dataesr/scientific-paragraphs-categorization huggingface dataset:
We expect the volumes of credit insurance to be particularly significant in firms with liquid CDSs as liquidity enables debtholders to easily create and unwind insured positions. A potential objection to our argument is that the activity in liquid CDSs may mostly reflect offsetting naked positions held by market dealers, rather than credit portfolio hedges. To address this critique, we compute the ratio of CDS net notional amounts outstanding to total debt for all North American high-yield reference entities included in the DTCC list at December 2008. We then compare average and median values of this ratio for the reference entities whose CDSs were also included in the CDX. NA. HY index at that date, and for the ones not included in the index. Given the lack of data on naked and covered CDS positions, the net notional amounts outstanding provided by the DTCC Warehouse CDS Data represent the best available proxy for covered CDS volumes.
Industry dummies, defined according to the first-digit SIC code, are also added to capture any residual industry-related effects. Information on recovery rates, seniority, size, and number of the securities in default is taken from the Moody's Default and Recovery Database. Firm-specific variables are obtained from the 10-K filings, while industry characteristics are taken from Compustat. NOTES: This table presents OLS estimates of determinants of recovery prices for out-of-court debt workouts according to a baseline model and four additional specifications with refined proxies for the existence of empty creditors. Seniority class equals 0 if the issue in default is secured, 1 if it is senior unsecured, 2 if it is subordinated. Median industry Q is the median of the ratio of market value of the firm to book value, for all firms in the three-digit SIC code of the defaulted firm.
18. Demiroglu and James (2013)  document that traditional bank loans are easier to restructure out of court than loans from institutional lenders. The notes to the SEC financial statements report the overall amount of bank debt, the proportion of bank debt which is secured, and how bank loans are divided between revolving lines of credit and term loans but do not provide information on lenders. To better investigate the structure of bank debt, we restrict our attention to the loans covered by the DealScan's database, which includes 99. 3% of all bank loans in our sample. The following indicators are computed to proxy for loan complexity and lenders' coordination problems: (i) proportion of bank debt in the form of institutional term loans (B-term, C-term, or D-term), which are designed to be placed with nonbank institutional investors, such as investment banks, pension funds, hedge funds, insurance companies; (ii) number of lead arrangers, that is, number of banks that establish a relationship with the firm and organize the loan; (iii) number of participants, that is, number of banks and/or institutional investors that join the lead arrangers to fund part of the syndicated loan.
To assess whether the distressed conditions of bank lenders during the financial crisis might have affected their ability to refinance their borrowers and, ultimately, might have played a role in their restructuring process, we compute measures of both profitability and profitability shocks, that is, the annual return-on-assets (ROA) and the change in annual ROA, of the lead arranger at the end of the fiscal period prior to the borrower's default. Average values are calculated when there are multiple lead arrangers. Other indicators of distress, such as Tier 1 capital ratios or net charge-off ratios, were considered, but had to be excluded due to the lack of data for a significant number of the lead arrangers in our sample. Data on banks' ROA are taken from BankScope. Table  D1  reports mean and median values of the bank debt characteristics for firms that restructure out of court against those that file for bankruptcy (Panel A for the entire sample, Panel B for reference entities only, and Panel C for nonreference entities only), and for reference entities against nonreference entities (Panel D).
Dataset We consider both American (ticker OEX R ) and European (ticker XEO R ) LEAPS R options written on the S&P 100 TM index traded between the 2 January 2008 and 29 May 2015, covering 1,826 days, spanned over the eight years. 19 We collected ask and bid prices, open interest, and volume together with the contract specifications (strike, maturity date and exercise style). Additionally, we used the Libor as a proxy of the risk-free rate. We collected the last prices of the S&P 100 TM index and the US dollar Libor for one week, one month, three months, six months and one year. Since S&P 100 TM index is a dividend-paying asset, we proceed by estimating the dividend yield over the life of each contract. As in  Linaras and Skiadopoulos (2005) , we imply the dividend yield for each trading day and maturity from the at-the-money put-call parity.
To perform the analysis, we select from the VentureXpert database a random sample of 1000 CVC investments done by US public corporations during the time period 2002-2012 (out of a full sample of 2588 CVC investments). Using the Factiva database, we then manually collect information on which of these investments have been publicly announced and at which exact date. We find that about 2/3 of these investments have been publicly announced, while 1/3 not. Half of the announcements occur exactly on the day the agreement is signed. Two third are announced within an event window of [-1,+1] days of the agreement date. Interestingly, we observe little differences between the sample of announced investments and unannounced investments; the only statistically significant difference relates to seed investments, as such investments are less often present in the sub-sample of un-announced investments.
