From noise to signal

In the fiercely competitive world of capital markets, easy wins are a thing of the past. With 30,000 hedge funds, 167,000 mutual funds, and countless asset managers all chasing Alpha, the competition is intense. Armed with billions of dollars in research, data, and analytics, and backed by some of the brightest minds, these entities are constantly striving to outperform the market. Yet, despite these vast resources, less than 25% of actively managed funds have outperformed the S&P 500 over the past decade. It’s clear: Alpha isn’t lying around waiting to be found.

At Zenpulsar, we dedicated 3 years of intensive R&D to build a sophisticated infrastructure and machine learning pipeline that consistently delivers robust signals for quantitative strategies. Many have attempted to crack the code of sentiment signals, but few have succeeded. Why? Because sentiment analytics demands deep specialization and precision at every step.

Data Collection:?The first challenge is managing the live data collection process to acquire billions of data points daily from diversified data sources. Understanding the moderation and operational rules of each data source is critical, as they directly impact the completeness and accuracy of the information. Constant monitoring and adjustments are essential to maintain the speed and consistency of real-time data collection.

Noise Filtering:?Next, we face the daunting task of filtering relevant information from the hundreds of millions of posts, news articles, and reports generated daily. Simple keyword searches won’t cut it, as topics, terms, and slang evolve rapidly. Moreover, bots, spam, and irrelevant content account for more than 90% of the data flow.?

Sentiment Extraction:?Once the data is filtered, it’s time for labeling and sentiment extraction. Each taxonomy label requires a finetuned model specific to the financial domain. Assessing reactions, audience quality, and the reach of particular narratives are crucial steps, all managed by a pipeline of specialized machine learning models.

Training and Quality Control:?Precision is everything. To meet hedge fund standards, we must carefully control data on each step,how and when our models are trained, ensuring that issues like using future data during model training or non-replicable results don’t undermine the value of the data and its alpha-generating potential.

Efficiency and Speed:?All this—data collection, filtering, and analysis—must be executed within seconds of the information being published.

Alpha Extraction: The final challenge is to determine whether the dataset contains Alpha. How stable is it? What does the PnL simulation show? Answering these questions requires experienced quant researchers and advanced simulation platforms. Multiple recalibrations and re-trainings are often needed to improve data quality, reprocess historical data, run backtests, simulate PnL and validate the signal.

The Reward:?Discovering Alpha is a significant achievement, placing you in the elite group of fewer than five companies globally who have mastered this process. You see that vanilla off-the-shelf solutions like GPT-4-powered sentiment extraction simply aren’t up to the task.

?As you can see, this is a complex, time-intensive, and resource-heavy process that requires a top-tier data science and quant research team. Even with the best resources, success isn’t guaranteed. That’s why hedge funds and institutional investors are always on the lookout for high-quality datasets and validated signals. They know the journey from noise to signal is costly, that’s why they’re willing to pay proper cheques for data that delivers Alpha.?

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