The world of finance has exploded with new datasets, tools and capabilities in recent years. This sea change has opened endless possibilities for investors to generate alpha. But without the right framework to aggregate, process and visualize all this data – being data-rich but insight-poor is a real risk.
Financial data platforms solve this by delivering the right mixture of breadth, scale, automation and analytics. Let‘s explore how leading solutions can serve as an enlightened guide through the complex world of finance.
Note: Throughout this guide, I try to simplify concepts for an everyday investor. Please feel free to email any questions or ideas for future coverage to [email protected].
The Data Big Bang Recalibrates Analysis
In 2020 alone, the amount of data created, captured, copied and consumed in the world reached 64 zettabytes. That‘s equivalent to every person on earth tweeting for 100 straight years!
Within this data explosion, finance has seen Hockey Stick growth:
Financial data created is projected to grow from 2 zettabytes in 2019 to over 10 by 2025. (Source: IDC)
Driving this are new alternatives datasets from satellites, shipping logs, credit cards, mobile phones that cast ever-expanding light onto consumer behavior, supply chains and more. Then there‘s machine data from programmatic trading – time series every microsecond. Plus real-time analytics requires concurrent data from markets across geographies. Lastly, unstructured data like earnings call transcripts, news, social posts and reddit threads contain invaluable alpha signals once unlocked by NLP.
Traditional analysis stacking these new sources on old statistical methods quickly breaks. This has triggered a wave of AI-powered platforms purpose built to ingest, process and visualize endless datasets for sound investing.
The Promise of AI in Financial Analysis
AI is transforming systems across industries from self-driving cars to medical diagnosis and more. In finance, AI drives everything from high-speed trading to automated advisors. Under the hood, machine learning has become a versatile tool for discovering patterns and making predictions from data:
Specialized techniques empower modern platforms to deliver advanced analytics like:
- Algorithmic trading strategies adaptive to dynamic markets
- Automated analysis of earnings call transcripts and executive profiles
- Backtesting investment ideas across decades in minutes rather than months
- Predicting future prices, economic events and detecting financial fraud
- Optimizing portfolios for risk-return balance personalized to any client
Democratization has also enabled individual investors real-time apply such institutional-grade analytics from their desktop.
Okay, let‘s now deep dive into the 12 most powerful financial data and analytics platforms available in 2023:
1. FactSet – The Protocol for Investment Professionals
For over 40 years, FactSet has provided financial intelligence to investment professionals. Across industries from banking to asset management, clients rely on FactSet for its accuracy, coverage and flexibility.
Specific capabilities empowering clients every day include:
Integrated Data and Analysis – Streaming integration across asset classes ensures investment decisions are based on complete and concurrent market realities. For example, seeing impacts of rising oil prices on energy stocks and downstream manufacturers happen in sync.
Flexible Development Platform – Factset offers a range of APIs allowing teams to build custom solutions from prototype to production based on FactSet data and analytics or other proprietary data streams.
Collaboration Across Teams – Workspaces, chat and co-development fosters firmwide transparency. Junior analysts can learn from senior portfolio managers when analysis and communication live in connected platforms.
With over $150 billion in assets under management by FactSet users, the platform has proven itself the analytics standard for investment professionals worldwide.
2. YCharts – Excelling at Investment Research
YCharts mission has remained unchanged for over 15 years since inception at Yale University – help investment professionals conduct research faster with transparent, unbiased content and time-tested analytical tools.
The web-based platform offers specialized capabilities for RIAs, multifamily offices, financial advisors, and individual DIY investors including:
Streamlined Research – Quickly find investment ideas using pre-built datasets for screening, comparing or modeling purposes without reinventing the wheel every time. Then easily customize analysis by adding proprietary models as needed.
Time Tested Methods – Regardless of role, years of experience or assets advised – having a battle-tested analytical framework removes guesswork and builds confidence. YCharts provides exactly this.
Simplified Delivery – Research, analysis and communications framed in easy-to-understand language helps justify recommendations better while avoiding complex jargon.
With strong roots in academic finance and focus on pragmatic solutions for real world advisors, YCharts continues proving itself an excellent addition to any investment research workflow.
3. Alternative Data Cloud – Unique Signals for Alpha Seekers
Alternative data offers invaluable insights into consumer demand by amplifying real-world signals around supply chains, transportation networks, eCommerce transactions, geolocation movements and more.
For alpha-seeking investors, Alternative Data Cloud aggregates hard-to-find alternative datasets to fuel predictive modeling. CEO Peter Hafez shares that "Alternative Data Cloud helps quants save weeks of valuable time in discovering and procuring the exact right dataset for their strategies."
