Evaluation of VI Index Forecasting Model by Machine Learning for Yahoo! Stock BBS Using Volatility Trading Simulation

Sasaki, Kodai
Suwa, Hirohiko
Ogawa, Yuki
Umehara, Eiichi
Yamashita, Tatsuo
Tsubouchi, Kota
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The risk avoidance is very crucial in investment and asset management. One commonly used index as a risk index is the VI index. Suwa et al. (2017) analyzed stock bulletin board messages and predicted it rise. In our study, we developed a simulation of trading Nikkei stock index options using intra-day data and verified the validity of the VI index prediction model proposed by Suwa et al. In a period from November 18, 2014, to June 29, 2016, we conducted a simulation using a long straddle strategy. The profit and loss from trading with the instructions of their model was +3,021 yen. The benchmark's profit and loss was -3,590 yen. The improvement with their model was +6,611 yen. Therefore, we confirmed that Suwa et al.'s VI index prediction model might be effective.
Data Analytics, Data Mining and Machine Learning for Social Media, intra-day data, machine learning, stock bulletin board, trading simulation
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