Quantitative analysis requires massive financial data for investment strategy modeling. In addition, based on different circumstances and the difference between data frequencies (daily, weekly, or monthly), the information acquired in the time period needs to be backtested on a recurrent basis.
Due to the time difference of financial data being updated and disseminated (financial statements, restatements, change of dividends, difference resulting from declared and actual shares repurchase, etc.), misusing information in modeling will not effectively reflect actual valuations and might result in inaccurate investment strategies.
TEJ TQuant LAB–Quantitative datasets allow decision-makers to get access to accurate data in real time.
Using point-in-time data can avoid look-ahead biasand reduce inaccurate assumptions.
The strategies adopting point-in-time data outperform those with lagging assumptions because real-time access to information allows us to adjust our holdings and improve investment strategies quickly.
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Quantitative Datasets Key Features
Access to historical and point-in-time financial statements, operational data, stock prices and company news.
Quarterly, monthly, and daily market data across listed and unlisted companies.
Rich data history of more than 20 years.
Quantitative Datasets Key Advantages
The full data set could be dated back to the exact dates and will help users minimize the survivorship bias.
Quantitative Analytics data with a point-in-time update.
Exclusive data that include standard measures used for analysis.
Convenience in building strategies when analyzing the relationship between stock returns and data.
Quantitative Datasets Key Benefits
Curated data enhancing effective factors efficiency.
Finding effective factors that facilitate quantitative modeling.