Let‘s analyze key capabilities offered:
Aggregated Data Catalog – Having curated relevant datasets eliminates the need for quants to do their own resource-intensive research. AD Cloud has aggregated datasets from partners specializing across derivative sources.
Quality & Trust – Each dataset lists provider details alongside dimensions like collection methodology, frequency and delivery specifics so suitability can be judged upfront.
Ease of Access – Technical documentation helps quickly integrate data into existing analyst workflows involving Python, R, Matlab without headaches. Samples help visualize and test relevance.
For funds pursuing data-centric strategies, a platform pooling hard-to-find yet highly relevant alternative datasets offers a valuable headstart.
4. Sentieo – The Next-Gen Financial Research Platform
Legacy financial research workflows centered around Excel, pitchbooks and web portals struggle with growing data volume, team collaboration and communicating context in analysis.
Sentieo overcomes these pain points with its next-generation AI platform. Let‘s see how:
Streamlined Research – Context-aware search across earnings calls, filings and news surfaces insights. Auto-generated transcripts and notes boost productivity. Executable excel models centralize analysis.
Collaboration – Team workflows like live presentations, shared annotations over documents and centralized models enable transparency.
Insights Communication – Smart visualizations contextualize trends and patterns. Auto-generated summaries highlight key takeaways and narratives.
Top hedge funds globally now use Sentieo to accelerate finding, testing and communicating investment ideas as markets rapidly evolve. Through configurable modules, Sentieo flexibly serves investment analysts, accountants, IR advisory teams and corporates alike via a unified platform.
5. Thinknum – The Power of Web Data
Thinknum offers breadcrumbs to discovering highly valuable company insights from publicly available web data.
While investors traditionally focus on earnings reports, conference calls and press releases for intel – Thinknum enables analyzing dynamic web data like:
- Job listings revealing new projects or business initiatives
- Menu price changes explaining revenue or margin shifts
- Vendor orders uncovering supply chain issues
Such datasets nurture predictive signals and complementary intel not captured in financial filings. Thinknum now tracks over 400K datasets across sectors like technology, retail, transportation and geos like US, China, India.
Let‘s examine crucial functionality provided:
Broad Web Coverage – Platform ingests HTML, XML, JSON, API data from public sites without needing engineering resources. Custom integrations also available for proprietary data streams.
Historical Data – Shows trends requiring long-term hindsight – like hiring across locations, SKU-level revenue changes.
Contextual Alerts – Monitoring vendor order declines or regional executive restructuring signals shifts way before financial impact.
Thinknum accelerates discovering niche investment ideas and momentum shifts – all from publicly available web data.
Additional Benefits of Purpose-Built Platforms
While Excel, Python and web data provide flexible functionality – dedicated financial data platforms offer additional advantages:
Time Savings – Curated datasets, pre-built templates and example analysis reports eliminate tedious duplicate work for analysts.
Confidence – Platforms have validated methodologies, data sources and financial models giving users trust. Data scales across firms rather than spreadsheet sprawl across individuals.
Technology Leverage – Cloud infrastructure, visualization libraries and APIs process endless data flows into actionable insights better than desktop software.
Community – Platform forums with expert participation foster learning. Some offer dataset sharing across client firms to benefit all.
New Ideas – Specialized alternative data and AI uncovers non-traditional insights for creative modeling and strategy innovation.
Cost Savings – Bulk enterprise contracts significantly lower incremental data costs as usage scales firmwide. IT infrastructure savings too.
Okay, let‘s round up key recommendations for making an informed platform selection.
Finding the Right Platform Fit
With dizzying choice, identifying the optimally aligned financial analysis platform is key for long-term ROI. Start by analysing along five dimensions:
User Profile – Individual quants have different needs vs collaborative analyst teams vs C-suite leaders depending on use case complexity, data needs and communication preferences.
Core Capabilities – Are niche datasets, customizable modelling, mobile access or specific asset class analytics valued more? Rank must-have features.
Platform Integration – Will the platform interface well with internal data streams, visualization dashboards, Excel, Python or R environment and proprietary models?
Usability – Intuitive interfaces accelerate user adoption across beginners to advanced experts. Uniform user experience retains customers.
Budget – Purchase value not cost. Total cost of ownership with employee ramp up time & opportunity cost is vital.
While Excel enjoys ubiquity, financial data platforms greatly enhance scope, scale and speed for investment analysis today. Just be sure what specifically needs solving for your use case before purchasing.
We‘ve only skimmed capabilities offered by some incredible platforms in this guide. Please reach out if you need any assistance identifying the right solution or discussing partnership opportunities around analytics.
Here‘s wishing you much success in generating alpha!