Data and Sample Statistics To As a next step, we identify which of these investments were publicly announced and, if they were, on which day. To this end, we use the Factiva database to search each of our 1000 investments for whether it was announced either by the CVC firm (or program), parent company or start-up company by searching for their names in Factiva. Our search window is six months prior to the investment date and three months post investment date. Overall, less than 3% of the cases we have identified involve multiple announcement dates. In the event of multiple announcement dates, we use the earliest date to the investment date as announcement date. We consider an investment is announced if any article in the Factiva database mentions the investment. We do not use any coded algorithm to search for announcements.
We collect accounting data and stock prices information of the parent companies from Compustat and CRSP databases. This includes information on market value of the parent company (measured as the product of stock price and number of common shares outstanding), current assets, total assets, capital expenditure, cash, long term debt, current liabilities, net income, property plant and equipment, sales, working capital and research and development. To measure information asymmetry of the parent company, we create a composite index following  Maskara and Mullineaux (2011). The index is constructed using six common information asymmetry benchmarks. The first is analyst forecast errors, measured as the absolute difference between analysts predicted earnings and actual earnings. The second is dispersion of analyst opinions, measured as the standard deviation of analysts forecasts of annual EPS in the last month prior to earning announcement.
Analyst forecast errors and dispersion of analysts opinions are standardized by share price and collected from IBIS database. Third, is volatility of abnormal returns around the earnings announcement, measured as the standard deviation of 3-days abnormal returns around earnings in the five years preceding deal announcement. Fourth is residual volatility measured as the standard deviation of market adjusted daily stock returns in the year of deal announcement. Fifth, is parent company age, measured as the number of years since the first firm observation in Compustat. Sixth and final is bid-ask spreads measured as the average ratio of the difference between the daily bid and ask closing prices to the midpoint of the bid and ask closing prices. This measure is also similar to  Chung and Zhang (2009).
---TABLE 5 ABOUT HERE ---Next, we examine the impact of the structure of syndicates, by controlling for the possible heterogeneity in the syndicate members. Most of the venture capital is provided by independent VC firms that are not affiliated to a specific corporation or financial institution. These different types of VC players are likely to have different incentives schemes in terms of information disclosure policy. For instance,  Gompers (1996)  shows VC at times they need to raise new funds. This is likely to affect also the way the amount of information they disclose to the market on their current investments. To capture specificities of independent VC firms, we construct a variable that corresponds to the fraction of independent VC firms participating in the syndicate. In the VentureXpert database, they are categorized as fund type PRIV We then include this additional variable in our regressions, which we denote Independent VC Participating.
This is particular important when the corporate link is more distant, as it is the case for externally managed programs. Admittedly, the categorization of internal versus external program is difficult to realize. However, one simple way to perform this categorization is by comparing the name of the CVC program in the VentureXpert database with the name of the parent company. In case of a separate legal entity (thus, an external program rather than an internal business department), one expect two O structured as a separate legal entity. In Table  6 , we adopt this categorization and offer an analysis of the impact of the organizational structure of CVC programs on the disclosure probability. The constructed dummy variable is denoted External CVC. We find that externally managed programs lead to more announcements.
This is consistent with the idea that such programs have greater incentives to attract attention of the market and the parent company, as its relationship with top management is not as immediate as for internally managed programs. ---TABLE  6  ABOUT HERE ---Relatedly, the incentives to disclose may be driven by the degree of organizational complexity of the parent company. In less hierarchical structures, CVC managers can more easily communicate in an informal way to the management of the parent company. In more hierarchical structures however, informal communication may be more difficult. To shed light into this specific channel, we explore whether information disclosure is more likely when the parent companies has a more hierarchical organization. As proxy, we use the number of industries it is active by counting the number of industries reported in the COMPUSTAT Segment database.
We consider companies that are active in more industries STYPE = BUSSEG to be more likely to have more complex organizational structures. As second proxy, we use the same database to assess whether parent companies are selling STYPE = GEOSEG. In unreported results, we find that information disclosure on the CVC investment is more likely when the parent company is more diversified (more industry segments) and operates internationally. However, the result is only statistically significant for industry segments (at 5% level). Combined with the previous results, these findings strengthen the notion that announcements are more likely when communication with the parent company is less formal due to its organizational complexity.
examine our research question, we extracted from the VentureXpert database a random sample (i. e. , sampling without replacement) of 1000 investments done by corporate-affiliated US venture capital firms during the time period 2002-2012 from a pool of 2588 CVC investments extracted from the database. To constitute our random sample, we only consider CVC firms that are held by public parent companies, thereby excluding private ones. We only consider We use VentureXpert to collect investment-level information on deal characteristics such as round amount, round number, number of investors participating in the considered round, investment stage of development and investment date (i. e. , the date in which the contract is signed and reported in VentureXpert). We then manually search through the profiles of the CVC firms mentioned in VentureXpert and using other online sources to identify the ultimate parent company of the CVC firm or program.
