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**A**: Over the past two decades, not only has the study of static risk measures flourished, but also dynamic theories of risk measurement have developed into a thriving and mathematically refined area of research. Dynamic risk measures represent a sophisticated and evolving field within risk management, extending the analysis beyond static frameworks to account for temporal changes in risk. Unlike traditional static risk measures that provide a snapshot assessment, dynamic risk measures recognize the fluid nature of financial markets and aim to capture how risk evolves over time. Introduced by Riedel (2004), dynamic coherent risk measures offer a framework that allows for a more nuanced understanding of risk dynamics. This advancement enables a comprehensive assessment of risk in the context of changing market conditions and evolving investment portfolios. Additionally, the introduction of dynamic convex risk measures by Detlefsen and Scandolo (2005) further enriched the field, providing insights into the time consistency properties of risk measures over different time horizons.
Cheridito et al**B**: (2021) constructed some general continuous-time equilibrium dynamic risk measures through using a adapted solution to a backward stochastic Volterra integral equation. Chen et al. (2018) and Sun et al. (2018) extended convex risk measures to loss-based cases. More recent research on dynamic risk measures reference in Chen et al. (2021), Chen and Feinstein (2022), Mastrogiacomo and Rosazza (2022), Yoshioka and Yoshioka (2024)**C**: (2006) considered dynamic coherent, convex monetary and monetary risk measures for discrete-time processes modelling the evolution of financial values. Acciaio et al. (2012) extended dynamic convex risk measures in Cheridito et al. to take the timing of cash flow into consideration. Sun and Hu (2018) introduced a new class of set-valued risk measures which satisfies cash sub-additivity and investigated dynamic set-valued cash sub-additive risk measures. Wang et al | ACB | BCA | BAC | BAC | Selection 1 |
**A**: This paper described the short-maturity asymptotic analysis of the Asian option having an arbitrary Hölder continuous payoff in the local volatility model**B**: We were mainly interested in the Asian option price and the Asian option delta value**C**: The short-maturity behaviors of the option price and the delta value were both expressed in terms of the Asian volatility, which was defined by
| ABC | CAB | BAC | BCA | Selection 1 |
**A**: Massimo Tavoni acknowledges financial support from the European Research Council, ERC grant agreement no. 101044703 - project EUNICE. The authors would also like to thank three anonymous reviewers for the insightful comments provided.
**B**: 336155 - project COBHAM ’The role of consumer behaviour and heterogeneity in the integrated assessment of energy and climate policies’**C**: Matteo Fontana acknowledges financial support from the European Research Council, ERC grant agreement no | BAC | BAC | ABC | CBA | Selection 4 |
**A**: Other examples could be found in [59].**B**: There are many situations where the parametrization approach is not applicable.
For example, when the investors do not know precisely what null and sure events are, the quasi-sure approach is appropriate**C**: The parametrization framework is not suitable in such situations because these events are known to the investors under a fixed probability measure | CBA | CAB | ACB | ACB | Selection 2 |
**A**: For that case and with binary states, Smith and
Sørensen (2000) show that, given any nontrivial preferences, there is learning if and only if beliefs are unbounded**B**: A number of papers on sequential Bayesian social learning only consider the complete observational network: each agent observes all her predecessors’ actions**C**: For the complete network but with multiple states, Arieli and | CAB | ACB | BAC | CAB | Selection 3 |
**A**: We complement this literature (as well as the statistical literature discussed below) by developing a model that explicitly incorporates the incentives and constraints of the researchers**B**: Wager (2021) for recent contributions**C**: Relative to the decision-theoretic approach, this has two main advantages. First, it lets us characterize when MHT adjustments are appropriate—and also when they are not—as a function of measurable features of the research process. Second, it allows us to justify and discriminate between different notions of compound error (e.g. FWER and FDR) in the same framework based on these same economic fundamentals.
| ACB | ACB | CAB | BAC | Selection 4 |
**A**: These factors should have been present in Britain but absent in China.**B**: This claim is supported by recent estimates of GDP per capita, as plotted in Figure 1**C**: The figure shows that Britain’s GDP per capita was similar to that of China before 1750, but diverged after that.
To understand why the Industrial Revolution occurred in Britain in the 18th century, researchers need to identify factors that caused what he refers to as the Great Divergence–the divergence in economic growth between Europe and China since the 19th century | ABC | CAB | ACB | ACB | Selection 2 |
**A**: We distill the responses down to three key attributes using a principal components analysis, representing trust, overall reciprocity, and positive reciprocity**B**: Incorporating these heterogeneous characteristics into our estimations, we observe clear and intuitive effects of heterogeneity on behavior. In the baseline condition, higher scores on the trust attribute are consistent with trustworthy behavior (stronger effects of generalized reciprocity), but predict less trusting decisions as subjects tend to be more cautious about sharing altruistically, perhaps out of mistrust in the notion that others will reciprocate. In the treatment condition, knowing that others observe the source of incoming benefits allows subjects with higher trust attributes to be more trusting.**C**:
The estimates of our structural parameters are robust to the inclusion of several heterogeneous individual characteristics, which we construct based on subjects’ responses to a post-experiment survey | CBA | BCA | CBA | CBA | Selection 2 |
**A**: Roughly speaking, time consistency refers to the property that smaller scores in future epochs guarantee a smaller score in the current epoch. We refer to [11] for a survey on various definitions of time consistency**B**:
One popular criterion is based on convex risk measures [4, 27, 37]. A naïve combination of convex risk measures and discounted total costs, however, lacks time consistency, hindering the derivation of a corresponding DPP**C**: There is a stream of literature (see, e.g., [29, 54, 52, 55, 33, 50, 22, 5]) that studies time consistency from multiple angles and/or attempts to integrate convex risk measures and their variations into MDPs. While here we are not concerned with model uncertainty, we would like to point out [10] and the references therein for a framework that handles model uncertainty in MDPs. | BCA | BCA | BAC | ABC | Selection 3 |
**A**: This problem is referred to as random yield in the literature**B**: Existing supply-uncertainty literature assumes that retailers know their suppliers’ true supply distributions (see e.g. Yano and Lee,, 1995; Grasman et al.,, 2007; Tomlin,, 2009). Noori and Keller, (1986) were among the first to address problems where supply and demand are both random, deriving the optimal order quantity for the unconstrained newsvendor problem with random yield. Parlar et al., (1995) allow for non-stationary supply by assuming that supply follows a Bernoulli process, i.e. the realisation of no or complete supply.
**C**: In general, retailers additionally face the risk of supply shortages, e.g. due to supply constraints in the distribution channels | BAC | BCA | CAB | ABC | Selection 2 |
**A**: To test our hypothesis, we estimate the relationship between external debt and the index of policies and institutions for environmental sustainability developed by the World Bank**B**: Descriptive statistics can be found in the Appendix A. This new dataset starts in 2005 and has a lower country coverage (52 countries).
**C**: This index measures the extent to which environmental policies promote the protection and sustainable use of natural resources and pollution management (1=low to 6=high) | CBA | CAB | ACB | BAC | Selection 3 |
**A**: Then in Section 3, we study this test from a numerical point of view. We start by studying its power using synthetic data in settings that are realistic in view of insurance applications and then, we apply it to real historical data. We also discuss several challenges related to the numerical implementation of this approach, and highlight its domain of validity in terms of the distance between models and the volume of data at hand.
