Working Paper – Please do not cite without permission
This version: December 9, 2024
First version: November 2024
The semiconductor industry represents a critical component of modern technology infrastructure, characterized by high volatility, cyclical behavior, and complex market dynamics. Recent global events, including supply chain disruptions and geopolitical tensions, have highlighted the need for sophisticated risk management approaches in this sector.
This paper presents a hybrid framework combining Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) networks for detecting market regimes in semiconductor equities. The methodology integrates traditional statistical methods with deep learning techniques, featuring robust fallback mechanisms and multi-horizon analysis across short-term (21-day) and medium-term (63-day) market dynamics.
The empirical analysis examines daily return data from four major semiconductor companies (NVDA, AMD, INTC, ASML) from 2017–2023, with portfolio weights allocated based on market capitalization and liquidity (NVDA: 40%, AMD: 30%, INTC: 15%, ASML: 15%). The framework identifies three distinct market regimes: low volatility (56.62% occurrence, mean return: 4.36%, t=3.42, p < 0.05), medium volatility (31.89% occurrence, mean return: 2.81%, t=2.15, p < 0.05), and high volatility (11.48% occurrence, mean return: -0.25%, t=-0.18).
Initial implementation results from my proof-of-concept system demonstrate promising performance with a Sharpe ratio of 0.73 [0.65, 0.81] and Information ratio of 0.68 [0.61, 0.75]. The regime detection framework achieves persistence rates exceeding 80% across all states, with rare direct transitions between extreme regimes. Portfolio risk metrics show strong statistical significance (p < 0.001), including Value at Risk (-3.99% [-4.2%, -3.7%]) and Expected Shortfall (-5.74% [-6.1%, -5.4%]). While these results suggest potential utility for dynamic risk management in semiconductor equity portfolios, further development is needed for production deployment.
Disclaimer: This research is conducted independently by Lucas Kemper and is not affiliated with HEC Lausanne or any other organization.
Notes: The website mentioned in this abstract isn't related to my digital portfolio which was developed using different methods and technologies. Also, I'm currently looking for coauthors and funding—feel free to contact me if you're interested.
This paper investigates the capabilities of Anthropic's Claude Computer Use API through a hands-on exploration using the Claude 3.5 Sonnet model. In a controlled virtual environment, a fully functional, multi-component web system was developed without prior web development experience, showcasing the API's potential to streamline and enhance complex projects.
This system, comprising frontend, backend API, database, and caching layers, was constructed in only 20 minutes of active AI coding and completed in 10 hours, generating over 150 MB of production-ready code. Key achievements include an impressive token efficiency ratio of 58.25 and seamless integration of tools like Vue.js 3 and Tailwind CSS, emphasizing the API's capacity for producing robust, scalable solutions.
Uniquely, this study demonstrates Claude's effectiveness in delivering real-world, end-to-end implementations, even for users without specialized technical backgrounds. The research provides insights into the evolving role of AI in development, addressing both the technical and security dimensions essential for professional applications.
Findings suggest significant implications for accelerated development processes, democratization of complex technology, and AI-driven collaboration, pointing towards a future where sophisticated technical projects become accessible to a broader range of users. Future research could explore deploying Claude's capabilities across various industry domains to further validate its transformative potential.