knitr::opts_chunk$set( message = FALSE, warning = FALSE, collapse = TRUE, comment = "#>", out.width = "100%" )
The disposition effect consists in the realization that investors are more likely to sell an asset when it is gaining value compared to when it is losing value. A phenomenon which is closely related to sunk costsâ€™ bias, diminishing sensitivity, and loss aversion.
This irrational phenomenon has strong implications on financial markets. In particular, it is a violation of the well-known "efficient market hypothesis" that bases its foundations on the theory of rational agents.
Hence, timely capturing and understanding irrational behaviors
on the financial markets is of primary interest both of
researchers and investors. For this reason, the dispositionEffect
package allows to quickly collect both aggregate and time series
results of the disposition effect, allowing to deeply study
the evolution in time of irrationalities.
To load the package simply use the usual library
function.
library(dispositionEffect) library(dplyr) library(tidyr) library(purrr) library(ggplot2)
The disposition effect analysis is performed on two fundamental types of data frames:
portfolio transactions, that is all the financial transactions an investor did during a specific period of time. A single transaction is made up of 6 features: the investor id, the asset id, the type of the transaction (it can be a buy or a sell), the traded quantity, the traded price, and the datetime.
market prices, that is the prices found on the stock markets for each traded asset and each transaction datetimes.
The portfolio_compute
function is the core interface of the package
and it is used to perform all the gains and losses computations.
In particular, the argument time_series_DE
is used to enable time
series disposition effect computations.
portfolio_results_ts <- portfolio_compute( portfolio_transactions = investor, market_prices = marketprices, time_series_DE = TRUE )
Setting it to TRUE
makes the function return two different results:
portfolio <- portfolio_results_ts$portfolio dplyr::select(portfolio, -datetime)
method
timeseries <- portfolio_results_ts$timeseries head(timeseries)
For every transaction datetime two different disposition effect are computed:
Note that, by the moment, the time series computations of disposition
effect are allowed for "count"
and "value"
methods only (with
the latter the disposition difference is implemented instead of
disposition effect).
The disposition_summary_ts
function can be used to summarize
the evolution of disposition effect over time.
disposition_summary_ts(timeseries)
A visual time series analysis can be performed as usual
with ggplot2
.
timeseries %>% tidyr::pivot_longer(cols = dplyr::starts_with("DE")) %>% ggplot2::ggplot(ggplot2::aes(x = datetime, y = value, col = name)) + ggplot2::geom_line(size = 1.5) + ggplot2::scale_colour_viridis_d(alpha = 1) + ggplot2::labs( title = "Time Series Disposition Effect results", subtitle = "Method Count", x = "", y = "" ) + ggplot2::theme(legend.position = "bottom")
The time series analysis of disposition effect can be
greatly improved also by computing the disposition effect
over time on specif assets traded by the investor.
Indeed, the argument assets_time_series_DE
allows to specify
a character vector of assets' id (that must be traded by the
investor) on which to compute the disposition effect over time.
portfolio_results_ts_assets <- portfolio_compute( portfolio_transactions = investor, market_prices = marketprices, time_series_DE = TRUE, assets_time_series_DE = c("ACO", "LSUG") )
timeseries_assets <- portfolio_results_ts_assets$timeseries head(timeseries_assets)[, 2:6] head(timeseries_assets)[, c(2:4, 7:8)]
disposition_summary_ts(timeseries_assets)[, 2:6] disposition_summary_ts(timeseries_assets)[, c(2:4, 7:8)]
timeseries_assets %>% tidyr::pivot_longer(cols = dplyr::contains("DE")) %>% ggplot2::ggplot(ggplot2::aes(x = datetime, y = value, col = name)) + ggplot2::geom_line(size = 1.5) + ggplot2::scale_colour_viridis_d(alpha = 1) + ggplot2::facet_wrap(~ name, ncol = 2) + ggplot2::labs( title = "Assets Time Series Disposition Effect results", subtitle = "Method Count", x = "", y = "" ) + ggplot2::theme(legend.position = "bottom")
This way it is possible to better understand the behavior of an investor on his traded assets. It may be possible, in practice that, disposition effect behaviors are only present on some specific assets, and understanding what assets are more subject to irrationality can be critical.
In order to better understand the relevance of the time
series analysis of disposition effect, we can test the
results on the DEanalysis
real sample dataset.
See "Disposition Effect in Parallel" to speed up computations with parallel computing in R.
trx <- DEanalysis$transactions mkt <- DEanalysis$marketprices investor_id <- unique(trx$investor) res_list <- vector(mode = "list", length = length(investor_id)) for (i in seq_along(investor_id)) { tmp_trx <- trx %>% dplyr::filter(investor == investor_id[i]) tmp_res <- tryCatch( dispositionEffect::portfolio_compute( portfolio_transactions = tmp_trx, market_prices = mkt, time_series_DE = TRUE ), error = function(e) "Error" ) res_list[[i]] <- tmp_res # save results rm(tmp_trx, tmp_res) } # extract time series results for each investor timeseries_10_investors <- res_list %>% purrr::map("timeseries")
load("figures/ts_res.RData")
purrr::map(timeseries_10_investors, disposition_summary_ts) %>% dplyr::bind_rows() %>% dplyr::filter(stat == "Mean") %>% dplyr::arrange(desc(DETs_count))
timeseries_10_investors %>% dplyr::bind_rows() %>% tidyr::pivot_longer(cols = dplyr::contains("DE")) %>% ggplot2::ggplot(ggplot2::aes(x = datetime, y = value, col = investor)) + ggplot2::geom_line(size = 0.75) + ggplot2::scale_colour_viridis_d(alpha = 0.9) + ggplot2::facet_wrap(~ name, nrow = 2, ncol = 1) + ggplot2::labs( title = "10 Investors Time Series Disposition Effect results", subtitle = "Method Count", x = "", y = "" ) + ggplot2::theme(legend.position = "bottom")
For more tutorials on disposition effect visit dispositionEffect.
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