A large amount of research is currently being done on the relationship between stocks in order to find a more accurate and efficient method for predicting the price of stocks. This work presents an algorithm that allows to find the relationship and its degree between two stocks based on historical data of the prices. Finding the degree of correlation between stocks will allow to build a model that predicts the price of a stock based on the historical data of another stock.
The relevance of the topic of this work is due to the problem of finding a more accurate trend based on the behavior of asset prices from one or related sectors. Traditionally, we have relied on manual analysis, which is becoming impractical as data volumes grow exponentially. New big data analytics technologies help analyze huge amounts of data and extract useful patterns and relationships. From the point of view of various factors influencing the relativity of stocks, this paper examines the relationship between the similarity of different assets and different degrees of correlation in stock prices, and then analyzes the similarity of correlation structures under the strongest correlation scenarios. Thus, the purpose of the study is to provide investors with broad information to reduce the complexity of developing their portfolio strategies.
- Python - version 3.8
- Anaconda - version 2020.11
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