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README.Rmd
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---
output: github_document
editor_options:
markdown:
wrap: 72
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
message = FALSE,
warning = FALSE,
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
options(tibble.print_min = 5, tibble.print_max = 5)
options(scipen = 1, digits = 2)
```
# dispositionEffect <a href='https://marcozanotti.github.io/dispositionEffect/index.html'><img src="man/figures/logo.png" align="right" height="200"/></a>
<!-- badges: start -->
[](https://CRAN.R-project.org/package=dispositionEffect)
[](https://github.com/marcozanotti/dispositionEffect/actions)
[](https://app.codecov.io/gh/marcozanotti/dispositionEffect?branch=master)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://marcozanotti.github.io/dispositionEffect/index.html)
[](https://github.com/marcozanotti/dispositionEffect/issues)
<!-- badges: end -->
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](https://CRAN.R-project.org) with:
```{r, eval = FALSE}
install.packages("dispositionEffect")
```
Otherwise, you can also install the development version from
[GitHub](https://github.com/) with:
```{r, eval = FALSE}
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_compute`and `disposition_summary`interfaces that allow
to easily compute disposition effect and summary statistics.
## Tutorials
* [Getting started](https://marcozanotti.github.io/dispositionEffect/articles/getting-started.html)
* [The Analysis of Disposition Effect](https://marcozanotti.github.io/dispositionEffect/articles/de-analysis.html)
* [Disposition Effect in Parallel](https://marcozanotti.github.io/dispositionEffect/articles/de-parallel.html)
* [Time Series Disposition Effect](https://marcozanotti.github.io/dispositionEffect/articles/de-timeseries.html)
## 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](https://marcozanotti.github.io/dispostionEffect-useR2021/), 2021, useR! Conference
## Getting help
If you encounter a clear bug, please file an issue with a minimal
reproducible example on
[GitHub](https://github.com/marcozanotti/dispositionEffect/issues).
For questions and other discussion, mail us at
[zanottimarco17\@gmail.com](mailto:[email protected]).
## Acknowledgements
A special thank to [Claud Graphics](https://www.behance.net/claudiocec3c4f) for our logo.