wuxxx949 / stock_embedding

Based on paper Learning Embedded Representation of the Stock Correlation Matrix using Graph Machine Learning

TODO: - add viz - make it a proper python package

Stock Embedding

This repo implements the methodology based on paper Learning Embedded Representation of the Stock Correlation Matrix using Graph Machine Learning.

The goal is to learn a vector representation of a ticker given the daily returns of a set of tickers. For example, AAPL is represented as a numpy array of size 32, just like word2vec.

'AAPL': array([-0.5099518 , -0.06737634,  0.48559666, -1.4538945 ,  1.6606339 ,
         0.3644821 , -0.01464582, -0.78605974,  0.47729206, -0.5726769 ,
         0.07515597, -1.2660204 , -0.42653227,  0.5527185 ,  0.07249957,
        -1.4414234 ,  0.6071886 , -0.22588927,  0.27790403,  0.898469  ,
        -1.2963198 ,  0.5084794 , -0.5935085 , -0.10640397, -0.94240546,
        -0.03292902,  0.741543  ,  0.8465433 , -0.81097144,  0.28370696,
        -0.12066484,  0.22394317], dtype=float32)

With the vectorized ticker, we can pass these elements in the vector to the downstream ML jobs.

Example

This repo is able to replicate most of the results presented in the paper. Here are some examples from the notebook under src.

# stock data are fetched via yfinance package, the log returns and correlations are cacluated internally
se = StockEmbedding(start_date='2021-01-01', end_date='2022-08-04')

# hyperparameter is tuned by maximizing average v-measure
param_lst = [{'l': 50, 'r': 10, 'p': 0.5, 'q': 2, 'w': 5, 'dim': 16},
             {'l': 100, 'r': 50, 'p': 2, 'q': 0.5, 'w': 5, 'dim': 16},
             {'l': 200, 'r': 10, 'p': 2, 'q': 0.5, 'w': 5, 'dim': 32}]

summary, opt_param = se.hyperparam_tuning(param_lst)

similar to Table 3 of the original paper

The stock embedding enables us to answer the following questions

• stock similarity

  se.find_similar_stocks(ticker='JPM')

  All the top similar stocks are from Financial sector with most of them from Banks industry.

• Analogical Inference: answer question like "JPM is to GS as JNJ is to ?"

  se.analogical_inference(ticker='AAPL', ticker_1='JPM', ticker_2='GS')

  The result is JPM is to GS as AAPL is to AMZN with a similarity of 0.9349383.

  se.analogical_inference(ticker='JNJ', ticker_1='JPM', ticker_2='GS')

  The result is JPM is to GS as JNJ is to GILD with a similarity of 0.8816782.

• Not Match Stock: Answer questions like: Does not match from JPM, MS, GS, GOOGL: GOOGL

  se.identify_not_match_stock(['JPM', 'MS', 'GS', 'GOOGL'])

  The result is Does not match from JPM, MS, GS, GOOGL: GOOGL.

  se.identify_not_match_stock(['JNJ', 'BMY', 'PFE', 'HD'])

  The result is Does not match from JNJ, BMY, PFE, HD: HD.

  se.identify_not_match_stock(['UAL', 'AAL', 'DAL', 'TSLA'])

  The result is Does not match from UAL, AAL, DAL, TSLA: TSLA.