The fact that about half are announced at the same time as the investment date in VentureXpert can also be attributed (at least in part) to the way information is collected for the construction of the VentureXpert database. Indeed, some of the information stems from public announcements. Presumably, VentureXpert then reports the announcement date as the investment date. However, this does not affect our analysis, since we are primarily interested in the announcement date and not the investment date reported by VentureXpert.
DATA In this paper we have examined the price of several flights through web pages devoted to booking and purchasing tickets. In order to include a sufficient number of flights and an in-demand route, we chose three routes: from Madrid to Paris; Madrid to New York; and Paris to New York, without stipulating a specific airport in the case of the latter destination. These routes consist of flights between the two capitals cities of the countries analyzed in this paper -France and Spain -and between the capital cities of these two countries and the high-demand destination of New York. Firstly, we selected the companies that offer the specific flights analyzed in this paper and chose seven flights for each route, operated by different companies. For the Madrid-Paris route, we selected only direct flights operated by Air France (two flights at two different times), Iberia (two flights at two different times), Vueling and Easyjet (two flights at two different times).
RESULTS The data is analyzed in terms of price level and price dispersion. To analyze the price level we used the average price available each day and with this information we developed analyses of variance (ANOVA). We used the coefficient of variation to measure price dispersion as this is the best way to make comparisons. To analyze the price average we have developed several analyses of variance (ANOVAs) in order to evaluate the differences in the average price with regards to the factors considered. In this case, we have examined two factors, each with two categories; the first factor is the country where the travel agency markets their product, that is, the geographical location. In this respect, we included Spanish and French travel agencies although the data were obtained via the internet.
included the Spanish travel agencies Barcelo viajes, Nautalia and El Corte Inglés, while the French travel agencies included were Havas voyages, Look voyages and Promovacances. The time period studied included flights departing on December 16, 2013 and returning five days later on December 21, 2013, in order to avoid peak holiday times and to obtain a representative sample of the off-peak season. The timeframe for the airfares in our database ranges from August 26 to two days before departure, because Nautalia, El Corte Inglés, Havas voyages and Look voyages do not sell flights any closer to the departure. Flight prices were monitored daily.
The assessment of the added clinical benefit of a new drug is always a comparative one; it is based on its comparison to an adequate comparator or treatment strategy/ies defined by HTA bodies, by using appropriate clinical endpoints, relevant to the main characteristics of the disease/condition to treat, the target population and the aim of treatment. Therefore, in the case of an irrelevant comparator, and/or a wrong choice of endpoints, or clinically irrelevant difference in OS, added benefit may not be granted. In the absence of OS data, lower added benefit may be granted if it is based on PFS data only. As mature data are requested whenever possible, interim analysis is generally not recommended, especially on PFS, but also on OS data if too premature. As REA should support clinical practice guidelines, data to support the potential place of the product in the treatment pathway (treatment after progression, possibility/effectiveness of subsequent treatments) and effectiveness in relevant patient subpopulations (slowly progressing versus fast progressing patients) are also of interest.
reA OF cANcer iMMUNOtHerAPies As stated, outcome data requested by HTA bodies to support reimbursement of anticancer drugs are based on final ("hard") endpoints coming from completed comparative phase 3 trials. In addition to OS (final endpoint), other patient-relevant endpoints are also assessed (such as time to symptomatic progression, symptoms, HRQoL). The recent arrival of new treatments that target identified functional genetic mutations ("targeted therapies") or PD-1/ PD-L1,2 axis ("immunotherapies") and their combinations have profoundly changed treatment strategies in cancers as they considerably improve patient survival, but have also raised new challenges in REA and decision-making process in Europe as compared to the REA of "classical" chemotherapies. In addition, recent regulatory initiatives to support accelerated clinical development and approval of cancer immunotherapies, based on intermediate or surrogate endpoints such as PRIME through the EMA, represent an additional challenge for HTA bodies and decision makers.
