This dataset has been meticulously curated to assist investment analysts, like you, in performing mean-variance optimization for constructing efficient portfolios. The dataset contains historical financial data for a selection of assets, enabling the calculation of risk and return characteristics necessary for portfolio optimization. The goal is to help you determine the most effective allocation of assets to achieve optimal risk-return trade-offs.

Dataset for most widely held stocks across top investor portfolios

A list of the top 50 Berkshire Hathaway holdings showing which stocks are owned by Warren Buffett's hedge fund.

INTRODUCTION

This case study is the Capstone Project of **upGrad PG Diploma - Data Science **. The 6 steps of Data Analysis is used to present this analysis.

Title: Financial & Risk Analytics

Author: Ankur Napa

Date: 14 Feb 2022

Portfolio Manager : How to identify the right investment opportunity and recommend a portfolio as per the client's exact need?

STEP 1: ASK

1.0 Background

We have 2 investors here:

1. Patrick Jyengar - He is a conservative investor and he wants to invest 1 million dollars and expects double of his capital with less risk in the coming 5 years. He also wants to invest 500K dollars in a magazine(Naturo) and later wants to buy a minority portion of the same.

2. Peter Jyengar - He is an aggressive investor and he wants to invest 1 million dollars into most high margin stocks & expects retunes within 5 years.

1.2 Business Task:

Analysing the portfolio of stocks to provide consultation on investment management based on the client’s requirements.

1.3 Business Objectives:

1. What are the trends identified?
2. How could these trends apply to customers?
3. How could these trends help influence investment strategy?

1.4 Deliverables:

1. A clean version of the final dataset.
2. A well commented Jupyter notebook containing the entire work.
3. A file containing a dashboard with all the important visualisations used in this project.
4. A PPT file with an executive summary containing your understanding of the investor, insights and recommended steps of action for the investors.
5. A video explaining the presentation: As the portfolio manager, you are expected to share a video presentation that you will share with the investors.

1.5 Key Stakeholders:

1. Patrick Jyengar - A successful entrepreneur - Jayengar Waterworks
2. Peter Jyengar - Inheritor of Patrick Jyengar

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F2b87409e296a59d20dab602e6501f340%2Ffile9e063b84e35.gif?generation=1707771596337465&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F9d574862156fdd14299b6bcdf1d7c0e8%2Ffile9e048912e2.gif?generation=1707771713059014&alt=media" alt="">

US Funds Dataset: Unlocking Insights for Informed Investment Decisions

Exchange-Traded Funds (ETFs) have gained significant popularity in recent years as a low-cost alternative to Mutual Funds. This dataset, compiled from Yahoo Finance, offers a comprehensive overview of the US funds market, encompassing 23,783 Mutual Funds and 2,310 ETFs.

Data

The dataset provides a wealth of information on each fund, including:

General fund aspects: total net assets, fund family, inception date, expense ratios, and more. Portfolio indicators: cash allocation, sector weightings, holdings diversification, and other key metrics. Historical returns: year-to-date, 1-year, 3-year, and other performance data for different time periods. Financial ratios: price/earnings ratio, Treynor and Sharpe ratios, alpha, beta, and ESG scores. Applications

This dataset can be leveraged by investors, researchers, and financial professionals for a variety of purposes, including:

Investment analysis: comparing the performance and characteristics of Mutual Funds and ETFs to make informed investment decisions. Portfolio construction: using the data to build diversified portfolios that align with investment goals and risk tolerance. Research and analysis: studying market trends, fund behavior, and other factors to gain insights into the US funds market. Inspiration and Updates

The dataset was inspired by the surge of interest in ETFs in 2017 and the subsequent shift away from Mutual Funds. The data is sourced from Yahoo Finance, a publicly available website, ensuring transparency and accessibility. Updates are planned every 1-2 semesters to keep the data current and relevant.

Conclusion

This comprehensive dataset offers a valuable resource for anyone seeking to gain a deeper understanding of the US funds market. By providing detailed information on a wide range of funds, the dataset empowers investors to make informed decisions and build successful investment portfolios.

Access the dataset and unlock the insights it offers to make informed investment decisions.

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.

What are the most successful trading algorithms? (NTAP Stock Forecast)

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

Context

These are the publicly traded U.S. stocks owned by Warren Buffett’s holding company Berkshire Hathaway, as reported to the SEC.

Warren Edward Buffett is an American investor, business tycoon, philanthropist, and the chairman and CEO of Berkshire Hathaway. He is considered one of the most successful investors in the world and has a net worth of US$78.9 billion as of August 2020, making him the world's seventh-wealthiest person.

The information can be found in the SEC 13F File. The Securities and Exchange Commission's (SEC) Form 13F is a quarterly report that is required to be filed by all institutional investment managers with at least $100 million in assets under management. It discloses their equity holdings and can provide some insights into what the smart money is doing in the market.

