Using the Markov-Switching Autoregressive Model to Classify Semiconductor Regimes and Identify Inflection Points

Abstract

Investing in semiconductor companies is particularly challenging due to complicated cyclical dynamics and rapidly changing technology. Recently, the industry has seen a massive influx of capital from governments shoring up supply chains and investors capitalizing on AI tailwinds, introducing new investors to the space. We see a need for company-specific quantitative tools to help investors forecast margins in order to make better investment decisions surrounding turning points in the cycle. Anticipating inflection points is one of the biggest challenges semiconductor investors face and can be a significant source of returns.

In order to capture the complicated cyclical dynamics, we used the Markov-Switching Autoregressive (MS-AR) model to classify historical semiconductor regimes and forecast gross margins on a company level. We used the Granger Causality test to select exogenous factors for each company from a collection of industry-specific and economy-wide indicators. Our work focused on modeling the thirty companies in the PHLX Semiconductor Index (SOX). Finally, we tested the performance of our forecasts using long/short and long only trading strategies.

Our MS-AR models successfully classified contraction and expansion regimes, identifying different regime dynamics across companies and verticals. Our resulting forecasts performed better than consensus estimates during cycle inflection points, when companies were transitioning between regimes. Finally, our long only strategy generated excess returns compared to the SOX index with a similar Sharpe ratio, and the long/short strategy had a significantly higher Sharpe ratio than the index.

Ultimately, this thesis provides investors with a tool to better anticipate when semiconductor margins will reach an inflection point in the cycle. It is particularly helpful for investors who want to make company-specific investments, which will be increasingly relevant as the optimism surrounding the current AI hype cycle fades, resulting in a greater dispersion of company outcomes within the industry.