dataesr/scientific-paragraphs-categorization huggingface dataset:
Program Participants as of February 2020 In The operational rules of DG-ECHO imposed a number of the central design features of the ESSN. One of these, as discussed in Chap. 5, was the multiagency nature of the program. While DG-ECHO, acting in the name of the FRiT trust fund and its steering committee, played the role of the funding agency, implementation was delegated to the WFP in partnership with the TRC. These, in turn, worked closely with Turkish authorities at both the national and local levels. Although it remained distinct, the ESSN was embedded in the infrastructure of Turkish national social assistance. The National Integrated Social Assistance System (ISAIS), managed by the Ministry of Family, Labor, and Social Services (MoFLSS) became the national database of all ESSN participants.
" (DG-ECHO 2019: 13)  Even the initial scaling-up of the program was complete, the same assessment estimates that the "exclusion error" attributable to the use of the demographic criteria was on the order of 5% (  14 ). In principle, it might have been possible to overcome this imperfect targeting through direct inspection. For Turkish citizens, as we saw in the case of R, eligibility is based on individual assessment using both nationally determined objective criteria-information in the ISAIS database and the "poverty score" calculated for each family-and locally organized onsite inspection carried out by the district SASF. An analogous procedure of on-site inspection was included in the design of the ESSN, with local SASFs once again tasked with carrying it out. An advantage attributed to this system by national officials, as we noted in the previous chapter, is the local knowledge provided by this decentralized system.
The high institutional startup costs of the system established a strong dynamic of path dependence, leading to this design being maintained even after the program's size had stabilized. Its sustainability should EU resources be withdrawn, finally, remains in question. At any scale, information about the situation and needs of individuals is among the critical resources required for effective targeting. The cursory nature of home visits carried out by untrained inspectors in R contributed to inadequate information there. In the case of ESSN, officials interviewed claimed that training was provided for SASF inspectors, but the lack of any evidence for this in R suggests considerable local-level variation. A distinct set of challenges were unique to ESSN. The impossibility of knowing what, if any, assets migrant families might be able to access from Syria and the refusal of the Turkish government to allow implementing agencies full access to personal information contained in national databases combined to hinder any systematic in-depth evaluation of impact at the individual level.
This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e. g. , Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the 'best' model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin's spot and futures prices.
html, accessed 19 February 2021). Investment managers are doubtful with respect to the forecastability of Bitcoin, much like currency forecasters. For instance, the Chief Investment Officer of Citi Private Bank, David Bailin, reminds that "to get an exposure to Bitcoin, if you do not own the actual Bitcoin, any such fund or structure can be a very, very inefficient way to do that". (See Yahoo Finance (2021) at https://autos. yahoo. com/unstoppable-trends-better-bitcoin-long-142808181. html, accessed on 17 March 2021). Bitcoin's private key custody problem has essentially three practical solutions: (i) kept on an exchange, it constitutes a "honey pot" for hackers (recall the Magic The Gathering Online eXchange (Mt. Gox) where 650,000 BTC were lost); (ii) kept in banks, it undermines the 21 million Bitcoins scarcity by paving the way for securitization all over again (recall the 2008 sub-primes crisis); (iii) kept on a hardware wallet (such as Trezor or Ledger), there is the risk of theft and physical harm (Ledger's marketing database was famously hacked, containing the clients' private addresses).
BlackRock, the world's largest asset manager "started to dabble" in BTC. Tesla invested $1. 5B in BTC and announced plans to accept crypto payments. North America's first Bitcoin Exchange-Traded Fund (ETF), the Canadian-based Purpose Bitcoin, amassed $421M in Asset Under Management in its first two days of trading) and academics, this study focuses on machine learning modeling. We illustrate some features that could explain the dynamic behavior of Bitcoin's price by taking into account the non-stationary behavior of the data in place of classical parametric modelings (ARMA, related-GARCH, VAR modelings). For recent extensions in econometrics, see for instance Abedifar et al. [15] , Ahelegbey et al. [16]  regarding correlation networks, Billio et al. [17]  for multivariate models such as Granger Causality, or Baumöhl  [18]  for connectedness à la Diebold and Yilmaz approaches for crypto-assets and exchanges  (Dahir et al.
