
SSi
An AI-driven platform for comprehensive equity research, trading, and risk management utilizing social sentiment analysis.
About SSi
FinSoftAi's Social Sentiment Insights (SSi) is an advanced platform combining AI and Blockchain technology for equity research and trading. It analyzes social media and news data to generate actionable insights for investment decisions, ESG scoring, risk mitigation, and trading strategies. SSi empowers institutional investors with real-time, unique insights to enhance returns and reduce risks.
How to Use
SSi aggregates and analyzes data from various social media and news sources to produce actionable insights. Users can access sentiment scores, social buzz trends, customizable alerts, and trending word clouds via intuitive dashboards and APIs for smarter investment decisions.
Features
- Risk management tools driven by social media buzz and market volatility
- FAANG Stock Sentiment Dashboard
- API integration for quantitative trading models
- AI-based sentiment analysis of social media and news streams
- Real-time insights for investment strategy and trading
- ESG sentiment scoring for impact investment decisions
Use Cases
- Investment Research: Identify stocks with strong social sentiment signals to build profitable portfolios.
- Trading: Combine sentiment analysis with technical indicators to enhance trading strategies.
- Social Risk Monitoring: Track social buzz and volatility to manage portfolio exposure effectively.
- Impact Investing: Use ESG sentiment scores to guide responsible investment choices.
Best For
Pros
- Highly customizable architecture supporting diverse use cases
- Utilizes AI and Blockchain for enhanced accuracy and transparency
- Claims to outperform industry benchmarks by 60%
- Provides real-time, actionable insights
- Comprehensive tools for investment research, ESG, risk management, and trading
Cons
- Access to social media and news data may involve costs or restrictions
- Back-testing results do not guarantee future performance
- Full utilization of API features may require quantitative analysis expertise
