dataesr/scientific-paragraphs-categorization huggingface dataset:
Our unique dataset not only provides novel insights into the financial history of Australia but also offers a rare opportunity to perform an authentic out-of-sample replication of equity anomalies. First, the SEM sample is uncharted territory in asset pricing as it has never been used before in the context of discovering or verifying anomalies. Second, as it contains companies from a foreign market, it provides a non-U. S. perspective and does not carry any form of "pre-test bias". Third, the study period precedes the discovery and publication of the vast majority of anomalies, thereby avoiding the influence of investor learning and improvements in market efficiency. Summing up, this exceptional setting enables a genuine out-of-sample study, helping to diagnose the scale of false discoveries in the anomaly literature.
Data sources and sample preparation Australian researchers and practitioners have had ample access to Australian stock market data since the mid-1990s. Beforehand, the availability of equity data in digital form was varied and fragmented, with gaps in company coverage, uneven time series, and missing time variables. 2 Our study is the first to take advantage of a unique dataset, covering all the equities listed on the no-longerexisting Stock Exchange of Melbourne (SEM). Our study period runs from January 1926 to March 1987. The beginning of the sample period is dictated by data availability. The end date is associated with the creation of the Australian Stock Exchange Limited (ASX). The ASX was created on April 1st, 1987, under the legislation of the Australian Parliament. The development of the national securities exchange involved the amalgamation of the six independent markets that used to function in each state's capital city.
The new national trading body effectively absorbed the SEM  (Carew, 2007). 3 he data is sourced from the newly created Australian Equities Database (AED) developed by the Monash Centre for Financial Studies (MCFS). 4 To the best of our knowledge, the AED has never been used in asset pricing studies before. The dataset examined in this research contains monthly observations of four principal variables: total returns, prices, number of shares outstanding, and dividend yields. Because this information has not been previously available in digital format, it was manually retrieved from the hard copies of monthly gazettes of the Stock Exchange of Melbourne and the Australian Stock Exchange. 5 Fig. 1  presents examples of the original data sources used to construct the dataset. Preparation of the dataset required significant work, including data collection, recording, and storage.
The details of the dataset preparation (e. g. , handling of missing variables, data validation, etc. ) are summarized in ACFS (2019) and are available upon request. One important feature of this unique dataset is reported currency. Notably, Australia not only switched from pounds to dollars on February 14th, 1966, but also from the imperial (pre-decimal) to the decimal system. Initially, all data observations were recorded in the currency of the published stock exchange data. Thus, a large proportion of the early part of the database (pre-February 1966) is recorded in the original imperial Australian currency '3-part' system of pounds, shillings, and pence. Subsequently, all the values were converted to "current" Australian dollars and cents. Importantly, because there was no direct equivalent for the penny coin in the new system (i.
As advocated by  Schmidt et al. (2017) , we exclude monthly returns and associated market capitalizations in the case of return spikes (return >500%). 6 Furthermore, as in  Jegadeesh and Titman (2001) ,  Gutierrez and Kelley (2008), and Hou et al. (2011) , among others, we control for penny stocks: specifically, we exclude observations when the price at the beginning of the month falls below AUD 0. 10. However, unlike, for example,  Ball et al. (1995) , De Moor and Sercu  (2013), and van der Hart et al. (2005) , we do not exclude microcaps, that is, companies characterized by tiny market capitalization, because we can control for and explore the role of firm size in a separate test. Next, as  Ince and Porter (2006)  suggested, we delete zero returns at the end of the return time-series. This is to avoid any stale prices displayed in the database after a delisting event occurs.
