Posts tagged Value Pickr
Sunflag Iron & Steel (12-07-2024)
Hello fellow investors,
I wanted to share some thoughts and seek your insights on Sunflag Iron’s recent financial decisions. As many of you might know, Sunflag Iron holds shares worth 4000 crore rupees in Lloyd Metals. Rather than liquidating these shares to pay off its own debt or to invest in expanding its operations, the company has chosen to further subscribe to the warrants of Lloyd Metals, committing even more cash.
Reference: https://www.bseindia.com/xml-data/corpfiling/AttachHis/7d477533-6d46-440b-aaf3-f1b17866edc1.pdf
This decision has raised a few questions for me:
Strategic Alignment: What might be the strategic reasons behind this move? Is there a potential synergy or future benefit that Sunflag Iron sees in Lloyd Metals that justifies further investment?
Financial Health: How does this decision impact Sunflag Iron’s financial health in the short and long term? What risks does this additional cash commitment entail?
Alternative Uses: Would it have been more prudent for Sunflag Iron to sell the shares and use the proceeds for debt reduction or to invest in its core business operations?
I’m eager to hear your perspectives and analyses on this. Why do you think Sunflag Iron is taking this approach, and what do you believe the implications might be for us as investors?
Sunflag Iron & Steel (12-07-2024)
Hello fellow investors,
I wanted to share some thoughts and seek your insights on Sunflag Iron’s recent financial decisions. As many of you might know, Sunflag Iron holds shares worth 4000 crore rupees in Lloyd Metals. Rather than liquidating these shares to pay off its own debt or to invest in expanding its operations, the company has chosen to further subscribe to the warrants of Lloyd Metals, committing even more cash.
Reference: https://www.bseindia.com/xml-data/corpfiling/AttachHis/7d477533-6d46-440b-aaf3-f1b17866edc1.pdf
This decision has raised a few questions for me:
Strategic Alignment: What might be the strategic reasons behind this move? Is there a potential synergy or future benefit that Sunflag Iron sees in Lloyd Metals that justifies further investment?
Financial Health: How does this decision impact Sunflag Iron’s financial health in the short and long term? What risks does this additional cash commitment entail?
Alternative Uses: Would it have been more prudent for Sunflag Iron to sell the shares and use the proceeds for debt reduction or to invest in its core business operations?
I’m eager to hear your perspectives and analyses on this. Why do you think Sunflag Iron is taking this approach, and what do you believe the implications might be for us as investors?
Oil India- has its time come? (12-07-2024)
It’s ministry data.
Oil India- has its time come? (12-07-2024)
It’s ministry data.
DIY Momentum QnA and Discussion (12-07-2024)
The NSE Bhavcopy contains daily information about all securities traded on the NSE, including the list of stocks and their trading data. You can use the Bhavcopy to compile historical lists of the top 200 stocks. Here’s how you can do it:
Steps to Access NSE Bhavcopy:
- Visit the NSE Website: Go to the NSE Historical Data.
- Download Bhavcopy:
- Navigate to “Equity” → “Historical Data” → “Bhavcopy.”
- Select the date range you are interested in.
- Download the Bhavcopy files for the desired dates.
Compiling Historical Lists:
- Extract Data: Extract the downloaded Bhavcopy files (usually in CSV format).
- Filter Top 200 Stocks: Use criteria like trading volume, market capitalization, or other metrics to filter the top 200 stocks for each date.
- Automate with Python: Write a Python script to automate the extraction and filtering process.
Example Python Script to Process Bhavcopy:
import pandas as pd
import glob
# Path to the folder containing the downloaded Bhavcopy files
path = "path/to/bhavcopy/files"
# Load all CSV files
all_files = glob.glob(path + "/*.csv")
# Initialize an empty DataFrame to store top 200 stocks
top_200_stocks = pd.DataFrame()
# Process each Bhavcopy file
for file in all_files:
df = pd.read_csv(file)
df['Date'] = pd.to_datetime(file.split('/')[-1].split('.')[0], format='%d%m%Y')
# Sort by desired metric, e.g., market capitalization
df = df.sort_values(by='MARKET_CAPITALIZATION', ascending=False)
# Select top 200 stocks
top_200 = df.head(200)
# Append to the main DataFrame
top_200_stocks = pd.concat([top_200_stocks, top_200])
# Save the result to a CSV file
top_200_stocks.to_csv('top_200_stocks_historical.csv', index=False)
Key Points:
- Regular Downloads: Ensure you download Bhavcopy files regularly to maintain an updated historical dataset.
