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Resubmission 3

This is a resubmission. In this version I have added some new features and fixed a few bugs.

New Features

  • We have updated the initials_adjpin() function, which generates initial parameter sets for the adjusted PIN model, to align with the algorithm outlined in Ersan and Ghachem (2024).

  • We have introduced the ivpin() function, which implements an improved version of the Volume-Synchronized Probability of Informed Trading (VPIN), based on Lin and Ke (2017). The function uses maximum likelihood estimation to provide more stable VPIN estimates, particularly for small volume buckets or infrequent informed trades, and improves the predictability of flow toxicity.

Bugfixes

  • We have rectified an issue in the mpin_ecm() function. In cases where an observation in the E-step of the ECM algorithm had a zero probability of belonging to any cluster, we decided to assign it a uniform probability of belonging to each cluster, calculated as 1 divided by the total number of clusters (cls). Previously, the code erroneously assumed a fixed number of clusters (6), which has now been replaced with the variable cls to accommodate varying cluster sizes.

  • We have enhanced the format and performance of polynomial root calculations within the conditional-maximization steps of the ECM algorithm. These enhancements are implemented in the solve_eqx function.

  • We've addressed two concerns related to the dependency package future. Firstly, previous code assigned variables to the global environment for parallel calls of the function .get_lagged_value, potentially yielding unexpected results. The updated code employs lexical scoping using the local() function to manage the variable .lwbound between function calls. Secondly, the previous code set the maximum size of futures to +Inf upon package loading, potentially impacting other packages. The option to adjust this value is now left to the user.

Test environments

  • local windows 11 , R 4.2.1
  • macOS-latest (release) (on GitHub)
  • windows-latest (release) (on GitHub)
  • windows-latest (4.1) (on GitHub)
  • ubuntu-latest (devel) (on GitHub)
  • ubuntu-latest (release) (on GitHub)
  • ubuntu-latest (oldrel-1) (on GitHub)
  • ubuntu-latest (oldrel-2) (on GitHub)
  • Windows Server 2022, R-release, 64 bit - R 4.2.1
  • Windows Server 2022, R-devel, 64 bit - R 4.3
  • macOS 10.13.6 High Sierra, R-release, brew - R 4.3
  • macOS 10.13.6 High Sierra, R-release, CRAN's setup - R 4.3

R CMD check results

0 errors | 0 warnings | 0 notes

Reverse dependencies

There are currently no downstream dependencies for this package.

Resubmission 2

This is a resubmission. In this version I have added some new features and fixed a few bugs.

New Features

  • We introduce a new function called classify_trades() that enables users to classify high-frequency (HF) trades individually, without aggregating them.
    For each HF trade, the function assigns a variable that is set to TRUE if the trade is buyer-initiated, or FALSE if it is seller-initiated.

  • The aggregate_trades() function enables users to aggregate high-frequency (HF) trades at different frequencies. In the previous version, HF trades were automatically aggregated into daily trade data. However, with the updated version, users can now specify the desired frequency, such as every 15 minutes.

Bugfixes

  • We identified and corrected an error in the mpin_ecm() function. Previously, the function would sometimes produce inconsistent results as the posterior distribution allowed for the existence of information layers with a probability of zero. We have now fixed this issue and the function produces correct results.

  • We have made some updates to the mpin_ml() function to better handle cases where the MPIN estimation fails for all initial parameter sets. Specifically, we have fixed an error in the display of the estimation results when such failure occurs. With these updates, the function should now be able to handle such failures more robustly and provide appropriate feedback.

  • We have simplified the ECM estimation functions, with a particular focus on the adjpin() function. We have improved the convergence condition of the iterative process used in the ECM estimation. Moreover, we rounded the values of the parameters at each iteration to a relevant number of decimals. This shall result in a faster convergence and prevent issues with decreasing likelihood values."

