Tribots read and write

Fu-Yuan Cheng
, and
Posted on Jun 22, 2017

10-Ks, annual financial corporate reports, is the basis of intelligent investment. These reports often reveals key information about the company.

Intro to Word to Vec

Word2Vec is a method that would try to represent the document into a vector form

General idea is that for the vocabularies used in the document, one would come up with a probability distribution of words that would be used in a nearby expression

2 Methods for doing this is continuous-bag-of-words and skip-gram

Continuous-bag-of-words tries to produce probability distribution of a word given a list of words

Skip-gram tries to provide probability distribution of words that show up in similar content given a vocabulary

Authorship attribution

We have also tried to apply authorship attribution analysis to the reports

It seems that doc2vec analysis is not quite effective at distinguishing authors unless the writings are drastically different

One methodology is to use key features that would give characteristics about the authors and use k-means clustering

Lexical Features:

average number of words per sentence

sentence length variation(standard deviation of words per sentence)

Lexical diversity(number of unique words/words used in document)


Frequencies for common Parts of Speech types(singular/plural noun, proper noun, determiner, preposition/conjunction,adjective)

Bag of Words:

Count the most common words in the documents and apply clustering


Misclassification Rate A Tale of Two Cities vs. The Great Gatsby The Great Gatsby vs

Sherlock Holmes

A Tale of Two Cities vs. Shakespeare Amazon 10K vs.

Altria 10K

Lexical 14% 0% 46% 0%
Syntax 14% 25% 46% 0%
BOW 46% 38% 46% 0%


Good and Bad Companies Classification

If the company’s stock price  has decreased by 50% we have considered them “bad”

If the company’s stock price has increased by more than 100% we have considered them “good” (over 5 years)



Random Forest For Doc2Vec


Confusion Matrix Prediction






11 33


5 130


Logistic Classification For Doc2Vec


Confusion Matrix Prediction






24 20


36 109


Recurrent Neural Networks

Neural Network has been around since 1940s but were not so useful until about 5 years ago

Whereas regular Neural Networks does not have a sense of time, Recurrent Neural Networks try to capture time element while working with neural networks by using previous output as another input

Recurrent Neural Networks by itself is not as effective so people have incorporated LSTM to forget or remember previous outputs


Generating Corporate Report using RNN/LSTM

10K reports were too large for us to train using RNN

Following the example of writing a similar story in Aesop’s fable, we have written summary using company profiles in CNBC

“the bell outwit met . nobody will all mouse got up and said that is all very well , but he thought would meet the case . you will all agree , said attached chief'”(Aesop’s Fable LSTM RNN output)

“other service offerings for play and retailers through the consumer and sale beverages. Hasbro, The Investment segment focuses sales and facilitates that sectors. Wholesale in food countries in China its in fundamental storage, equity, and”(somewhat makes sense)


While we made some progress in using natural language processing to investigate 10-Ks, here are some future research direction:
1. Further analysis is needed to examine the relationship between long-term company health with different sections of 10-K (i.e., risks, business overview)
2. Features generated by NLP alone might not have the necessary predicative power and therefore more quantitative measures, such as valuation models should be included as well
3. 10-Qs, quarterly financial reports, are filed after each quarter and might better reflect company’s current status.
The future of using NLP to understand financial reports are unbound. This similar analysis can be applied to other financial documents that might contain crucial information for intelligent investment. By combining NLP with recurrent neural network (RNN) and long and short term memory (LSTM), machine learning might be able to automatically compose a 10-K!

About Authors


Daniel Rim

Daniel Rim has been working as Quant Analyst working to analyze undervalued equity investments in Emerging Markets. His educational background has been in fields such as mathematics,physics, and statistics. He has been immersing himself into the innovation and...
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Jason Chiu

Jason is a public health researcher and healthcare quality professional. He is passionate about using data to improve social enterprises. Jason is also an avid music aficionado and an amateur composer. He enjoys a wide range of music,...
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Fu-Yuan Cheng

Fu-Yuan Cheng

Fu-Yuan comes from a background in mathematical science and is pursuing a master's degree in applied analytics from Columbia University. While applying time series models to financial data, he discovered he had a passion for machine learning and...
View all posts by Fu-Yuan Cheng >

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