**B**: The present article is organized as follows: as a preliminary, we introduce in Section 2 the Maximum Mean Distance and the signature before describing the statistical test proposed by Chevyrev and Oberhauser (2022)**C**: This test is based on these two notions and allows to assess whether two stochastic processes have the same law using finite numbers of their sample paths | CBA | CAB | CBA | BCA | Selection 2 |
**A**: The split of the population between one closely following the anchor and another biased by it but keeping close to realistic wage values is quite different in Prolific Workers, where the split is 62%/28%, than it is in GPT-3, where it is 1%/99% for the anchor value $100 (Fig. 2). This phenomenon deserves further exploration.**B**: The unimodal average shows increased variance for the anchor $50 but reduced variance for $100, where most of the bot population coalesces to a more realistic response but is still affected by the anchor as is visible in an offset toward the anchor**C**:
For the unrealistic anchors $50 and $100, GPT-3 exhibits substantial differences from Prolific workers | ACB | BCA | CBA | ACB | Selection 3 |
**A**: A transaction initiated by an EOA may be accompanied by multiple sub-transactions related to the smart contract at the same time, as shown in transaction ❺**B**:
Notably, the process of contract call is accompanied by the flow of Ether**C**: During graph modeling, for complex interaction scenarios, we consider multiple sub-transactions with the same transaction hash as different interaction edges but with the same timestamp. The time-aware metapaths can identify whether they belong to the sub-transactions in the same transaction behavior by comparing the timestamp information. | CBA | ABC | CBA | BAC | Selection 4 |
**A**: So, the only change is in the uncertainty about FDA’s decision, and the approval announcement resolves such uncertainty.**B**:
By equating the change in the firm’s market value to the change in the market value of the drug, we can identify the value of a drug at approval**C**: In our context, it is reasonable to assume that by the time the FDA announces its approval, the payoff-relevant information about the drug has already been disclosed to the market | CAB | CBA | BAC | BCA | Selection 1 |
**A**: i𝑖iitalic_i votes at all q′(i)>q(i)superscript𝑞′𝑖𝑞𝑖q^{\prime}(i)>q(i)italic_q start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ( italic_i ) > italic_q ( italic_i ))**B**: As we document in the Appendix,
individual behavior in the experiment is mostly monotonic**C**: There is | CAB | ABC | CBA | CAB | Selection 2 |
**A**: We numerically demonstrated the applicability of our algorithm in this low-dimensional setting. Moreover, we have discussed the scalability of our algorithm by explaining how one could extend our code for the general d𝑑ditalic_d-dimensional setting.**B**: The OptionPricing class within this package enables the user to input all parameters of the underlying stocks, to choose the class of CPWA payoff functions within the ones presented in Example 2.5, as well as to specify to error tolerance level**C**:
Furthermore, we have developed a package we named qfinance within the Qiskit framework which can be used to run Algorithm 1 on a computer for the case d=1,2𝑑12d=1,2italic_d = 1 , 2 | CBA | ACB | BCA | ABC | Selection 1 |
**A**: Similarly, Figure 1-(c) plots the Consumer Price Index for Urban Wage Earners and Clerical Workers (CPI-W) from 1984 to 2023, and Figure 1-(d) plots the total cost of U.S. undergraduate students over time from 1963 to 2021, which both exhibit the same increasing trend in the long run.
**B**: Figure 1-(a) illustrates the increasing trend of simulated sample paths of (1.3), which is consistent to Figure 1-(b) that displays the long term growing trend of the observed data of S&P500, NASDAQ and Dow Jones from April 1, 2010 to November 01, 2020**C**: The benchmark process in the general form of (1.3) can effectively capture the long-term increasing trend of many typical benchmark processes, such as S&P 500, NASDAQ and Dow Jones, or the movements of CPI index and higher education costs in the long run | CBA | CAB | ABC | BCA | Selection 1 |
**A**: Considering the systematic importance of individual firms can substantially mitigate negative economic side effects of decarbonization strategies**B**: Simultaneously considering systemic relevance and CO2 emissions could lead to new decarbonization policies, such as a supply-chain sensitive CO2 tax. Similar ideas were explored in financial networks [33] [34] [35]. Future research should investigate the potential of such a taxation scheme to accelerate the restructuring of production networks towards decarbonization.
**C**: In conclusion, our study emphasizes the importance of firm-level production networks in managing the economic outcomes of decarbonization | CBA | BAC | BCA | CAB | Selection 3 |
**A**: However, this would require formulating a set of separate subproblems for each of the GenCos at the lower level which would further increase the computational burden. An alternative avenue for future research could involve the integration of storage batteries into the grid. This would potentially alleviate the intermittency problem regarding the availability of variable renewable energy sources and hence provide new insights, thereby offering novel perspectives on the outcomes of numerical experiments**B**: However, one should bear in mind that this would also most probably further complicate the computational tractability of the problem. Another possible enhancement could stem from the development of an efficient solution method that allows one to consider a continuous range of investment decisions for the TSO at the upper level. Lastly, if the modelling simplifications caused by limitations of state-of-the-art solvers are overcome one could investigate how policy-related insights differ in case the model allows for a higher level of detail for energy system representation, investment projects, uncertainty formulation and the planning horizon.**C**:
Regarding further research, one could pinpoint a few possible directions. The first one is related to considering an imperfectly competitive market defining a Cornout oligopoly (Ruffin, 1971) instead of perfect competition. The imperfectly competitive market structure may more closely represent the reality (Oikonomou et al., 2009) as the oligopolies have access to information helping them in the decision-making process | BCA | CAB | ACB | ABC | Selection 1 |
**A**: If M=L=γNβ𝑀𝐿𝛾superscript𝑁𝛽M=L=\gamma N^{\beta}italic_M = italic_L = italic_γ italic_N start_POSTSUPERSCRIPT italic_β end_POSTSUPERSCRIPT it holds that**B**: (i) Assume density f𝑓fitalic_f satisfies Assumption A1**C**: Let β∈(0,JJ+3)𝛽0𝐽𝐽3\beta\in\left(0,\frac{J}{J+3}\right)italic_β ∈ ( 0 , divide start_ARG italic_J end_ARG start_ARG italic_J + 3 end_ARG )
and γ>0𝛾0\gamma>0italic_γ > 0 | BCA | BCA | CBA | CAB | Selection 4 |
**A**: Additionally, so-called market impact games, in which a finite number of large traders aims to minimize their liquidation/execution cost, have for example been considered by [40, 42, 37, 23]. Moreover, [14] considers two agents who interact strategically through their linear impact on the return of the risk free asset**B**: The majority of literature considers the case of a single large trader. [43], however, consider a continuous time financial market where the price impact - both temporary and permanent - results from the investment of n+1𝑛1n+1italic_n + 1 ’strategic players’**C**: Maximizing their terminal wealth under CRRA utility, he derives the unique constant pure-strategy Nash equilibrium. Risk-averse investors competing to maximize expected utility of terminal wealth have also been considered by [41].
| BCA | BAC | BCA | ACB | Selection 2 |
**A**: We show that trade data can be used to approximate the flow of mineral resources in a meaningful way when combined with other data sources. Our flow analysis provides a useful foundation for the analysis of global P flows in terms of phosphate rock, fertilizers and related goods before biomass production**B**: We provide the information on (a) the origin of P flows, (b) their destinations and approximate material composition and (c) the resulting complex system of dependencies in supply.
This work also provides the means to analyze possible inconsistencies in different data sets by comparing model-based estimates to different official data sources. Another important aspect of this study is the general applicability of the approach to other raw material flows, such as sulfur, nitrogen or potassium.**C**: As such, it allows to derive valuable information for the analysis of vulnerabilities in countries’ supply relationships, including food security. For this the translation of nominal bilateral trade flows into material flows of P is an important step in terms of accuracy | ACB | BCA | BAC | BCA | Selection 1 |
**A**: When comparing the empirical densities of original and net trailing returns on S&P 500 and S&P/ASX 200 indices, we can observe that the net returns are more tightly distributed and exhibit a smaller variance**B**: It is common in the economic literature to assume that investors are risk-averse and pay more attention to losses, rather than gains. Although the net trailing returns have compressed positive parts, there are ample reasons to contend that EPS products would prove advantageous for holders of superannuation accounts since their protection leg is an effective tool to mitigate portfolio’s losses.
**C**: Furthermore, the frequencies of positive and negative values for both the original and net trailing returns are similar, with positive (resp., negative) net returns being systematically lower (resp., higher) for the net returns | ABC | ACB | BAC | BCA | Selection 2 |
**A**:
Table 1: Summary statistics of our main variables, 2010, balanced panel. Individuals who experienced a harsh default before or in the same year as a soft default in the sample period (i.e**B**: Similarly, the top 1% of total credit limit, total balance on revolving trades and total revolving limit have been trimmed. **C**: from 2004 onwards) have been dropped. Special codes credit scores lower than 300 have been trimmed | BCA | CAB | CBA | ACB | Selection 4 |
**A**: Indeed, if we have a look at the time series data again, then we see that in times of crises, at some points the price level jumps drastically followed by weaker fluctuations around the new price level. This could be an indicator of changing regimes.
