Prediction markets aggregate dispersed beliefs into prices that act as probabilistic forecasts of uncertain events. Classical theory establishes a clean equivalence between forecasting accuracy and trading profit, but only for the specific automated market maker (AMM) design. However, the largest exchanges today are based on central limit order books in which informed forecasters routinely lose money while uninformed strategies can profit on simple heuristics. We resolve this discrepancy by establishing a formal equivalence between predictive accuracy and profitability. For any strictly proper scoring rule $S$, we exhibit a "proper" betting strategy that depends only on the forecaster's prediction $\mathbf{p}$ and the market price $\mathbf{q}$, and earns positive expected profit whenever $\mathbf{p}$ outperforms $\mathbf{q}$ under $S$ and the market has sufficient liquidity. Moreover, this proper betting is essentially the only strategy with such robust profitability guarantee. The proof rests on a decomposition of expected profit that strictly generalizes the classical AMM guarantee and also explains how strategies can profit without an accuracy edge. Empirically, across thousands
Retention campaigns in customer relationship management often rely on churn prediction models evaluated using traditional metrics such as AUC and F1-score. However, these metrics fail to reflect financial outcomes and may mislead strategic decisions. We introduce e-Profits, a novel business-aligned evaluation metric that quantifies model performance based on customer lifetime value, retention probability, and intervention costs. Unlike existing profit-based metrics such as Expected Maximum Profit, which assume fixed population-level parameters, e-Profits uses Kaplan-Meier survival analysis to estimate tenure-conditioned (customer-level) one-period retention probabilities and supports granular, per-customer profit evaluation. We benchmark six classifiers across two telecom datasets (IBM Telco and Maven Telecom) and demonstrate that e-Profits reshapes model rankings compared to traditional metrics, revealing financial advantages in models previously overlooked by AUC or F1-score. The metric also enables segment-level insight into which models maximise return on investment for high-value customers. e-Profits provides a transparent, customer-level evaluation framework that bridges pred
Existing studies that introduce the concept of fixed capital into the Okishio Theorem show that, under an unchanged real wage, cost-reducing technical progress still necessarily raises the equilibrium rate of profit. This paper argues that this conclusion depends on how cost is defined in fixed capital models. In the original model of Okishio (1961), cost corresponds to what is termed operating cost in accounting, which excludes any form of profit; by contrast, the classical fixed capital frameworks adopted by Roemer (1979) and Woods (1985) cannot mathematically separate depreciation from profit, so that cost comparison in fact corresponds to capitalized cost inclusive of profit. This paper employs the annuity method to treat fixed capital, thereby separating depreciation from profit, and grounds the capitalist's technology choice on the basis of operating cost. The results show that two distinct thresholds exist in the fixed capital model: a profit-rate threshold that determines the direction of change in the rate of profit after the diffusion of the new technology, and an operating-cost threshold that determines whether the capitalist will adopt the new technology. When the lifet
Profit Maximization is one of the key objectives for social media marketing, where the task is to choose a limited number of highly influential nodes such that their initial activation leads to maximum profit. In this paper, we introduce a variant of the Profit Maximization Problem where we consider that instead of nodes, benefits are assigned to some of the motifs of the graph, and these benefit values can be earned once a given threshold count of nodes from the motifs is influenced. The goal here is to choose a limited number of nodes for initial activation called seed nodes such that the motif-oriented profit gets maximized. Formally, we call our problem the Motif Oriented Profit Maximization Problem. We show that the problem is NP-hard to solve optimally. We propose a Reverse Reachable Set-based framework to solve our problem. The proposed methodology broadly divides into three steps: KPT Estimation and RR Set generation, Seed Set Selection, and Motif Oriented Profit Estimation. The proposed methodology has been analyzed to understand its time and space requirements. It has been implemented with real-world social network datasets, and the results are reported. We observe that t
This paper investigates the extent of political rent seeking in Hungary in the 2010s. Political capitalism--where powerful private interests influence public policy for private gain--creates opportunities for rent seeking that vary across sectors. The analysis is based on a theoretical model assuming rent seeking occurs in a three-stage process: changes in economic institutions granting regulatory privileges, which are enhanced by political-business networks; this leads to scarcities, and increased market power in certain markets; which then generates rents. To quantify this, the study evaluates Hungarian political capitalism by examining the impact of political decisions on firms' rents, analysing the profit trends of the 1,000 largest Hungarian firms (selected annually by net sales) and comparing their mean profit share (earnings before tax) across two periods: 2008-2012 and 2019-2023. A significant increase in a sector's mean profit share was assumed to indicate increased rent seeking. Using Welch's two-sample t-tests, three sectors were identified as potentially experiencing increased rent seeking: agriculture, construction, and financial and insurance activities. Quantitative
