Using Alternative Data, News Signals, and Transaction Cost Analysis in Quant Trading

Summary

This presentation transcript surveys quantitative trading practices described for overseas markets. It explains how nontraditional inputs such as news sentiment, ESG reports, satellite imagery, and weather can be converted into structured signals, while noting that some sources are delayed or difficult to quantify. It also outlines transaction cost analysis: compare order decision and execution timing, slippage, and routing channels to inform execution choices. Other topics include algorithmic order slicing, latency, event-driven research, and time-of-day patterns in foreign exchange.

Examples include grouping stocks by sentiment rankings, observing a sentiment shift around a corporate controversy, and using news metadata and entity relationships to identify companies connected through shared coverage or supply chains. These are illustrative cases rather than fully documented tests: the transcript gives no methodology, controls, or risk-adjusted performance results. It also notes practical limits such as ambiguity in text interpretation and the challenge of turning high-volume, unstructured information into reliable trading inputs.

Key ideas

• Alternative inputs such as sentiment, ESG, satellite imagery, and weather need conversion into structured variables before quantitative use.
• Transaction cost analysis compares execution timing, slippage, and routing channels to guide order placement.
• News metadata and entity relationships can help identify relevant companies beyond exact keyword matches.
• The examples are illustrative and do not establish predictive performance or account for implementation risks.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.