**B**: Since this is the first study that tests factor models for electricity spot prices in recent times of crisis, there is still space for improvements. But if practitioners would like to develop a model by themselves, then our results suggest to use the 3-factor model as starting point**C**: As already mentioned, a possible extension could be the use of a stochastic jump intensity rate. Alternatively, also regime-switching models could help to include larger price jumps | BAC | CBA | CAB | BAC | Selection 3 |
**A**: Some RR methods resample all historical data (e.g., 500 million samples) into a new training set that shares a similar distribution with future data (Li et al., 2022). However, such a coarse-grained adaptation fails in IL where incremental data is of limited size (e.g., one thousand samples) and contains deficient samples to reveal future patterns. To address this limitation, we propose to adapt all features and labels of the incremental data to mitigate the effects of distribution shifts in a fine-grained way.**B**: A critical yet under-explored direction is to adapt data into a locally stationary distribution so as to mitigate the effects of distribution shifts at the data level**C**:
Data Adaptation | ABC | CBA | BCA | CAB | Selection 2 |
**A**: Conversely, in Appendix C Table C.3 conveys analogous information, drawing from Twitter posts. The panels include the percentage of posts that expressed happiness, sadness, hate, surprise, and fear related to each IPO, as well as the total number of posts and users. I used EmTract to extract emotions from my data and employed two emotion models: one specifically designed for StockTwits, and one general emotion model.**B**:
Table 1 presents the descriptive statistics for the analysis variables using the StockTwits sample**C**: The table captures the emotions expressed on StockTwits concerning each IPO, complemented by the relevant IPO and financial data | BAC | ACB | BAC | CAB | Selection 4 |
**A**: These techniques allow exact sampling of subsequent d𝑑ditalic_d-DPP samples in O(poly(d))𝑂poly𝑑O(\text{poly}(d))italic_O ( poly ( italic_d ) ), independent of the size of the full basis set n𝑛nitalic_n. Many of these algorithms are implemented in the open-source DPPy library[33], which we used in the experiments in this paper.
**B**: In a counter-intuitive result, [31] and [32] proposed methods that avoid performing the full DPP sampling procedure on large parts of the basis set**C**: This approach resulted in a significant reduction in runtime, making DPPs more practical for mid-to-large-scale datasets | ACB | CAB | CBA | BCA | Selection 2 |
**A**:
The key to understanding the results of fits in Sec**B**: 4 is the analysis of the structure of RV used by the markets – a square root of realized variance (1)**C**: At its core is the average of the consecutive daily realized variances (2). Distribution of daily realized variance can be modeled using a duo of stochastic differential equations – for stock returns and stochastic volatility – which produces distributions of daily variance such as mGB liu2023rethinking and GB2 dashti2021combined . Via a simple change of variable, daily RV would then follow the same distributions but with renormalized parameters. | ACB | ABC | ACB | ACB | Selection 2 |
**A**:
Also, two strong bidders with value 2 participate with i.i.d**B**: probability of 0.5**C**: Consider soft-floors from 0 (which corresponds to a simple second-price auction) to 2 (effectively, a first-price auction with a reserve of 2). For each parameter value, we run 500,000 simulation periods and compute average revenues in the final 50,000 periods. We report the average of the results over 5 repetitions of the simulation. | CBA | ABC | BAC | BCA | Selection 2 |
**A**: . The subjects were primarily Lancaster University undergraduates (82.2%) from various disciplines, mainly Business and Economics (60%), Social Sciences (23%) and Science and Medicine (17%)101010A potential criticism could be that using a student subject pool could corroborate the results since students tend to be more socially connected compared to a more representative population. Nevertheless, a common finding in the literature is that students tend to behave in a less prosocial way compared to representative populations or professionals (see for example Anderson et al**B**: 2013; Bellemare and Kröger 2007; Belot et al. 2015), giving less in public goods experiments (Gächter et al. 2004; Carpenter and Seki 2011) or behaving in a similar manner to non-student populations (Exadaktylos et al. 2013).**C**:
To test the predictions of the model presented in the previous section, we designed and conducted an incentivised economic experiment. The experiment took place at the Lancaster Experimental Economics Lab (LExEL) in February 2023, involving 96 subjects across three treatments999The power analysis, conducted with a significance level of 0.05 and aiming for 80% power, to detect a moderate effect size (Cohen’s h = 0.65) in the specified one-sided test, indicated a minimum sample of 29.3 subjects per treatment. The power analysis was conducted using the pwr.2p.test function from the library pwr in R | CBA | BCA | ACB | BAC | Selection 2 |
**A**: The goal is to optimize the coin parameters of a multi-SSQW to achieve the targeted distribution of the position space, and then we only compute the position space**B**: The coin space of a multi-SSQW that performs a controlled motion of a walker on the position space is similar to the ancilla qubit taking a controlled rotation in Eq.(3)**C**: We will accomplish this by using parameterized quantum circuits (PQC). The steps for this process are as follows:
| CBA | BAC | BCA | CAB | Selection 2 |
**A**: Thus, delegating to experts may encourage the investor to take on greater risk.777The importance
of trust is echoed by Germann et al. (2022) who provide experimental evidence that the higher the level of trust in a given advisor is, the more risk clients ask this advisor to take.**B**: Finally, according to the increasing risk tolerance motivation (see e.g**C**: Gennaioli et al., 2015), trust in the expert may reduce an investor’s utility cost of taking risk | CBA | BAC | CBA | CAB | Selection 4 |
**A**:
It is well-known that the existence of a period three cycle implies that of a Li-Yorke chaos (by the famous Li-Yorke theorem [Li and Yorke, 1975, Thm. 1]), and this argument has been used a lot in economic literature, see [Benhabib and Day, 1980], [Benhabib and Day, 1982], [Day and Shafer, 1985], [Nishimura and Yano, 1996] for example**B**: In the first part of this paper, extending Proposition 1.1, we obtain:**C**: However, this is a bit overkill: by [Block and Coppel, 1992, Chap. \@slowromancapii@], we know that the existence of a cycle of any odd length (not necessarily of period three) implies that of a Li-Yorke chaos | CAB | BAC | ABC | ACB | Selection 4 |
**A**: While the factors required to minimize price-impact are well understood, this is not the case for slippage**B**: Second, slippage attracts the most adversarial attention. Thus, for any given swap, slippage arguably matters the most to execution quality.
**C**: In contrast, price-impact and slippage directly affect execution price on a per-trade basis | CAB | BCA | ABC | BAC | Selection 2 |
**A**: By comparing the VASPs cryptoasset holdings to balance sheet data, we show that the major issues are related to the different management of cryptoasset wallets in different DLTs, the lack of wallet addresses attribution data for VASPs, and the absence of breakdowns by cryptoasset types in balance sheets.
**B**: Currently, supervisory auditing of VASPs does not fully exploit the public availability of DLT transactions**C**: We believe our work provides valuable insights toward a better and more systematic assessment of their solvency, and might help make the process more effective and less error-prone | BAC | BCA | BCA | CAB | Selection 4 |
**A**:
see, e.g., [14]**B**: However, we will see that such a polyhedral approximation (7) will be a first step to reach our goal.**C**: This is not sufficient for our purposes as we need on one hand not weakly, but Pareto optimal points, and on the other hand, we need the set of all Pareto optimal points (or ϵitalic-ϵ\epsilonitalic_ϵ-Pareto optimal points) | BCA | BCA | BCA | ACB | Selection 4 |
**A**: This data is extracted from the ENTSO-E Transparency Platform and Gestore Mercati Energetici (GME) and are available hourly until 31st December 2022. We decided to average hourly data into daily data to avoid additional intra-day noise and follow literature practices [29]**B**: This granularity choice will equally enable us to match the granularity of other dynamics like the temperature data’s. Additionally, we exclude 2019 to 2022 years as energy price time series show considerably erratic paths due two major macroeconomic shocks: the COVID-19 pandemic and the Ukrainian war.
**C**: The above model is tested in real world data. In particular, we study day-ahead log spot energy prices in France and North Italy from 5th January 2015 to 31st December 2018 | CAB | CBA | BCA | ACB | Selection 3 |
**A**: Although we could not reconcile the changes in open interest with trading volume, the frequency and magnitude of the discrepancies is such that it leaves room for some relatively more benign explanation (see Section 5)**B**: The last group of exchanges is formed by Kraken and HTX, who have the lowest number of discrepancies. For these exchanges we could reconcile changes in open interest with trading volume on almost all sub-periods (see Tables 4 and 5).
