Herding behavior measurement models should consider the time dimension since herding behavior studies support that investors enter the market in sequence. Generally, the more recent information and sentiment should be given greater weight because of a greater impact on prices. This research aims to improve the previous herding sentiment model to explain and predict investors' behavior and market by adding a time dimension better accurately. This study finds that herd sentiment could be more volatile when the decay speed of information is faster. Moreover, this study utilizes daily tick data from JoinQuant to conduct a simple test of the HS model. The findings suggest that a decrease in the decay parameter of a specific stock or portfolio may signify reduced volatility of herding sentiment and increased volatility of future prices. During a period of volatile prices, the increase of bullish market sentiment created by herd behavior could lead to an asset price increase in the future.