Our research based on these data analytics techniques focuses on (i) their capability to fit a data set. We observe that the Adaboost method and the random forest processes are the winners inside a competition based on six competitors. (ii) Regarding their predictability power, we observe a high variability of the results depending on the period on which we work and the input data used for the training. Thus, the question that emerges from this work is the possibility of predicting the spot or the future for this cryptocurrency against luck or uncertainty, without obviously calling into question the methodology used. Central banks convey this idea of Bitcoin being an extremely inefficient way to process transactions, highly speculative, and used mainly for the financing of illicit activities.
In the view of the Treasury Secretary Ms. Janet Yellen, Central Banks Digital Currencies (CBDC) should be the only solution for printing digital money (through its proprietary core ledger). This view is largely echoed in finance journals. To cite a few, Foley et al. [28]  estimate that around $76 billion worth of illegal activity per year involve Bitcoin (46% of bitcoin transactions), which is close to the scale of the U. S. and European markets for illegal drugs. Among other "Silk Roads" dismantled by the FBI regarding drug trafficking, the risks of "black e-commerce" are heightened by the anonymous file server Tor (The Onion Router), and by secret cryptocurrencies' operational design such as Zcash or Monero. Spanning daily data from 13 January 2015, to 31 December 2020, our analysis is based on several steps to analyze the main drivers of the Bitcoin currency.
Regarding the central methodological and empirical contributions, our paper stresses the key ingredients to make a 'good' machine learning model in quantitative economics, a. k. a: (i) proceeding to an excellent data collection (our 'financial markets' approach as opposed to a 'blockchain approach' feeding the models with technical and non-stationary data) while controlling for low multi-collinearities; (ii) assessing using a wide array of visualization tools (clusters, maps, diagrams) the main finding of segmentation of Bitcoin concerning traditional financial and commodity markets (e. g. , Bitcoin reacts mainly to the information content of other cryptocurrencies); and (iii) favoring either the AdaBoost or Random forest algorithms as predictors of the Bitcoin spot and futures prices, which allows us to implement trading strategies; and to open the debate on the forecasting accuracy of Bitcoin.
Some references are Phillip et al. [44] , Mensi et al. [45]. There exists a large literature on the bubble behavior of Bitcoin, which has been observed since 2014. We can cite, among others, Su et al. [46] , Guegan and Frunza  [47] , Geuder et al. [48]. At the same time, looking at the evolution of the price on specific periods, authors try to show that Bitcoin can be considered a commodity (the idea is that this cryptocurrency corresponds to a limited resource), e. g. , Guesmi et al. [49] , or as gold  (Dyhrberg [50] ). In many cases, the authors are interested in discussing the potential (or not) of Bitcoin for diversification, (e. g. , Polasik et al. [51] , Bouri et al. [52] , Selmi et al. [53] ). Some papers use high-frequency data and are interested in shock transmission: using realized volatility of the cryptocurrencies, some authors detect asymmetries in shock transmissions between the cryptocurrencies and traditional assets, see, for instance, Kurka  [54]  and references therein.
For both classification and regression purpose, the output f of the additive tree model is a weighted sum of all the tree outputs as follows: f (x) = p ∑ j=1 ω j f j (x), (5) where ω j ∈ R is the weight associated to tree j. The previous formulation is very general and includes some popular models as special cases, like random forests. This additive tree model is widely used in realworld applications and appears to be the most popular and influential off-the-shelf classifier. It can cope with regression and multi-class classification on both categorical and numerical datasets with superior accuracy. In these ensemble methods, several weaker decision trees are combined into a more robust ensemble. A bagged decision tree consists of trees trained independently on data that is bootstrapped from the input data.
