Sold by: MphasisÂ
Deployed on AWS
This GenAI based solution aids research analysts in identifying pivitol disparities between stock reports, extracting key financial ratios.
Overview
"This solution uses Claude 2.1, a Large Language Model to extract intricate financial data from stock analyst reports. By leveraging the prowess of generative pre-trained transformers, this solution accurately retrieves information from various lengthy and complex stock analyst reports supplied in PDF format spanning different stocks within the same industry, over specified time periods. This solution leverages GenAI concepts and AWS Bedrock to run pre defined set of prompts on the reports to facilitate comparisons which empowers investors with a comprehensive understanding of the market landscape. By enabling the comparison of multiple reports and extracting meaningful insights swiftly, it eliminates the need for arduous manual analysis, ultimately saving time and enhancing decision-making processes. The reports undergo a comprehensive comparison, where key financial ratios such as EBITDA growth, revenue growth, and valuations are meticulously evaluated. "
Highlights
- Large language models are used to select and retrieve information from financial analyst stock reports. This is an easy solution to carve out necessary information and provide comparative analysis across stocks of the same sector.
- The investors in stocks need to take buy/sell/hold decisions. This requires analyzing several financial reports of stocks in the same industry. Large language models help in quickly checking the similarity across stocks by segregating the data from the financial analyst report. This provides fast solutions with reliable estimates.
- Mphasis uses large language models to compare the performance of different stocks from the financial reports provided by the stock analysts. Need Customized Large Language Model Learning Solutions? Get in Touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $2.00/host/hour |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $2.00/host/hour |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $2.00/host/hour |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $2.00/host/hour |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $2.00/host/hour |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $2.00/host/hour |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $2.00/host/hour |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $2.00/host/hour |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $2.00/host/hour |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $2.00/host/hour |
Vendor refund policy
Currently, we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
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Content disclaimer
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
1.1
Additional details
Inputs
- Summary
A zip folder with the following directory structure and availability of AWS Claude2.1 in AWS bedrock. *input.zip credentials credentials.json >aws_access_key_id : >aws_secret_access_key: >region_name:
data Your files go here. hyperparams Look At example
- Limitations for input type
- Data should be PDF files only
- Input MIME type
- application/zip
https://github.com/Mphasis-ML-Marketplace/Stock-Report-Comparisons-and-analysts/blob/main/input.zip
https://github.com/Mphasis-ML-Marketplace/Stock-Report-Comparisons-and-analysts/blob/main/input.zip
Support
Vendor support
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AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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