**C**: Binance, Deribit and BitMEX form, conceptually, another cluster of exchanges | BAC | ACB | BAC | BCA | Selection 4 |
**A**:
The classical Yard-Sale Model with redistribution also has a Gaussian tail**B**: Hence, for both w≪1much-less-than𝑤1w\ll 1italic_w ≪ 1 and w≫1much-greater-than𝑤1w\gg 1italic_w ≫ 1, the redistributive versions of the modified and classical Yard-Sale Model have the same characteristic shapes**C**: Since the latter has successfully been used to fit empirical data, we take these results to indicate that the modified system holds similar promise. | BCA | ABC | BCA | BAC | Selection 2 |
**A**: Prior to this, there are two potential challenges in high-dimensional scenarios**B**:
Inspired by a version of Regression Monte Carlo Methods, proposed by [10], which applied GPR to estimate the continuation value within the Longstaff-Schwartz algorithm in low-dimensional cases, we aim to extend the application of GPR to the pricing of high-dimensional American options**C**: The first challenge is the unreliability of the Euclidean metric in high-dimensional spaces. Common kernels, such as the Radial Basis Function (RBF) kernel, employ the Euclidean metric to measure the similarity between two inputs; however, this may not be optimal for high-dimensional data [24]. | ACB | BAC | ACB | ACB | Selection 2 |
**A**:
Osband’s principle can be used to create new MK divergences**B**: Indeed any strictly monotonic transformation of an elicitable risk functional leads to a new MK divergence, where the optimal coupling follows from Proposition 3.17**C**: Here, we give an example of the reciprocate of risk functionals. | ABC | BAC | ACB | ACB | Selection 1 |
**A**: There are many important problems in mathematical finance and economics incurring time-inconsistency, for example, the mean-variance selection problem and the investment-consumption problem with non-exponential discounting. The main approaches to handle time-inconsistency are to search for, instead of optimal strategies, time-consistent equilibrium strategies within a game-theoretic framework. Ekeland and Lazrak [14] and Ekeland and Pirvu [15] introduce the precise definition of the equilibrium strategy in continuous-time setting for the first time. Björk et al**B**: [20] introduce the concept of open-loop equilibrium control by using a spike variation formulation, which is different from the closed-loop equilibrium concepts. The open-loop equilibrium control is characterized by a flow of FBSDEs, which is deduced by a duality method in the spirit of Peng’s stochastic maximum principle. Some recent studies devoted to the open-loop equilibrium concept can be found in [2, 3, 29, 18]. Specially, Alia et al. [3], closely related to our paper, study a time-inconsistent investment-consumption problem under a general discount function, and obtain an explicit representation of the equilibrium strategies for some special utility functions, which is different from most of existing literature on the time-inconsistent investment-consumption problem, where the feedback equilibrium strategies are derived via several complicated nonlocal ODEs; see, e.g., [26, 6].
**C**: [5] derive an extended HJB equation to determine the equilibrium strategy in a Markovian setting. Yong [30] introduces the so-called equilibrium HJB equation to construct the equilibrium strategy in a multi-person differential game framework with a hierarchical structure. The solution concepts considered in [5, 30] are closed-loop equilibrium strategies and the methods to handle time-inconsistency are extensions of the classical dynamic programming approaches. In contrast to the aforementioned literature, Hu et al | ABC | CBA | ACB | CAB | Selection 3 |
**A**: By unbundling the PBS auction as outlined, the HFT builders specialized in non-atomic arbitrage would still dominate the top-of-block opportunities in times of high volatility, but would minder the effects on the rest of the block body. While we believe that this would be a step in the right direction, some problems remain**B**:
Separating Top of Block. One possible avenue to work against these centralizing effects of non-atomic arbitrage suggested by Gupta et al. [4] is to separate top-of-block extractions, i.e., non-atomic arbitrage that generally takes place top-of-block as these swaps wish to be the first to execute in the respective pools, and block-body extraction**C**: For one, the size of the top-of-block would likely have to be limited as otherwise, these top-of-block opportunities are likely to take up more than half the block space in times of high volatility. Additionally, while this approach would limit the centralizing effects of non-atomic arbitrage, it would not target the security implications (i.e., time-bandit attacks) outlined previously. | ABC | CAB | BAC | ACB | Selection 3 |
**A**: Validating the aforementioned observations, prominent players in the financial sector, including JPMorgan and Bloomberg, have recently launched AI-powered initiatives. Announcements of an AI-enable advisory platform CNBC (2023) and the release of a finance-centric LLM Wu et al**B**: (2023) from these entities, respectively, reaffirm the prominence of generative AI within the financial sector. Furthermore, Morgan Stanley has utilized OpenAI’s models to develop a chatbot that aids financial advisors by leveraging the bank’s extensive research data OpenAI (2023b)**C**: In a similar vein, Broadridge, through its subsidiary LTX, has introduced BondGPT, a chatbot powered by GPT-4 designed to assist institutional investors in bond trading LTXtrading (2023). It is crucial to note, however, that many major financial institutions like Goldman Sachs and BlackRock have dedicated AI departments working on specialized initiatives. Nonetheless, the details of such innovations in investment banking are often closely guarded within these institutions for competitive and proprietary reasons, thereby limiting the public disclosure of comprehensive information about these significant developments.
| ABC | BCA | BAC | ACB | Selection 1 |
**A**: The second main feature, switches between different regimes, is implemented by a form of concatenation of stochastic processes**B**: We define a family of randomised processes with different coefficients, labelling each one as a component process**C**: Then, a composition rule is defined from which a composite process emerges, which exhibits switching behaviour whenever its constituting component process changes.
| ABC | CAB | ACB | ACB | Selection 1 |
**A**: In this way we are able to adjust SOFR futures prices not only for the inherent convexity, as was done by Mercurio [2018], but also for the interest rate skew and smile observed in options markets**B**:
The intention of the present work is to model SOFR rates instead through the recent Hull-White model extension of Turfus and Romero-Bermúdez [2023] which captures skew and smile effects through use of a local volatility which is a quadratic function of the short rate**C**: This is to our knowledge the first calculation attempting to capture these two effects together in an analytic expression.222During the completion of this work, Sepp and Rakhmonov [2023] showed preliminary results on Eurodollar convexity in a stochastic volatility model. | CBA | CAB | BAC | ABC | Selection 3 |
**A**: Finally, experts choose for each consumer whether they want to implement the HQT or the LQT, and proceed to a summary screen that shows how many consumers approached them and how much money they made that round. Consumers first decide whether they want to leave the market, or choose one of the three experts. Consumers do not observe an expert’s ability level, but they can identify each expert, which allows for reputation building. After choosing an expert or leaving the market, consumers wait to be treated and proceed to a summary screen that shows which expert they approached in this round (as well as in all previous rounds), what prices the expert chose, and how much money they earned.
**B**: Subjects play 25 rounds in groups of six. The first 10 rounds are constant across treatments and always follow the same sequence. Experts choose their price vector, and then diagnose all three consumers by completing a short prediction task**C**: Here, high-ability experts use 3 out of 5 possible input factors, and low-ability experts use 2 input factors to identify the problem. Input numbers are pre-filled for each expert, who automatically make a prediction by clicking on a ”diagnose” button. Then, experts receive a diagnostic signal that depends on their ability type | ABC | ABC | CAB | BAC | Selection 3 |
**A**:
Figure 5: Hybrid Model**B**: In this diagram, diamonds represent on-chain smart contracts, rectangles represent off-chain databases, and ellipses represent off-chain servers**C**: We use blue background to indicate elements differentiating Hybrid Model from the CEX Model. | BAC | ACB | ABC | CBA | Selection 3 |
**A**: In the USS simulations of 2018, the funding ratio is measured every year as assets divided by SfS liabilities**B**: SfS liabilities are calculated from remaining cashflows of promised benefits using a SfS discount rate**C**: So the paths of the simulation fail the 90% funding ratio condition in a given year if the assets drop below 90% of the remaining SfS liabilities. It is worth noting that the mechanism to calculate the SfS DR from the SfS liabilities requires a SfS DR as an input.
| BAC | ABC | BAC | CBA | Selection 2 |
**A**: The out-of-sample results for the simulated portfolios in Section 4 already illustrated the superior performance of our NN GA compared to the analytic methods.
