First we train the model using: \train_models\LDA_model_estimation.py or LDAMarius.slurm if using a multi-core system.

• The model files are saves under .\pretrained_models
• Estimation logs are saved under .\train_models\Logs
• Perplexity scores are saved under .\train_models\Output

Second, we run the \train_models\study_topics.py file to plot perplexity against the number of topics and select the optimal topics.

• Output is a graph, perplexity_topics.pdf.
• The file also outputs the top ten words for each topic, given a (manually) prespecified number of topics X: topics_terms_n=X.pdf.

The file industry_gettopics.py generates a quarter-industry panel of topic loadings, saved in the file: .\IndustryAnalysis\topic_loadings_by_industryquarter.csv'

Code .\IndustryAnalysis\industry_toptopics.py generates the top 2 topics (with list of words) for each GIC code and saves in TopTopics_Industries.csv.

Code build_shapley.py (together with ShapleyMarius.slurm) generate panels of Shapley values by analyst-ticker-quarter (including information diversity, contribution), saved in OutputShapley folder.

Use merge_shapley.py in the OutputShapley folder to generate a DataShapley.csv file.

Run get_technicaldummy.py to get a file with analyst-level topic loadings on technical analysis topics (`DataShapley_TechnicalTopicWeights.csv')

The complete merged file (DataShapley.csv + DataShapley_TechnicalTopicWeights.csv') is saved as Data_InfoContributionAnalyst.csv'