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

Request Access

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

Request Access

Now more than ever, it is essential to augmenting alternative data from global sources in order the capture alpha, mitigate risk and assessing new investment opportunities.

1. Data Driven

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 industries.

1. Data Driven

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 industries.

2. Extancive Coverage

Covering 10,000 companies globally with more than 400 events categories and subcategories.

2. Extancive Coverage

Covering 10,000 companies globally with more than 400 events categories and subcategories.

3. Human Still Matters

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.

3. Human Still Matters

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.

4. Data Output

The output is an accurate and clean dataset 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).

4. Data Output

The output is an accurate and clean dataset 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).

5. Data Delivery Methods

The data is presented via visually compelling, interactive dashboards or as CSV file via AWS S3.

5. Data Delivery Methods

The data is presented via visually compelling, interactive dashboards or as CSV file via AWS S3.

Use case

Quants / Quantamental

Standardized data to evaluate both companies and industries on a global scale

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
Point in time data collection methodology
Delivered daily via S3, Dashboards
A trial period for backtesting

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

January 2020

Industry overview using alternative data: Oil & Gas Integrated

Industry overview using alternative data: Oil & Gas Integrated An integrated oil and gas company is a business entity that engages in the exploration, production, refinement, and distribution of oil and gas. Given the high entry costs relating to many oil and gas industry operations, many of the world's [...]

Alternative data for Quantamental

Alternative Data for Quantamental Quantamental investing is a term that is now being used to describe a combination of these methods on different data sets to improve on the accuracy of predicting outcomes – such as stock prices or a company (or country)’s financial performance. In the most [...]

Alternative data for private equity funds

Alternative data for private equity funds In today’s fast-paced M&A environment, deal making is increasingly a challenging task. Competition is only increasing as private equity (PE) firms, strategies, and other investors aggressively vie for the most attractive targets. The biggest PE managers are investing in alternative data to [...]

December 2019

Alternative data corporate event 2019 statistics

Alternative data corporate event 2019 statistics The stock markets will finish 2019 with double-digit gains for the 8th time out of the last 11 years. During 2019, our system capture 38K events (68% bullish events vs. 32% bearish once). Here is 2019 statistics: We are excited to see [...]

Previous 123 Next

READY TO EXPLORE OUR DATA?

Request Access

As alternative data goes mainstream, but the deal cycle takes between 6-12 months, it is critical to understand the key challenges in implementing an alternative data program in order to know how to streamline the process.

If you are a data buyer or user, we’ll appreciate your answer below. It will help us, the data vendors, to understand better and streamline the process.

Quants / Quantamental

Standardized data to evaluate both companies and industries on a global scale

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
Point in time data collection methodology
Delivered daily via S3, Dashboards
A trial period for backtesting

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

January 2020

Industry overview using alternative data: Oil & Gas Integrated

Industry overview using alternative data: Oil & Gas Integrated An integrated oil and gas company is a business entity that engages in the exploration, production, refinement, and distribution of oil and gas. Given the high entry costs relating to many oil and gas industry operations, many of the world's [...]

Alternative data for Quantamental

Alternative Data for Quantamental Quantamental investing is a term that is now being used to describe a combination of these methods on different data sets to improve on the accuracy of predicting outcomes – such as stock prices or a company (or country)’s financial performance. In the most [...]

Alternative data for private equity funds

Alternative data for private equity funds In today’s fast-paced M&A environment, deal making is increasingly a challenging task. Competition is only increasing as private equity (PE) firms, strategies, and other investors aggressively vie for the most attractive targets. The biggest PE managers are investing in alternative data to [...]

December 2019

Alternative data corporate event 2019 statistics

Alternative data corporate event 2019 statistics The stock markets will finish 2019 with double-digit gains for the 8th time out of the last 11 years. During 2019, our system capture 38K events (68% bullish events vs. 32% bearish once). Here is 2019 statistics: We are excited to see [...]

Previous 123 Next

READY TO EXPLORE OUR DATA?

Request Access

As alternative data goes mainstream, but the deal cycle takes between 6-12 months, it is critical to understand the key challenges in implementing an alternative data program in order to know how to streamline the process.

If you are a data buyer or user, we’ll appreciate your answer below. It will help us, the data vendors, to understand better and streamline the process.

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

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

Now more than ever, it is essential to augmenting alternative data from global sources in order the capture alpha, mitigate risk and assessing new investment opportunities.

Now more than ever, it is essential to augmenting alternative data from global sources in order the capture alpha, mitigate risk and assessing new investment opportunities.

1. Data Driven

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 industries.

1. Data Driven

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 industries.

2. Extancive Coverage

Covering 10,000 companies globally with more than 400 events categories and subcategories.

2. Extancive Coverage

Covering 10,000 companies globally with more than 400 events categories and subcategories.

3. Human Still Matters

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.

3. Human Still Matters

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.

4. Data Output

The output is an accurate and clean dataset 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).

4. Data Output

The output is an accurate and clean dataset 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).

5. Data Delivery Methods

The data is presented via visually compelling, interactive dashboards or as CSV file via AWS S3.

5. Data Delivery Methods

The data is presented via visually compelling, interactive dashboards or as CSV file via AWS S3.

Use case

Use case

Quants / Quantamental

Standardized data to evaluate both companies and industries on a global scale

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

Point in time data collection methodology

Delivered daily via S3, Dashboards

A trial period for backtesting

Private equity / Venture capital

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

DD on target company

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

January 2020

December 2019

READY TO EXPLORE OUR DATA?

As alternative data goes mainstream, but the deal cycle takes between 6-12 months, it is critical to understand the key challenges in implementing an alternative data program in order to know how to streamline the process.

If you are a data buyer or user, we’ll appreciate your answer below. It will help us, the data vendors, to understand better and streamline the process.

Which factors impact alternative data long implementation?

Quants / Quantamental

Standardized data to evaluate both companies and industries on a global scale

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

Point in time data collection methodology

Delivered daily via S3, Dashboards

A trial period for backtesting

Private equity / Venture capital

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

DD on target company

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

January 2020

December 2019

READY TO EXPLORE OUR DATA?

As alternative data goes mainstream, but the deal cycle takes between 6-12 months, it is critical to understand the key challenges in implementing an alternative data program in order to know how to streamline the process.

If you are a data buyer or user, we’ll appreciate your answer below. It will help us, the data vendors, to understand better and streamline the process.

Which factors impact alternative data long implementation?