The out-of-sample results based on real portfolio data in this subsection document that our NN GA is also highly accurate in real data applications, i.e**B**: The accuracy of the NN GA of course increases when the distributions used for training fit more closely to the empirical distributions of the real portfolios. While our NN was trained using parametric distributions with parameters fit to the real portfolios, training based on empirical distributions of historical portfolio data could substantially increase precision. In this way, MDBs or rating agencies that have access to such historical portfolio data, could use our methodology to obtain a highly accurate and very fast estimate for the name concentration risk in current portfolios.**C**: when applied to portfolios with characteristics that are not just sampled from the training distributions | CAB | CBA | CAB | ACB | Selection 4 |
**A**: This approach optimises the interaction with subsequent LLM-based analysis stages, ensuring that the questions remain within acceptable limits for processing and analysis**B**: This efficiency is crucial for maintaining the system’s performance, scalability and responsiveness.
**C**: Efficiency: By grouping similar questions and summarising inquiries related to missing values into a single question, the agent efficiently manages the context window | BAC | CBA | BAC | BCA | Selection 4 |
**A**: The continual training group has the smallest variation in the inventory, while the untrained group has the largest variation. We further separate the MM’s PnL into profit from spread and profit from holding inventory**B**: To assess realistic behavior, the MM agents should control their inventory and rely on the Profit and Loss (PnL) from spread (providing liquidity) rather than from inventory (long-term investments). Figure 6 shows the average inventory evolution of all MM agents in the three groups over time**C**: The MMs in the continual training group profit on average $1.26 million from spread and lose $0.22 million from inventory. The results for MM agents from other groups are similar.
| CAB | BAC | ACB | CBA | Selection 2 |
**A**: Our methodology includes using two main types of inputs: the attributes from the Multiple Listing Service (MLS) and the images themselves**B**: We specifically investigate the performance of neural networks and OLS, both in isolation and in combination**C**: For image processing, we employ encoders to categorize the images.
| ABC | BAC | BCA | ABC | Selection 2 |
**A**: The general form of an ARIMA model is ARIMA(p,d,q), where ’p’ is the number of AR terms, ’d’ is the degree of differencing, and ’q’ is the number of MA terms.**B**:
ARIMA models, known for their flexibility in handling various time series patterns, were individually tailored for each sector**C**: The model selection process, guided by the auto.arima function, determined the optimal combination of autoregressive (AR), differencing (I), and moving average (MA) components for each time series | ABC | ABC | CAB | BCA | Selection 3 |
**A**: The generalizations concern marginal distributions (Proposition 1), dependence structures (Proposition 2), a tail risk model (Proposition 3), a classic insurance model (Proposition 4), and bounded super-Pareto losses (Theorem 2)**B**:
We provide several generalizations of the inequality that the diversification of WNAID super-Pareto losses is greater than an individual super-Pareto loss in the sense of first-order stochastic dominance**C**: These results strengthen the main point made by Chen et al. (2024): As diversification increases the risk assessment of extremely heavy-tailed losses for all commonly used decision models, non-diversification is preferred. | BCA | ACB | CBA | BAC | Selection 4 |
**A**: Deep Learning application have also shown clear improvement and proved their ability to provide consistently reliable sentiment to a complex text (Zhang et al., 2018).
**B**: Likewise, SentiWordNet 3.0, provides an enhanced lexical resource for sentiment analysis, showing about 20% accuracy improvement over its earlier version (Baccianella et al., 2010)**C**: Recent advancements like FinEntity focus on entity-level sentiment classification in financial texts, demonstrating its utility in investment and regulatory applications (Tang et al., 2023) | CBA | BCA | BAC | CAB | Selection 4 |
**A**: The input features that are used in the dataset are net profit, total liabilities, working capital, current assets, retained earning, EBIT, book value of equity etc. (see (Tomczak, 2016) for details). The dataset contains the observations of five years, during 2007-2013 among which 7027 instances are given in the first year, 10173 in the second year, 10503 in the third year, 9792 in the fourth year, and 5910 in the fifth year. The dataset contains several missing values, and it is also highly imbalanced.
**B**: This dataset is generated from EMIS (Emerging Market Information Service) dataset. This data was collected within the time period of 2000 to 2013. It contains two classes: class 0 and class 1. Class 0 shows that the company is not bankrupt and class 1 shows the Polish bankrupt companies**C**: Here in this work Polish companies bankruptcy datasets (Tomczak, 2016) have been used for experimentation. The data set is about the prediction of bankruptcy of Polish companies. This data set contains 64 quantitative features. This dataset describes the bankruptcy status of Polish companies | CBA | CAB | CAB | BAC | Selection 1 |
**A**: Furthermore, it is essential to ensure that these feature maps cannot be easily replicated by classical devices, lest the quantum device’s unique advantage be nullified [6]. Under widely-believed computational complexity assumptions, Instantaneous Quantum Polynomial (IQP) embedding have been used as go-to quantum embedding aiming at better classification power without an easily constructed classical analogue [10]**B**: To navigate this balance, various algorithms have been developed to identify the most effective quantum embedding for particular tasks [13]. Among these, genetic algorithms have been explored to sift through the extensive array of potential data embedding circuits, assessing their utility and innovativeness [3]. Additionally, integrating different Quantum Kernels has been shown to yield strong performance outcomes [29], suggesting a promising avenue for enhancing quantum computational applications.
**C**: Nonetheless, there exists a well-documented balance between the ability of classical systems to reproduce such embedding and the practical utility of quantum embedding in addressing specific problems. For that reason, several algorithms were devised to generate the optimal quantum embedding for solving specific tasks [13] | CBA | ACB | CBA | CAB | Selection 2 |
**A**: Numeric values in nodes represent distinct Signature Group node features**B**:
Figure 4: Examples of building blocks with shared common subgraphs highlighted in red**C**: Building blocks of same protocols, while differing in financial functionalities, can either contain the entire subgraph of another (left) or share common subgraphs (right), suggesting the presence of protocol-specific patterns reuse. | CAB | ABC | BAC | CAB | Selection 3 |
**A**: In this phase, we focus on a task that involves predicting connections between nodes. We posit that if a node u𝑢uitalic_u is connected to node i𝑖iitalic_i at time t𝑡titalic_t, this scenario constitutes a positive pair**B**: Conversely, a negative pair is formed by randomly sampling a node from the entire set of nodes. We denote such a negatively sampled node as k𝑘kitalic_k**C**: To refine the model’s ability to differentiate between these pairs, we utilize the Binary Cross-Entropy Loss (BCELoss):
The objective is to increase the probability of an edge existing in positive pairs while decreasing it for negative pairs. Mathematically, this can be represented as follows: | BCA | CBA | ABC | ACB | Selection 3 |
**A**: In this environment, employers have little ability to offer firms-specific incentives to retain workers (Kryscynski et al., 2021). Second, the job is carried out remotely. While remote work, or work-from-anywhere policies have become important post-Covid, providing valuable amenities to workers and productivity gains to firms (Choudhury et al., 2021), this trend has a notable downside: it reduces employee embeddedness within firms because it makes it more difficult to build meaningful relational links with co-workers(Yang et al., 2022). These features constrain the strategies managers within the industry have for retention.**B**:
In comparison to the national average voluntary separation rate of 2.2%, trucking industry turnover is extreme.111December 2023; Latest figures: https://www.bls.gov/news.release/pdf/jolts.pdf These high rates of turnover provide variation that makes the trucking industry a particularly useful testbed for understanding successful worker retention strategies more generally. There are two important features of trucking, in particular, that make worker retention challenging**C**: First, the job is highly-routinized across firms. Employees develop and depend little on firm-specific human capital so that their skills are highly substitutable between firms | ABC | CBA | CAB | BAC | Selection 3 |
**A**:
There are, of course, many other potential approaches that we did not include in our experiments. In particular, the Planar Maximal Filtering Graph (PMFG) has been advocated in the previous literature [18]**B**: For this reason, we have tested it. We have found that it is an extremely slow preprocessing step, and therefore, it does not fit with our criteria as it fails the Computational efficiency requirement**C**: However, because it had been used previously, we have run it for a selected subsets of centrality measures. As we will see in more detail below, the PMFG is never appearing among the best 100 methods, neither when comparing methods based on cumulative return nor when ranking them based on Sharpe Ratios. We therefore believe that it does not bring any particularly precious added value to the idea of centrality measure based portfolio selection. | BAC | BAC | CBA | ABC | Selection 4 |
**A**: Furthermore, Table 13 shows the precision and recall of the svc classifier by class**B**: Note that all metrics exceeded 80% as pursued, a level that, compared to other Machine Learning financial applications in the literature (Zhu et al., 2017; Atkins et al., 2018; Zhu et al., 2019; Dridi et al., 2019; De Arriba-Pérez et al., 2020), is similar and even superior.**C**:
Table 12 shows that, with this second selection, we attained well over 80% precision and recall performance with the svc classifier, which takes considerably less time to train than the nn | CAB | BCA | BAC | CAB | Selection 2 |
**A**: We then detect dependencies between these key terms. Next, we replace all references to stock markets, assets, asset abbreviations and currencies with the tags stock, ticker, ticker_abr and currency, respectively. Using the same lexica, we also replace financial terms and abbreviations with the tag fin_abr.**B**: on stock markets, tickers and currencies. In addition, we search for words such as company, enterprise, manufacturer and shareholder, which may refer to an asset**C**: After text segmentation and co-reference resolution, the tag processing stage homogenises the input for the subsequent lda stage. First, asset identifiers are detected using our financial lexica333Available at https://www.gti.uvigo.es/index.php/en/resources/14-resources-for-finance-
knowledge-extraction, October 2022 | ACB | ACB | ABC | CBA | Selection 4 |
**A**: It employs maximum likelihood estimation for parameter estimation and provides a stronger statistical foundation compared to LSA. Additionally, PLSA excels in handling polysemous words by establishing a latent semantic space and demonstrates better results in dealing with domain-specific synonyms.**B**:
To address these limitations, Hofmann introduced an improved method called Probabilistic Latent Semantic Analysis (PLSA) in 1999 [16]**C**: PLSA models the word generation process in a document as a mixture of multiple implicit topics, each associated with a set of words | BAC | ABC | BAC | CAB | Selection 4 |
**A**: Peña et al**B**: [19] studed the problem of computing the upper and lower bounds on basket and spread option prices when the prices of other basket and spread option prices are known.**C**: [21] developed a linear programming-based approach for the problem of computing the upper price bound of a basket option given bid and ask prices of vanilla call options.