Dataset Quality: A 'Financial Markets' Approach As opposed to a 'Blockchain' approach that would add, as an input to the machine learning models, non-standard econometric variables (e. g. , hash rates, mining difficulty, block size, block version, number of transactions, the time between blocks, block size votes) about which we have neither theoretical grounding nor economic meaning virtually, we favor, in this paper, a 'Financial Markets' approach based on price relationships between various asset classes (e. g. , stocks, bonds, foreign exchange, commodities, as well as other cryptocurrencies). Indeed, Koutmos  [104]  argues that Bitcoin prices, despite their seemingly attractive independent behavior relative to economic variables, may still be exposed to the same types of market risks which afflict the performance of conventional financial assets.
They are listed in Table  2. They provide us some information regarding volatility, clustering, and extreme behavior. For instance, notice that the maximum Bitcoin spot price recorded is equal to 29,026$ by the year-end of 2020. We introduce a cluster analysis to find groupings between all these variables as a classic unsupervised learning technique. In cluster analysis, data are partitioned into groups based on some measure of similarities or shared characteristics. Clusters are formed so that objects in the same cluster are very similar, and objects in different clusters are very distinct. Louvain clustering detects communities in a network of nearest neighbors. More precisely, the Louvain clustering algorithm converts the dataset into a graph, where it finds highly interconnected nodes (Blondel et al.
[105] , Lambiotte et al. [106] ). When applied to this dataset, this method confirms the existence of at least 11 different clusters. The clustering of the dataset is represented graphically in Figure  3. This data projection by color regions reveals that cryptocurrencies (highlighted in yellow) tend to share common data attributes, especially Bitcoin with Monero, Tether, and Stellar. It also features several relatively segmented financial securities: international stocks, bonds, exchange rates, and commodities. This preliminary data analysis helps us detecting patterns depending on the asset class under consideration (e. g. , stocks, bonds, commodities, or cryptocurrencies) that will be further assessed by the machine learning models.
Sub-Samples Decomposition We specify the different periods on which we analyze all the datasets. • Different sets of data: To predict the Bitcoin price, described as a cryptocurrency without clearly established fundamentals, we follow an approach based on financial markets. That is to say, we include in the pool of predictors several underlying and analyze their contribution in explaining Bitcoin. As shown in Table  1 , we retain 56 variables (besides the Bitcoin price) that belong to the following categories: (i) cryptocurrencies, (ii) stocks, (iii) bonds, (iv) foreign exchange rates, and (v) commodities. To predict Bitcoin following the schemes proposed in Section 3, Ŷt = f (X t ), we proceed step-by-step for the choice of the variables using up to five different vectors X t : (1) X t composed of sixteen cryptocurrencies, (2) X t composed of eleven stocks, (3) X t composed of four bonds, (4) X t composed of four foreign exchange rates, (5) X t composed of twenty-one commodities, and (6) X t is composed of fifty-six variables.
15, which is inferior to that of Stocks (0. 36) and Commodities (0. 39). From this first round of results dedicated to Bitcoin spot prices, we obtain a similar conclusion as Klein et al. [109] : "Bitcoin as an asset does not resemble any other conventional asset from an econometric perspective. " Thanks to this result, we begin to specify the characteristics of this asset. Next, if we use the whole database of 56 series, we acknowledge that the smallest forecast errors are reached in this setting: MAPE all = 0. 11 < MAPE cryptocurrencies = 0. 15; RMSE all = 19. 18 < RMSE cryptocurrencies = 23. 42. Thus, we note that over this period, using an extensive set of information improves the forecast of the Bitcoin, with the Adaboost algorithm, in the sense of the forecast error. The regression with a Ridge regularization giving a very indirect result.
