The best investment app in India is here. Nobias Finance is a platform that processes thousands of articles daily, employing machine learning to categorise them into different rating levels. Whether you're a new investor or a pioneer in investment, leveraging this platform can provide valuable insights. Users can follow analysts/bloggers on Nobias to understand how a stock will likely perform based on their ratings.

1. Unlock the secrets of successful
long-term investing.
where credible information meets
opportunity

2. Who Are We?
Nobias was founded in 2017 in the USA with a mission to
protect individuals from deceptive or misleading content on the
internet and help them make better-informed decisions.
Nobias India aims to enlighten and empower young Indian
investors by helping them assess financial information more
effectively and without bias. Using AI / ML, our monitoring tools
equip investors with credible, relevant, and unbiased
information so that they can make smart financial decisions.

3. How We Rate
Bias Rating
This article is neutral on this stock. That means the author expects the
stock to hold in value
This article is bullish on this stock. That means the author expects the stock
to rise in value
This article is bearish on this stock. That means the author expects the stock
to fall in value
Expectation Unknown
Neutral
Bullish
Bearish
Unknown
Explaination

4. How We Determine Bias Rating
Nobias processes thousands of articles every day and uses machine learning to surface
whether an article is bullish or bearish.
We also zoom out and give you context on the overall market sentiment on a particular stock.
Let’s see how we break this down.
You can see in the image to the right that we highlight the sentiment of the article on Maruti
Suzuki India being bullish. We also show you in the donut chart on the top right the
distribution of sentiments across all articles written on Maruti Suzuki India—this is the market
sentiment. Hover over the segments to view how many articles are written from each side.
To obtain our bias rating, we employ proprietary Natural Language Processing (NLP) and Deep
Learning methods, built on Nobias’ own and the latest research in Machine Learning.

5. Nobias processes thousands of articles every day and tracks the performance of the analysts and bloggers you read
using unbiased, transparent methodologies. We want you to decide for yourself who to trust.
Let’s see how we break this down.
As shown in the image above, this analyst is ranked #3 in our database of 14,535 analysts and bloggers. Out of his 597
total recommendations, he has been accurate 39% of the time. If you followed all of his recommendations over three
months, you would have an average return of 45%. At the time of this snapshot, this analyst was bullish on 80% of the
stocks and ETFs he wrote about.
All of this results in a five-star credibility rating for this author. We determine our stars using a combination of:
Accuracy Rate: The percent of their recommendations that were correct three months following publication.
1.
Average Expected Return: The expected return of the author’s recommendation in the 3-month period (for
bloggers) or in a 1-year period (for analysts) following publication.
2.
Downside: This is the maximum loss experienced during the 3-month period (for bloggers) or in a 1-year period
(for analysts) following publishing.
3.
The number of articles an author has written.
4.
How We Determine Credibility Rating

6. Dive Deeper Into Our Bias Ratings
Background
BERT (Bidirectional Encoder Representations from Transformers) is a recent paper published by researchers at Google AI Language.
BERT’s key technical innovation is applying the bidirectional training of Transformer, a popular attention model, to language modelling.
This is in contrast to previous efforts which looked at a text sequence either from left to right or combined left-to-right and right-to-left
training.

7. How it works
BERT makes use of Transformer, an attention mechanism
that learns contextual relations between words (or sub-
words) in a text. In its vanilla form, Transformer includes
two separate mechanisms – an encoder that reads the
text input and a decoder that produces a prediction for
the task. Since BERT’s goal is to generate a language
model, only the encoder mechanism is necessary. The
chart below is a high-level description of the Transformer
encoder. The input is a sequence of tokens, which are first
embedded into vectors and then processed in the neural
network. The output is a sequence of vectors of size H, in
which each vector corresponds to an input token with the
same index.

8. Dive Deeper Into Our
Analyst Ratings
Nobias seeks to devise a statistically sound method for rating financial authors
and analysts. For every author, there are 3 fields: the total number of calls made,
accuracy, and expected return. All were collected by keeping track of articles
they have published (LexisNexis live feed), determining the stock they were
discussing (regex-like checks), determining the positive/negative slant in the way
they discussed the stock to generate a buy/sell/neutral slant, and then checking
during the 3 months that follow whether the slope of the stock price was in fact
aligned with his recommendation and yielded a profit (or a loss) as well as the
maximum loss observed. The expected return is based on the purchase of $100
worth of shares on the day of his prediction (this may be fractional), and the total
average return after holding the stock for a specified time period (3 months in our
first case). Accuracy, Expected return, and Maximum Loss are averaged across all
calls. In the case of analysts, we allow for a longer (1 year) time frame or until the
Price Target recommended by the Analyst is achieved or the Analyst revises his
recommendation.

9. Thank You!