Research on all kinds of NLP for market forecasting, experts estimation, etc.

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    Mo[[<a href='https://github.com/completelyboofyblitzed/NLP-in-STOCK-MARKET/blob/main/README.md#models'>Models</a>]]
    A(<b>NLP</b>) -->|for| F{<a href='https://github.com/completelyboofyblitzed/NLP-in-STOCK-MARKET/blob/main/README.md#1-market-forecasting'>Market Forecasting</a>} 
    
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    Fc{{<a href='https://github.com/completelyboofyblitzed/NLP-in-STOCK-MARKET/blob/main/README.md#forecasting'>Forecasting</a>}}
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    Ts[(<a href='https://github.com/completelyboofyblitzed/NLP-in-STOCK-MARKET/blob/main/README.md#text-sources'>Text Sources</a>)]
    
    %% Se<-.-Mo
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    F--> Di{{Direct}}

    Sc(((<a href='https://github.com/completelyboofyblitzed/NLP-in-STOCK-MARKET/blob/main/README.md#scrapping'>Scrapping</a>)))
    A -->|to identify| M((<a href='https://github.com/completelyboofyblitzed/NLP-in-STOCK-MARKET/blob/main/README.md#2-market-movers-identification'>Market Movers</a>))
    A -->|to estimate| E[<a href='https://github.com/completelyboofyblitzed/NLP-in-STOCK-MARKET/blob/main/README.md#3-experts-competence-estimation'>Expert's Competence</a>]
    F -->|"  "| MF{{?}}
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    %%I><a href='https://github.com/completelyboofyblitzed/NLP-in-STOCK-MARKET/blob/main/README.md#interpretability'>Interpretability?</a>]
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Models

• FinancialBERT - A Pretrained Language Model for Financial Text Mining 2022 Ahmed Rachid Hazourli [pdf] [demo]
• FinBert - A Large Language Model for Extracting Information from Financial Text 2019-2022 Allen H. Huanga, Hui Wang, Yi Yang [pdf] [repo] [website] [demo]
• NFinBERT: A Number-Aware Language Model for Financial Disclosures 2021 Hao-Lun Lin and Jr-Shian Wu, Yu-Shiang Huang, Ming-Feng Tsai, Chuan-Ju Wang [pdf]

1. Market Forecasting

Projection of the market states (high/low volatility, up/down index movements).

Sentiment Based

• Predicting the Stock Market with Sentiment Analysis of Newspaper Text Juan Luis Ruiz-Tagle 2020 [master thesis (draft)] [repo]

Text source: newspapers
Sentiment model: BERT
Predictions model: LSTM?

Claimed predictions for Tesla based solely on newspaper text

Sentiment Analysis

Models
Hugging Face Hub #financial-sentiment-analysis

Forecasting

Algorithms

To be added

Interpretability

Essentially we want an answer to a question "what..", but can we as well know "why"?
Is this problem related to long-form question-answering (LFQA)? I don't know. If so, here's something to read:

• WebGPT: Browser-assisted question-answering with human feedback 2021 OpenAI [blog] [pdf] [demo] + the explanation on habr (by @stalkermustang I presume)
  
• GopherCite: Teaching language models to support answers with verified quotes 2022 DeepMind [blog] [pdf]
  
• (And obviously) Toolformer: Language Models Can Teach Themselves to Use Tools 2023 Meta [pdf]
  

Can the task of prediction can be solved as collateral to the Market Movers identification?

2. Market Movers Identification

Market moving information is a term used in stock market investing, defined as information that would cause any reasonable investor to make a buy or sell decision. (Wikipedia)

Example of a stock mover identification taken from Benzinga:

How can such pipeline look:

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stateDiagram

    classDef Important stroke:#f66,stroke-width:2px,stroke-dasharray: 5 5,text-decoration: none
    direction LR
    A: Get top N gainers/losers
    b: Get latest tweets/news
    c: Summarize as an explanation
    B: ~
    C: Show
    [*] --> A
    A --> B
    B --> C
    state B {
      direction LR
      b --> c
    }
    C --> [*]
    class c Important

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3. Expert's Competence Estimation

Ranking experts, qualification of experts, based on their past predictions/performance.

Tools

Pythonic ways to download market data from Yahoo!Ⓡ finance:
yfinance
yahooquery

Text Sources

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graph TD
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    Sc(((<a href='https://github.com/completelyboofyblitzed/NLP-in-STOCK-MARKET/blob/main/README.md#scrapping'>Scrapping</a>)))
    Ts[(<a href='https://github.com/completelyboofyblitzed/NLP-in-STOCK-MARKET/blob/main/README.md#text-sources'>Text Sources</a>)]
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                Cm[(Plain mentions/tags)]
                Ex[("''Experts''")]
                In[(Highly influential<br> individuals)]
                Of[(Official Media)]
                end
            end
        end
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    Ts-->Cm
    Ts-->Ex
    Ts-->In
    Ts-->Of
    Sc-.-Fb
    Sc-.-Tw
    Sc-.-Ws
    Sc-.-Tg

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Scrapping

To be added

Websites

To be added

Twitter

To be added

Facebook

To be added

Telegram

To be added

🤝 Contributing

Contributions, issues and feature requests are welcome.