DiscUssiON The improvement in OS remains a gold standard for REA to inform reimbursement decisions for an anticancer drug. Intermediate endpoints such as response rate, duration of response, and PFS are not easily accepted as predictors of clinical benefit and assessed (only) in the absence of OS data. Surrogacy of these endpoints to predict OS depends on tumor type and stage of disease, as well as on the therapeutic agent used. Delayed responses and pseudo-progressions on some immunotherapies make this task even more difficult. With the current HTA mind-set, limited development programs that might be sufficient for an accelerated assessment and regulatory approval might not be sufficient to support reimbursement. A recent survey on reimbursement status of medicines granted conditional approval shows that HTA decisions still remain independent on the regulatory route of approval and consider only data available to support REA and not the regulatory approval pathway  (9).
We investigate these research questions for VC and buyout funds located both in the United States and internationally. In this paper, we use detailed deal-level and fund-level cash flow data coming from the Center for Private Equity Research (CEPRES) database. Our international sample covers 18,256 unique investments in portfolio companies done by 769 VC and buyout funds during the 1980 2009 period. Slightly more than half the investments are realized (i. e. , divested). Although portfolio companies are anonymized, the database enables linking portfolio companies to funds and funds to management firms. Previous versions of the data have been used by  Franzoni, Nowak and Phalippou (2012)  for buyout and  Cumming, Schmidt, and Walz (2010) ,  Cumming and Walz (2010)  and  Krohmer, Lauterbach, and Calanog (2009)  for VC.
Data Description We use data from the CEPRES database, which is unique in that it provides detailed information and cash flow data at deal and fund levels; other databases tend to provide data for private equity at either the fund level or the investment level only. CEPRES data are described in detail in  Franzoni, Nowak and Phalippou (2012). Several studies have used the database, including  Cumming, Schmidt and Walz (2010) ,  Cumming and Walz (2010) ,  Franzoni, Nowak and Phalippou (2012), and Krohmer, Lauterbach and Calanog (2009). Through its special data collection method (based on the so-called Private Equity Analyzer), CEPRES effectively anonymizes all information related to investments to meet the confidentiality requirements of the VC and PE firms that provide data to CEPRES. This means that third parties are not able to identify individual portfolio companies, funds or management firms.
This is crucial and eliminates the incentives for management firms to overstate the results they report to CEPRES. Lack of anonymity in other databases may result in overstating, partial reporting and back-filling of information, amounting to positive self-reporting biases. We have details on 392 buyout funds that invested in 6,702 deals, 3,729 of which are fully realized deals. We   1  shows, VC deals are more than twice the number of buyout deals, while for the US subsample, the VC deals are approximately three times the number of buyout deals. [Please insert Table  1  about here].
89 for the buyout and 1. 12 for the full sample. Table 2 also shows how the risk measures differ between the first and fourth quartile funds. Fund quartiles are calculated by sorting data based on the IRRs of funds. The mean loss rate is high for the first quartile and slightly lower for the fourth quartile. The mean of intra-fund volatility for the first quartile is lower than the fourth quartile and consistent for the full sample, buyout and VC funds. We observe the same for the upside volatility, while for the downside volatility, the mean is high for the first quartile and low for the fourth quartile. In other words, funds with a low performance show, on average, a higher downside risk. In terms of skewness, the intra-funds IRR are highly positively skewed in the fourth quartile and less positively skewed in the first quartile.
On the other hand, as trade gets freer, everything else being equal, it becomes more profitable to serve the small market through export. Trade integration thus improves the attractiveness of the largest country thanks to agglomeration economies, and this will allow its government to raise taxes without suffering from a large capital outflow. Importantly, there is a trade cost threshold under which it becomes so cheap to serve the foreign market through exports that a core-periphery structure emerges, characterized by the complete concentration of manufacturing firms in the biggest country where the tax base elasticity is equal to zero. In what follows, we ignore the latter possibility because we test predictions of the model on a dataset in which even the less industrialized countries attract manufacturing firms.
More importantly, the 'new' EU member states -which are often considered as peripheral countries (at least during the time period considered in our empirical study) -succeed in attracting FDI from western countries, for example in the car industry. Therefore, our dataset does not include the kind of coreperiphery pattern predicted by New Economic Geography models. 3 Therefore, there are no tax interactions. The tax outcome is such that the tax differential between both countries (2010), the downward trend in corporate taxes casts doubt on the existence of such a taxable agglomeration 'rent'. The explanation provided in their model is that intra-regional tax competition within core countries could make it impossible to tax the agglomeration rent. Here, we analyze another possibility. We consider that agglomeration economies are not strong enough to give rise to a core-periphery pattern.