Peña et al | CBA | BCA | ACB | CAB | Selection 3 |
**A**:
The resulting portfolio compositions for the discrete and rounded continuous cases are shown in Fig. 3, both in terms of the number of stocks bought per ISIN and the invested budget per ISIN**B**: We observe that the respective portfolio compositions are strikingly different**C**: The rounded continuous approach yields a solution, which is well diversified in terms of allocated budget. The discrete approach on the other hand yields a portfolio, which is slightly more concentrated in terms of budget allocation. This effect is likely due to the strict budget constraint in the discrete case, which forces the optimization to pick allocations that fit the specific budget constraints. | ACB | CBA | CBA | ABC | Selection 4 |
**A**: Figure 1 illustrates how karma works out to the benefit of everyone at hand of an example involving three intersection encounters. Let’s start with the encounter in the top center. The high-urgency lila car has a current karma account of 9 and bids 4, thus outbidding the low-urgency blue car whose karma account is also 9 but bids 2**B**: Now the orange karma goes up by 4 to 13. Let’s move along clockwise. Orange now has high urgency and bids 4, thus outbidding low-urgency lila who has 5 karma left and bids 1. Thus the circle closes, et cetera, et cetera.
**C**: As a result, the blue car’s karma account goes up by 4 to 13. Let’s move along clockwise. Blue now happens to have high urgency and bids 4, thus outbidding and getting priority over orange whose karma account is nine and bids 3 | ACB | BAC | BCA | BAC | Selection 1 |
**A**: However, quotes and replies can also be used to express opposite opinions or debunking, and thus might be affected differently by the treatment**B**: Retweets normally mean alignment and lead to sharing the exact same tweet (with the Community Note if one is rated as helpful) but without adding any additional information. Thus, we believe that it represents the best estimate of the impact of Community Notes on user’s behavior.
**C**: When we use the log number of replies and the log number of quotes as our dependent variable, we respectively find a reduction by 32.4% and 34.6% with the DiD (38.6% and 43% using the pre-treatment outcome matching) | CBA | BCA | BAC | ABC | Selection 2 |
**A**: The 95% confidence interval is calculated using the delta method.
**B**: The tariff equivalent measure is expressed in percentage points**C**: Notes: The figures show the tariff equivalent of the trade costs for West-leaning countries (Switzerland, Ireland, and Sweden) | CAB | ABC | CBA | BAC | Selection 3 |
**A**: Interestingly, I do find persistent negative impacts for sadness, suggesting its uniquely enduring influence on market dynamics. Furthermore, in Panel B, when I consolidate these emotional measures into a singular metric, valence444I follow Breaban and Noussair (2018) and define valence as:
**B**: Table 7 explores the impact on emotions before the market opens today on returns the following four days**C**: As expected, I find that the predictive power of investor emotions diminishes over time | BCA | CBA | CAB | ABC | Selection 3 |
**A**: However, these studies on sex and race anti-discrimination laws also omit local laws, potentially skewing their estimates..**B**:
This paper is the first to examine both local and state-level sexual orientation anti-discrimination laws in a quasi-experimental design**C**: This paper is also the first to analyze the effects of any local anti-discrimination laws on sex, race, or sexual orientation in a modern difference-in-difference framework666Using state variation in anti-discrimination laws has also been analyzed to understand racial- and sex-based discrimination (Neumark and Stock (2006); (Donohue III and Heckman, 1991);((Margo, 1995); Goldin and Margo (1992)) | BAC | CAB | ABC | ACB | Selection 2 |
**A**: As the relative reduction in costs of uncertainty if an additional customer subscribes is stronger when the share of remaining customers ordering at random is small, we obtain a convex relation between β𝛽\betaitalic_β and the marginal effect**B**: Thus, if a retailer is able to offer prices sequentially this result should be considered. In particular, this might lead to a situation where it is beneficial to offer discounts that exceed the marginal benefit of first customers subscribing in order to gain the higher benefit from customers ordering at a later stage (or offering subscriptions at all even if it is not beneficial when only a small number of customers subscribe) as also revealed in Implication 4.
**C**: Figure 7 gives the marginal effect of an additional customer subscribing depending on the share of customers already subscribed β𝛽\betaitalic_β | ACB | ACB | ABC | BCA | Selection 4 |
**A**: Only three non-Western countries are represented, all from East Asia. There is no reason to believe that people from different parts of the world would systematically differ in their interpretation of the evidence about climate change and its impact, but they may well hold different attitudes to the future. The literature on the social cost of carbon may thus be biased towards Western attitudes.**B**: The USA contributed most (46%) followed by the UK (20%). Africa and Latin America did not contribute to this literature**C**:
Figure 1 groups 323 papers on the social cost of carbon by the country of affiliation of the authors of these papers (data from Tol, 2024a). Papers of mixed nationality are attributed proportionally to the number of authors | ABC | ABC | ACB | CBA | Selection 4 |
**A**: Yet, this does not mean that the trader will be making profit, since the expense of buying, and the commission prices have also to be considered., or deep out of the money555When the option has what is also called an extrinsic value, i.e. a value at a strike price higher than the market price of the underlying asset. In such a case, the Delta, i.e. the Greek which quantifies the risk, is less than 50.. This work opens the way to an empirical investigation and an inverse problem of the probability measure μ𝜇\muitalic_μ.**B**: convergence condition for the associated finite difference scheme is determined and written explicitly. For the proper calibration, our model can avoid overpricing options at the money, and underpricing options at the ends, either deep in the money444When the option has what is also called an intrinsic value, i.e. the real value of the option, that is to say the profit that could be made in the event of immediate exercise. It means that the value is at a favorable strike price relative to the prevailing market price of the underlying asset**C**: In this paper, we give a definition of a general version of the Black-Scholes model, based on the theory of non-symmetric Dirichlet forms, and on the abstract theory of partial differential equations. The key point is to consider the Black-Scholes equation describing an average evolution, while the exact dynamics depends on uncertainty captured by a mathematical measure. The analysis goes so far as proving the existence of a Generalized Black-Scholes operator.
