We study investor sentiment on a non-classical asset such as cryptocurrency using machine learning methods. We account for context-specific information and word similarity using efficient language modeling tools such as construction of featurized word representations (embeddings) and recursive neural networks. We apply these tools for sentence-level sentiment classification and sentiment index construction. This analysis is performed on a novel dataset of 1220K messages related to 425 cryptocurrencies posted on a microblogging platform StockTwits during the period between March 2013 and May 2018. Both in-and out-of-sample predictive regressions are run to test significance of the constructed sentiment index variables. We find that the constructed sentiment indices are informative regarding returns and volatility predictability of the cryptocurrency market index.
In this paper, we study the statistical properties of the moneyness scaling transformation by Leung and Sircar (2015). This transformation adjusts the moneyness coordinate of the implied volatility smile in an attempt to remove the discrepancy between the IV smiles for levered and unlevered ETF options. We construct bootstrap uniform confidence bands which indicate that the implied volatility smiles are statistically different after moneyness scaling has been performed. An empirical application shows that there are trading opportunities possible on the LETF market. A statistical arbitrage type strategy based on a dynamic semiparametric factor model is presented. This strategy presents a statistical decision algorithm which generates trade recommendations based on comparison of model and observed LETF implied volatility surface. It is shown to generate positive returns with a high probability. Extensive econometric analysis of LETF implied volatility process is performed including out-of-sample forecasting based on a semiparametric factor model and uniform confidence bands' study. It provides new insights into the latent dynamics of the implied volatility surface. We also incorporate Heston stochastic volatility into the moneyness scaling method for better tractability of the model.
We study investor sentiment on a non-classical asset such as cryptocurrency using machine learning methods. We account for context-specific information and word similarity using efficient language modeling tools such as construction of featurized word representations (embeddings) and recursive neural networks. We apply these tools for sentence-level sentiment classification and sentiment index construction. This analysis is performed on a novel dataset of 1220K messages related to 425 cryptocurrencies posted on a microblogging platform StockTwits during the period between March 2013 and May 2018. Both in-and out-of-sample predictive regressions are run to test significance of the constructed sentiment index variables. We find that the constructed sentiment indices are informative regarding returns and volatility predictability of the cryptocurrency market index.
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