dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion In this study, we compare machine learning models for credit scoring that pass the bar of ECB minimum requirements. We evaluate their statistical and financial performance on a specific dataset. Our analysis reveals that traditional models like LR, ELN, and LDA perform poorly in both statistical and financial terms. MLP is the best financial performer but among the worst statistical one, while LGBM is a good statistical model with poor financial performance. This highlights the importance of computing the financial performance. We also examine the performance of newly developed explainable models, such as EBM and GAMI, which rely on black-box algorithms to derive GAM outputs. These models demonstrate excellent statistical and financial performance, with the ability to compete with and even outperform decision tree models like XGB in financial terms.
We compute the cost of explainability associated with these inherently explainable models compared with best performing black-box MLP, and find a 0. 14% to 0. 21% cost of explainability, depending on the risk appetite. Our study further investigates a way to improve explainability by introducing isotonicity as human supervision. By introducing expert judgment to obtain isotonic GAMs, we improve the explainability of the GAM without compromising the financial or statistical performance. If confirmed with other datasets, these findings could further enhance the attractiveness of GAM models and contribute to their acceptability from a regulatory point of view. In summary, our findings demonstrate that explainable models, such as EBM and GAMI, are promising alternatives to traditional models, and that isotonicity that expresses expert judgment can be used to enhance their interpretability while preserving their financial and statistical performance.
Perspectives While the proposed approach shows promising results, it has only been tested on a single dataset over a limited period, with a low granularity in the risk appetite threshold. Further analysis with different datasets, longer periods, and more granular risk appetite threshold would help confirm or challenge these initial findings. Longer periods would allow the use of more complex neural network architectures and, hence, a possibly higher cost of explainability. Additionally, exploring the use of explainable models for feature selection could be a valuable avenue for future research. It is also worth noting that the study highlights inconsistencies between statistical and economic results that warrant further investigation. As academic research mostly relies on statistical measures, improving the understanding of how statistical measures reflect or not the economic performance of a model could enhance the application and adoption of academic research in the financial world.
Acknowledgments I am grateful for the support of the  Jean Monnet Fellowship  at the  Robert Schuman Center   for Advanced Studies, European University Institute , which provided a wonderful environment and precious institutional resources to write this article. My thanks also go to the thematic issue's editors for their support and for organising a panel at the 2023 18th  Biennial EUSA Conference , where I first presented this article and received very helpful feedback. Last, I thank the anonymous reviewers for their precious comments.
Data Our main sample includes U. S. domestic mergers from January 1995 to December 2020 for which corresponding press release was found in the SEC EDGAR system. We chose 1995 as the starting year because the EDGAR system started in late 1994. We included all the deals of U. S. public firms which were officially announced between 1995 and 2020 and were completed or withdrawn before the end of 2020. The primary source for M&A data is Refinitiv SDC Platinum. Consistent with the M&A literature (e. g. ,  Derrien et al. , 2023) , we excluded firms from the financial industry (primary Standard Industry Classification (SIC) codes 6000-6999) and utilities sector (SIC codes 4900-4999) because of their highly regulated environment. To avoid the results being driven by smaller deals, we introduced a transaction threshold and only included deals with a minimum transaction value of $1 m (deflated in 2009).
An additional criterion for our analysis was that both the acquirer firm and the target firm are publicly listed companies. To avoid including takeovers motivated by a low target stock price, we further excluded inactive target firms, defined as those with a one-month stock price below $1 prior to the M&A announcement. In addition, we eliminated all 'rumor' deals and only considered majority acquisitions, i. e. , acquisitions in which the acquirer held <50% of the target before the announcement and obtained a controlling stake (above 50%) through the M&A transaction. To exclude share buybacks, we removed deals in which the acquirer and target were the same company. We also excluded deals where the acquirer announced more than one deal on a given day. This gave us a dataset with 2944 M&A deals.
Next, we manually matched the official M&A press release of the announcement to the respective M&A deal. Note that the initial public announcement differs from the proxy statement. While proxy statements are rather technical and follow certain regulatory requirements, the SEC does not have mandatory requirements for the initial public announcement and therefore allows flexibility in format and content. 4  For each deal, we obtained the central index key (CIK) identifiers of the target and acquirer firms from a CIK-CUSIP database 5  and then matched the press release to the corresponding M&A event. 6  This resulted in a final dataset comprising 1152 M&A deals with initial public announcements. We applied the sentiment textual analysis technique of  Loughran and McDonald (2016)  to analyze the content of the M&A press release.
