Tech-driven investment management business seen rapid growthHighlights - Completed acquisition of Deep Neural Computing Company Limited (DNCC) and expanded tech-driven management business
- Focus on deep neural networks, artificial intelligence, distributed computing, and quantitative trading algorithms
- Newly acquired DNCC has contributed HK$9.22 million revenue in merely 3 months
- Gross profit ratio improved from 0.95% in previous year to 5.76% for the year
HONG KONG SAR - Media OutReach Newswire - 30 September 2024 - International Genius Company ('IGC'; stock code: 0033.HK) announced its annual results for the year ended 30 June 2024 (the 'Reporting Period'). During the Reporting Period, the Group's tech-driven investment management business has achieved rapid growth, driving IGC's transformation into a global company with financial transaction innovation driven by artificial intelligence(AI). The total revenue for the Reporting Period was approximately HK$227 million. Among which, the newly acquired Deep Neural Computing Company Limited ('DNCC') has contributed around HK$9.22 million revenue to the tech-driven investment management segment in merely 3 months. Its income has significantly increased IGC's profit margins. Gross profit of the year has increased 4 folds during the Reporting Period. On 22 March 2024, the Group completed the acquisition of DNCC, a leading R&D and application company specializing in artificial intelligence, deep neural networks, distributed computing, and quantitative trading algorithms. DNCC boasts a team of experts with years of experience in AI research development. The acquisition has further enhanced IGC's capability in R&D and technology, enabling a breakthrough in AI trading algorithms in order to provide a more specialized and efficient solutions for clients' trading strategies and technology needs. With the completion of the acquisition of DNCC, IGC has successfully expanded its tech-driven investment management business. The segment has quickly generated income with a high profit margin, and has enhanced the Group's income structure and quality, laying a solid foundation for IGC's transformation. IGC has now established an advanced and mature trading technology system, with deep neural network, distributed computing and quantitative trading algorithm at its core. IGC will continue to develop trading algorithm based on machine learning and deep learning, in order to form our core product 'IGC Prophet'. The technology conglomerate will provide clients with customized one-stop AI trading technology solution that can be commercialized and applied to multiple international financial trading sectors, giving our clients an unparallel competitive edge in the global market. Looking forward, IGC will continue on the path of technology and model innovation. By increasing the Group's investment in AI and related technology, and focusing on R&D and scalable application of AI trading algorithms in global financial trading, we strive to drive the innovation in financial trading around the world through AI-driven trading tools. This will in turn expand our client network and increase the Group's income and profit, further enhancing IGC's competitiveness in the global market. Hashtag: #InternationGeniusCompany #IGC #ANNUALRESULTS
The issuer is solely responsible for the content of this announcement. About International Genius CompanyThe International Genius Company (IGC; stock code: 0033. HK) is a global company with financial transaction innovation driven by artificial intelligence(AI) with top AI R&D capabilities and quantitative trading experience, working to combine advanced technology with market insight and redefine financial asset trading through AI. Based on cutting-edge technologies and massive data analysis capabilities such as deep neural networks and distributed computing, IGC provides algorithmic trading strategies and customized one-stop extensible AI trading technology solutions for investment institutions, asset management companies, family offices, etc International Genius Company
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