Financial risk is an essential indicator of investment, which can help investors to understand the market and companies better. Among the many influencing factors of financial risk, researchers find the earnings conference call is the most significant one. Predicting financial volatility after the earnings conference call has been critical to beneficiaries, including investors and company managers. However, previous work mainly focuses on the feature extraction from the word-level or document-level.The vital structure of conferences, the alternate dialogue, is ignored. In this paper, we introduced our Multi-Round Q&A Attention Network, which brings into account the dialogue form in the first place. Based on the data of earnings call transcripts, we apply our model to extract features of each round of dialogue through a bidirectional attention mechanism and predict the volatility after the earnings conference call events. The results prove that our model significantly outperforms the previous state-of-the-art methods and other baselines in three different periods.