Equities (Individual Stocks)

Forecasting Liquidity Withdraw with Machine Learning Models ArXiv ID: 2509.22985 “View on arXiv” Authors: Haochuan, Wang Abstract Liquidity withdrawal is a critical indicator of market fragility. In this project, I test a framework for forecasting liquidity withdrawal at the individual-stock level, ranging from less liquid stocks to highly liquid large-cap tickers, and evaluate the relative performance of competing model classes in predicting short-horizon order book stress. We introduce the Liquidity Withdrawal Index (LWI) – defined as the ratio of order cancellations to the sum of standing depth and new additions at the best quotes – as a bounded, interpretable measure of transient liquidity removal. Using Nasdaq market-by-order (MBO) data, we compare a spectrum of approaches: linear benchmarks (AR, HAR), and non-linear tree ensembles (XGBoost), across horizons ranging from 250,ms to 5,s. Beyond predictive accuracy, our results provide insights into order placement and cancellation dynamics, identify regimes where linear versus non-linear signals dominate, and highlight how early-warning indicators of liquidity withdrawal can inform both market surveillance and execution. ...

Decision Trees for Intuitive Intraday Trading Strategies ArXiv ID: 2405.13959 “View on arXiv” Authors: Unknown Abstract This research paper aims to investigate the efficacy of decision trees in constructing intraday trading strategies using existing technical indicators for individual equities in the NIFTY50 index. Unlike conventional methods that rely on a fixed set of rules based on combinations of technical indicators developed by a human trader through their analysis, the proposed approach leverages decision trees to create unique trading rules for each stock, potentially enhancing trading performance and saving time. By extensively backtesting the strategy for each stock, a trader can determine whether to employ the rules generated by the decision tree for that specific stock. While this method does not guarantee success for every stock, decision treebased strategies outperform the simple buy-and-hold strategy for many stocks. The results highlight the proficiency of decision trees as a valuable tool for enhancing intraday trading performance on a stock-by-stock basis and could be of interest to traders seeking to improve their trading strategies. ...

Revisiting Cont’s Stylized Facts for Modern Stock Markets ArXiv ID: 2311.07738 “View on arXiv” Authors: Unknown Abstract In 2001, Rama Cont introduced a now-widely used set of ‘stylized facts’ to synthesize empirical studies of financial price changes (returns), resulting in 11 statistical properties common to a large set of assets and markets. These properties are viewed as constraints a model should be able to reproduce in order to accurately represent returns in a market. It has not been established whether the characteristics Cont noted in 2001 still hold for modern markets following significant regulatory shifts and technological advances. It is also not clear whether a given time series of financial returns for an asset will express all 11 stylized facts. We test both of these propositions by attempting to replicate each of Cont’s 11 stylized facts for intraday returns of the individual stocks in the Dow 30, using the same authoritative data as that used by the U.S. regulator from October 2018 - March 2019. We find conclusive evidence for eight of Cont’s original facts and no support for the remaining three. Our study represents the first test of Cont’s 11 stylized facts against a consistent set of stocks, therefore providing insight into how these stylized facts should be viewed in the context of modern stock markets. ...