Robustness Checks Full sample results convey the idea that using cryptocurrencies only is enough to predict Bitcoin. Adding other variables from the financial markets deteriorates the forecasting accuracy. In Section 5, we have established that the Bitcoin spot's predictive strategies and futures are not the same. On the one hand, for the Bitcoin spot during the whole sample (13 January 2015 to 31 December 2020), we have identified that cryptocurrencies are segmented from traditional financial and commodity markets. On the other hand, from the creation of Bitcoin futures in December 2017 until the end of our database in December 2020, it appears that CME's Bitcoin derivatives instrument is better explained by stocks and commodities. Maybe because the CME also heavily trades futures for stocks and commodities (see the discussion on the birth of the Bitcoin futures market in Baur and Dimpfl  [102] ).
Year 2019 We narrow down our analysis to the latest trading year in our dataset, a. k. a the year 2019, which did not bring bulls run or rallies in the price path of Bitcoin, which stayed at around 8000$. It is also far away from the next halving period, which should occur in May 2020 at current hash rates. The mining premium is halved for every 210,000 transaction blocks. About 50 bitcoins were generated every 10 min or so during the first four years, this value increased to 25 Bitcoin on 28 November 2012, and to 12. 5 Bitcoin on 9 July 2016.
Depending on the dataset we use, the best RoR is provided by (i) an AR(1) for cryptocurrencies (25. 55%), (ii) an AR(1) for stocks and bonds (25. 55%), (iii) an ANN using commodities (26%), (iv) by Ridge using all the variables (30. 47%). There is no segmentation since the best results are obtained as we add financial variables. The cryptocurrency world is not the best trading strategy. ANN using commodities, we have the same result as all (26-26. 88%). Our results stand in sharp contrast with Atsalakis et al. [64]  (PATSOS neuro-fuzzy = 37. 34%): although the ML algorithms deliver high forecast accuracy, they do not necessarily convert into money (candlestick patterns can be tricky to non-experienced investors). For sensitivity analysis, we run again the trading strategy with coins bought in January 2019 and sold at the end of our study period (aka, December 2020).
Conclusions Machine learning algorithms attempt to find natural patterns in data to enhance decision-making. Machine learning typically prescribes a vast collection of high-dimensional models attempting to predict quantities of interest to solve problems in computational finance while imposing regularization methods. In a seminal paper, Zhao and Hastie  [8]  warns us against the pitfalls of 'black-box' machine learning models and urge us to make proper use of (i) a good predictive model, (ii) a sound selection of the dataset, and (iii) visualization tools to ensure the quality of the research work. This paper addresses the following central research questions: How far the is use of machine learning techniques useful to forecast daily movements of the price of Bitcoin? How strong is the relationship between cryptocurrencies and traditional financial assets? How to perform a trading strategy based on cryptocurrencies? We overcome the risks of machine learning in predicting Bitcoin spot and futures prices by documenting: (i) either the AdaBoost or Random forest algorithms perform as the 'best' machine learning models among the six considered and could be implemented in a banking institution's internal computing system for Bitcoin forecastability.
(ii) A financial market approach favors price relationships between asset classes (cryptocurrencies, stocks, bonds, foreign exchange, and commodities) for the dataset's quality. (iii) We implement a myriad of visualization tools (e. g. , Louvain clustering, self-organizing map, t-distributed SNE, Sieve diagrams, and multidimensional scaling) to strengthen our findings. During the period under study, the key takeaway is that Bitcoin appears as a problematic asset to forecast, subject to frequent price swings, as highlighted by the latest end-of-year 2020 run-up. From our empirical results, Bitcoin appears segmented from traditional financial and commodity markets. It seems to react more to the information content stemming from other cryptocurrencies, enhancing the forecast accuracy of Bitcoin.
[93]. 5. k-Nearest-Neighbors (k-NN). k-NN categorizes objects based on the classes of their nearest neighbors in the dataset. Distance metrics are used to find the nearest neighbor. The k-NN algorithm searches for k closest training instances in the feature space and uses their average prediction. k-NN predictions assume that objects near each other are similar. When mobilizing k-NNs, memory usage and prediction speed of the trained model are of lesser concern to the modeler. The k-NN regression method is probably the simplest non-parametric method we can propose. It works as follows: given a value for k and a prediction point of x 0 , k-NN regression first identifies the k training observations closest to x 0 , represented by S 0.
