Topic: [ENDED][SHP] Sharpe Capital - Sentiment & Financial Protocol. - page 77. (Read 47810 times)

And how long will this be? Just so we Know approximate timeframe of initial HODL-ing

It WILL BE a dividend paying project when the fund gets the appropriate license from the regulating bodies. Currently it's not and therefore it's open to residents from anywhere in the world.

Yeah, probably. You can't really reinvent the wheel anymore. With so many projects coming in daily

So what you are saying is that they will not start with dividends payment but as time goes on they will start paying. Tpo me as long as  dividends payment is part of their plan, it is still a dividends paying project.

At the beginning the SHP won't entitle to receive any dividends and therefore it will be a pure utility token. When the fund recevies the green light from the diverse regulators where Sharpe Capital is applying for a license, SCD tokens will be emitted to all SHP holders and dividend payment will start. Therefore, there are no territorial restrictions for investors.

At the beginning the SHP won't entitle to receive any dividends and therefore it will be a pure utility token. When the fund recevies the green light from the diverse regulators where Sharpe Capital is applying for a license, SCD tokens will be emitted to all SHP holders and dividend payment will start. Therefore, there are no territorial restrictions for investors.

this is a dividends paying project, that means US citizens unless you are an accredited investors will not be able to invest into it plus the Chinese citizen that has been ban also. Please correct me if I'm wrong

This project seems solid... although I think I've already seen several similar ones

Funny how you have 2 team members with countries fornames 

How much is the dividend in percentage?

Joining you on this one. Seems like a well thought out project

Joining you on this one. Seems like a well thought out project

people like us (including me) know what is good and what have future 

I believe this project is good and I invest in this.

Hey, thanks for your question. So, contextual frame analysis (CFA), is one of the more hypothetical elements of the platform. It is not necessary for its operation, but we believe it would serve to further reduce risk and increase returns by improving accuracy as compared to the more standard sentiment analysis approaches - of which there are many.

In the simplest terms, CFA is an attempt to capture how language use (contextual frames) drives decision making. The seminal example given is the 'Virus vs Beast' study by Thibodeau and Boroditsky (2011), in which an identical article about a crime wave - except a one word difference - is shown to two groups of people. One article used the word 'beast' and the other used 'virus'. The consensus of the group that saw 'beast' was that punitive action should be taken against the criminals, however, the group that saw 'virus' leaned strongly toward rehabilitation. When the people in these groups were asked to justify their decision, nobody mentioned the virus/beast word usage, and quoted data/statistics from the article.  (See pp. 28-29 in our white paper)

That is, people came to opposing decisions while justifying them with the same data, driven by the contextual frame in which they saw the data. We are working with Mieke, a linguistic & expert in frame analysis, to develop a strategy that enables us to determine whether these 'contextual frames' are more likely to drive an investor toward 'bullish' or 'long' vs 'bearish' or 'short' when consuming news media and other data sources from which they make their investment decision. Underpinning this theory is the concept that decision making is, to a significant extent, emotional - even when investors insist their actions are data driven, they are ultimately slaves to the cognitive decision making processes we all evolved with.

At the moment we are developing a manual CFA process based on 'traditional' linguistic frame analysis as a proof-of-concept on a relatively small number of news articles, as this is a novel idea in both investment and linguistics. Once the relationship between frames and responses is established in a few key examples, we will quickly scale it to operate on a large scale using natural language processing techniques within artificial intelligence and machine learning, such that our algorithms can learn to optimally identify contextual frames based on historical data. We aim to have this operational along with the rest of the platform - however, this is a scientific endeavour, and as such may be subject to further development before we release it in the wild.

It is probably the most exciting of the linguistic analysis approaches we're taking, but it is not mission critical to operation of the fund - it will simply serve to enhance it.

Let me know if you have any more questions. If you're interested in the linguistics side of things, Mieke hangs out in the Telegram group.

James