We first included media coverage and media sentiment in news articles around the deal announcement. 10 This information was obtained from the Ravenpack News database  (Hossain and Javakhadze, 2020)  and controls for how managerial sentiment is associated with media sentiment, as most of the published studies on M&A sentiment use media sentiment for their analyses (e. g. ,  Liu and McConnell, 2013). We further control for deal characteristics (e. g. , transaction value, percentage of stock payment, number of bidders, tender offer, horizontal/vertical deal, industry merger wave) and include firm-level control variables for both acquirer and target firms. These include firm size, return on assets (ROA), cash ratio, market-to-book ratio, leverage, and product competition, as per  Hoberg et al.
(2014). Accounting data for the firms was obtained from Compustat. Our control variables are based on commonly used variables in the M&A literature and textual analysis research (see e. g. ,  Jegadeesh and Wu, 2013; Boyson et al. , 2017; Bonaime et al. , 2018; Lee et al. , 2018; Guernsey et al. , 2022; Stewart, 2023). In addition, we control for CEO characteristics and governance-related factors (e. g. , CEO gender, age, education, ownership, the fraction of independent directors, board size, staggered board), using data obtained from BoardEx. Finally, we also follow  Karpoff et al. (2013)  and  Masulis and Simsir (2018)  and proxy for information asymmetry to examine whether the use of sentiment is associated with information asymmetry between insider and outsider investors. The full list of our variables and the corresponding definitions are provided in Table  A.
This is consistent with theories that media content is a proxy for new information on fundamental asset values (e. g. ,  Hossain and Javakhadze, 2020)  and provides first evidence that media sentiment and managerial sentiment may not be comparable or even serve as substitutes. Moreover, compared to targets, acquirers show higher levels of CEO ownership and have more institutional investors as well as larger boards and higher levels of board independence. Acquirer CEOs also have longer tenure, more often take on a second role as chairman, and show higher levels of overconfidence than target CEOs. Table  3  splits the sample according to whether a press release exists in the EDGAR database and, if available, we divide them into positive and negative levels of sentiment by acquirer and target.
The information value of initial public announcements We start by examining the stock market reaction around M&A announcements conditional on whether a press release is found on the EDGAR database and whether it contains a specific management statement by key executives. The dependent variables of our regressions are the three-day CARs ([-1,+1] event window) of the target and the acquirer firm, respectively. We chose three independent variables as our main variables of interest. First, we analyze whether the existence of a press release generally matters. The This table shows the regression results of deal attitude (friendly versus hostile deals) on issuing press releases and managerial sentiment used in press releases. For the dependent variables, we set the dummy of acquirer (target) statement in the press release (dummy acq , dummy tgt ) respectively, which is equal to 1 if acquirer (target) gives a quote in the press release, and 0 otherwise.
Merger waves are defined following the methodology of  Harford (2005). verti_deal Following the methodology of  Fan and Goyal (2006) , a merger is defined as a vertical integration if the vertical relatedness coefficient exceeds 1% and merging firms are from different industries. hori_deal Following the methodology of  Fan and Goyal (2006) , a merger is defined as a horizontal integration if the vertical relatedness coefficient is <1% and merging firms are in same industries. tender Binary variable defined as 1 if the acquirer makes a tender offer, and 0 otherwise. The variable is obtained from SDC. numbidder The number of bidders reported in the SDC database. relative_size The transaction value divided by the acquirer's equity market value at the end of the year prior to the merger announcement.
The solution to the optimal control problem dynamically allocates the vaccine demand to reach the endemic equilibrium point. Finally, to illustrate the applicability and performance of the proposed model and solution approach, the paper reports extensive numerical experiments on a real case study of the vaccination campaign in France. The computational results show that the proposed Benders decomposition algorithm is 12 times faster, and its solutions are, on average, 16% better in terms of quality than the Gurobi solver under a limited CPU time. In terms of vaccination strategies, our results suggest that delaying the recommended time interval between doses of injection by a factor of 1. 5 reduces the unmet demand up to 50%. Furthermore, we observed that the mortality is a convex function of fairness and an appropriate level of fairness should be adapted through the vaccination.
The performance of the proposed ABD algorithm is tested through a set of 36 test problems with a different number of vaccination centers and time periods. All test problems except the last one are indeed smaller parts of the case study with fewer vaccination centers or fewer time periods. Since the adjusted SIR model is run for each populated area for a time horizon of 365 days, and it is also impossible and illogical to plan the MVMDLIRP daily, each period of the MVMDLIRP model represents a week and the demand for each period is the cumulative demand of the whole week. Both the MVMDLIRP model and the ABD algorithm were coded in Python 3. 8 using the Gurobi 9. 1. 2 library, and all experiments were done on a server containing four Intel XEON processors with 5 gigabytes of RAM memory running at 2.
