On the Impact of Feeding Cost Risk in Aquaculture Valuation and Decision Making

ArXiv ID: 2309.02970 “View on arXiv”

Authors: Unknown

Abstract

We study the effect of stochastic feeding costs on animal-based commodities with particular focus on aquaculture. More specifically, we use soybean futures to infer on the stochastic behaviour of salmon feed, which we assume to follow a Schwartz-2-factor model. We compare the decision of harvesting salmon using a decision rule assuming either deterministic or stochastic feeding costs, i.e. including feeding cost risk. We identify cases, where accounting for stochastic feeding costs leads to significant improvements as well as cases where deterministic feeding costs are a good enough proxy. Nevertheless, in all of these cases, the newly derived rules show superior performance, while the additional computational costs are negligible. From a methodological point of view, we demonstrate how to use Deep-Neural-Networks to infer on the decision boundary that determines harvesting or continuation, improving on more classical regression-based and curve-fitting methods. To achieve this we use a deep classifier, which not only improves on previous results but also scales well for higher dimensional problems, and in addition mitigates effects due to model uncertainty, which we identify in this article. effects due to model uncertainty, which we identify in this article.

Keywords: Schwartz-2-factor model, Deep Neural Networks, Aquaculture, Stochastic feeding costs, Deep classifier, Commodities (Agricultural/Livestock)

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced stochastic calculus (SDEs for Schwartz-2-factor models) and deep learning for optimal stopping, resulting in high mathematical complexity. It demonstrates strong empirical rigor through the use of real market data (soybean futures), extensive calibration algorithms (Kalman filter and nested minimization), and includes code and data availability for reproducible backtesting.

  flowchart TD
    A["Research Goal: <br>Assess impact of stochastic feeding costs <br>on aquaculture valuation & decisions"] --> B["Model Inputs: <br>Stochastic Soybean Futures & Salmon Feed <br>via Schwartz 2-Factor Model"]
    B --> C["Methodology: <br>Deep Neural Network Classifier"]
    C --> D{"Decision Rule Comparison"}
    D --> E["Scenario 1: <br>Deterministic Feeding Costs"]
    D --> F["Scenario 2: <br>Stochastic Feeding Costs"]
    E & F --> G["Computational Process: <br>Inference of Harvest/Continue <br>Decision Boundary"]
    G --> H["Key Findings & Outcomes"]
    H --> I["Stochastic rules outperform <br>deterministic proxies in key cases"]
    H --> J["DNNs mitigate model uncertainty <br>& scale efficiently"]
    H --> K["Feeding cost risk integration <br>adds significant value <br>with negligible computational cost"]