Special treatment is given to the self-similar case by writing an explicit formula, which enables computation of the solution. The CFL333Courant-Friedrichs-Lewy | CBA | CAB | ABC | ABC | Selection 1 |
**A**:
Article 2(1)(a) requires that the payments toward the Positions be dependent upon the performance of the Exposures**B**: Therefore a scheme or transaction is not considered a securitization if the Exposures ongoing losses are always zero ∀tfor-all𝑡\forall\ t∀ italic_t**C**: In fact, if this were the case, there would be no credit risk to \saytranche, and thus the letter (b) requirement would not be satisfied. Whilst it is necessary the presence of potential losses, it is not sufficient per sé. Article 2(1)(b) additionally requires to \saytranche the credit risk in such a way that the losses be allocated to the different Positions so as to reflect the subordination of tranches continuously during their ongoing life. | CAB | CBA | ABC | BAC | Selection 3 |
**A**: Uniswap v2, the market leader before the introduction of v3, further maintains a substantial amount of liquidity (about 70% as much as Uniswap v3, at the time of writing).
Fee earnings are computed based on the historical trading activity within the liquidity pools. To simulate arbitrage losses, we assume that the pools are consistently rebalanced to historical prices on Binance, the most liquid cryptocurrency exchange.**B**: We compare the historical earnings from trading fees to the amount of incurred arbitrage losses for the most-traded Uniswap v2 and v3 pools**C**: This covers a considerable fraction of the total liquidity in AMMs, as Uniswap v3 is the market-leading and highest-volume AMM on Ethereum | ABC | ABC | CBA | CAB | Selection 4 |
**A**: (2010); Orsi et al. (2015). However, results regarding the inverse parabolic behaviour between relatedness and performance are inconclusive due to the lack of a standardized and recognized method for robust performance and relatedness measurements Jo et al. (2016); Cimini et al. (2022).
**B**: A notable section of M&A studies focuses on the patenting activities of involved firms, centring on technological relatedness. Ahuja-Katilia Ahuja and Katila (2001) introduces a measure of technological similarity between acquirer and target firms, revealing an inverse parabolic relationship between technological similarity and innovation performance post-acquisition. Many subsequent authors develop different measures of technological relatedness and investigate their links to post-acquisition performance Cloodt et al**C**: (2006); Cassiman et al. (2005); Hagedoorn (2002); Valentini and Dawson (2010); Jo et al. (2016); Makri et al | CAB | CBA | BCA | ABC | Selection 1 |
**A**: As shown in the example in Table II, the splitter divides the term acuerdocomercial successfully as acuerdo comercial ‘commercial agreement’.
**B**: We decompose hashtags in words with a splitter that uses our own lexica [68, 69] and the Spanish frequency reference corpus (CREA) by Real Academia Española de la Lengua888Available at http://corpus.rae.es/lfrecuencias.html, August 2020.**C**: Hashtag and mention splitting | CBA | CAB | ACB | CAB | Selection 1 |
**A**: Ignoring this input entirely would be imprecise, as the information from an earnings conference call can continue to affect stock price movements after its publication**B**: To incorporate the continuous influence of earnings conference call information even on days without new input, we will introduce a hyper-parameter to measure the decaying speed of its relevance. We will apply the Exponential Decay function:
**C**: Consequently, the input from earnings conference calls may be absent on some training days | BAC | ABC | BCA | CAB | Selection 3 |
**A**: Since elaborate report becomes less extreme when they become more frequent, Part 1 of Corollary 1 follows directly from Proposition 3**B**: For instance, as the size of region A becomes larger, there are more elaborate negative reports**C**: However, as the size of region A increases and more elaborate negative reports are disclosed, the disclosed value of x𝑥xitalic_x becomes closer to point O𝑂Oitalic_O and thus less extreme. If we map these comparative statics to the real world and consider elaborate reports by hedge funds, those that are more successful should, on average, issue less aggressive reports. In addition, firms with better information environments tend to have more aggressive negative reports and less aggressive positive reports.
| CBA | ABC | ACB | CBA | Selection 2 |
**A**:
The second common approach is to use flows between different position titles and infer promotions as common transitions from one position title to another. This revealed preference approach is appealing in settings in which internal labor markets are homogeneous and consist of a relatively limited number of positions.121212For instance, the firms studied in Huitfeldt et al. (2023) have an average of ten occupations, and the firms in Baker et al**B**: Another challenge for more complex labor markets is that position titles are often both noisy and relatively coarse, making it difficult to make granular distinctions between different hierarchy levels. For example, in my sample, 26% of employees share a position title with either their supervisor or their supervisor’s supervisor. My use of direct measures of job authority allows me to avoid relying on (noisy) position titles.**C**: (1994) (a medium-sized service-sector firm) and Ransom and Oaxaca (2005) (a supermarket) have a relatively small set of possible career trajectories. However, this is not the case for many firms, particularly larger firms. In the firm that I study, the internal labor market consists of over 200 different occupations and multiple non-intersecting career paths, making it difficult to construct a universal hierarchy ranking based on transitions between position titles | ACB | BCA | CAB | ABC | Selection 1 |
**A**: Finally, we show some concrete examples of distortion factor risk measures such as CoESCoES\mathrm{CoES}roman_CoES, the distortion of conditional VaRVaR\mathrm{VaR}roman_VaR and the expectation of conditional ESES\mathrm{ES}roman_ES. Most of those factor risk measures are new and they offer new angles to evaluate the risk affected by some factors. To some extend, our characterization results in Section 3 extend the results in Wang and Ziegel (2021). It is also worth mentioning that our characterization with the aid of the distortion functionals on a set of Borel measurable functions is very different from Gong et al. (2022), where the axioms are state-wise and the expressions are state-wise based.
**B**: Under this new law-invariance, we study factor risk measures satisfying monotonicity and comonotonic-additivity which are called the distortion factor risk measures in Section 3. The classical distortion risk measures have been widely applied in decision theory (Yaari (1987)), insurance and option pricing (Wang (1996) and Wang (2000)), performance evaluation (Cherny and Madan (2009)) and quantitative risk management (McNeil et al. (2015) and Föllmer and Schied (2016))**C**: Hence, its generalization to the case with factors is important from both theoretical and practical perspective. In our characterization in Theorem 1, the distortion factor risk measure can be represented by a Choquet integral defined by a distortion functional on a set of Borel measurable functions, which extends the classical distortion risk measures given by a Choquet integral with a distortion function on the real line. Moreover, we find a necessary and sufficient condition on the distortion functionals such that the distortion factor risk measure is coherent | ACB | CAB | CBA | BCA | Selection 2 |
**A**: The first is a single-stage scheme as a baseline, using the classifiers in Section 3.3. The second is the multi-class stacking ensemble described in the same section**B**:
In this section, we evaluate the final performance of our system to detect financial opportunities and precautions. The results were computed using two different streaming approaches**C**: Since both were implemented in streaming mode, they were progressively tested and trained by sequentially using each sample from the experimental data set to test the model (i.e., to predict) and then to train the model (i.e., for a partial fit). Performance metrics are obtained as their incremental averages. In particular, we employed the EvaluatePrequential313131Available at https://scikit-multiflow.readthedocs.io/en/stable/api/generated/skmultiflow.evaluation.EvaluatePrequential.html, January 2023. library. | CAB | CAB | BAC | CAB | Selection 3 |
**A**: The methodology for our PageRank calculation emphasizes incoming edges—interpreted, within the framework of our analysis, as import flows. Consequently, our centrality measure is designed to recognize a country’s influence within the network based on import performance.