Finally, as De Moor and Sercu (2013) argued, automatic filters may not necessarily guarantee the elimination of all possible errors. Potential problems include decimal sign shifting, high reported returns not corresponding with a similar change in market capitalization, price not mirroring dividend payouts, or some other obvious types of wrongly handled corporate actions. Therefore, we follow Rouwenhorst (1999) and manually filter suspicious data coming from potential errors. 7  It is important to note that, despite our best efforts and all the tests conducted, we acknowledge that our data may be of lesser quality in earlier decades. This fact is an essential limitation of the study that stems inherently from the nature of our long dataset. However, we expect that any errors should be randomly distributed and cancel each other out in the long run.
Our final sample for analysis contains 3610 different companies and 365,707 monthly return observations. Notably, while our dataset begins in January 1926, we use the initial 12 months only to obtain different return predictive signals. As a consequence, the actual sample of returns is from February 1927 to March 1987. 8 Naturally, the number and market value of firms in our sample are not constant through time but increase along with the development of the Australian capital markets. The overall size of our sample through time is reported in Fig. 2. The number of companies covered increases gradually from 157 in February 1927 to 1020 in March 1987. The most significant spike is recorded in 1972 when a national listing of all Australian securities was introduced. This brought an influx of a considerable number of small company listings.
Second, we take into account only one-dimensional anomalies. As a consequence, we exclude the return regularities requiring double-sorting, such as various enhanced momentum strategies  (Hanauer and Windmüller, 2020). Third, the anomalies must be derived from the elementary market data available in our dataset, including stock prices, total returns, market capitalizations, and dividend yields. Hence, we do not cover the accounting anomalies such as profitability or investment (e. g. ,  Cooper et al. , 2008; Novy-Marx, 2013) , even if they are broadly acknowledged. Finally, due to the limitations of our data frequency, we are constrained to returnpredicting variables based on monthly data. As a result, we cannot test the anomalies inherently requiring daily or intraday data, such as the trend factor  (Han et al.
Group 7: Other. Finally, the last group of anomalies contains signals related to the issuance, information uncertainty, or market frictions, which cannot be easily classified into any earlier categories. This group includes four return-predictive signals, that is, composite equity issuance (CEI), firm age (AGE), market value or size effect (MV), and share price (SP). The final anomaly refers to the so-called low-price effect. We replicate all the anomalies closely following the approach in the original papers. Nonetheless, in some cases, we introduce minor modifications to accommodate the variables to the limitations of our dataset. For instance, the original value uncertainty anomaly of  Bali et al. (2018)  was based on the volatility of the book-to-market ratio (B/M). Due to the lack of relevant accounting data, we use the dividend yield instead of B/M.
The dividend yield strategy performance is unimpressive even in the undeveloped frontier markets, where it fares weaker than other value indicators  (de Groot et al. , 2012). Moreover, the recent broad replication studies, examining extensive arrays of various anomalies, found little support for the dividend yield anomaly  (Hou et al. , 2020; Qiao, 2019; Hollstein, 2020). Interestingly, unlike the studies based on recent data, the dividend yield anomaly typically works well in the studies using long-run historical datasets. For example,  Dimson et al. (2003)  document substantial profits by utilizing the DY strategy over the long-run on U. K. data, and  Dimson et al. (2017)  provide similar evidence from over a century of data from 23 international markets. The value effect measured with DY is also confirmed in the pre-World War I data from the U.
Concluding remarks This study employs a novel dataset comprised of securities listed in the Stock Exchange of Melbourne in the years 1926 to 1987. We use stock returns, prices, capitalizations, and dividend information retrieved from stock exchange gazettes and records. Our unique dataset has never been examined in an asset pricing context and offers a genuine out-of-sample perspective on stock return predictability. We find that the majority of anomalies cannot be successfully confirmed in this unique out-of-sample setting. When considered on a standalone basis, eight anomalies report positive alphas at the conventional 5% confidence level using single hypothesis tests. However, only three anomalies, constituting just 10% of our sample, survive the more stringent hurdle when we consider a multiple testing framework.
