This dataset is about book subjects. It has 5 rows and is filtered where the books is The invisible hands : top hedge fund traders on bubbles, crashes, and real money. It features 2 columns including publication dates.

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 ---

A dataset of mentions, growth rate, and total volume of the keyphrase 'Hedge Fund Manager' over time.

Analysis of ‘Investment funds statistics broken down by investment policy - Growth rates’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/ecb-investment-funds-investment-policy-growth-rates on 07 January 2022.

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

Investment funds can be distinguished by investment policy (equity funds, bond funds, mixed funds, real estate funds, hedge funds, other funds). This dataset covers annual percentage changes.

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

This research was conducted on Islamic money market mutual funds registered with the OJK using three years (2015-2018) with a total sample of 36 Islamic money market mutual funds. There is one dependent variable in this research, namely the Islamic money market mutual funds’ performance, and three independent variables: asset allocation policy, investment manager performance, and risk level. The asset allocation policy variable was measured using Sharpe’s Asset Class Factor Model, the investment manager performance using the Treynor-Mazuy Model, the level of risk using the Standard Deviation Formula, and the performance of the Islamic money market mutual funds using the Shape Ratio

Analysis of ‘Investment funds statistics broken down by investment policy - Flows’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/ecb-investment-funds-investment-policy-flows on 07 January 2022.

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

Investment funds can be distinguished by investment policy (equity funds, bond funds, mixed funds, real estate funds, hedge funds, other funds). This dataset covers financial transactions.

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

Download Historical ETF - Financial Select Sector SPDR ETF Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

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

Download Historical Gilt-Long(8.75-13yr) (All Sessions) Futures Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Analysis of ‘Investment funds statistics broken down by type of fund - Stocks’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/ecb-investment-funds-type-of-fund-stocks on 07 January 2022.

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

Investment funds can be distinguished by type of fund (open-end or closed-end). This dataset covers outstanding amounts at the end of the period.

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

Download Historical ETF - Materials Select Sector SPDR ETF Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

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Download Historical TOPIX Index (Day) Futures Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Download Historical US 5 Year Bond Yield Fixed Income Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Analysis of ‘Investment funds statistics broken down by type of fund - Growth rates’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/ecb-investment-funds-type-of-fund-growth-rates on 12 November 2021.

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

Investment funds can be distinguished by type of fund (open-end or closed-end). This dataset covers annual percentage changes.

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

Download Historical Brazilian Real (Globex) Futures Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Download Historical Nikkei 225 - OSE(Day) Futures Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Download Historical Bund (Settlement) Futures Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Download Historical Canadian 6 Year Bond Yield Fixed Income Data. CQG daily, 1 minute, tick, and level 1 data from 1899.