After obtaining the list of companies in which Berkshire Hathaway invests, we proceeded to search the historical data of each company on NASDAQ.

Content

In total there are 49 files in csv format. They are composed as follows: - 45 files contain the U.S. stocks owned by Berkshire Hathaway. - 2 files contain the historical data from Berkshire Hathaway. Class A stock (BRK-A) and Class B stock (BRK-B). - 1 file contain the list of all the companies with additional information. - 1 file contain the SEC Form 13F.

Column Description

Every company file has the same structure with the same columns: - Date: It is the date on which the prices were recorded. - Close/Last: Is the last price at which a stock trades during a regular trading session. - Volume: Is the number of shares that changed hands during a given day. - Open: Is the price at which a stock started trading when the opening bell rang. - High: Is the highest price at which a stock traded during the course of the trading day. - Low: Is the lowest price at which a stock traded during the course of the trading day.

The two other files have different columns names:

Company List - Name: Name of the company. - Symbol: Ticker symbol of the company.
- Holdings: Number of shares. - Market Price: Current price at which a stock can be purchased or sold. (10/18/20) - Value: (Holdings * Market Price).
- Stake: The amount of stocks an investor owns from a company.

SEC Form 13F

Name of Issuer, Title of Class, CUSIP Number, Market Value, Amount and Type of Security, Investment Discretion (Sole, Shared-Defined, Shared-Other), Other Managers, Voting Authority.

You can find detail information of each column in the SEC General Instructions Form 13F in page 5.

Acknowledgements

SEC EDGAR | Company Filings NASDAQ | Historical Quotes

Inspiration

Possible questions which could be answered are:

• Of the companies that make up the Berkshire Hathaway portfolio, which are the most profitable companies?
• On February 21, 2020, Berkshire Hathaway suffered a large drop in its share price, what was the reason for this large drop?
• The presidential elections in the United States will be soon, do you think this will affect a particular company in the Warren Buffett portfolio and if so, which would affect them more?

More Datasets

Optimal portfolios of the most profitable strategy.

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.

What are the most successful trading algorithms? (BC Stock Forecast)

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

As of 2024, the top-ranking product among Millennials and Gen Z was stocks, with roughly ** percent of Millennials and ** percent of Gen Z survey respondents stating they held positions. The next most popular financial security was retirement accounts, with ** percent of Millennials and ** percent of Gen Z currently holding retirement accounts in their portfolio.

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

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.

What are the most successful trading algorithms? (NSE SONATSOFTW Stock Forecast)

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

Investment Portfolio Management Software Market Outlook

The global investment portfolio management software market size is projected to reach USD 15.3 billion by 2032, from USD 6.7 billion in 2023, exhibiting a compound annual growth rate (CAGR) of 9.5% during the forecast period. This significant growth can be attributed to the increasing demand for sophisticated investment management tools, the growing adoption of cloud-based solutions, and the rising need for effective risk management and regulatory compliance in the finance sector.

One of the primary drivers of this market is the increasing sophistication and complexity of investment portfolios. As investors seek higher returns, they are diversifying their portfolios with a mix of traditional and alternative investments. This diversification necessitates advanced tools capable of handling complex calculations, real-time data analytics, and comprehensive reporting. Investment portfolio management software meets these needs by providing robust features that facilitate better decision-making and risk management. Additionally, the rise of robo-advisors and automated trading systems has further propelled the demand for such software, ensuring that both individual and institutional investors can maximize their returns while minimizing risks.

Another significant growth factor is the increasing adoption of cloud-based solutions. Cloud technology offers several advantages, including cost efficiency, scalability, and accessibility. By leveraging cloud-based investment portfolio management software, firms can access real-time data and analytics from anywhere, enabling better collaboration and more efficient management of portfolios. Moreover, cloud solutions often come with advanced security features and regular updates, ensuring that users are protected against the latest cybersecurity threats and benefit from the latest technological advancements. The shift towards cloud-based solutions is expected to continue driving market growth, as more firms recognize the benefits of this technology.

The need for stringent regulatory compliance and effective risk management is also a critical driver for the investment portfolio management software market. Financial institutions and investment firms operate in a highly regulated environment, and failure to comply with regulations can result in significant penalties and reputational damage. Investment portfolio management software helps firms stay compliant by automating regulatory reporting, monitoring for potential risks, and providing comprehensive audit trails. This not only reduces the risk of non-compliance but also enhances operational efficiency by minimizing manual processes and reducing the likelihood of human errors.

