Orbit Platform Documentation
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  • Welcome
  • 1. Introduction
    • 1.1 Orbit Platform Overview
    • 1.2 Key Features
    • 1.3 Target Audience
    • 1.4 Benefits of Using Orbit Platform
    • 1.5 Overview of this Documentation
  • 2. Quick Start
    • 2.1 Accessing Orbit Platform
    • 2.2 Navigating the User Interface
    • 2.3 Basic User Cases
      • 2.3.1 Conducting a Semantic Search
      • 2.3.2 Copilot Chat
      • 2.3.3 Browsing and Using Pre-Defined Bots
    • 2.4 Exploring the Bot Marketplace
    • 2.5 Understanding SaaS Features and Limitations
  • 3. Platform Overview
    • 3.1 Overview of Orbit Platform
    • 3.2 Orbit AI Studio
      • 3.2.1 Data Loaders
      • 3.2.2 Metadata Management
      • 3.2.3 PDF Pre-Processing
      • 3.2.4 LLM Integration
      • 3.2.5 Workflow Automation
    • 3.3 Custom Knowledge Base Creation
    • 3.4 Chat and Search Capabilities
    • 3.5 Bot Marketplace
      • 3.5.1 Overview of the Bot Marketplace
      • 3.5.2 Creating and Managing Bots
      • 3.5.3 Automating Manual Tasks with Bots
  • 3.6 Data Connectors
  • 4. User Guide
    • 4.1 General User Interface
      • 4.1.1 Portfolio Management
      • 4.1.2 Concept Management
      • 4.1.3 Share
    • 4.2 Semantic Search and Chat
    • 4.3 Features on Single Document
    • 4.4 Create Your Knowledge Base
  • 5. Orbit Knowledge Bases
    • 5.1 Introduction
  • 5.2 Global Exchange Filings
  • 5.3 China Earnings Transcripts
  • 5.4 Global Sustainability Reports
  • 5.5 Global Regulation Documents
  • 5.6 Global Earnings Transcripts
  • 5.7 Listed Companies Official Documents
  • 5.8 Private Companies Official Documents
  • 5.9 Google News
  • 5.10 China Bond Documents
  • 6. Off-the-Shelf Bots
    • 6.1 Data Transformer
    • 6.2 Filings Insight Extractor
    • 6.3 Portfolio News Tracker
    • 6.4 Summary Composer
    • 6.5 Financial Statement Navigator
    • 6.6 Earning Call Calendar
    • 6.7 News Flow Tracker
  • 6.8 SmartMonitor Bot
  • 7. Pricing
    • 7.1 Product Options
    • 7.2 SaaS Pricing Structure
  • 7.3 Product Selection Guide
  • 8. Enterprise Deployment
    • 8.1 Deployment Options
    • 8.2 Security and Compliance
    • 8.3 Scaling and Performance
    • 8.4 Integration with Existing Systems
  • 9. Use Cases and Examples
    • 9.1 Investment Research Use Cases
      • 9.1.1 Generate a Research Report with Copilot Chat
      • 9.1.2 Analyse Investment Themes from Annual Reports
    • 9.2 Sustainability Use Cases
      • 9.2.1 Generate an ESG Report with Copilot Chat
      • 9.2.2 Orbit vs Claude vs Perplexity
    • 9.3 Service Provider Use Cases
    • 9.4 Case Studies: Success Stories
  • 10. FAQ and Troubleshooting
    • 10.1 Common Questions
    • 10.2 Contacting Support
  • 11. Appendices
    • 11.1 Glossary of Terms
    • 11.2 Whitepapers
      • Advancing News Analytics for Financial Decision Making
    • 11.3 Release Notes
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On this page
  • Introduction
  • Coverage
  • Latency
  • Delivery
  • Case Studies
  • Use case 1 – Institutional On-Site Research Testing Scenario
  • Use case 2 – On-Site Q&A’s Transcripts
  • Use case 3 – CSI-300 & Institutional On-Site Research (Sentiment Applied) Testing Scenario
  • Data Dictionary
  • Earnings Call & Institutional On-Site Research: ECM and BRD File Format
  • Online Forum Transcripts: OQA File Format

5.3 China Earnings Transcripts

Previous5.2 Global Exchange FilingsNext5.4 Global Sustainability Reports

Last updated 9 months ago

Introduction

Reports are classified into one of below three types

Earning Call Transcripts: This is the transcripts of earning call meetings after annual or quarterly reports are filed with the Shanghai and Shenzhen stock exchanges.

Institutional On-site Research Transcripts: By regulation, Financial Professionals from the institutional sector (i.e Brokers, Asset Managers, Investment Banks, Researchers ect.) carry out onsite research on publicly listed companies. The companies then have the obligation to disclose the Q&A responses. Usually, 75%-80% of all companies disclose the Q&A meetings within 3 days.

Online Forum Transcripts: Retail investors will ask the publicly listed companies questions on the online forums - In which the executives of those companies will have to provide an answer for. These Q&A’s are shown in real time and have between 350,000 – 535,000 individual company interactions per year, shown across the full coverage of the China A market.

Coverage



Orbit provides full coverage of the China A-Shares, with history going back 10+ years starting from 2012. The exchanges that we cover are both the Shanghai and Shenzhen stock exchanges.

