Finding relevant features within Form 4s based on insider info, transaction details, post-transaction position, historical context, regulatory flags

Finding relevant features within DEF 14As based on metadata, proposals, board composition, compensation, shareholder rights, governance

Finding relevant features within 10-Ks based on filing structure, risk disclosures, MD&A analytics, financial statement metrics, footnotes, ESG, semantic NLP, visuals.

Finding relevant features within 10-Qs based on metadata, financial statement metrics, qualitative sections and risk disclosures, footnote, ESG, semantic NLP

Finding relevant features within 8-Ks based on filing metadata, item code information, text metrics, sentiment features, financial disclosures, topic modeling, named entity recognition, timing and temporal metadata

Finding relevant data to extract from PPMs to enable better onboarding of fund documents. Some categories are issuer information, offering structure, fee, performance targets, fund management, strategy, legal risks, ESG

Finding relevant data to extract from startup pitch decks to evaluate investment potential based on market opportunity, business traction, technology and product, go-to-market strategy, team, funding, investors, risks

Finding relevant features within earnings call transcripts based on call metadata, sentiment metrics, emotion signals, Q&A interaction dynamics, market reaction.

Finding relevant features within credit/debit card data based on identifiers, temporal, transaction content, merchant, behavioral rolling, customer profile, clustering

Finding relevant features within news headlines based on headline content, event annotations, publisher metrics, temporal context, behavioral signals, interheadline relations

Finding relevant features within reddit data based on volume metrics, sentiment, buy/sell intent, user quality, themes

Finding relevant features within tweets based on metadata, sentiment, volume, user diversity, topic, engagement, volatility