Asset Performance Management Software is becoming increasingly integral to the financial sector, particularly in the realm of investment portfolio management. As firms strive to optimize the performance of their assets, they are turning to advanced software solutions that provide real-time analytics and predictive insights. This software helps in identifying underperforming assets and reallocating resources to maximize returns. By integrating Asset Performance Management Software, financial institutions can enhance their decision-making processes, reduce operational costs, and improve overall asset utilization. This trend is particularly evident in sectors where asset-heavy investments are prevalent, and the need for precise performance tracking is critical.

Regionally, North America is expected to hold the largest market share due to the presence of a large number of financial institutions and investment firms. The region's robust technological infrastructure and early adoption of advanced software solutions further contribute to its dominance. However, the Asia Pacific region is anticipated to witness the highest growth rate, driven by the increasing digitalization of financial services, rapid economic growth, and a growing number of high-net-worth individuals. Additionally, countries like China and India are making significant investments in fintech, which is expected to boost the adoption of investment portfolio management software in the region.

Component Analysis

The investment portfolio management software market can be segmented by component into software and services. The software segment dominates the market and includes various types of software solutions designed to enhance portfolio management effici

ABSTRACT This article aims to analyze the shares that make up the Brazilian IBRX100 index, verifying which sectors had the greatest influence on the Stock Exchange in 2018, 2019, and 2020. For this purpose, the theory of graph centrality measures was used to discover the most central shares. A balance analysis of the graphs was also performed, since balanced graphs are more stable, generating a more predictable stock portfolio. This study may help investors to compose a safer stock portfolio and identify which stocks are most correlated with each other. The most central shares can aid in perceiving stock market trends.

Model Portfolio Platform Market Outlook

According to our latest research, the global Model Portfolio Platform market size reached USD 4.8 billion in 2024, driven by rapid digitalization in the wealth management sector and the increasing demand for efficient portfolio management solutions. The market is expected to grow at a robust CAGR of 13.2% during the forecast period, reaching a projected value of USD 14.1 billion by 2033. This impressive growth trajectory is primarily fueled by the rising adoption of advanced analytics, cloud-based deployment, and the need for scalable, customizable investment solutions across financial institutions worldwide.

The growth of the Model Portfolio Platform market is significantly influenced by the evolving landscape of wealth management and investment advisory services. As investors seek more personalized and cost-effective investment strategies, financial institutions are increasingly leveraging model portfolio platforms to automate and optimize portfolio construction and rebalancing processes. These platforms enable wealth managers and advisors to deliver tailored investment solutions at scale, enhancing client experience and operational efficiency. Furthermore, the proliferation of digital channels and the integration of artificial intelligence and machine learning are transforming traditional portfolio management, making it more data-driven and responsive to market dynamics. This digital transformation is a key driver propelling the expansion of the model portfolio platform market.

Another major growth factor for the Model Portfolio Platform market is the regulatory environment, which is pushing financial institutions towards greater transparency and fiduciary responsibility. With regulations such as MiFID II in Europe and the SECÂ’s Regulation Best Interest in the United States, firms are compelled to adopt technologies that ensure compliance, auditability, and consistent investment processes. Model portfolio platforms offer robust compliance tools and documentation capabilities, allowing firms to meet regulatory requirements efficiently while reducing operational risks. The ability to standardize investment strategies and provide detailed reporting also enhances trust and credibility with clients, further boosting market adoption.

Additionally, the increasing complexity of financial markets and the growing diversity of investment products are prompting advisors and asset managers to seek sophisticated portfolio management solutions. Model portfolio platforms facilitate the management of multi-asset portfolios, enable seamless integration with third-party data sources, and support real-time performance tracking. The scalability of these platforms allows both large enterprises and small and medium-sized firms to manage a wide range of model portfolios, adapt to changing client preferences, and respond quickly to market volatility. This adaptability and scalability are critical growth drivers, particularly as firms strive to differentiate themselves in a highly competitive marketplace.

From a regional perspective, North America continues to dominate the Model Portfolio Platform market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The strong presence of leading wealth management firms, advanced financial infrastructure, and a high level of digital adoption in North America are key factors supporting market growth. Meanwhile, Asia Pacific is emerging as the fastest-growing region, driven by the rapid expansion of financial advisory services, increasing wealth accumulation, and the adoption of digital investment platforms among a younger, tech-savvy population. Europe remains a mature market, benefiting from stringent regulatory standards and a well-established financial advisory ecosystem.

In recent years, the concept of a Virtual Renewable Portfolio Risk Engine has emerged as a transformative tool in the financial sector, particularly within the realm of model portfolio platforms. This innovative technology leverages advanced algorithms and data analytics to assess and manage the risks associated with renewable energy investments. As the demand for sustainable investment solutions grows, the integration of such engines into portfolio management systems is becoming increasingly crucial. They provide financial ins

Multifamily was the most popular property sector among investors in the United States in 2025. About ** percent of respondents showed preference for this type of real estate. Industrial and logistics real estate came in second with ** percent of respondents.

