DOI: 10.5061/dryad.1c59zw42z
title: Craving money? Evidence from the laboratory and the field

description: We propose a new model of choice under repeated exposure to gambles. In
 it, the agent may come to choose a negative expected value, negative skew
 gamble, due to a behavioral bias that has a neurobiological foundation. We
 run laboratory experiments as a first step in testing the model and
 supplement the experimental findings with suggestive evidence from
 observational data. In the process of doing so, we identify a new asset
 pricing anomaly. The findings bring novel insights into the motivations
 underlying investor decisions and the impact of temptation and
 self-control in contexts of repeated risk-taking. 
description: # Craving Money? Evidence from the Laboratory and the Field
 All the data and code used
 to generate the findings for the paper can be found here. You can find the
 code to generate the lab/theoretical results and the data for the option
 prices and transaction level data. **1st.) For the experimental and Lab
 results:** All experimental data needed to evaluate the conclusions
 reported in the article and the Supplementary Materials are available at:
 **** The code used to generate the
 experimental findings reported in the article and its Supplementary
 Materials can be found here in the repository. The code covers all the
 models listed below. * Base model (Risk neutral, Deterministic): * Base
 model (Risk neutral, softmax): * Base model (prospect theory,
 Deterministic): * Base model (prospect theory, softmax): * Base model with
 Gambler's Fallacy: * Base model augmented with CbD (Risk neutral,
 Deterministic): * Base model augmented with CbD (Risk neutral, softmax): *
 Base model augmented with CbD (prospect theory, Deterministic): * Base
 model augmented with CbD (prospect thoery, softmax): * Base model
 augmented with CbD (modified probability function, Risk neutral, softmax):
 * Base model with CbD(Risk neutral,Deterministic,multiplicative): * Base
 model with CbD model (Prospect theory,Deterministic, same alphas): *
 Time-Varying DA model (risk neutral, softmax): * Time-Varying DA model
 (prospect theory, softmax): * Time-Varying DA model (prospect theory,
 softmax, same alphas): * Data Generator for Time-varying DA model
 (Prospect theory, softmax): * Data Generator for Base model (prospect
 theory, softmax): Folder containing all the simulated datasets: Each File
 starts wither a B (Base model) or TV (Time Varying). There are 120 Base
 and Time-Varying datasets, respectively. Pre-Requisites For any given
 dataset, the user needs to run all the cells in a model (in respective
 order) to get the results. Parameter optimization and obtaining results
 are separate parts in each model: One can look at the output of the
 hyperparameter optimization step in a code to get the values of all the
 free parameters. The results for a dataset (accuracy score of a particular
 model in %) can be obtained from the output of the bottom-most code cell.
 In some datasets, the 'betting column' (whether or not the
 subject chooses to bet, denoted by 1 or 0 respectively) is col.No.4 and in
 some datasets, it is col.No.5, so while training the model, variable
 'Y' (which is actually the betting column) needs to be changed
 (to df[4] or df[5]) depending on datasets. The user needs to change the
 drive link in each code to whatever is the location of dataset. In CbD
 models, n-value of 4 is used, meaning that DA is zero for any trial t such
 that 1&lt;= t &lt;= 4 There are no separate codes for the varying n-value
 test (as everything is exactly the same except for n-value), so the user
 may change the value of n in the CbD model (Prospect theory,
 Deterministic) code for that test. Simulated participants' datasets
 are similar to original datasets, only they contain 2 columns instead of
 6/7 columns, so while testing the models on these datasets for model
 recovery, df[4] should be replaced by df[2], everything else remaining the
 same. For model recovery procedure 1, Both time-varying DA model (prospect
 theory, softmax) and Base model (prospect theory, softmax) are trained and
 tested on simulated participants for Time-varying model. For model
 recovery procedure 2, Both time-varying DA model (prospect theory,
 softmax) and Base model (prospect theory, softmax) are trained and tested
 on simulated participants for Base model. **2nd. )Option Empirics:** For
 the empirical analysis of the options prices, two data sources were used.
 First, all the option price data came from *Optionmetrics*. Second, the
 transaction-level data is from the *International Securities Exchange*
 (ISE). This data covers the period from May 2005 through 2019. The columns
 represent: Option ID, Date, Call or Put, Option Expiration date, Open
 Interest, firm CUSIP, best bid, best offer, Volume, Strike Price, Implied
 Volatility, Firm Open Buy, Firm Closing Buy, Firm Open Sell, Firm Closing
 Sell, Customer Open Buy, Customer Closing Buy, Customer Open Sell, and
 Customer Closing Sell.