Powered by OpenAIRE graph
Found an issue? Give us feedback
ZENODOarrow_drop_down
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

The Unlucky Investor – Chapter 4: Methodology and Settings (Biomagnification Contamination Model)

Authors: Gandolfi, Philipp;

The Unlucky Investor – Chapter 4: Methodology and Settings (Biomagnification Contamination Model)

Abstract

his deposit contains the complete reproducible materials for Chapter 4: Methodology and Settings of the thesis: “The Unlucky Investor: How Even “Clean-Shot” Investments Are Contaminated by Upstream Pollution – A Natural-Science Approach to Financial Decision-Making in Opaque Markets” by Philipp Gandolfi (May 2026). Contents Full polished text of Chapter 4 (sections 4.1–4.7) Mixed-methods research design and operationalisation of the Biomagnification Contamination Model Detailed hardware, software, and containerised environment specifications (Docker 27.1 + Singularity 3.11) Data sources, preprocessing pipelines, and financial food-web network construction Monte-Carlo simulation design (10,000 runs per scenario), parameter calibration, random-seed strategy, and full reproducibility protocols These materials form the methodological foundation for the empirical analysis in Chapters 5 and 6. All Monte-Carlo simulations, sensitivity analyses, and figures presented in the thesis can be exactly reproduced using the accompanying codebase. Links GitHub repository: https://github.com/philippgandolfi/unlucky-investor-thesis Zenodo DOI for this record: (assigned automatically upon publication) Reproducibility declaration: All empirical results, figures, and tables in this thesis that depend on Chapter 4 can be regenerated exactly from the publicly released codebase, containerised environment, provided data, and fixed random seeds.

Keywords

unlucky investor, biomagnification, supply chain risk, reaction-diffusion model, financial contamination, greenwashing, ESG risk, sustainable finance, Scope-3 emissions, Monte Carlo simulation

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average