This practical challenge is exacerbated further when attempting to construct and transact a short portfolio of micro-caps. Furthermore, some of these anomalies, such as the short-run residual reversal, require high portfolio turnover and do not work for extended holding periods. Overall, even if some anomalies genuinely exist, they may not matter from a practical investor perspective. The major limitation of our study is data quality issues inherent in the long-term SEM dataset. First, our dataset is limited to several simple features, such as prices, returns, and dividend yields, and does not include financial statement-related variables. Hence, we are not able to verify the extensive universe of accounting anomalies in the literature. Second, our data originates from old historical sources; thus, it may potentially contain record-keeping inaccuracies and omissions.
We acknowledge this and expect that the random errors in the dataset should cancel each other out. Our findings provide new insights into asset pricing in stock markets. They cast doubt on the validity of the return predictive signals identified in the anomaly literature. Notably, the findings also matter from a practical perspective. Our results indicate that quantitative anomaly-based stock-picking strategies should be treated with caution. The principles that many of them rely on, as well as their future profitability, may be far from reliable. Future studies should continue to focus on separating the wheat from the chaff in the anomaly literature. The development of new methods may help to cope with p-hacking and recognize genuine anomalies in asset pricing.
Acknowledgements The authors are grateful to  Jean-Philippe Bouchaud ,  Mardy Chiah ,  Taufiq Choudhry ,  John Fan ,  Richard Grossman ,  Bin Li ,  Mingyi Li ,  Benjamin Liu ,  Andrew Odlyzko ,  Mikhail Samonov ,  Terry Walter ,  Tingxi Zhang , and  Angel Zhong , as well as to the participants of the 35th EBES Conference in Rome, whose valuable feedback and constructive comments have significantly improved the quality of the paper. We also thank  Yolanda Wang  for her contribution to this study. The remaining errors (if any) are the responsibilities of the authors. Adam Zaremba acknowledges the support of the  National Science Center of Poland  [grant no. 2019/33/B/HS4/01021 ].
The model to reversal obtain the residuals is estimated based on months t-36 to t-1. 25 LTREV Long-term reversal DeBondt and Thaler (1985) Total return from t-60 to t-13. 26 MVCH Market value change Gerakos and Linnainmaa (2018) Log-change in market value from t-36 to t-1. Panel G: Other 27 CEI Composite equity issuance Kumar et al. (2008)84)6)Change in logarithms of market value from t-60 to t-1 minus the total log-return from t-60 to t-1. 28 AGE AgeBarry and Brown (1984);Jiang et al. (2005),Kumar et al. (2008)Number of months since the first coverage in the database. (continued on next page) N. Cakici et al.
This minimizes the number of searches to be carried out and double counting. These search strings are quite precise in order to focus on environmental risks and exclude other climate change-relevant topics that do not deal with risk management concepts. Table  A. 2 provides an overview of the associated number of search results. The search is conducted on the three electronic databases EBSCOhost, Emerald Insight, and ScienceDirect, whereby for EBSCOhost the sub databases "Business Source Complete" and "EconLit with Full Text" are used. The use of these three different databases, in turn, ensures the holistic and comprehensive nature of the research and is intended to prevent subject-relevant literature from not being recorded in the search due to limitations of individual databases. Using suitable inclusion and exclusion criteria, the results of the search are filtered according to their relevance.
In a broader view, banks take various approaches to cover climate risk management in the investment process, including carbon footprint analyses of portfolio firms and reduction of portfolio footprint as well as analyses and reduction of stranded asset risk. Still, the two mostly used approaches, analyses of carbon footprints and stranded asset risks, have been employed by less than 40% of interviewed investors. Other approaches are portfolio diversification, ESG and ESG rating integration, firm valuation models, and climate risk hedging. In order to directly assess potential impacts of climate risk on portfolios, investors rely on carbon footprint measurements, return impact measurements of climate risk, scenario analyses, and stress tests considering climate scenarios. The data used to assess the carbon footprint of portfolio firms is derived from corporate disclosures (self-collected), private reporting channels, the MSCI ESG database, or the Carbon Disclosure Project database  (Solomon et al.
