What is Aster?
Aster is a cutting-edge AI-powered prediction and analysis platform for the next generation. By analyzing real-time data across various industries, including financial markets, Aster supports business decision-making with high-precision forecasts. This enables companies to swiftly respond to market fluctuations and enhance their competitiveness.
Key Features:
1. Personalized Investment Advisory Chatbot
Provides personalized investment advice based on individual investors' risk tolerance, investment goals, and financial situation. This allows individual investors to benefit from professional investment strategies. Humans are known to feel losses more acutely due to emotional factors, as described in prospect theory. Aster-GPT provides personalized investment advice taking into account such human psychology, based on the investor's risk tolerance, investment goals, and financial situation.
2. Real-time Market Analysis and Prediction
Aster-GPT receives and analyzes multifaceted data and news in real-time to predict market trends. This allows investors to respond quickly to market fluctuations and enhance their competitiveness. The information is sourced from APIs directly connected to NASDAQ data centers, enabling extremely fast and reliable information-based investments alongside you.
3. Identical Distribution
By applying a distributed network, investors participating in Aster can analyze the psychological state of society like hedge fund traders. As a result, Aster can behave as if it were the collective opinion of the market.
History of AI
- 1990s
The emergence of machine learning in the 1990s brought attention to mathematical analysis methods known as "technical analysis." Analytical techniques using algorithms such as neural networks and decision tree analysis began to gain prominence. This was initially a groundbreaking invention and became widely used among investors.
- late 90s
However, this method had the drawback of only analyzing past data and could not predict future market trends. Past data alone is insufficient to forecast future market movements. So how can we predict future market trends?
- 2000s
In the 2000s, the concept of "big data" emerged, and the amount of data available for analysis increased dramatically. This led to the development of AI algorithms that could analyze vast amounts of data in real-time. The use of AI in financial markets became more common, and the demand for high-precision prediction tools grew.
The solution was to perform large-scale analysis, known as ensemble learning, combining past data that is the direct learning target with other media text information beyond financial indicators. This made high-frequency trading (HFT) possible, allowing investors to generate profits with less risk.
- 2010s
In the 2010s, with the introduction of deep learning, the accuracy of data analysis further improved, enabling more precise predictions of market fluctuations. This allowed investors to generate profits with even less risk.
2020s
As we entered the 2020s, with the proliferation of AI, data analysis became even more widespread and widely used among investors.
Aims
Aster aims to achieve extremely high prediction accuracy and contribute to strategic decision-making for society as required:
- By providing high-precision forecasts, we aim to contribute to strategic decision-making for society.
- We aim to provide a flexible revenue model that can be tailored to the needs of each user.
- We aim to provide a platform that can be used by a wide range of users, from individual investors to YOUR CHILDREN.
The Future of Aster:
Aster continues to grow with technological advancements and is expected to be used in more industries. In the future, we will not only enhance AI technology to improve prediction accuracy but also focus on improving user experience and exploring new markets.
Aster is not just a tool, but a powerful platform that supports entire businesses, shaping the future of business.
Case Studies
Application in the Financial Industry
Aster has been implemented by major financial institutions, significantly improving market prediction accuracy. Particularly in high-frequency trading, it has contributed to performance improvement by minimizing trading slippage.
Application in the Logistics Industry
Aster has been successfully applied to logistics management, optimizing inventory management and delivery routes. By utilizing real-time data, it has achieved an efficient supply chain by preventing inventory shortages and delays.
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