Investment Research
Our investment research leverages advanced machine learning techniques to enhance understanding of agricultural productivity and market dynamics. Through these efforts, we provide actionable insights that inform investment decisions, risk management, and policy development within the agricultural sector.
2024: Ongoing research
Driving evidence-based policies and investments in climate-smart agricultural practices
Zhenshan Chen and Le Wang
We are investigating the impact of agricultural practices on productivity and agricultural output using advanced machine learning techniques. By leveraging these state-of-the-art methods, we aim to uncover key patterns and insights that may not be detectable through traditional approaches. This allows us to assess the sources of productivity gains and understand how specific agricultural practices contribute to improving output.
The growing complexity of agricultural systems and the increasing variability of external factors, such as climate and market conditions, require more sophisticated tools to accurately assess the effectiveness of different practices.
The insights gained from this research have the potential to inform better decision-making for both farmers and policymakers. By identifying the most effective agricultural practices, we can help optimize productivity and sustainability, ultimately enhancing food security and economic stability. Furthermore, the actionable insights derived from our analysis can guide policy development and contribute to innovations in agricultural technology and resource management.
Systemic Risk Indicator for U.S. agricultural commodities
Our project aims to develop a novel measure systemic indicator that captures the relative susceptibility of different commodities to external shocks.
The volatility of agricultural commodity prices has increased significantly over the past two decades, both domestically and globally. In this uncertain environment, some commodities have shown greater vulnerability or susceptibility to external shocks than others. It is crucial to understand the extent to which commodity prices are influenced by external factors and to identify the key drivers behind this increased volatility.
Our project will help regulators and practitioners gauge which commodities are most vulnerable to certain shocks and disasters and develop expectations and policies accordingly.