Learning Module 02 · Week 2 · Individual mission
Turn Market Data into a Decision Signal
Trace a market claim from raw observations to a decision-ready signal.
- 1BriefingCurrent
- 2Worked exampleNext
- 3Case boardNext
- 4CheckpointsNext
- 5ReflectionNext
- 6CompleteNext
Start here
Briefing
Market innovation depends on a trustworthy chain from source to signal. A signal is not decision-ready merely because it is timely.
Classify the evidence, check timestamp and source quality, and compare the proposed signal with a transparent baseline. Then convert the article text into a lightweight JSON record, not heavier XML.
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Game economy
Build Evidence Momentum without risking Decision XP
You begin with 3 Evidence Momentum. Any held points return when you solve that checkpoint. Evidence Momentum never falls below zero and never changes assessment marks. Complete the mission to open one transparent reward draw.
What you will practise
- Classify structured, semi-structured, and unstructured market evidence.LO1
- Evaluate whether a proposed innovation improves a defined financial decision.LO2
6 terms for this mission
- Structured data
- Data organised into fixed fields, such as prices and volumes.
- Semi-structured data
- Data with tags or fields but flexible content, such as a news feed.
- Unstructured data
- Text, audio, or images without a fixed analytical table.
- Signal
- A transformed measure intended to inform a decision.
- Named-entity recognition (NER)
- Automatically labelling the entities a text names, such as a firm, amount, or date.
- Sentiment scoring
- Converting the tone of a text into a score, such as positive, neutral, or negative.
Every organisation, person, figure, and dataset in this mission's scenario is invented. Real institutions, laws, and standards are named only as general context.
Sources and further reading
Every organisation, person, figure, and dataset in this mission's scenario is invented. Real institutions, laws, and standards named in this mission, including in the sources below, appear only as general context, and the sources support the concepts and methods, not the events in the scenario.
Aldridge, I., & Avellaneda, M. (2021). Big Data Science in Finance (1st ed.). Wiley. ISBN 978-1-119-60298-9
Chapter 1 and Chapter 7 set out how large financial datasets are classified and transformed, and how to judge whether a constructed series carries decision-relevant signal rather than noise.
Hasbrouck, J. (2007). Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press. ISBN 978-0-19-530164-9
Explains how trade and quote records are generated and time-stamped, and why price series are handled as time-series objects, for example modelling returns rather than raw price levels.
Loughran, T., & McDonald, B. (2016). Textual analysis in accounting and finance: A survey. Journal of Accounting Research, 54(4), 1187-1230. doi:10.1111/1475-679X.12123
Surveys how financial text is converted into measured fields such as tone, and documents the measurement choices that determine whether such a score is trustworthy.
Nguyen, H. H., Ngo, V. M., Pham, L. M., & Nguyen, P. V. (2025). Investor sentiment and market returns: A multi-horizon analysis. Research in International Business and Finance, 74, 102701. doi:10.1016/j.ribaf.2024.102701
A published study building a sentiment measure from a large body of social-media text and testing its relationship with Vietnamese stock market returns over both long and short horizons.