Acknowledgments: For useful comments on earlier drafts, we thank  Sofiane Aboura  and  Laurent Gauthier. The authors also wish to thank conference participants at the  University of Geneva's MAF (Mathematical and Statistical Methods for Actuarial Sciences and Finance ) in November 2020, at the 1st Research Day of the  Macro-Finance Axis (Research Lab LED, University Paris  8) in April 2021, as well as at the  RIFED Global Virtual Conference on Fintech, Business Ecosystem & Economic Development in June 2021.
Hence, country-specific factors such as the legal environment seem to matter for explaining announcement effects of equity rights offerings. We use an international dataset to examine these cross-country differences and expand the literature on equity rights offerings to an international context. In our study, we conjecture that the discount is only relevant in countries with a ceteris paribus higher information asymmetry between (uninformed) shareholders and management, as signalling is necessary and possible in those environments. Thus, the discount should be an essential factor to explain announcement effects of equity rights offerings in those countries. We employ the level of capital market transparency as a proxy for information asymmetry on the country level. As institutional factors have been largely documented to affect financing decisions (Fan et al.
Sample description We obtain an international database of 6458 equity rights offerings. We lose observations due to several data requirements. First, for 587 observations, we do not have sufficient stock data to calculate cumulated abnormal returns. Furthermore, we drop 11 observations with a cumulated abnormal return of lower than -100% and 67 observations with a cumulated abnormal return of higher than 100%. 3 This leaves 5793 observations from 86 countries between 1996 and 2011 for our event study. Unfortunately, we cannot use all of those observations for our following regressions due to some data constraints. For our regressions, we drop all observations with a discount smaller than zero. A negative discount indicates a potential measurement error, since negative discounts in an equity rights offering seem implausible.
In line with our event study, we continue to exclude all observations with CAR smaller than -100%. We also delete equity rights offerings for which we do not have sufficient data for all control variables. This reduces our dataset substantially and leaves 1071 observations for our regressions. Table  1  breaks down our dataset in detail. We obtain data on announcement dates and firm-specific variables from Capital IQ. We add country-specific data from various external sources, such as the World Bank, the Global Competitiveness Report, or established papers (Doidge et al. 2007;  Djankov et al. 2008). We assign firms to countries via the location of the headquarter. We detail the sources for these country-level variables in Table  3.
Descriptive statistics Table  3  reveals some interesting aspects about our dataset. The median of Discount amounts to 30. 52%. The median of ROA is -2. 79%, which already indicates that firms announcing a rights offering might on average suffer from poor firm quality, which is in line with the prediction by Myers and Majluf (1984). Furthermore, 45. 29% of all offerings are underwritten. The median of Relative Size amounts to 26. 16%, which shows that the offerings in our sample have a quite high offering size. To further characterize our dataset, we calculate the correlation coefficients for all variables based on the data included in our main regression. Table  4  presents the correlation matrix. Some interesting findings emerge. At a first glance, there seems to be no direct connection between our proxies for capital market transparency Market Development or Governance Rating and CAR.
Hence, the decision to underwrite an equity rights offering seems to generate ceteris paribus lower announcement effects. Although the coefficient of Underwritten is only significant in one model, its sign is negative across all regressions. This result is in contrast to the theoretical model of Heinkel  and Schwartz (1986), which predicts a positive effect for underwritten offerings. We trace this difference back to the fact that we use an international dataset of rights offerings. As a certain floatation method might be especially popular in a certain country (see, e. g. , Bøhren et al. 1997; or Eckbo and Masulis 1992), the coefficient of Underwritten might result from particular country-specific effects. Our results are also important in terms of economic significance. For instance, in countries with low values of Market Development, a one percentage point increase in the discount leads to a ceteris paribus 0.