One of the main challenges when using observational data for causal inference is the presence of confounding. A classic approach to account for confounding is the use of propensity score techniques that provide consistent estimators of the causal treatment effect under four common identifiability assumptions for causal effects, including that of no unmeasured confounding. Propensity score matching is a very popular approach which, in its simplest form, involves matching each treated patient to an untreated patient with a similar estimated propensity score, that is, probability of receiving the treatment. The treatment effect can then be estimated by comparing treated and untreated patients within the matched dataset. When missing data arises, a popular approach is to apply multiple imputation to handle the missingness.
An issue for propensity score matching, as for any other adjustment method, is the presence of missing data on confounders. Multiple imputation is a powerful and increasingly popular approach to handle missing data. M completed datasets are built by drawing missing values from their posterior predictive distribution. Treatment effect estimates are then obtained for each completed dataset and are averaged into a single treatment effect estimate. Rubin  19  proposed rules to obtain a variance effect estimate that correctly accounts for the additional variability caused by the missing data. Multiple imputation has been proposed in combination with propensity score analysis. Leyrat et al  20  discuss challenges in applying multiple imputation within propensity score analysis in general. Here, we focus on specific issues encountered when applying propensity score matching.
2. 4 Combining propensity score matching and multiple imputation Problems arise when there are missing data in at least one of the baseline covariates so that the propensity score is not directly estimable. In this case, a widely used approach is based on the combination of propensity score matching with multiple imputation. 19 Multiple imputation samples from the posterior distribution of the missing data conditional on the observed data to impute missing values. Typically this is done under the missing at random assumption. The most commonly used implementation of multiple imputation is based on the chained equation method. 29, 30 Implementations of this approach are available in most standard software, for example in the r package mice by van Buuren and Groothuis-Oudshoorn. 31  By imputing multiple times, nb imp imputed datasets are created.
For each of the nb imp imputed datasets, a propensity score model can be estimated. From there, to obtain an estimate of the treatment effect using propensity score matching, different approaches can be used, notably what have been termed the within and across approaches. 20 In the within approach, the matching is done separately for each one of the nb imp imputed datasets leading to nb imp matched datasets and nb imp treatment effect estimates that are aggregated into one estimate. 19 In the across approach, the propensity scores are averaged over all nb imp imputed datasets and the matching is done from this averaged propensity score leading directly to one treatment effect estimate only. Following initial debate of these two approaches,  [32] [33] [34] [35] [36]  Leyrat et al  20  demonstrated that only the within approach could lead to consistent estimates, subsequently confirmed by other authors.
Reiter's rules In the context of multiple imputation for measurement error, Reiter  21  proposed a modification of Rubin's rules to combine estimates across imputed datasets in scenarios where some of the patients used for imputation are not used for further analysis. In classic multiple imputation, a parameter draw computed from the imputation model is used to generate a single imputed dataset. This operation is repeated nb imp times, resulting in nb imp completed datasets each corresponding to a different parameter draw. Reiter  21  proposed generating not one but nb rep datasets from the same parameter draw and to repeat this nb imp times leading to a total of nb imp × nb rep imputed datasets. The treatment effect estimates θk,j for k = 1, … , nb imp and j = 1, … , nb rep are aggregated and the variance is estimated using the following formulae: θ = 1 nb imp nb rep nb imp ∑ k=1 nb rep ∑ j=1 θk,j = 1 nb imp nb imp ∑ k=1 θk where θk = 1 nb rep nb rep ∑ j=1 θk,j , Var( θ) = W + ( 1 + 1 nb imp ) B - ( 1 + 1 nb rep ) U, where W = 1 nb imp nb rep nb imp ∑ k=1 nb rep ∑ j=1 Var( θk,j ), B = 1 nb imp -1 nb imp ∑ k=1 ( θk - θ) 2 , U = 1 nb imp (nb rep -1) nb imp ∑ k=1 nb rep ∑ j=1 ( θk,j -θk ) 2.
In the above formula, W is the average within imputation variance of the treatment effect estimate, B is the between imputation variance of the average treatment effect estimate across parameter draws and U is the variability of the treatment effect within parameter draws but across imputed datasets. When W and B gets close to W and B, that is, for nb imp and nb rep big enough, the variance formula above includes a new positive term compared to Rubin's rules, U, which is subtracted, leading to a smaller estimated variance and hence narrower confidence intervals. In this two-stage approach, because we are imputing nb rep times for each parameter draw generated from the imputation model, the successive matching will lead to nb rep matched samples for each parameter draw, thus increasing the probability of a patient being included in one of the final analyses.
Informally, the second term in Rubin's rules accounts for the additional variance introduced due to uncertainty about the missing values. This is estimated by the empirical variance across treatment effects estimated from the different sets of imputed values. However, in the case of propensity score matching, treatment effects resulting from these different sets of imputed values differ not just due to uncertainty about the missing values but also because of the stochastic nature of the sampling process (ie, the propensity score matching). The latter is accounted for within the second term ( B), but is also accounted for in the original within-sample variance estimate ( W), thus we need to subtract an estimate of the additional variability induced by the propensity score sampling process within a fixed dataset (U).
