Investment | Lucid Sphere AI

Investment Analytics: Key Features

Capital market analytics, including technical and fundamental analysis.
LLM-supported analysis of web-wide investment sentiment.
Providing AI-powered stock buying recommendations (e.g., buying stocks if the fair market value is higher than the market price).
Predictive analytics to forecast financial returns.
What-if modelling based on the analysis of historical investment performance and the current market situation.
ML/AI-powered investment prescriptions based on the analysis of an investor's financial data (e.g., income, expenses, assets, and liabilities) and investment goals.

Automated monitoring of the total return of a portfolio and individual investments, including capital gains, dividends, interest, and distributions.
Tracking portfolio performance vs. asset-specific benchmark index.
Factor exposure analysis and identification of return drivers (e.g., interest rates, inflation, company earnings).
Asset allocation analytics (e.g., what-if models of various asset allocation strategies).
Rule-based portfolio rebalancing (e.g., based on risk tolerance, market conditions, tax implications), enabled by robotic process automation (RPA).
Alerts on reaching predefined limits (e.g., for price, price-to-earnings ratios).

Assessing risk-adjusted return through case-specific calculations.
Standard deviation calculation.
Risk attribution analysis (e.g., across individual securities, asset classes).
Continuous monitoring of competitor activity, borrowers' credit scores, trading volumes, bid-ask spreads to detect possible market, credit, and liquidity risks.
Tracking hedging transactions across various derivative instruments.
What-if analysis for various risk factors and portfolio rebalancing options.

Analyzing internal processes to improve operational efficiency (e.g., identifying delays or errors in trade execution).
Insight into financial management (e.g., financial analytics, (e.g., calculating financial performance KPIs like FCF, ROA, employee compensation vs performance comparison)).
Analyzing investment managers' performance and identifying the impact their decisions have on portfolio performance.
Real-time analytics of asset purchasing, selling, reinvestment, switch, split, STP, SWP.

AI-supported identification of fraud related to insider trading, pump and dump schemes, HFT manipulation.
Continuous monitoring of investment accounts to identify attempts of account takeover or theft.
Immediate alerts on detected suspicious activity.

Monitoring of transactions, client information, trading activities, reporting procedures, advertising and client communication, etc. to support compliance with regulations like SEC, GLBA, OFAC and FINRA, SOX, GDPR, AML/KYC, CMA.
Alerts on compliance violations.
Reports for regulatory authorities in compliance with the established reporting forms, e.g., form PF (for private fund advisers), form ADV (for investment advisers), and form CRS (for broker-dealers).
Automated report submission to regulators.

Identifying the most tax-efficient investment funds.
Assistance in tax-loss harvesting, e.g., by forecasting potential losses in a portfolio, pinpointing to sell underperforming investments.
Assessing tax-related impact of holding different assets in taxable, tax-deferred, and tax-exempt accounts.
What-if modeling for tax implications of investment decisions, including incurred tax liabilities, regulations, and efficiency.
Calculation of incurred tax liabilities under various tax jurisdictions.

AI-powered verification of investor data, including identity, taxpayer identification number, bank account information, and compliance with company-specific eligibility criteria.
Location-based AML/OFAC, KYC checks for investors.
Automated client segmentation, e.g., by risk tolerance, investment preferences, financial goals.
Continuous monitoring of clients' investment activities for timely segmentation refining.