<example>
H: <text>Calculate the average value for AAPL US Equity.</text>
A: <response>To calculate the average closing price over the last 3 months for AAPL US Equity, use the BQL query: `get(avg(px_last(dates=range(start=-3m,end=0d)))) for('AAPL US Equity')`</response>
</example>| def analyze_with_claude_2(ten_q_content): | |
| url = "https://api.promptperfect.jina.ai/your-api-endpoint" | |
| headers = {"Content-Type": "application/json"} | |
| response = requests.post(url, headers=headers, json={"parameters": {"earnings_call_transcript": ten_q_content}}) | |
| if response.status_code == 200: | |
| return response.json() # Assuming the response contains the risks identified | |
| else: | |
| print(response.text) | |
| return [] |
| from datasets import load_dataset | |
| # Load the dataset from Hugging Face | |
| dataset = load_dataset("kurry/sp500_constituents_10q") | |
| # Placeholder list for companies with known accounting issues | |
| companies_with_issues = ["CompanyA", "CompanyB"] # This needs to be populated | |
| # Analyze each company's 10-Q using Claude 2 | |
| true_positives = 0 |
Let's evaluate the alignment between the response and the SEC's findings on accounting fraud:
-
Revenue Recognition and Sales Performance:
- Your Analysis: Concerns about aggressive revenue recognition, potential channel stuffing, and discrepancies.
- SEC Findings: Misleading investors about core sales growth, pulling sales into earlier quarters, and accounting practices inconsistent with GAAP.
- Alignment: Strong alignment. Both your analysis and the SEC's findings highlight discrepancies in revenue recognition and sales performance.
-
Accounting Practices:
- Your Analysis: Lack of transparency around sales trends and accounting maneuvers.
- SEC Findings: Inconsistent accounting practices with GAAP and overriding internal accounting controls.
Here is my analysis of the potential accounting and financial risks based on the earnings call transcript:
Meta-data:
Industry: Consumer Goods
Company: Newell Brands
Call Date: February 7, 2017
Risk Score: 0.61 (Medium-High)
Confidence Scores:
| Thanks, Mike, and good morning, everyone | |
| As Mike noted, we made significant progress throughout 2016 in transforming our organization | |
| We achieved 3.7% core sales growth while delivering over $210 million in synergies and Project Renewal savings and progressed deleveraging the balance sheet | |
| We also refined our portfolio with some strategic divestitures and acquisitions | |
| Our fourth quarter results were solid and in line with guidance | |
| Fourth quarter reported net sales were $4.14 billion, a 165% increase versus last year, which was largely attributable to the Jarden transaction | |
| Core sales increased 2.5%, driven by strong results from the Writing, Baby, Home Solutions, and Outdoor Solutions businesses | |
| For the full year, core top line increased 3.7% | |
| The year-over-year improvement in operating results down the income statement was driven by a number of overarching themes: strong operating income growth on the legacy business, the profit contribution from the Jarden and Elmer's acquisitions, and Project Renewal saving |
| { | |
| "startDate": "2016-01-01", | |
| "endDate": "2016-12-31", | |
| "year": "2016", | |
| "quarter": "FY", | |
| "symbol": "NWL", | |
| "data": { | |
| "bs": [ | |
| { | |
| "label": "Cash and cash equivalents", |
Here is my analysis of the potential accounting and financial risks based on the earnings call transcript:
Meta Data
- Industry: Consumer Goods
- Company: Newell Brands Inc.
- Call Date: February 2017 (Discussing 2016 FY results)
Risk Score: 0.63 (Medium-High Risk)
Summary of Findings:
Objective:
Your primary task is to conduct a step-by-step, rigorous analysis of the earnings call transcript enclosed within the <document></document> XML tags, identifying potential accounting and financial risks.
Pre-analysis Steps:
1. Initial Reading: Begin by reading the entire transcript to understand the general themes and topics discussed.
2. Identify Key Sections: Mark sections of the transcript that discuss financials, future projections, governance, and any other relevant topics.
| package FocusPackage; | |
| import com.assylias.jbloomberg.DataChangeEvent; | |
| import com.assylias.jbloomberg.DefaultBloombergSession; | |
| import com.assylias.jbloomberg.IntradayStudyData; | |
| import com.assylias.jbloomberg.IntradayStudyField; | |
| import com.assylias.jbloomberg.IntradayStudyRequestBuilder; | |
| import com.assylias.jbloomberg.RealtimeField; | |
| import com.assylias.jbloomberg.TypedObject; | |
| import com.bloomberglp.blpapi.CorrelationID; |