Wall Street Alt-Data Pipeline — Earnings, Filings & Insider Activity for a NYC Hedge Fund

Industry

Fintech / Hedge Fund / Investment Research

Geography

United States — New York City based, US-listed equities universe

Data Coverage

SEC filings, earnings transcripts, insider trades, web traffic signals, app downloads, satellite imagery metadata

Client Overview

The client is a $4.2 billion AUM multi-strategy hedge fund operating from New York with a long/short equity desk and a quantitative research arm. Their investment edge comes from being faster and broader than competitors in identifying market-moving information.

Traditional Bloomberg-style feeds are commoditized. The alpha now lives in alternative data — SEC filings parsed in milliseconds, insider Form 4 filings, earnings-call language, and consumer behavior signals.

Business Challenges

Filing Latency Race

SEC filings move markets within minutes. Even a 90-second delay in parsing 8-Ks meant lost trading edge.

Earnings Transcript NLP

Earnings call language carried predictive signals — tone, hedging, forward-looking statements — but parsing free-text PDFs/audio was non-trivial.

Insider Trading Signal Decay

Form 4 filings (insider buys/sells) were public but scattered; aggregating into clean signals required infrastructure.

Consumer Behavior Cross-Reference

App downloads, web traffic, and product reviews needed correlating with company tickers — most signals lacked clean ticker mappings.

Project Objectives

The client partnered with Actowiz Solutions to:

• Parse SEC EDGAR filings within 60 seconds of posting
• Extract earnings transcript signals (sentiment, forward guidance, Q&A tone)
• Aggregate Form 4 insider activity into per-ticker daily signals
• Cross-reference consumer-behavior data (Similarweb, App Annie public data) to tickers
• Deliver signals via low-latency Kafka stream into the fund's quant systems

Actowiz Solutions Approach

Sub-60-Second EDGAR Pipeline

Built a real-time EDGAR RSS poller + parser combo; 8-K filings reached the fund's quant desk within 30–50 seconds of SEC posting.

Earnings Transcript NLP Stack

Multi-stage pipeline using OpenAI for sentiment, custom regex for guidance extraction, and fine-tuned classifiers for management hedging detection.

Insider Activity Aggregator

Daily aggregation of all Form 4 filings, computed per-ticker insider net-buy/sell scores adjusted for executive seniority.

Ticker-Mapped Consumer Signals

Built a master mapping linking 8,000+ public companies to their consumer-facing brands, apps, and web properties — enabling alt-data correlation.

Kafka Stream Delivery

All signals published to Kafka topics; the fund's quant systems consumed signals with sub-second latency.

Key Features

• Sub-60-second SEC EDGAR filing parser
• NLP-based earnings transcript signal extraction
• Insider activity aggregator with seniority weighting
• Consumer-behavior cross-reference to 8,000+ tickers
• Kafka stream delivery for low-latency consumption
• Backtested signal libraries with documented edge

Business Impact

Within 12 months:

• 23 basis points of incremental alpha attributed to alt-data signals
• $47M+ in additional fund profits generated from faster filing response
• 5× faster information edge vs the fund's previous Bloomberg-only workflow
• Eliminated $1.2M/year spend on commercial alt-data vendors
• Sub-60-second filing-to-desk latency sustained across 100,000+ filings

Testimonial

"Actowiz didn't just give us alt-data — they gave us speed. We're seeing 8-Ks before our competitors finish loading their Bloomberg terminals."

— Head of Quantitative Research, $4.2B NYC Hedge Fund

Conclusion

Wall Street is a millisecond business — and information edge is the only sustainable edge. Actowiz Solutions delivered a real-time alt-data pipeline that turned public-domain data into a measurable alpha source for a top-tier hedge fund, replacing commercial vendors and accelerating decisions across every strategy.