Analysis of ‘Ratio of non-state investment leveraged to MHT administered funds awarded’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/233a4303-4a0b-45ac-b8b2-75c542f97b21 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This data shows how much private investment is generated with awards of state funds.

--- Original source retains full ownership of the source dataset ---

In 2022, the global total corporate investment in artificial intelligence (AI) reached almost 92 billion U.S. dollars, a slight decrease from the previous year. In 2018, the yearly investment in AI saw a slight downturn, but that was only temporary. Private investments account for a bulk of total AI corporate investment. AI investment has increased more than sixfold since 2016, a staggering growth in any market. It is a testament to the importance of the development of AI around the world.

What is Artificial Intelligence (AI)?

Artificial intelligence, once the subject of people’s imaginations and the main plot of science fiction movies for decades, is no longer a piece of fiction, but rather commonplace in people’s daily lives whether they realize it or not. AI refers to the ability of a computer or machine to imitate the capacities of the human brain, which often learns from previous experiences to understand and respond to language, decisions, and problems. These AI capabilities, such as computer vision and conversational interfaces, have become embedded throughout various industries’ standard business processes.

AI investment and startups

The global AI market, valued at 142.3 billion U.S. dollars as of 2023, continues to grow driven by the influx of investments it receives. This is a rapidly growing market, looking to expand from billions to trillions of U.S. dollars in market size in the coming years. From 2020 to 2022, investment in startups globally, and in particular AI startups, increased by five billion U.S. dollars, nearly double its previous investments, with much of it coming from private capital from U.S. companies. The most recent top-funded AI businesses are all machine learning and chatbot companies, focusing on human interface with machines.

Context

First few tries to bring the logloss down on the Numerai Dataset (nr. 52).

Content

You will find 21 features in the datasets. All of them range between 0 and 1. These are encrypted features, that later (after your prediction submission) will be de-crypted back to trade-able signals.

Target Feature is a Boolean ( 0 or 1 ).

Your Prediction CSV File has to have two columns: id , prediction

The prediction column , unlike the target feature, has to range between 0 and 1 (float,double) assigning the probability of row[id] being a 1.

Acknowledgements

This Dataset was downloaded from www.numer.ai

Quant Fund Market Outlook

As of 2023, the global quant fund market size is estimated to be USD 1.2 trillion, with a projected CAGR of 8.5% leading to an anticipated market size of approximately USD 2.47 trillion by 2032. The rising adoption of algorithmic trading and advanced analytics stands out as a key growth factor driving this remarkable proliferation. The integration of artificial intelligence (AI) and machine learning (ML) to enhance trading strategies has been transforming the landscape, providing unprecedented opportunities for growth and efficiency gains.

One of the primary growth factors for the quant fund market is the increasing reliance on data-driven decision-making in financial markets. Institutional investors are progressively leveraging quantitative models to optimize their investment strategies, minimize risks, and capitalize on high-frequency trading opportunities. These sophisticated models, powered by AI and ML, allow for the processing of vast amounts of market data to uncover patterns and insights that would be nearly impossible to detect manually. This trend is expected to continue, further pushing the market's expansion.

Another significant factor contributing to the growth of the quant fund market is the technological advancements in computing power and data storage. The development of high-performance computing systems and the advent of cloud computing have enabled quantitative funds to process and analyze massive datasets in real-time. These technological innovations have not only enhanced the accuracy and efficiency of trading algorithms but also reduced the operational costs associated with running complex quantitative models. This evolution in technology is likely to sustain the market's growth trajectory in the coming years.

Furthermore, the increasing demand for diversification and risk management among investors is also driving the market's growth. Quantitative funds are designed to employ sophisticated strategies that aim to provide consistent returns while mitigating market risks. The ability to implement market-neutral strategies, statistical arbitrage, and trend-following techniques allows these funds to perform well even in volatile market conditions. This appeal of stable and diversified returns is attracting a broader range of investors, from institutional to retail, thereby expanding the market size.

The regional outlook for the quant fund market indicates that North America currently holds the largest market share, driven by the presence of numerous established quant funds and a mature financial ecosystem. However, the Asia Pacific region is anticipated to witness the highest growth rate over the forecast period, fueled by rapid economic development, increased adoption of advanced financial technologies, and a growing number of high-net-worth individuals seeking sophisticated investment solutions. Europe and Latin America are also expected to contribute significantly to the market growth, albeit at a slower pace compared to Asia Pacific.

Fund Type Analysis

The quant fund market can be segmented by fund type into equity funds, fixed income funds, multi-asset funds, and alternative funds. Within the equity funds segment, quantitative strategies have been particularly advantageous in identifying undervalued stocks and arbitrage opportunities, leading to a steady influx of investments. The application of machine learning algorithms to analyze stock performance and predict future trends has allowed equity-focused quant funds to generate consistent returns, attracting both institutional and retail investors.

Fixed income funds, on the other hand, have gained traction due to their ability to navigate the complexities of bond markets. Quantitative models in this segment are often employed to analyze interest rate movements, credit spreads, and economic indicators. The precision offered by these algorithms in predicting bond price movements has made fixed income quant funds a preferred choice for investors seeking stable returns with lower volatility compared to equity markets. Moreover, the inclusion of government and corporate bonds in their portfolios adds an additional layer of security for risk-averse investors.

