Hur estimeras riktkurser av analytikerna? Analys av estimerade aktiepriser på Stockholmsbörsen från år 2000 – 2024 med maskininlärningsmodellen Random Forest

Stock analysts have a great influence on how investors choose to invest their money and which companies that will receive a share of the ever-growing amount of money that Swedish households choose to invest in stocks. With the influence that analysts have on the company inflows and outflows of capital, one can wonder what drives their target prices. This report analyzes which financial variables most affect quarterly consensus estimated stock prices for companies on the Stockholm Stock Exchange from 2000 to 2024, by using the machine learning model Random Forest. The main part of the report describes the approach of how to use machine learning to obtain a result that shows which variable is most important, this is described step by step. Extensive acquisition and formatting of financial variables through S&P Capital IQ Pro was significantly time-consuming due to limitations in the database. Hyperparameter settings for missForest and Random Forest were used before they were applied to the final testing data. Based on the results, technical variables such as price movements were most important to the analysts, but also variables connected to fundamental analysis. The conclusion was not particularly surprising. It became a new insight that the technical analysis would be so dominant as it was. The work has provided many new insights and lessons when it comes to machine learning, programming and data management.