以個股選擇權價量資訊預測美國半導體股票市場景氣

The Predictability of US Semiconductor Stock Market Using the Information of Individual Option Price and Volume

張芮綺(Rui-Chi Chang)

指導教授 ： 石百達

國立臺灣大學/管理學院/財務金融學研究所/碩士(2021年)

https://doi.org/10.6342/NTU202101189

摘要

本文透過由下而上累積個股選擇權的資訊來判斷美國半導體產業未來4週的股票趨勢，將微笑曲線和P/(C+S)比率的十年相對排名、以及四個月的排名差作為模型變數，並以Random Forest和XGBoost兩種演算法預測股價指數的空頭訊號。研究結果發現模型在預測跌幅超過6.37%時有不錯的表現；此外短期內微笑曲線排名的變化可以提升模型預測空頭的能力，而短期內P/(C+S)比率的排名變化則需要搭配其他變數方能區分有用的資訊抑或雜訊。

關鍵字

半導體 ； 隱含波動度 ； 微笑曲線 ； 選擇權交易量 ； 危機預測 ； 隨機森林 ； 極限梯度提升

並列摘要

We use bottom-up approach in individual options to predict the trend of the U.S. semiconductor stock market in the next four weeks. The ten-year rolling ranking of the volatility smirk and the P/(C+S) ratio, and the four-month ranking difference of the first two are taken as model variables, and we use Random Forest and XGBoost algorithms to predict the crash signal of the stock price index. The strategy result shows that the model has a good performance when predicting index collapsing more than 6.37%; in addition, the difference of the volatility smirk ranking in the short term can improve the model’s ability to predict crash signal, while the ranking difference of the P/(C+S) ratio in the short term needs other variables to distinguish between useful information and noise.

並列關鍵字

Semiconductor ； Implied Volatility ； Volatility Smirk ； Volatility Skew ； Option Trading Volume ； Crash Signal ； Crisis Forecasting ； Random Forest ； XGBoost

參考文獻

An, B., A. Ang, T.G. Bali, and N. Cakici. 2014. The joint cross section of stocks and options. Journal of Finance 69, 2279-2337.

Chakravarty, S., H. Gulen, and S. Mayhew. 2004. Informed trading in stock and option markets. Journal of Finance 59, 1235-1257.

Cremers, M. and D. Weinbaum. 2010. Deviations from put–call parity and stock return predictability. Journal of Financial and Quantitative Analysis 45, 335–367.

Johnson, T.L. and E.C. So. 2012. The option to stock volume ratio and future returns. Journal of Financial Economics 106, 262–286.

McLean, R.D. and J. Pontiff. 2016. Does academic research destroy stock return predictability? Journal of Finance 71, 5-32.