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  • John Snow Labs
  • Jan 20, 2023

Resolver Company Names to Tickers using Nasdaq Stock Screener

  • en
  • licensed
  • finance
  • nasdaq
  • ticker

Description

This is an Entity Resolution / Entity Linking model, which is able to provide Ticker / Trading Symbols using a Company Name as an input. You can use any NER which extracts Organizations / Companies / Parties to then send the input to finel_nasdaq_company_name_stock_screener model to get normalized company name. Finally, this Entity Linking model get the Ticker / Trading Symbol (given the company has one).

Predicted Entities

Copy S3 URI

How to use

document_assembler = nlp.DocumentAssembler()\
    .setInputCol('text')\
    .setOutputCol('document')

tokenizer = nlp.Tokenizer()\
    .setInputCols("document")\
    .setOutputCol("token")

ner_embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_sec_bert_base","en")\
    .setInputCols(["document", "token"])\
    .setOutputCol("embeddings")

ner_model = finance.NerModel.pretrained('finner_orgs_prods_alias', 'en', 'finance/models')\
    .setInputCols(["document", "token", "embeddings"])\
    .setOutputCol("ner")

ner_converter = nlp.NerConverter()\
    .setInputCols(["document", "token", "ner"])\
    .setOutputCol("ner_chunk")

chunkToDoc = nlp.Chunk2Doc()\
    .setInputCols("ner_chunk")\
    .setOutputCol("ner_chunk_doc") 

ticker_embeddings = nlp.UniversalSentenceEncoder.pretrained("tfhub_use", "en")\
    .setInputCols("ner_chunk_doc")\
    .setOutputCol("ticker_embeddings")

er_ticker_model = finance.SentenceEntityResolverModel.pretrained('finel_nasdaq_ticker_stock_screener', 'en', 'finance/model')\
    .setInputCols(["ticker_embeddings"])\
    .setOutputCol("ticker")\
    .setAuxLabelCol("company_name")

pipeline = nlp.Pipeline().setStages([document_assembler,
                              tokenizer, 
                              ner_embeddings,
                              ner_model, 
                              ner_converter,
                              chunkToDoc,
                              ticker_embeddings,
                              er_ticker_model])

empty_data = spark.createDataFrame([[""]]).toDF("text")

model = pipeline.fit(empty_data)

lp = nlp.LightPipeline(model)

text = """Nike is an American multinational association that is involved in the design, development, manufacturing and worldwide marketing and sales of apparel, footwear, accessories, equipment and services."""

result = lp.annotate(text)

result["ticker"]

Results

['NKE']

Model Information

Model Name: finel_nasdaq_ticker_stock_screener
Compatibility: Finance NLP 1.0.0+
License: Licensed
Edition: Official
Input Labels: [sentence_embeddings]
Output Labels: [normalized]
Language: en
Size: 54.6 MB
Case sensitive: false

References

https://www.nasdaq.com/market-activity/stocks/screener