Multi-asset funds, which combine equities, bonds, and other asset classes, have also seen significant growth. These funds leverage quantitative techniques to allocate assets dynamically based on market conditions. The ability to diversify across multiple asset classes while employing sophisticated risk management strategies makes multi-asset funds attractive to

Success.ai’s Private Equity (PE) Funding Data provides reliable, verified access to the contact details of investment professionals, fund managers, analysts, and executives operating in the global private equity landscape. Drawn from over 170 million verified professional profiles, this dataset includes work emails, direct phone numbers, and LinkedIn profiles for key decision-makers in PE firms. Whether you’re seeking new investment opportunities, looking to pitch your services, or building strategic relationships, Success.ai delivers continuously updated and AI-validated data to ensure your outreach is both precise and effective.

Why Choose Success.ai’s Private Equity Professionals Data?

1. Comprehensive Contact Information

   • Access verified work emails, direct phone numbers, and social profiles for PE fund managers, analysts, partners, and principals.
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   • Includes profiles of professionals involved in leveraged buyouts, growth capital, venture investments, and secondary market deals.
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   • Identify partners receptive to new growth capital deployments, early-stage investments, or strategic acquisitions.

2. Advisory and Professional Services

   • Offer due diligence, valuation, legal, or consulting services directly to decision-makers at private equity firms.
   • Position your expertise to streamline deal execution, portfolio optimization, or exit planning.

3. Fundraising and Investor Relations

   • Connect with IR professionals, placement agents, and fund administrators to discuss fundraising targets, investor outreach strategies, or institutional capital requirements.
   • Engage with professionals managing limited partner relationships, capital calls, and reporting obligations.

4. Market Research and Competitive Intelligence

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   • Understand investment patterns, portfolio performance, and sector preferences to refine your business strategies.

Why Choose Success.ai?

1. Best Price Guarantee

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...

Suppose there is an investment fund planning to invest in properties at hundreds of locations. 🏠 Based on the previous millions of property sales over the past few years, the fund house wants to identify the property which can result in a higher gain on investment. 💰 They can not go by analyzing all the properties one by one. 🤔 So they want the segmentation of properties so that they can look into their target segments. 🎯 So this challenge is going to help them by easily identifying their target properties using advanced AI and Analytics. 🔍

In the first week, you will receive a dataset of real estate properties with locality, estimated price, and selling price for the last 23 years. 📊 The task is to identify the input features in the dataset and use them to predict the sale price of a property. 🔮 After this modelling of input and output features, predict the sale price of all the properties in the test dataset. 💵 Once the sale prices for the test data are predicted, put these properties into 4 segments. 🔢 These segments can be formed according to the gain. 💹 The gain is calculated based on the estimated price and predicted sale price (Gain = (Sale price - Estimated price)/100).

Finally, you need to submit your results as the segment level for each of the properties given in the test data. 📝 For reference, the properties need to be segmented into the following 4 segments according to the gain calculated based on the predicted sale:-

0: Premium Properties 💰🏰 1: Valuable Properties 💎🏡 2: Standard Properties 🏘️💸 3: Budget Properties 🏠💵

The Infrastructure Investment and Jobs Act (IIJA) also known as the Bipartisan infrastructure Law contains a large amount of formula funds. This report details those calculations.

Success.ai provides unparalleled access to Venture Capital Funding Data, meticulously curated to support organizations in identifying, connecting with, and analyzing global venture capital professionals. Our robust database includes verified profiles of VC analysts, fund managers, investment partners, and other key decision-makers. With AI-driven validation, continuously updated data, and extensive global coverage, our solutions empower businesses to excel in fundraising, partnership development, and strategic investment decisions.

Key Features of Success.ai's Venture Capital Funding Data:

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Industry-Specific Data: Tailored to industries such as technology, healthcare, renewable energy, e-commerce, and more, ensuring highly relevant data for targeted outreach.

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Why Choose Success.ai for Venture Capital Funding Data?

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Comprehensive Use Cases for VC Funding Data:

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With Venture Capital Funding Data, Success.ai empowers you to connect with the right investors, streamline your fundraising efforts, and gain unparalleled insights into the global VC ecosystem. Whether you’re a startup founder, corporate executive, or investment professional, our data solutions provide the tools you need to succeed.

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Locations of offices associated with the Los Angeles Housing and Community Investment Department which provide services to city residents.

This table contains 32 series, with data for years 1970 - 1988 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Investments (32 items: Total portfolio at market value;Investments in Canada;Term deposits;Chartered banks; ...).

This list contains the following details for all funds registered with the program: * name * address * contact information * registration date * registration number * status About the labour sponsored investment fund program

This table contains 8 series, with data for years 1970 - 1990 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Share capital and surplus (8 items: Closing balance of share capital and contributed surplus;Opening balance;Add, proceeds from sale of shares;Other additions; ...).

Monthly data on international investment position, foreign portfolio investment in Canadian equity and investment fund shares by type of instrument, by North American Industry Classification System (NAICS), at market value.

Quarterly data on foreign portfolio investment in Canadian bonds and Canadian money market instruments by sector and geographic region.

This table contains 8 series, with data for years 1970 - 1990 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Mortgage and investment reserves and reserve fund (8 items: Balance at end of quarter;Opening balance;Add, provisions charged to current expenses;Add, transfers from retained earnings; ...).

This table contains 28 series, with data for years 1991 - 2017 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada);  Type of instrument (2 items: Canadian bonds; Canadian money market instruments);  Geographic region (7 items: All countries; United States; United Kingdom; Other European Union countries; ...);  Valuation (2 items: Book value; Market value).