We have all original documents, in structured JSON format, and translated into English. As a part this dataset service, Orbit also provides the updated transcripts data.

Latency

As a standard we provide all data before the next market open. However due to our inhouse data management an processing capabilities, we’re able to structure the latency based on your individual requirements.

Delivery

Flat file delivery

The delivery of the data is provided in a JASN formatted flat file via AWS S3. You’ll be given an Access Key ID, Secret Access Key, Default Region Name, and AWS S3 bucket.

Orbit Insight: VIP China A Share Content



Orbit Insight is a highly powerful and intuitive investment research tool, which harnesses AI and powerful large language models (LLMs) to derive predictive indicators and investment opportunities from a universe of unstructured company documents and news sources.

Access to this data remains a challenge. Even though information is publicly available on the internet, there still lacks a single, consolidated platform, especially for non-US and non-English content. Added to this challenge, effective investment research needs specific logic for accurate investment decision making (not currently available on search engine type solutions), as well as access to current trends and a wider range of potential opportunities.

Our revolutionary approach of integrating LLMs (such as ChatGPT) allows clients to seamlessly generate ad-hoc research across millions of unstructured sources. Global coverage of data across public and private companies is provided, as well as the ability to conduct tailored and targeted searches of all underlying company documents specific to financial services.

(Dashboard falls into an additional subscription, please enquire for more information.)

Case Studies

Use case 1 – Institutional On-Site Research Testing Scenario

Measuring the historical relationship of on-site research Q&A’s Vs Share price movement:

  1. Buy/hold strategy for 5 & 10 days from next market open after on-site research Q&A filed with the exchange

  2. Measured over a 3-year period

  3. 3,500 stocks & a total of 1991 published on-site research Q&A’s

Conclusion

Looking historically, those Companies seeing large requests for on-site research / Q&A sessions are seeing a positive impacts on share price over a 5 – 10 day period (based on trading at next market open from filing)

Filing Numbers in the period ​

Number of Companies ​

Average % Return​

5 day holding ​

Accuracy ​

Average % Return​

10 day holding ​

​

Accuracy​

More than 50​

37​

20.10%​

81.0%​

27.08%​

64.8%​

21 - 50​

125​

23.10%​

69.6%​

27.70%​

61.6%​

11 – 20​

278​

18.23%​

53.3%​

21.67%​

51.0%​

Up to 10​

1551​

(0.55%)​

47.0%​

(10.64%)​

38.3%​

Metric Definition:

  1. Filing Numbers in the period: No. of BRD reports from Jan 2018 to July 2020

  2. No_of_Companies: No. of companies have BRC reports fit into the group

  3. Yield_Holding_xdays: Accumulated return in % to buy when report is released and hold for x days

  4. Accuracy: The percentage that trades on a stock profit.

Use case 2 – On-Site Q&A’s Transcripts

Testing Scenario

Measuring the historical return on the back of on-site research Q&A’s, when sentiment is taken into account:

Buy/hold strategy for 15 & 30 days from the next market open after on-site research Q&A filed with the exchange Measured the return based on sentiment Measured over a 2-year period

Conclusion

Looking historically, those Companies seeing large requests for on-site research / Q&A sessions are seeing a positive impacts on share price over a 5 – 10 day period (based on trading at the next market open from filing)

Use case 3 – CSI-300 & Institutional On-Site Research (Sentiment Applied) Testing Scenario

Measuring the historical relationship between institutional on-site research sentiment Vs CSI300 Index

Measuring sentiment of 6 months of institutional on-site research transcripts Plot against the daily value of the CSI300 Index

Conclusion

Strong relationship between the sentiment of institutional on-site research and the overall price movement of the CSI300



Data Dictionary

Earnings Call & Institutional On-Site Research: ECM and BRD File Format

Json file contains below keys:

stockcode – stock ticker of local exchange

exchangecode – MIC code of Shanghai stock exchange (XSHG) and Shenzhen stock exchange (XSHE)

typesOfInvestorRelationsActivities: ecm and brd (ecm is for the earning call transcript and brd is for broker research disclosure)

transcriptuniqueid – our internal unique ID

transcripttitle – transcript title

nameOfParticipatingUnitAndPersonnel – name of investor participants

time: { start_time – meeting start date and time

end_time – meeting end date and time, optional

published – date and time when the report is available on exchange website

}

Location – meeting location

listedCompanyReceptionistName – name of company participants

content:{ contented – internal unique ID

contenttype – statement is for the opening statement from company, question and answer are for the Q&A sessions

content – detailed transcript

}

versioned – version of the transcript, all defaulted to be 1 for now

uploadtime – date and time when transcript file is uploaded to Orbit S3 bucket

Online Forum Transcripts: OQA File Format

stockcode - stock ticker of local exchange

exchangecode - MIC code of Shanghai stock exchange (XSHG) and Shenzhen stock exchange (XSHE)

typesOfInvestorRelationsActivities - default to OQA

transcriptuniqueid - our internal unique ID

date - date of the online Q&As by answer date

versioned– version of the transcript, all defaulted to be 1 for now

content: [

{

question: {

question_id - internal unique ID

platform - name of the source platform

speak_user_name - person name

speak_time - date and time the person ask the question

content_cn/en – statement

reply: [

{

reply_id- internal unique ID

speak_time - date and time the person answer the question

content_cn/en – content

speak_user_name – person name

}

]