Assessment Approaches As reported by  Krueger et al. (2020) , research addressing general risk management approaches mainly focuses on portfolio strategies (hedging, diversification) and risk measurement methods (carbon footprint, stranded assets, ESG rating, and firm valuation). Regarding risk measurement methods and specifically the carbon footprint,  Görgen et al. (2019)  construct measures of carbon risk. The authors use four ESG databases to construct the "Brown-Green-Score" as an advanced approach to carbon risk. The Brown-Green-Score includes three indicators describing a firm's sensitivity to carbon risk (policy). This score is used to compute the "Brown-Minus-Green" portfolio that reflects the global market carbon risk. The "carbon beta" of a firm describes the firm's sensitivity to the global market carbon risk.
Andersson et al. (2016b)  and De Jong and Nguyen (2016) construct measures of low carbon intensity to foster portfolio decarbonization. These low-carbon indices modify the weighting of index components that exhibit high carbon intensity. The carbon intensity for the portfolios is defined by carbon footprints extracted from the MSCI ESG database (originally collected by the Carbon Disclosure Project). The outcomes of the studies show that the low-carbon index-tracking portfolios achieve a reduction in carbon emissions of 50-60%. Both studies find no difference in the risk-adjusted return between their low-carbon portfolios and a benchmark portfolio. For their part,  Engle et al. (2020)  consider the possibility of carbon emissions pricing and report an outperformance of the low-carbon portfolio compared to its benchmark.
Finally, we discuss the results on environmental risk management, focus on current challenges, and put forward potential alleys of further research. The procedure of the systematic literature review in this work is to be reflected critically. By restricting the research to three databases and the exclusive use of English search terms and results, a holistic nature of the investigation cannot be guaranteed. By formulating selection criteria and limiting them to ranked journals, further filtering of the results also eliminated potential research. In order to achieve a scope of the study as holistic as possible, a snowball principle research was carried out in addition to the systematic literature review. While employing both research processes to select the final set of suitable and relevant articles, a subjective assessment of the journal articles cannot be ruled out.
, but] there are signs that this is beginning to change. " Later studies until 2007 comprise the analysis of the performance of ecoefficient funds  (Derwall et al. , 2005; Hoti et al. , 2007) , corporate governance-related issues  (Cowton and Thompson, 2000; Thompson and Cowton, 2004) , and research on the linkage between financial and environmental performance  (King and Lenox, 2001). 3. The VHB JOURQUAL 3. 0 ranking was conducted in 2012. The webpage states that the ranking does have a correlation of 0. 66 and 0. 70 with the British Academic Journal Quality Guide and Scimago Journal Rank, respectively. See https://vhbonline. org/vhb4you/vhb-jourqual/vhb-jourqual-3 (accessed 5 February 2020). 4. The result of the systematic literature research depends heavily on the keywords used, the search strings formulated from them, and the databases employed.
This becomes clear when looking at search results per database in Table  A. 2. While a total of 45 relevant articles could be found via EBSCOhost, the identical searches at Emerald Insight and ScienceDirect provided only 6 and 13 significant publications, respectively. 5. The study by  Hong et al. (2019)  is published in a journal outside of this ranking, but nonetheless comes with an 2018 impact factor of 1. 949. 6. Both interviews are conducted with analysts and investors, but differ in the number of interview partners (19 vs. 439) and structure of the interviews (unstructured and qualitatively vs. structured and quantitatively). 7. See also  Düsterhöft et al. (2020)  who show that energy utilities do not disclose climate-related risks at all in the risk section of the note to the annual financial reports for a sample from 2007 to 2017.
