3,000

Stocks

200+

Ratios and Multiples

1+ Billion

Datapoints

10+ Years

Historic Data

What Does SensaAI Provide?

Product Showcase

Return characteristics for a selection of our portfolios. Register for an account to subscribe to our predefined portfolios and/or create your own.

How do SensaAI Compare?

SensaAI fill the gap between low return passive exchange traded funds products and highly expensive active investment products.

Accuracy of Analysis
Passive Robo Investment
N/A
The SensaAI Way
Accurate
Traditional Active Investment
Prone to human errors
Adapting to Changes in Market Environment
Passive Robo Investment
None
The SensaAI Way
Constantly learning from new data in real time
Traditional Active Investment
Slow to react to changes in environment
Amount of Data Analyzed
Passive Robo Investment
None
The SensaAI Way
Millions of data points and connections analyzed
Traditional Active Investment
Limited capacity to analyze data
Cost
Passive Robo Investment
Some ETFs can be inexpensive but robo-advisor adding additional layer of costs
The SensaAI Way
Relatively inexpensive
Traditional Active Investment
Very expensive
Data Analysis Methodology
Passive Robo Investment
None
The SensaAI Way
Modern machine learning algorithms
Traditional Active Investment
Archaic (e.g. Excel spreadsheets)
Computer Hardware Needs
Passive Robo Investment
Low
The SensaAI Way
Very high
Traditional Active Investment
Low
Performance
Passive Robo Investment
Underperformance vs. market equal to cost of ETFs and robo-advisors
The SensaAI Way
Potentially significant outperformance
Traditional Active Investment
Hit and miss. On average underperformance equal to high cost of active investment
Rebalancing
Passive Robo Investment
Depending on index methodology
The SensaAI Way
Programmatic rebalancing
Traditional Active Investment
Ineffective and slow rebalancing
Speed of Analysis
Passive Robo Investment
N/A
The SensaAI Way
Fast
Traditional Active Investment
Slow and time consuming

Financial Fundamentals Augmented by Machine Learning

A strong mix of financial and technological knowledge applying modern machine learning technologies to fundamental financial analysis.

Research Driven
Our method is grounded in fundamental finance principles, rigorous quantitative research with tested models and strategies.
Technical Knowledge
A passion for data science and a strong belief that machine learning can uncover tendencies in historical data more efficiently than could be done by humans.
Investment Experience
25+ years of experience of investing in financial markets across multiple asset classes.

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