Milind Sharma, CEO, QuantZ Machine Intelligence Technologies
| July 30, 2019
Milind introduces a Quantamental investing model and synopsizes a sentiment signal, derived from alternative data. This model helps anticipate buyouts.
The author shows how a sentiment signal, derived from news and social media, performs as an overlay to an existing leveraged buyout strategy (LBO). The study should be of interest to activists, risk arbitrageurs and speculators on the long side as well as market participants (typically quants), looking to eliminate event risk on the short side.
The paper introduces the QMIT LBO model developed by Milind and his team. In addition to a long-term hit rate of 41%, the Top 100 model predictions can be traded profitably as an equal-weighted long portfolio with a Sortino ratio of close to 2.5 over the 19-year backtesting history.
To address potential event risk, a sentiment signal is introduced as an overlay into the model. In particular, they apply RavenPack’s Sum Excess Sentiment Indicator (SESI). Given that SESI captures news-based sentiment, which may include rumors on LBO names, it is logical to ascertain whether benefits may accrue from trading a combined quantamental signal.
Considering the weekly rebalanced LBO Top 100 signal with a SESI overlay, the authors find the following key results:
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We consider incorporating sentiment signals from news, earnings call transcripts, and insider transactions to
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