Quantitative market situation embeddings: utilizing Doc2Vec strategies for stock data

We introduce Quantitative Market Situation Embeddings (QMSEs), a pioneering artificial intelligence (AI)-driven methodology for encoding distinct temporal segments of stock markets into high-dimensional contextual embeddings exclusively leveraging quantitative stock data. Building upon prior research, we construe quantitative stock data analogously to Natural Language Processing (NLP) data, thereby adopting Doc2Vec methodologies to effectuate the embedding of stock data similar to document-level representations. We ascertain the efficacy of QMSEs in representing market dynamics by assessing their ability to discern various significant economic downturns post-2000, including but not limited to, the events of 9/11, the Subprime Crisis of 2008, and the Covid-induced market disruption. Moreover, we elucidate the practical utility of QMSEs through their application in employing distance metrics to gauge the rarity of market scenarios, serving as a regularizer in the training of quantitative stock AI models. Subsequently, we proceed to assess the algorithmic identification of analogous market conditions, aiming to elucidate their potential implications for future stock movements. Additionally, we demonstrate the efficacy of QMSEs in reducing data requirements for quantitative stock AI models by leveraging them as condensed representations of stock data.