Home2020-01-26T18:53:39-05:00
FIRST TO INVEST

ALTERNATIVE DATA FOR ALPHA

Big data and NLP technologies to collect, structure, and reveal events from news articles, press releases, and financial social media and share it with our clients via dashboards or API

FIRST TO INVEST

ALTERNATIVE DATA FOR ALPHA

Big data and NLP technologies to collect, structure, and reveal events from news articles, press releases, and financial social media and share it with our clients via dashboards or API

Sophisticated investors are increasingly augmenting their decision-making processes using alternative data sources from news and social media.

In today’s noisy environment, it is essential to have a bigger and clearer picture of companies and countries financial activities by being able to collect, structure, and reveal important events in order the capture alpha.

Using big data and NLP technologies powered by dedicated classifiers, dictionaries and clusters for the financial industry, provides the opportunity to sift through the noise and find only the relevant events which impact financial outcomes of companies and countries.

To empower the technology, a hybrid model of machine and human is being used to provide accuracy of data classification, context, and sources to ensure maximum value for our clients.

The output is an accurate and clean dataset that contains only the important events where each is scored based on its impact on the company’s performance, which indicates the direction (bullish/bearish) and duration (long/short term).

The data is presented via visually compelling, interactive dashboards or as CSV file via FTP or API.

Our data and system are being used by world-leading quants, quantamental, fundamental hedgefunds and assets managers, private equity and corporates.

NLP

Coverage

  • 30 Markets

  • 11 Sectors

  • 42 Event Categories

  • 80 HQ Countries

  • 136 Industries

  • 385 Event Subcategories

  • 700 ETFs

  • 7400 Companies

Events Distribution

    • 18% Consumer Discretionary

    • 18% Healthcare

    • 13% Technology

    • 13% Industrials

    • 10% Financial Services

    • 7% Communication Services

    • 6% Consumer Staples

    • 5% Basic Materials

    • 5% Energy

    • 3% Real Estate

    • 2% Utilities

Use case

Quants / Quantamental

Input data feed in quantitative models

Dataset

  • Clean dataset using a hybrid model for accuracy and advance clusters for data relationship

  • Each event is scored based on its impact on the company’s performance, which indicates the direction (bullish/bearish) and duration (long/short term)

  • Structure machine-readable dataset

  • Delivered daily via S3, API, FTP

  • Trusted by top tier quant shops

  • A trial period for backtesting

Read More

Private equity / Venture capital

Alternative data provides an element of nuance that can be valuable to PE investors

DD on target company

  • Use alternative data to identify fast growing companies by monitoring events of a target company to make data-driven decisions: new product launch, fundraising, expansion to new markets, partnerships, joint venture, acquisition, management change, changes in product pricing model, regulatory decisions, financial reports, and compare the data with public-traded peers

Portfolio companies

  • Track portfolio companies’ corporate events to monitor their business activities and make the right adjustment to their strategy

Special projects

  • Short-term DD, industry analysis and monitoring a basket of Private companies

Read More

Corporate

Decision-makers understand that combination of alternative data and in-house data resources provides more understanding around their customers and competitors

Competitors monitoring

  • Track competitor’s corporate events such new products, management change, M&A, new deals, new agreements, expansion to new markets, ESG and more

  • Benchmark against other peers and industry performance

Visual Analytic Platform

Embrace alternative data and effectively adopt a more quantitative approach

Analysis levels

  • Markets

  • Indices

  • Sectors

  • Industries

  • Companies

  • ETFs

  • Portfolio

  • Countries

Functionality

  • Friendly and easy to use interface

  • Event-driven score by date range

  • ML data query

  • Import/Export lists

  • Key influencer factor indicator

  • Advance filtering

  • Export to xls, csv, pdf, ppt

  • Email and phone alerts on event-driven score changes

  • Share notes and other information with colleagues to encourage organized collaboration

December 2019

Alternative data: ESG Case Study

Alternative data: ESG Case Study Environmental, social and corporate governance (ESG) refers to the three central factors in measuring the sustainability and ethical impact of an investment in a company or business. These criteria help to better determine the future financial performance of companies (Wikipedia). ESG is growing [...]

November 2019

Alternative data: Brexit vote prediction using NLP

Alternative data: Brexit vote prediction using NLP The United Kingdom’s divorce with the European Union, better known as Brexit, has become a drawn-out, contentious affair without an obvious resolution. Former British Prime Minister Theresa May’s government and her counterparts in the European Union negotiated a withdrawal agreement last [...]

