Analysis of Disposition Effect on Financial Portfolios

Evaluate the presence of disposition effect and others irrational investor's behaviors based solely on investor's transactions and financial market data. Experimental data can also be used to perform the analysis. Four different methodologies are implemented to account for the different nature of human behaviors on financial markets. Novel analyses such as portfolio driven and time series disposition effect are also allowed.

dispositionEffect

The dispositionEffect package allows to quickly evaluate the presence of disposition effect’s behaviors of an investor based solely on his transactions and the market prices of the traded assets.

Installation

You can install the released version of dispositionEffect from CRAN with:

install.packages("dispositionEffect")

Otherwise, you can also install the development version from GitHub with:

install.packages("devtools")
devtools::install_github("marcozanotti/dispositionEffect")

Overview

The package contains few user-friendly purpose specific interfaces:

• portfolio_compute is a wrapper function that compute realized and paper gains and losses from the investor’s transactions and the market prices of the traded assets and updates the investor’s portfolio

• gains_losses is the core function of the package. It performs all the necessary calculations and can be used for real-time processing (it is intended for advanced users only)

• disposition_effect computes the disposition effect

• disposition_difference computes the disposition difference

• disposition_computeand disposition_summaryinterfaces that allow to easily compute disposition effect and summary statistics.

Tutorials

• Getting started

• The Analysis of Disposition Effect

• Disposition Effect in Parallel

• Time Series Disposition Effect

References

• Mazzucchelli, 2022, An Analysis of Short Selling and Volatility Impact on the Disposition Effect (working paper)

• Filippin, Mazzucchelli, and Zanotti, 2022, Portfolio driven disposition effect: the wide framing approach (working paper)

• Mazzucchelli, and Zanotti, 2022, Mean reverting expectations to rationalize the disposition effect (working paper)

• Computing Disposition Effect on Financial Market Data, 2021, useR! Conference

Getting help

If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub.

For questions and other discussion, mail us at [email protected].

Acknowledgements

A special thank to Claud Graphics for our logo.