Investment Trust Market Outlook

The global investment trust market size was valued at approximately USD 2.5 trillion in 2023 and is projected to reach around USD 4.1 trillion by 2032, growing at a compound annual growth rate (CAGR) of 5.5% during the forecast period. The growth of this market is driven by several factors including increasing investor preference for diversified portfolios and the growing availability of various types of investment trusts to meet different investment goals. These factors are expected to propel the market significantly over the coming years.

Expanding middle-class populations and increasing disposable incomes in emerging economies are also contributing significantly to the growth of the investment trust market. With more individuals seeking avenues for better returns on their investments, investment trusts offer an attractive proposition due to their diversified nature and professional management. Additionally, the growing awareness about the benefits of investing in such diversified instruments, as opposed to individual stocks or bonds, is a crucial growth factor.

Technological advancements and digitalization have made it easier for investors to access investment trusts. Online platforms have simplified the process of investing, enabling real-time tracking and management of investment portfolios. This ease of access has broadened the market's appeal, attracting a younger, tech-savvy investor base. The integration of artificial intelligence and machine learning in these platforms further enhances their capabilities, making investment decisions more data-driven and informed.

The rising trend of sustainable and responsible investing is another significant driver for the investment trust market. Many investors are now seeking to align their portfolios with their personal values, focusing on environmental, social, and governance (ESG) criteria. Investment trusts that prioritize ESG factors are seeing increased demand, as investors look to not only generate financial returns but also contribute positively to society and the environment.

Regionally, North America and Europe dominate the investment trust market, primarily due to their well-established financial sectors and higher levels of investor sophistication. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The increasing economic development and growing middle-class population in countries like China and India are major contributors to this growth. As more individuals in these regions become financially literate, the demand for diverse investment options like investment trusts is expected to rise steadily.

Type Analysis

Equity investment trusts, fixed-income investment trusts, hybrid investment trusts, and other specialized types form the various segments of the investment trust market. Equity investment trusts, which primarily invest in stocks, remain the most popular due to their potential for high returns. These trusts appeal to investors looking for growth opportunities, particularly in sectors showing robust performance. The volatility of stock markets, however, poses a risk, making it essential for these trusts to maintain a well-diversified portfolio to mitigate potential losses.

Fixed-income investment trusts focus on bonds and other debt instruments, offering a more stable and predictable income stream, which is particularly attractive to conservative investors or those nearing retirement. These trusts typically have lower risk compared to equity trusts, but also potentially lower returns. With interest rates playing a critical role in their performance, the recent trends of fluctuating interest rates have made these trusts more appealing as they adapt to the changing economic landscape.

Hybrid investment trusts combine both equity and fixed-income investments, providing a balanced approach that appeals to a broader range of investors. These trusts aim to achieve a mix of income generation and capital appreciation, making them suitable for investors with moderate risk tolerance. The flexibility offered by hybrid trusts allows them to adjust their asset allocation based on market conditions, enhancing their appeal in uncertain economic climates.

Other types of investment trusts include those specializing in real estate, commodities, and niche sectors like technology or healthcare. These specialized trusts cater to investors looking to focus on specific sectors that they believe will outperform the broader market. While they offer t

In 2025, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the financial crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

The most popular financial security among investors in the United Kingdom (UK) in 2022 was stocks or shares, with over 40 percent of investors holding stocks or shares in their portfolio. CFDs - or contracts - for difference were the least popular form of investment in the UK.

Portfolio selection and management are two of the most important decisions in the financial field. The existence of uncontrollable factors affects the decision-making process, which is a problem for investors who are responsible for the final financial decisions on how to allocate their budgets to financial assets in their investment portfolios. To overcome the challenges involved in the selection of a stock portfolio, this article presents a three-stage optimization model. In the first stage, the pharmaceutical industry data collected from the Tehran Stock Exchange (TSE) website is used to apply the robust ratio data envelopment analysis (RR-DEA) in GAMS software with respect to some specific financial indicators to determine efficient stocks in conditions of data uncertainty. These selected stocks are then moved to the second stage, where the ANFIS algorithm is employed in MATLAB to predict the final closing prices and calculate the prediction error (RMSE). In the third stage, the fuzzy goal programming (FGP) method is applied, incorporating the prediction errors from the previous stage. The model is optimized in GAMS software, considering each Index’s objectives in a fuzzy context, with the results presented separately for different objectives. For this problem, in the first stage 27 stocks were selected as samples from the (TSE) website using the proposed methods, and 23 stocks were entered into the price prediction stage. Finally, in the FGP stage, optimization and purchase amount of each share was done. Illustrative results show that the proposed approach is effective for portfolio selection and optimization in the presence of uncertain data.