Accuracy-Risk Trade-off due to Social Learning in Crowd-sourced Financial Predictions

Directories:

• raw data: this directory contains most of the raw data we collected from participants, i.e. the predictions made by people during prediction rounds

Files:

• utilities.py:
  • this file contains various python function utilities for general usage. e.g. this contains a function that downloads futures asset price from barchart (you'll need to reqest an API from them), and then combines this data with various other data.
• generate belief update model data.py:
  • file reads the raw data and generate the residual of the belief update models and save it to 'learning_dic_all.csv'
• conjugate vs empirical.py:
  • file reads learning_dic_all.csv plots the performance of different belief update models. the plotting pipeline is as such: files are read in python (full python conda environment is detailed before), processed using pandas, etc. and then plotted using r's ggplot2 via rpy2. this creates fig. 2 in paper.
• bar chart improvement error bar.py:
  • file plots fig 3.
• momentum.py:
  • file pulls asset and futures data from barchart.com's API and calculates first-order momentum predictions that are used in Table 1 in the paper.
• brexit plot.py:
  • file plots Brexit candlestick plot in supplementary fig. S1.
• plotting improvement vs alpha BREXIT.py:
  • file subsets predictions by alpha and then plots fig. 5.
• generating_improvement_data.py:
  • file reads predictions from raw data/ directory, subsets predictions by alpha and then saves improvement data to csv
• generating_risk_data.py:
  • file generates risk subsets data for paretto curve improvements
• paretto curve.py:
  • file reads paretto data and plots paretto curve in fig. 4.
• python_environment.yml:
  • conda environment python packages and versions generated using https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#exporting-an-environment-file-across-platform