- Filter Criteria: Choose consistent criteria for filtering top stocks, like market cap or trading volume.
By using the NSE Bhavcopy and processing it with a script, you can compile a historical list of the top 200 stocks over the desired period.
DIY Momentum QnA and Discussion (12-07-2024)
The NSE Bhavcopy contains daily information about all securities traded on the NSE, including the list of stocks and their trading data. You can use the Bhavcopy to compile historical lists of the top 200 stocks. Here’s how you can do it:
Steps to Access NSE Bhavcopy:
- Visit the NSE Website: Go to the NSE Historical Data.
- Download Bhavcopy:
- Navigate to “Equity” → “Historical Data” → “Bhavcopy.”
- Select the date range you are interested in.
- Download the Bhavcopy files for the desired dates.
Compiling Historical Lists:
- Extract Data: Extract the downloaded Bhavcopy files (usually in CSV format).
- Filter Top 200 Stocks: Use criteria like trading volume, market capitalization, or other metrics to filter the top 200 stocks for each date.
- Automate with Python: Write a Python script to automate the extraction and filtering process.
Example Python Script to Process Bhavcopy:
import pandas as pd
import glob
# Path to the folder containing the downloaded Bhavcopy files
path = "path/to/bhavcopy/files"
# Load all CSV files
all_files = glob.glob(path + "/*.csv")
# Initialize an empty DataFrame to store top 200 stocks
top_200_stocks = pd.DataFrame()
# Process each Bhavcopy file
for file in all_files:
df = pd.read_csv(file)
df['Date'] = pd.to_datetime(file.split('/')[-1].split('.')[0], format='%d%m%Y')
# Sort by desired metric, e.g., market capitalization
df = df.sort_values(by='MARKET_CAPITALIZATION', ascending=False)
# Select top 200 stocks
top_200 = df.head(200)
# Append to the main DataFrame
top_200_stocks = pd.concat([top_200_stocks, top_200])
# Save the result to a CSV file
top_200_stocks.to_csv('top_200_stocks_historical.csv', index=False)
Key Points:
- Regular Downloads: Ensure you download Bhavcopy files regularly to maintain an updated historical dataset.
- Filter Criteria: Choose consistent criteria for filtering top stocks, like market cap or trading volume.
By using the NSE Bhavcopy and processing it with a script, you can compile a historical list of the top 200 stocks over the desired period.
Investing Basics – Feel free to ask the most basic questions (12-07-2024)
Hi, Based on the limited information that has been shared it appears as follows:
Loan accepted means company has taken a loan from the related party
No, it means company has paid more to the related party who now owes it the money. See for Hiren Kotecha - Loan repaid = 0.72 which is the Closing balance for 21-22 which is also the opening balance for 22-23. So it seems it means company has paid him 0.72 which he now owes to the company
Interest expense will be on the entire loan exposure for the tenure of the loan. You cannot get the interest calculation from the given information since the dates of the loan taken / repaid etc are not available.
Overall, the following formula should hold for the numbers given:
Opening Balance + Loan Accepted - Loan Repaid + Interest Expense = Closing Balance
If it doesn’t seek an explanation from the company.
Investing Basics – Feel free to ask the most basic questions (12-07-2024)
Hi, Based on the limited information that has been shared it appears as follows:
Loan accepted means company has taken a loan from the related party
No, it means company has paid more to the related party who now owes it the money. See for Hiren Kotecha - Loan repaid = 0.72 which is the Closing balance for 21-22 which is also the opening balance for 22-23. So it seems it means company has paid him 0.72 which he now owes to the company
Interest expense will be on the entire loan exposure for the tenure of the loan. You cannot get the interest calculation from the given information since the dates of the loan taken / repaid etc are not available.
Overall, the following formula should hold for the numbers given:
Opening Balance + Loan Accepted - Loan Repaid + Interest Expense = Closing Balance
If it doesn’t seek an explanation from the company.
DIY Momentum QnA and Discussion (12-07-2024)
(post deleted by author)