Test environments

  • local windows 11 , R 4.2.1
  • macOS-latest (release) (on GitHub)
  • windows-latest (release) (on GitHub)
  • windows-latest (4.1) (on GitHub)
  • ubuntu-latest (devel) (on GitHub)
  • ubuntu-latest (release) (on GitHub)
  • ubuntu-latest (oldrel-1) (on GitHub)
  • ubuntu-latest (oldrel-2) (on GitHub)
  • Windows Server 2022, R-release, 64 bit - R 4.2.1
  • Windows Server 2022, R-devel, 64 bit - R 4.3
  • macOS 10.13.6 High Sierra, R-release, brew - R 4.3
  • macOS 10.13.6 High Sierra, R-release, CRAN's setup - R 4.3

R CMD check results

0 errors | 0 warnings | 0 notes

Reverse dependencies

There are currently no downstream dependencies for this package.

Resubmission 1

This is a resubmission. In this version I have added some new features and fixed a few bugs.

New Features

  • The functions pin(), pin_*(), mpin_ml(), mpin_ecm(), adjpin(), vpin(), and aggregate_trades() accept now, for their arguments data, datasets of type matrix. In the previous version, it only accepted dataframes, which did not allow users, for instance, to use rollapply() of the package zoo.

  • Introduction of the function pin_bayes() that estimates the original pin model using a bayesian approach as described in Griffin et al.(2021).

Bugfixes

  • Fixed an error in the function initials_pin_ea() as it used to produce some parameter sets with negative values for trade intensity rates. The negative trade intensity rates are set to zero.

  • Fixed two errors in the function vpin(): one is related to an estimation error, and the argument verbose did not work properly.

  • Fixed an issue with resetting the plan for the future (future::plan) used for parallel processing.

Test environments

  • local windows 10 , R 4.2.1
  • Fedora Linux (on R-hub) R-devel
  • macOS-latest (release) (on GitHub)
  • windows-latest (release) (on GitHub)
  • ubuntu-latest (devel) (on GitHub)
  • ubuntu-latest (release)
  • ubuntu-latest (oldrel-1)
  • Windows Server 2022, R-release, 64 bit - R 4.2.1
  • Windows Server 2022, R-devel, 64 bit - R 4.3
  • macOS 10.13.6 High Sierra, R-release, brew - R 4.3
  • macOS 10.13.6 High Sierra, R-release, CRAN's setup - R 4.3

R CMD check results

There were no ERRORs or WARNINGs.

There is one NOTE that is only found on Windows (Server 2022, R-devel 64-bit):

* checking for detritus in the temp directory ... NOTE
Found the following files/directories:
  'lastMiKTeXException'

As noted in R-hub issue #503, this could be due to a bug/crash in MiKTeX and can likely be ignored.

R CMD check results

Local, using devtools::check()

SUCCESS!

-- R CMD check results ------------------------------------------------------------------------------------ PINstimation 0.1.0 ---- Duration: 3m 46.8s

0 errors √ | 0 warnings √ | 0 notes √

GitHub Actions

  • macOS-latest (release)
  • windows-latest (release)
  • ubuntu-latest (devel)
  • ubuntu-latest (release)
  • ubuntu-latest (oldrel-1)

All SUCCESS!√

Platform checks using rhub

There were no ERRORs or WARNINGs.

There was 1 NOTE:

Note 1

Maintainer: 'Montasser Ghachem montasser.ghachem@pinstimation.com'

New submission

Possibly misspelled words in DESCRIPTION: AdjPIN (11:232) Duarte (11:243) Easley (11:128, 11:157, 11:306) Ersan (11:204) MPIN (11:195) Multilayer (11:179) VPIN (11:297) al (11:167, 11:316) et (11:164, 11:313)

Explanation:

The presumably misspelled words are either:

  • concepts, or abbreviations related to statistical models in Finance (e.g. AdjPIN stands for Adjusted Probability of informed trading). This also concerns AdjPIN, MPIN, Multilayer, and VPIN.

  • Researcher names: Ersan, Duarte, Easley

  • 'et', and 'al', together form 'et al.', which is an abbreviation meaning “and others.”; used to shorten lists of author names in text citations.