Alternative data: Uber not a surprise

Alternative data: Uber not a surprise... Uber Technologies Inc (UBER) on Monday posted a wider third-quarter loss as costs at the ride-hailing company soared, sending shares down 6.7% in after-hours trading. Based on Uber's 3Q event analysis we can see that Uber's event-driven score is in a downtrend [...]

October 2019

Alternative data: benefits and challenges

Alternative data: benefits and challenges What benefits can the use of alternative data bring to financial institutions? Quarterly earnings reports and industry whitepapers are often slow and infrequent and as these traditional sources of information are readily available, they tend to offer limited actionable intelligence or insight. No [...]

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Use case

Quants

Input data feed in quantitative models

Dataset

  • Clean dataset using a hybrid model for accuracy and advance clusters for data relationship

  • Each event is scored based on its impact on the company’s performance, which indicates the direction (bullish/bearish) and duration (long/short term)

  • Structure machine-readable dataset

  • Delivered daily via S3, API, FTP

  • Trusted by top tier quant shops

  • A trial period for backtesting

Portfolio managers

Embrace alternative data and effectively adopt a more quantitative approach

Analysis levels

  • Markets

  • Indices

  • Sectors

  • Industries

  • Companies

  • ETFs

  • Portfolio

  • Countries

Functionality

  • Friendly and easy to use interface

  • Event-driven score by date range

  • ML data query

  • Import/Export lists

  • Key influencer factor indicator

  • Advance filtering

  • Export to xls, csv, pdf, ppt

  • Email and phone alerts on event-driven score changes

  • Share notes and other information with colleagues to encourage organized collaboration

Privet equity

Alternative data provides an element of nuance that can be valuable to PE investors

DD on target company

  • Use alternative data to identify fast growing companies by monitoring events of a target company to make data-driven decisions: new product launch, fundraising, expansion to new markets, partnerships, joint venture, acquisition, management change, changes in product pricing model, regulatory decisions, financial reports, and compare the data with public-traded peers

Portfolio companies

  • Track portfolio companies’ corporate events to monitor their business activities and make the right adjustment to their strategy

Special projects

  • Short-term DD, industry analysis and monitoring a basket of privet companies

Corporate

Decision-makers understand that combination of alternative data and in-house data resources provides more understanding around their customers and competitors

Competitors monitoring

  • Track competitor’s corporate events such new products, management change, M&A, new deals, new agreements, expansion to new markets, ESG and more

  • Benchmark against other peers and industry performance

November 2019

Alternative data: Uber not a surprise

Alternative data: Uber not a surprise... Uber Technologies Inc (UBER) on Monday posted a wider third-quarter loss as costs at the ride-hailing company soared, sending shares down 6.7% in after-hours trading. Based on Uber's 3Q event analysis we can see that Uber's event-driven score is in a downtrend [...]

October 2019

Alternative data: benefits and challenges

Alternative data: benefits and challenges What benefits can the use of alternative data bring to financial institutions? Quarterly earnings reports and industry whitepapers are often slow and infrequent and as these traditional sources of information are readily available, they tend to offer limited actionable intelligence or insight. No [...]

Alternative data: Netflix outperform in Q3 2019

Alternative data: Netflix outperform in Q3 2019 We saw the shift in Netflix $NFLX from the past quarter using key events that took place during 3Q. The streaming-video pioneer beat subscription estimates thanks to growth in countries like Brazil and India. The third season of Stranger Things helped drive [...]

Alternative data: clean dataset and why it’s impotent

Alternative data: clean dataset and why it's impotent Today, more than 80% of the data is unstructured. Data is being produced as we speak – from every conversation we make in social media to every content generated from news sources. In order to produce any meaningful actionable insight [...]

September 2019

Private equity alternative data Case Study – Snowflake

Private equity alternative data Case Study - Snowflake Snowflake provides a data warehouse as a service designed for the cloud, founded in 2012 and raised $928M to date. The company is headquartered in San Mateo, California, United States. Snowflake offers corporate users to store and analyze data using [...]

August 2019

Sift through the noise

Sift through the noise As data volume increase by the day, the noise level is tremendous. By noise, we mean the same data in different wording or even duplication of the same content. In the financial markets, data is key factor for outperforming the benchmarks. However, nowdays, it [...]

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Please enter your corporate email, so we can follow up with you.
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