from daily price and volume data. Genetic programming generates candidate factors: Rank IC is used to find factors with linear predictive relationships, while mutual information targets nonlinear…
This practitioner report reviews DeepAlpha, a neural model used to rank stocks from factor inputs, and compares it with other approaches. The author says a simple fully connected network appeared competitive with decision-tree and linear models, and…
This overview catalogs machine learning approaches that can be applied to quantitative investing, grouping them into tree models, linear models, unsupervised methods, deep learning, ensembles, and online learning. It links model families to tasks such as…
The document summarizes research on converting stock factors with strong negative-return associations into a long-only selection process. Because short selling is limited in the A-share market, it describes reversing the use of such signals: remove stocks…
This report describes a monthly stock selection model that uses a random forest, an ensemble of decision trees trained with bagging, to estimate each stock’s probability of rising in the next period. Its workflow covers feature and label preparation,…
This research review explains how earnings beta can measure a company’s exposure to systematic risk through the co-movement of its earnings with aggregate market earnings. It compares eleven earnings series built from realized or analyst-forecast earnings,…
This literature summary covers two separate topics. The first describes a framework for studying how mutual fund managers’ personal characteristics relate to performance, decomposing outcomes into overall results, return and risk, and timing and security…
This research summary describes an adaptation of Robert Whitelaw’s time-varying Sharpe ratio, or Tsharp, for timing the Shanghai Composite. Unlike a conventional Sharpe ratio, the measure estimates returns and variance through regression, making it…
This opinion article argues that algorithmic trading can harm investors who rely on widely known indicator signals or chase sharp price moves, while creating opportunities through the liquidity and volatility it adds. It describes three potential…
This study organizes fifty Wind order-flow measures into eight groups, including inflows, outflows, net active buying, and large-order net flows. It assesses their stock-selection value with industry-neutral portfolio sorts, cross-sectional regressions, and…
This document summarizes an ETF tracking workbook and describes the market landscape it was designed to monitor. It reports that broad-market and sector or theme ETFs formed the largest non-money-market categories, with examples of major index exposures and…
This market-monitoring report reviews Chinese equities during the early 2020 pandemic shock. It combines trading activity, valuation, turnover, sentiment, sector relative performance, fund concentration, and style-factor signals. Its readings describe…
The document reports a platform-specific issue in which an XGBoost classifier’s probability prediction function returns only endpoint values of zero and one. The user expected continuous class probabilities that could be used to rank securities, based on…
This introductory guide explains how machine learning can be applied to quantitative investing through a staged research workflow. Using a fruit-selection analogy, it describes gathering historical data, defining the prediction target, labeling observations,…
This discussion distinguishes machine learning for execution optimization from machine learning for directional trading. Execution begins with a known trade and seeks to reduce its cost; alpha generation must identify price direction and capture enough of…
This article describes combining two stock selection models designed for different Chinese equity market conditions: a rebound after an index decline, and a mild market pullback after stocks hit their daily price limit. Each model includes market environment…
This Chinese-language support post describes a feature-engineering question: how to count, over a recent window, the days when a five-day moving average exceeds a ten-day average. The proposed first feature assigns a positive or negative value according to…
This forum post raises a data-timing problem in a trading workflow. In the described backtest, a signal generated on one day is placed the next day, and pre-market processing can apparently retrieve that day’s opening and closing values through historical…
This article describes 25 reasons retail investors may lose money in equities. It groups them around information and research gaps, overreliance on one strategy, emotional and herd-driven decisions, limited planning and experience, concentrated portfolios,…
This review explains why diversification may help more during declines than during gains, and why average correlations can hide that difference. It recommends estimating an asset pair’s downside relationship by conditioning on the portfolio’s main growth…
This review describes a method for identifying crowded markets and distinguishing potential bubble build-up from unwinding. It measures sector or factor clustering from return covariances and principal components, then pairs that measure with relative…
The document raises a question about possible look-ahead bias in a stock-selection workflow. It points to a five-day return calculated from prices shifted forward in time and asks whether using that value as a feature in a test set leaks future information.…
The document presents a modified MACD indicator built by replacing standard exponential moving averages with a three-stage smoothing expression. It applies that transformation separately to closing prices over 12- and 26-period spans to form DIF, then…
This report summary examines a 2018 forecast of CSI 300 constituent changes and the potential trading flows associated with index funds adjusting their holdings. The forecast uses the index's periodic review rules, including measures of past-year trading…