Vectorspace AI and CloudQuant teamed up to launch datasets that reveal relationships between global equity products. Vectorspace AI datasets are designed to boost precision, accuracy, signal or alpha based on Natural Language Processing and Understanding (NLP/NLU) using the VXV utility token wallet-enabled API.
The algorithmically generated datasets are based on formal NLP/NLU models including OpenAI’s GPT-3, Google’s BERT along with word2vec and experimental models built at Lawrence Berkeley National Laboratory and the US Dept. of Energy (DOE).
“The ability to use language to generate event signals for specific companies opens a whole new range of investment opportunities,” said Morgan Slade, CEO of CloudQuant, in a statement. Datasets are updated and designed to augment or append existing proprietary datasets such as gene expression datasets in life sciences or time-series datasets in the financial markets.
In the financial markets, this can be used for generating investment signals based on (unlimited) topics, themes, and global events. These signals can be used to generate thematic portfolios (position baskets) for real-time investment and visualization.
Kasian Franks, CEO of Vectorspace AI, said in a statement that having its data available on the CloudQuant platform “will bridge the gap between raw data and alpha generation for our clients”.