**B**: PageRank (mixed): by leveraging the PageRank algorithm, we determine the relative importance or centrality of countries within the trade network**C**: A country’s centrality reflects not just its direct trade connections but also the significance of its trading partners | CAB | BCA | ACB | CBA | Selection 1 |
**A**: The price takes the form (6), where
**B**: This is the reverse combined demand function at equilibrium, and indicates linear price impact**C**: Indeed, even though the insider does not internalize impact in the price-taking case, in equilibrium it turns out the price is linearly impacted by her trade, combined with the noise trader’s demand | CAB | CBA | BAC | BCA | Selection 1 |
**A**: However, due to the conventional price series involving a lot of noise, the trend patterns may not be easy to discover by most of the existing methods under the highly turbulent financial market. In this work, a multi-agent and self-adaptive portfolio optimisation framework integrated with attention mechanisms and time series namely the MASAAT is proposed in which multiple trading agents are introduced to analyse price data from various perspectives to help reduce the biased trading actions**B**: In addition to the conventional price series, the directional changes-based data are considered to record the significant price changes in different levels of granularity for filtering any plausible noise in financial markets. Furthermore, the attention-based cross-sectional analysis and temporal analysis in each agent are adopted to capture the correlations between assets and time points within the observation period in terms of different viewpoints, followed by a spatial-temporal fusion module attempting to fuse the learnt information. Lastly, the portfolios suggested by all agents will be further merged to produce a newly ensemble portfolio so as to quickly respond to the current financial environment. The empirical results on three challenging data sets of DJIA, S&P 500, and CSI 300 market indexes reveal the strong capability of the proposed MASAAT framework to balance the overall returns and portfolios risks against the state-of-the-art approaches.**C**:
Financial Portfolio optimisation has been studied for a few decades yet is still a very challenging and significant task for investors to balance investment returns and risks under different financial market conditions. There are many studies trying to use various deep or reinforcement learning approaches such as convolution-based, recurrent-based, and graph-based neural networks to capture the spatial and temporal information of assets in a portfolio | BCA | ACB | ABC | CAB | Selection 1 |
**A**: One possibility is the absence of bots. Therefore, in the following section we introduce a new formulation for the pool in which LPs and bots interact, and we subsequently examine the effects of bots on the accuracy of model simulations.
**B**: While these results are promising, we note that there are still certain discrepancies between the real and simulated exchange rates and liquidity distributions. One such issue is that the error between the observed and simulated November 30 liquidity distribution (4.4313) is almost double the error from between the observed and calibrated distributions on November 29 (2.3902)**C**: In addition, we see that the observed pool exchange rates follow the market exchange rate almost perfectly (MAPE of 0.063%) while there is more than twice as much error when looking at the simulated pool exchange rate (MAPE of 0.1357%). One possible explanation for these discrepancies is a missing element | ABC | ACB | BAC | CAB | Selection 4 |
**A**: In this example, we use estimates by Campos et al**B**: We now turn to a more complicated example that uses several years of data**C**: (2023) of Spain’s “border thickness”. In their article, they measure the border thickness (a measure of how difficult it is to trade internationally) of Spain during the Franco regime and compare it to the border thickness of a synthetic control for Spain that is calculated as the average of other countries. They report estimates of the welfare loss implied by Spain’s differential border thickness and interpret this welfare loss as the effect economic policies followed by Spain in the post-war period until 1975.
| BCA | CBA | BAC | BCA | Selection 3 |
**A**: Traditional models are able to tell whether this behaviour is due to this person neglecting her prior beliefs (base rate neglect), or interpreting too much out of the information she receives (overinference). However, other potential biases could also explain her updating behaviour**B**: If she has preferences over different outcomes, is she updating her beliefs too much because she generally overinterprets information, or because she cares about the outcome (optimism/pessimism)? Or may she instead be jumping to conclusions because the information she receives is confirming her previous beliefs (confirmation bias)? Could it also be that she has very little doubt about the outcome that she believes to be more likely to happen (overconfidence)? By excluding some of these biases from the model, we may wrongly attribute her behaviour to a different bias than the one(s) she is actually exhibiting. This implies that some of the biases which happen to be present in the literature may only seem to be relevant because we lack a more complete model. For instance, base-rate neglect might appear to be driving behaviour because, say, confirmation bias and overconfidence are unaccounted for.
**C**: The essential reason why such a model would be desirable is that the identification of belief-updating biases can be confounded when some potential biases are not taken into consideration. For example, imagine a person who consistently updates her beliefs “too much” in the face of new information | CAB | BCA | ACB | CBA | Selection 2 |
**A**: [22] argued that deep hedging often depends on a specific choice of price process simulator, which could result in suboptimal performance**B**: They suggested that improved performance could be achieved by utilizing empirical data for training. However, the amount of historical market data available is often limited.**C**: In conventional deep hedging [3], a simulator based on the Heston model [4] was used for training.
Mikkila et al | BCA | CAB | CAB | CAB | Selection 1 |
**A**: We illustrate that it not only measures the centrality of a node in a network, providing insight into its influence and connectivity within the network but also contains other implicit information in the web graph, which can be used to classify a company and predict its economic performance. Its interconnectedness and influence within a network can reflect its economic diversity, as companies with a wide range of products or services often have diverse market connections and are more resilient to market changes. This centrality can also highlight opportunities for strategic partnerships and market expansion.**B**:
In our analysis, we found that harmonic centrality scores are complex measures that combine information related to the network graph structure, such as the size of domains and the number of inbound links, but also include information related to offline unstructured characteristics such as the size, scope, influence, and impact of the organization**C**: The contribution of this paper is to explore the nature of harmonic centrality further, examine structural and nonstructural features associated with these measurements, find significant correlations, and provide another view to understanding harmonic centrality | BCA | CAB | BCA | ACB | Selection 2 |
**A**:
In this section we discuss two examples of robust allocation mechanisms**B**: In each, we propose a model of market allocation based on a given robust allocation mechanism that allows the global frictional cost in the market to be parameterized by a single parameter**C**: This parameter can be understood as related to the fees imposed by the allocator on the participants. | ACB | ABC | CBA | BCA | Selection 2 |
**A**: Even small analytic firms often have teams of engineers and programmers who outnumber even a large group at a single financial institution**B**: The increasingly specialised nature of analytic technology means that external software can be a key leverage point for smaller PMs and investment managers.**C**: Robustness and support.
A key feature of external software is the size of the team and the technical advantage of specialised IT support | ACB | BCA | BAC | CAB | Selection 2 |
**A**: In the corresponding section, we will motivate the additional difficulties in the pricing of this early-exercise interest rate derivative. In order to avoid some of the drawbacks described above when using traditional numerical techniques, the ANN solutions have recently emerged as interesting alternatives, whose main advantage is precisely that they decouple the expensive computations (carried on in training phase) from the actual use**B**: An already significant number of solutions based on ANNs for financial problems have been proposed in the last few years [22]. Thus, ANNs have been applied for pricing all types of financial derivatives, with [5, 17] and without [25] early-exercise features, recovering implied volatilities [16, 26], solving valuation PDEs [32, 35], among others [13]. Still, there is much room to explore within this novel field in general and, in particular, regarding its application in the early-exercise pricing problem.**C**:
This paper addresses the problem of pricing involved financial derivatives by means of advanced forms of deep learning and ANN. Although the proposed methodology can be applied to other products, we will focus on the pricing of the Bermudan Swaption, which is a derivative whose underlying is the interest rate swap | BCA | ACB | ABC | CAB | Selection 1 |
**A**: The network takes the observed trajectory of the OU process as input and outputs the estimated parameters of the OU process**B**: We sample the number of paths given in the forward computation of the network,**C**: Formally, the loss function calculates
the mean squared error between the predicted and actual parameters of the OU process | ABC | ABC | ACB | CAB | Selection 3 |
**A**: Although these microscale networks provide new and useful information, they also pose several challenges for their interpretation and applications in, for example, policy design and analysis**B**: The structure of interannual of labor Networks vary over time due to both cyclical and structural factors ([3], [2], [5]).
We also applied the skill-relatedness (SR) indicator measure for the analysis of labor flow dynamics [6], and compare it with the original flows in order to differentiate the type of information that each of these techniques offers for characterizing the productive system based on the dynamics of private formal employment [7].**C**: Previously, in [3, 4] the inter-industry labor flows of Argentina have been studied at high level of details, and revealed that networks extracted are typically very dense, not sparse, with clear core-periphery structures, and present small-world properties | BCA | CBA | ACB | BAC | Selection 1 |
**A**: I demonstrate the performance of the risk-sensitive q-learning algorithm on two applications in Section 5. In particular, the theoretical guarantee for Merton’s investment problem is shown in Section 5.1.2. Finally, Section 6 concludes. All proofs are in the Appendix.**B**: Section 3 is devoted to introducing the definition of q-function for risk-sensitive problems and showing the martingale characterization of the optimal q-function and value function. The difference between q-learning and policy gradient, and the extension to ergodic problems are presented in Section 4**C**:
The rest of the paper is organized as follows. Section 2 describes the problem setup and motivates this formulation | CBA | ABC | BAC | BAC | Selection 1 |
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