This dataset is best for a comprehensive analysis of women's luxury watch market growth with demographic trend modeling and investment implications. Essential for hedge funds seeking exposure to expanding market segments. Analysis includes purchasing power evolution and optimal brand selection strategies. Critical for capturing demographic-driven alpha opportunities.

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Vision USA includes consumer transaction data on 100M+ credit and debit cards, including 35M+ with activity in the past 12 months and 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants, 800+ parent companies, 80+ same store sales metrics, and deep demographic and geographic breakouts. Review data by ticker in our Investor Relations module. Brick & mortar and ecommerce direct-to-consumer sales are recorded on transaction date and purchase data is available for most companies as early as 6 days post-swipe.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

Private equity and venture capital firms can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights teams and retailers can gain visibility into transaction data’s potential for competitive analysis, shopper behavior, and market intelligence.

CE Vision Benefits • Discover new competitors • Compare sales, average ticket & transactions across competition • Evaluate demographic and geographic drivers of growth • Assess customer loyalty • Explore granularity by geos • Benchmark market share vs. competition • Analyze business performance with advanced cross-cut queries

Private equity, venture capital, hedge funds, asset managers, and corporate clients use Consumer Edge data for:

Private Equity & Venture Capital Use Cases • Deal Sourcing • Live Diligences • Portfolio Monitoring

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Advanced sales data for statistical analysis decomposing Patek Philippe perpetual calendar returns into systematic (beta) and idiosyncratic (alpha) components. Essential for hedge funds building factor models for luxury watch investments. Includes stress testing during financial crises and quantified liquidity premiums across different market conditions.

This dataset is best for an econometric study of complexity premiums across different complication levels with risk-adjusted return calculations. Critical for hedge funds building factor-based models for luxury watch portfolios. Analysis includes liquidity impact of complications and optimal portfolio weighting strategies. Essential for understanding value drivers beyond brand premiums.

A list of the top 50 BlackRock holdings showing which stocks are owned by BlackRock Inc's hedge fund.

Middle Office Outsourcing Market size was valued at USD 8087.59 Million in 2023 and is projected to reach USD 14844.38 Million by 2031, growing at a CAGR of 8.70% from 2024 to 2031.

Key Market Drivers: Cost Efficiency and Scalability: One of the key reasons for middle office outsourcing is the possibility of cost savings. Outsourcing middle office operations such as risk management, compliance, and trade processing allows businesses to drastically cut operational expenses associated with keeping in-house staff. Outsourcing providers frequently have specialized knowledge and economies of scale allowing them to provide certain services more efficiently. Access to Advanced Technology and Expertise: Another important factor is having access to cutting-edge technology and specialized knowledge. Middle office operations necessitate complex tools and systems for data management, analytics, and compliance monitoring. Outsourcing providers invest extensively in these technologies allowing their clients to access cutting-edge solutions that would be prohibitively expensive to develop in-house. Regulatory Compliance and Risk Management: The growing complexity of regulatory regulations is another major driver of middle office outsourcing. Financial organizations face severe rules that necessitate strong compliance and risk management systems. Companies that outsource these services can reduce the risk of non-compliance and the resulting penalties. Outsourcing firms specialize in keeping up with changing rules and have the means to keep their clients compliant.

Context

Gold, the yellow shiny metal, has been the fancy of mankind since ages. From making jewelry to being used as an investment, gold covers a huge spectrum of use cases. Gold, like other metals, is also traded on the commodities indexes across the world. For better understanding time series in a real-world scenario, we will work with gold prices collected historically and predict its future value.

Content

Metals such as gold have been traded for years across the world. Prices of gold are determined and used for trading the metal on commodity exchanges on a daily basis using a variety of factors. Using this daily price-level information only, our task is to predict future price of gold.

Data

For the purpose of implementing time series forecasting technique , i will utilize gold pricing from Quandl. Quandl is a platform for financial, economic, and alternative datasets. To access publicly shared datasets on Quandl, we can use the pandas-datareader library as well as quandl (library from Quandl itself). The following snippet shows a quick one-liner to get your hands on gold pricing information since 1970s.

import quandl gold_df = quandl.get("BUNDESBANK/BBK01_WT5511")

The time series is univariate with date and time feature

Starter Kernel(s)

-Start with Fundamentals: TSA & Box-Jenkins Methods

This notebook is an overview of TSA and traditional methods

Acknowledgements

For this dataset and tasks, i will depend upon Quandl. The premier source for financial, economic, and alternative datasets, serving investment professionals. Quandl’s platform is used by over 400,000 people, including analysts from the world’s top hedge funds, asset managers and investment banks.

Inspiration

• Forecast gold price

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The levels of research & development expenditure at Samsung Electronics between 2009 and 2024 has been steadily increasing in recent years. In 2024, total spending on research and development at Samsung Electronics amounted to approximately 29 billion U.S. dollars, The highest-ever recorded R&D expenditure by the Korean giant. Samsung R&D spending Samsung, a global leader in semiconductors, telecommunications and digital media technologies, spent nearly 21 billion U.S. dollars on research and development in 2023. This amount is considerably higher than the 2009 figure, when Samsung spent around seven billion U.S. dollars on research and development. Their expenditure has tripled. Samsung’s investment manifests, for example, in the more than six thousand U.S. patents. Investment in R&D Software and computer services companies, and technology hardware and equipment companies tend to invest heavily in research and development. In the last few years, companies from these two industrial sectors spent, on average, about seven to nine percent of their total revenue on R&D.