Cryptocurrency investment is inherently difficult due to its shorter history compared to traditional assets, the need to integrate vast amounts of data from various modalities, and the requirement for complex reasoning. While deep learning approaches have been applied to address these challenges, their black-box nature raises concerns about trust and explainability. Recently, large language models (LLMs) have shown promise in financial applications due to their ability to understand multi-modal data and generate explainable decisions. However, single LLM faces limitations in complex, comprehensive tasks such as asset investment. These limitations are even more pronounced in cryptocurrency investment, where LLMs have less domain-specific knowledge in their training corpora. To overcome these challenges, we propose an explainable, multi-modal, multi-agent framework for cryptocurrency investment. Our framework uses specialized agents that collaborate within and across teams to handle subtasks such as data analysis, literature integration, and investment decision-making for the top 30 cryptocurrencies by market capitalization. The expert training module fine-tunes agents using multi-modal historical data and professional investment literature, while the multi-agent investment module employs real-time data to make informed cryptocurrency investment decisions. Unique intrateam and interteam collaboration mechanisms enhance prediction accuracy by adjusting final predictions based on confidence levels within agent teams and facilitating information sharing between teams. Empirical evaluation using data from November 2023 to September 2024 demonstrates that our framework outperforms single-agent models and market benchmarks in classification, asset pricing, portfolio, and explainability performance.

The global Artificial Intelligence Chips market is valued at xx million US$ in 2020 is expected to reach xx million US$ by the end of 2026, growing at a …

The Automotive Artificial Intelligence (AI) market is an intrinsic study of the current status of this business vertical and encompasses a brief synopsis about its segmentation. The report is inclusive of a nearly accurate prediction of the market scenario over the forecast period – market size with respect to valuation as sales volume. The study lends focus to the top magnates comprising the competitive landscape of Automotive Artificial Intelligence (AI) market, as well as the geographical areas where the industry extends its horizons, in magnanimous detail. The market report, titled'Global Automotive Artificial Intelligence (AI) Market Research Report 2019 – By Manufacturers, Product Type, Applications, Region and Forecast to 2025′, recently added to the market research repository of details in-depth past and present analytical and statistical data about the global Automotive Artificial Intelligence (AI) market. The report describes the Automotive Artificial Intelligence (AI) market in detail in terms of the economic and regulatory factors that are currently shaping the market's growth trajectory, the regional segmentation of the global Automotive Artificial Intelligence (AI) market, and an analysis of the market's downstream and upstream value and supply chains.

Among the various products offered by Mitsubishi UFJ Kokusai Asset Management is AI Japan Equity Open, an investment trust launched at the beginning of February. As of March 31 it had attracted total investor assets of 10.7 billion yen ($98 million). Asked why it is selling so well, director Hideo Shirota said, "Many investors want their assets handled based on objective judgment rather than the professional sense of a fund manager." The company receives advice about the selection and timing of equity trades from Mitsubishi UFJ Trust Banking, where chief fund manager Noriyuki Okamoto, a man of over 20 years experience, and moreover, an artificial intelligence system are the brains behind the product. Arriving at the company's Tokyo head office at 8 every morning, he turns on his computer to read an AI-generated report on which equity prices will likely rise and the best timing to sell index futures.

We outline initial concepts for an immune inspired algorithm to evaluate and predict oil price time series data. The proposed solution evolves a short term pool of trackers dynamically, with each member attempting to map trends and anticipate future price movements. Successful trackers feed into a long term memory pool that can generalise across repeating trend patterns. The resulting sequence of trackers, ordered in time, can be used as a forecasting tool. Examination of the pool of evolving trackers also provides valuable insight into the properties of the crude oil market.