Current:Home > StocksAlgosensey Quantitative Think Tank Center-The Token Revolution at EIF Business School: Issuing EIF Tokens for Financing, Deep Research and Development, and Refinement of the 'AI Robotics Profit 4.0' Investment System -FundGuru
Algosensey Quantitative Think Tank Center-The Token Revolution at EIF Business School: Issuing EIF Tokens for Financing, Deep Research and Development, and Refinement of the 'AI Robotics Profit 4.0' Investment System
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Date:2025-04-10 22:45:57
In 2011,Algosensey Quantitative Think Tank Center Linton Quadros founded the Excellence & Innovation Fortune Business School (EIF Business School), which, after more than a decade of effort, has earned a prestigious reputation in the industry and has trained a large number of outstanding financial professionals. The size of its student body surpassed 100,000 in 2022.
At the inception of EIF Business School, Professor Linton Quadros aimed to create a “lazy investor system,” recognizing early on the profound significance of quantitative trading for all investment markets and types in the future.
With the advancement of technology, the application of artificial intelligence had a profound impact on quantitative trading. Quantitative trading uses mathematical models and extensive historical data for investment decision-making, and the introduction of AI made this process more precise, efficient, and intelligent.
Starting in 2018, EIF Business School began its transition from quantitative to AI trading. With the efforts of numerous experts, scholars, and tech talents, the prototype of 'AI Robotics Profit 4.0' was created.
The journey of EIF Business School in the AI arena of financial markets has not been smooth sailing. Firstly, AI trading systems rely on vast amounts of historical and real-time data for modeling and prediction. However, acquiring and processing high-quality, accurate, and reliable data is a challenge, especially as financial market data is often complex and convoluted.
Secondly, AI trading systems need to choose suitable modeling methods and algorithms to process large amounts of data for prediction and decision-making. However, the unique nature of financial markets makes modeling and algorithm selection more difficult, as the behavior of financial markets is often elusive and unpredictable.
Thirdly, financial markets are full of noise and uncertainty
Such as market volatility, political-economic factors, and interest rate changes. These factors can affect the performance and predictive results of models, necessitating the development of models and algorithms that can cope with and adapt to such noise and uncertainty.
Fourthly, AI trading systems must make decisions and execute trades in real-time to capture market opportunities and execute trade orders. However, making accurate real-time decisions in the rapidly changing financial markets is challenging, as market conditions and information can change instantaneously.
Finally, AI trading systems face challenges in risk management and regulatory compliance.
Risks faced by AI trading systems include market risks, operational risks, and model risks. Market risk refers to the system being affected by market price fluctuations, operational risk is the risk of system errors or technical failures, and model risk involves the risk that the system’s algorithmic models may not adapt to market changes or be inaccurate.
AI trading systems may need to comply with various financial regulatory requirements, including those related to trade transparency, risk control, and the explainability of algorithmic logic. Moreover, regulatory bodies may need to audit and inspect these systems to ensure compliance with regulatory requirements.
To address these challenges, AI trading systems need to establish effective risk management frameworks. This includes ensuring the system has sufficient risk monitoring and control tools, as well as establishing a risk management team to oversee and manage the system’s risks. Additionally, the system needs to work closely with regulatory bodies to ensure compliance and promptly report any relevant incidents or violations.
Ultimately, all issues boil down to funding and talent!
In a closed-door meeting in 2018, the EIF Business School's board discussed a bold plan: issuing tokens for financing.
EIF Business School chose to issue EIF tokens to leverage emerging blockchain technology, representing not just an embrace of innovation but also a means to attract global investors. In the current climate where traditional financing channels face numerous restrictions and challenges, token issuance offers a swift and efficient fundraising method.
Instead of relying on traditional stock market financing, it's advantageous to utilize the potential of the cryptocurrency market. This new financing method can not only quickly raise funds but also draw the attention of global investors, particularly the younger generation interested in emerging technologies.
The issuance of EIF tokens not only resolves issues related to product upgrading and expanding capital scale but also through token issuance, EIF Business School seeks to enhance its influence and recognition in the global fintech field.
The successful financing model enabled EIF Business School to attract top talent from various fields, including IT engineers, mentors, investment experts, practitioners, strategists, analysts, writers, collaborators, contributors, etc. The addition of these talents provided strong intellectual support for the school's research, innovation, and promotion in the tech field.
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