United States Assets: Flow: PDI: Debt Securities (DS) data was reported at 11.162 USD bn in Sep 2018. This records a decrease from the previous number of 13.509 USD bn for Jun 2018. United States Assets: Flow: PDI: Debt Securities (DS) data is updated quarterly, averaging 5.393 USD bn from Dec 1951 (Median) to Sep 2018, with 268 observations. The data reached an all-time high of 201.090 USD bn in Sep 2010 and a record low of -102.810 USD bn in Dec 2010. United States Assets: Flow: PDI: Debt Securities (DS) data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.AB012: Funds by Sector: Flows and Outstanding: Private Depository Corporations.

United States Assets: Outs: PDI: Debt Securities (DS) data was reported at 4,265.366 USD bn in Mar 2018. This records a decrease from the previous number of 4,286.232 USD bn for Dec 2017. United States Assets: Outs: PDI: Debt Securities (DS) data is updated quarterly, averaging 726.832 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 4,286.232 USD bn in Dec 2017 and a record low of 89.147 USD bn in Jun 1953. United States Assets: Outs: PDI: Debt Securities (DS) data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB012: Funds by Sector: Flows and Outstanding: Private Depository Corporations.

Pason Systems reported CAD14.85M in Debt for its fiscal quarter ending in March of 2025. Data for Pason Systems | PSI - Debt including historical, tables and charts were last updated by Trading Economics this last August in 2025.

United States Liabilities: Flow: PDI: Debt Securities (DS) data was reported at -1.356 USD bn in Mar 2018. This records an increase from the previous number of -8.218 USD bn for Dec 2017. United States Liabilities: Flow: PDI: Debt Securities (DS) data is updated quarterly, averaging 0.156 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 29.522 USD bn in Dec 2011 and a record low of -53.654 USD bn in Dec 2010. United States Liabilities: Flow: PDI: Debt Securities (DS) data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB012: Funds by Sector: Flows and Outstanding: Private Depository Corporations.

United States Liabilities: Flow: PDI: saar: Debt Securities (DS) data was reported at -5.424 USD bn in Mar 2018. This records an increase from the previous number of -32.872 USD bn for Dec 2017. United States Liabilities: Flow: PDI: saar: Debt Securities (DS) data is updated quarterly, averaging 0.586 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 118.088 USD bn in Dec 2011 and a record low of -214.616 USD bn in Dec 2010. United States Liabilities: Flow: PDI: saar: Debt Securities (DS) data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB012: Funds by Sector: Flows and Outstanding: Private Depository Corporations.

Pason Systems reported CAD803K in Interest Expense on Debt for its fiscal quarter ending in September of 2024. Data for Pason Systems | PSI - Interest Expense On Debt including historical, tables and charts were last updated by Trading Economics this last August in 2025.

United States Assets: Flow: PDI: saar: Debt Securities (DS) data was reported at -75.146 USD bn in Mar 2018. This records a decrease from the previous number of 254.428 USD bn for Dec 2017. United States Assets: Flow: PDI: saar: Debt Securities (DS) data is updated quarterly, averaging 21.741 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 751.966 USD bn in Sep 2010 and a record low of -359.598 USD bn in Dec 2010. United States Assets: Flow: PDI: saar: Debt Securities (DS) data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB012: Funds by Sector: Flows and Outstanding: Private Depository Corporations.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

PIMCO Dynamic Income Fund (PDI) Outlook: Riding the Interest Rate Rollercoaster

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

PSC Insurance reported AUD6.57M in Interest Expense on Debt for its fiscal semester ending in December of 2023. Data for PSC Insurance | PSI - Interest Expense On Debt including historical, tables and charts were last updated by Trading Economics this last August in 2025.

Debt-To-Assets-Ratio Time Series for Padini Holdings Bhd. Padini Holdings Berhad, an investment holding company, engages in the retail of garments and ancillary products. The company operates through three segments: Investment Holding, Apparels and Footwear, and Management Service. It deals in ladies' shoes, garments, and accessories; and children's garments and accessories. The company also provides bags. In addition, it offers management and electronic commerce services. The company provides its products through retail stores and consignment counters, as well as through online portals primarily under the Padini, Vincci, Vincci Accessories, Vincci Mini, BO Accessories, Blitz, Filanto, Gamesters, Garage Inc., Hotshots, Industrie Co., Move, Oceano, Portofino, Ropé, Studio, Padini Authentics, PDI, Seed, Miki, and P&Co brands. It operates stores in Malaysia, Cambodia, Bahrain, Brunei, Oman, Qatar, Thailand, and the United Arab Emirates. The company was founded in 1971 and is based in Shah Alam, Malaysia.

[Keywords] Market include WNRS, Oddcoll AB, EOS Global Collection, CobroAmericas, CyberCollect

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

PIMCO Dynamic (PDI) Dividend Dilemma?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

PSC Insurance reported AUD231.77M in Debt for its fiscal semester ending in December of 2023. Data for PSC Insurance | PSI - Debt including historical, tables and charts were last updated by Trading Economics this last August in 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

PSI-20: Where to Next?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data