In practice, implementation of these rules, which we will refer to as Reiter's rules as a parallel to Rubin's rules, requires a slight modification of the standard implementation of multiple imputation by chained equation: for each parameter draw, nb imp imputed datasets are created instead of only one. This can generally be done using existing options of standard packages for multiple imputation. For example, the ignore argument of the r function mice allows the imputation model to be fitted on a subset of the whole dataset. Therefore, if we concatenate nb rep duplicates of the initial dataset and fix the ignore argument to TRUE for all duplicates, except the first one, this will impute all duplicates using an imputation model based on the same parameter draw estimated from the initial dataset only.
Data generation We generated N sim = 1000 datasets, each with N = 10 000 patients. Three confounders x = (x 1 , x 2 , x 3 ) were generated as three independent standard Gaussian variables  (0, 1). Three levels of confounding (strong, moderate and weak) were considered. We expect the crude estimation of the treatment effect to be biased for both the strong and moderate confounding scenarios while we expect the crude estimation to be almost unbiased for the weak confounding scenario.
Assessing confounding in our simulated scenarios First, we evaluated the level of confounding generated in our simulation scenarios. To this end, we simulated a sample of N = 10,000 patients with a binary outcome, without any missing data and with 30% treated (ie, with  0 = -1). For each confounding scenario (weak, moderate and strong) we looked at the balance of the three confounders before matching (in the whole dataset) and after matching (in the propensity score matched dataset). We also looked at the absolute standardized mean differences (ASMD) for all three confounders before and after matching. The ASMD is a balance indicator that helps to identify confounding. Guidelines suggest that ASMD values above 0. 1 indicate potential confounding. 40 To plot balance and ASMD, we used the cobalt package.
Combining multiple imputation and propensity score matching For all simulations, we set nb imp = 20 and nb rep = 10. Simulations were conducted using R. First, multiple imputation was performed using the function mice with the outcome included in the imputation model as advised by previous work  20, 42  to generate nb imp imputed datasets. In the case of Reiter's rules, nb imp × nb rep imputed datasets were produced and we used the ignore argument of the mice function to generate nb rep imputed datasets for each nb imp parameter draws. Then, for each completed dataset, the propensity score was estimated using the glm function with a logit link including x 1 , x 2 and x 3 as covariates. For each completed dataset, each treated patient was matched to one untreated patient by their estimated propensity score, using a caliper of 0.
2 times the standard deviation of the logit of the propensity score. 14 Matching was performed without replacement. This matching was done using the package MatchIt. 43 For each matched dataset, the ATT was estimated with a generalized linear model of outcome on treatment only, as a risk difference between the treated and the untreated by using a linear link and as an odds ratio between the treated and the untreated by using a logit link. As advised by Austin  7  and Hill,  18  clustered standard errors that take into account the within-pair correlation due to the matched nature of the data were used. In R, this was done using the function glm. cluster from the package miceadds. Finally, all treatment effect estimates and estimation of their variance were aggregated using Rubin's rules or Reiter's rules.
APPLICATION In this section, we use data taken from the National Cancer Registry of the Office for National Statistics  44  to estimate the effect of age at diagnosis as a binary variable (using the median as the cutoff) on the receipt of surgery for the 31,351 patients diagnosed with lung cancer recorded in the registry. Tumor stage at diagnosis is classed as early versus late, based on a dichotomization (stages 1,2 vs 3,4) of Belot et al's algorithm. 44 The patient's performance status, assessing functional abilities, has two modalities, good and bad, based upon dichotomization of the five-category WHO classification. 44 Deprivation was measured using the Income Domain from the 2010 England Indices of Multiple Deprivation. 45 Comorbidities were adjusted for using the Charlson Comorbidity Index with a 6-year time window up to 6 months before diagnosis.
All these variables and the sex of the patient were considered to be confounders in our analysis. Table  1  provides a description of the sample by the outcome, receipt of surgery, summarizing potential confounder variables and any missing data. About 25% of performance status and 10% of tumor stage data were missing. Multiple imputation was performed using mice, by including the binary outcome, treatment and the fully observed confounders listed above in the imputation model. Propensity score matching was performed using nearest neighbor matching without resampling. A caliper of 0. 2 was applied on the scale of the logit of the propensity score values. We applied the procedure described in Section 3. 4. 2 to these data using both Rubin's rules and Reiter's rules. In order to assess the impact of the choice of nb imp and nb rep , several combinations of values were used: (nb imp ; nb rep ) = (20; 10), (20; 30), (30; 10), (30; 30), (50; 10) and (50; 30).