Acknowledgments We thank  Les Oxley  (the editor), an anonymous reviewer, and  Sebastian Utz  for their helpful comments on an earlier version of this study. Part of the work has been conducted during  Thomas Walther 's research time as an Assistant Professor at the  Institute for Operations Research and Computational Finance, School of Finance, University of St. Gallen. This publication is part of  SCCER CREST (Swiss Competence Center for Energy Research) , which is supported by  Innosuisse (Swiss Innovation Agency). Thomas Walther  is grateful for the funding received for conducting this research.
Instead of that, the dental probe has a sharp end. Since the data collected for the analysis of dental devices are not numerical, the linguistic fuzzy scale has been used to convert the obtained dataset to numerical values. As a result, the linguistic fuzzy scale is used as shown in Table  2. The Yager Index ðYI = 3na + ðb/3ÞÞ of the fuzzy number F = ðna, n, n + bÞ is applied to defuzzify the linguistic data. In this study, the input data are also treated as fuzzy numbers. This took into account the uncertainty contained in the data, which could give more valuable ranking results by considering the fuzziness. After the defuzzification, the PRO-METHEE process is then employed for the ranking of dental devices with minimizing cost, disadvantages, dose, and size of the alternatives and maximizing the advantages and comfortability.
To test the results obtained with Fuzzy PROMETHEE for the evaluation of the dental devices, the fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) technique is used, which is also an another successfully used MCDA technique. The TOPSIS technique has been defined by Hwang and Yoon in 1981  [30]. It evaluates the alternatives based on their distance to the positive ideal solution (A + ) and the negative ideal solution (A -) under conflicting criteria. The positive ideal solution is the combination of the best values of each criterion, while the negative ideal solution is the combination of the worst values of each criterion. It applies to the selection problems with the numerical dataset. Furthermore, relative closeness to the positive ideal solution (R i ) should be considered as the most preferred alternative with this technique.
Study population A prospective observational cohort study was conducted using nationwide data from a French SMO platform (https://www. deuxiemeavis. fr/). Patients were included if they resided in France or an overseas French territory and submitted a request for an SMO through the platform between 1 and 2016 and 1 October 2020. All participants consented to the use of their data for research purposes. Mental health disorders were not subject to a SMO in accordance with the existing literature  [12].
Data collection The following variables were recorded for all case submitted: disease, class of diseases, expert name, level of complexity (simple or complex), divergent or convergence of the SMO, and time duration of the review. For each patient, the age, sex, region of residence, employment category, and whether the GP was aware that the patient was seeking an SMO or not, were collected. The dependent variable used for statistical modelling was convergence or divergence of both diagnosis and treatment between the first and second opinion. For each case, if the diagnosis and treatment were fully consistent between the first and second opinion, the dependent variable was coded as convergent. In all other cases (diagnostic and/or therapeutic divergence) the dependent variable was coded as divergent.
Statistical analysis Quantitative variables were presented as median and 1st and 3rd quartiles (Q1, Q3) if non-normally distributed, and qualitative variables were presented as number (%). We compared patient characteristics between convergent and divergent SMO. Groups were compared using the chi square or Fisher's exact test as appropriate, and the Mann-Whitney U test was used to compare the time required to prepare the SMO. A planned exploratory analysis was performed to compare simple and complex cases. Logistic regression was used to estimate the probability of a divergent SMO according to patient characteristics. Missing data were handled by multiple imputation with m = 100 imputations. All analyses were performed using R version 4. 0. 2 (www. R-project. org). Multiple imputation was performed with the mice package (Stefan Van Buuren).
Ethical considerations The database used for this study was fully anonymous. The SMO platform received special authorization from the French national commission for data privacy (Commission Nationale Informatique et Libertés, CNIL) when it was launched (at the beginning of 2016), in compliance with a legislative requirement that has since been lifted. Patients provided informed consent that their anonymous data may be used for research and quality improvement purposes. No additional approvals were required according to the French legislation. According to the ethical rules of the SMO platform scientific council, an SMO expert was not allowed to be directly in charge of the patient.