Deception And Truth Analysis (D.A.T.A.) is a proprietary library of scientifically-based algorithms that rapidly scour financial texts for deception.
Deception And Truth Analysis - D.A.T.A.
HOW D.A.T.A. WORKS
Language Fingerprints Examined. Our algorithm works by examining how language is used, and not what language is used. Deception And Truth Analysis (D.A.T.A.) scrutinizes over 30 unique language fingerprints, making it exceedingly difficult to spoof its algorithms. Other text-based deception detectors look for the presence of "red flag" words. Sadly, recent research (Cao, Sean; Wei Jiang; Baozhong Yang; Alan L. Zhang. "How to talk when a machine is listening: Corporate Disclosure in the Age of AI." SSRN. October 2020) shows that the advantage from searching texts for these words has created an arms race, with companies now hiring speech and language coaches in order to excise offending "red flag" language from their texts and speech. Deception And Truth Analysis results are re-scaled with most Deception Scores falling between -100 (very likely deceptive) to +100 (very likely truthful).
Algorithm Developed Out-of-Sample. D.A.T.A.'s algorithm was developed out-of-sample. That is we evaluate how people deceive using language, in general, rather than how they use language to deceive within a specific context. This means that our technology is general enough to be applied in multiple domains. Thus far we have tested corporate communications, including Annual Reports, Management's Discussion & Analysis sections, earnings call transcripts, political statements, and personal communications.
Scientifically Demonstrated Accuracy. Our work is supported by scientific research into the ability of computers and text-based analyses. D.A.T.A.'s ability to surface deception is statistically significant and in excess of the capabilities of people. Our double-blind accuracy is 87.5% for communications in excess of two pages in length. This accuracy compares to a January 2021 study that placed human capabilities of detecting deception in texts at an even 50.0% chance level. Additionally in experiments D.A.T.A. is able to identify all 10 of the largest scandals in global corporate history and with an average 6.6 years lead time. Last in a test of Deception And Truth Analysis used for investment selection resulted in an average annual outperformance vs. the equal-weighted DJIA of 104 bps, outperformance in 9 of 11 years, and compounded outperformance of 11.85%. See our Insights section for more information.
No overfitting. Look, as investment managers ourselves we are dubious of back-testing and data-mining. We have literally seen and heard it all. In short, we are not fans. Consequently, in reporting Deception And Truth Analysis tests done on stock indices, we report the results of our initial hypotheses. In the case of the DJIA (see our Research section), our initial thought was, what happens if we score each component for deception, then wait until the start of the next year to not purchase the 5 biggest deceivers, and then repeat the process. That is it. Our result is that D.A.T.A. provides a 104 bps advantage per year, and in 9 of 11 years. We have run optimizations on D.A.T.A. and in the case of the DJIA research we conducted, it is possible to overfit and optimize to generate a maximum outperformance of 247 bps, outperformance in 11 of 11 years, and compounded outperformance of 27.48% by excluding 10 companies. Instead, the results we report and that we want you to focus on are those generated in deference to Occam's Razor.