We present ProFit, a new code for Bayesian two-dimensional photometric galaxy profile modelling. ProFit consists of a low-level C++ library (libprofit), accessible via a command-line interface and documented API, along with high-level R (ProFit) and Python (PyProFit) interfaces (available at github.com/ICRAR/ libprofit, github.com/ICRAR/ProFit, and github.com/ICRAR/pyprofit respectively). R ProFit is also available pre-built from CRAN, however this version will be slightly behind the latest GitHub version. libprofit offers fast and accurate two- dimensional integration for a useful number of profiles, including Sersic, Core-Sersic, broken-exponential, Ferrer, Moffat, empirical King, point-source and sky, with a simple mechanism for adding new profiles. We show detailed comparisons between libprofit and GALFIT. libprofit is both faster and more accurate than GALFIT at integrating the ubiquitous Serrsic profile for the most common values of the Serrsic index n (0.5 < n < 8). The high-level fitting code ProFit is tested on a sample of galaxies with both SDSS and deeper KiDS imaging. We find good agreement in the fit parameters, with larger scatter in best-fit parameters from fit
Diffusion of information, innovation, and ideas is an important phenomenon in social networks. Information propagates through the network and reaches from one person to the next. In many settings, it is meaningful to restrict diffusion so that each node can spread information to only a limited number of its neighbors rather than to all of them. Such social networks are called closed social networks. In recent years, social media platforms have emerged as an effective medium for commercial entities, where the objective is to maximize profit. In this paper, we study the Profit Maximization in Closed Social Networks (PMCSN) problem in the context of viral marketing. The input to the problem is a closed social network and two positive integers $\ell$ and $B$. The problem asks to select seed nodes within a given budget $B$; during the diffusion process, each node is restricted to choose at most $\ell$ outgoing links for information diffusion; and the objective is to maximize the profit earned by the seed set. The PMCSN problem generalizes the Influence Maximization problem, which is NP-hard. We propose two solution approaches for PMCSN: a sampling-based approximate solution and a margin
Cyclic arbitrage chances exist abundantly among decentralized exchanges (DEXs), like Uniswap V2. For an arbitrage cycle (loop), researchers or practitioners usually choose a specific token, such as Ether as input, and optimize their input amount to get the net maximal amount of the specific token as arbitrage profit. By considering the tokens' prices from CEXs in this paper, the new arbitrage profit, called monetized arbitrage profit, will be quantified as the product of the net number of a specific token we got from the arbitrage loop and its corresponding price in CEXs. Based on this concept, we put forward three different strategies to maximize the monetized arbitrage profit for each arbitrage loop. The first strategy is called the MaxPrice strategy. Under this strategy, arbitrageurs start arbitrage only from the token with the highest CEX price. The second strategy is called the MaxMax strategy. Under this strategy, we calculate the monetized arbitrage profit for each token as input in turn in the arbitrage loop. Then, we pick up the most maximal monetized arbitrage profit among them as the monetized arbitrage profit of the MaxMax strategy. The third one is called the Convex Op
This paper analyzes the relation between bank profit performance and business models. Using a machine learning-based approach, we propose a methodological strategy in which balance sheet components' contributions to profitability are the identification instruments of business models. We apply this strategy to the European Union banking system from 1997 to 2021. Our main findings indicate that the standard retail-oriented business model is the profile that performs best in terms of profitability, whereas adopting a non-specialized business profile is a strategic decision that leads to poor profitability. Additionally, our findings suggest that the effect of high capital ratios on profitability depends on the business profile. The contributions of business models to profitability decreased during the Great Recession. Although the situation showed signs of improvement afterward, the European Union banking system's ability to yield returns is still problematic in the post-crisis period, even for the best-performing group.
We consider islamic Profit and Loss (PL) sharing contract, possibly combined with an agency contract, and introduce the notion of {\em $c$-fair} profit sharing ratios ($c = (c_1, \ldots,c_d) \in (\mathbb R^{\star})^d$, where $d$ is the number of partners) which aims to determining both the profit sharing ratios and the induced expected maturity payoffs of each partner $\ell$ according to its contribution, determined by the rate component $c_{\ell}$ of the vector $c$, to the global success of the project. We show several new results that elucidate the relation between these profit sharing ratios and various important economic factors as the investment risk, the labor and the capital, giving accordingly a way of choosing them in connection with the real economy. The design of our approach allows the use of all the range of econometrics models or more general stochastic diffusion models to compute or approximate the quantities of interest.
The study takes the social media industry as its research subject and examines the impact of scientific innovation capabilities on profit distribution within the value chain of the social media industry. It proposes a specific solution to the profit distribution problem using an improved Shapley value method. Additionally, the AHP (Analytic Hierarchy Process) is employed to evaluate the profit distribution model, allowing the improved Shapley value method to better address the issue of profit allocation within the value chain of the social media industry. This approach ensures that each member receives a fair share of the profits, fostering strong cooperative relationships among members. Moreover, it compensates for the shortcomings of the traditional Shapley value method in addressing such problems to a certain extent.
We examine two types of binary betting markets, whose primary goal is for profit (such as sports gambling) or to gain information (such as prediction markets). We articulate the interplay between belief and price-setting to analyse both types of markets, and show that the goals of maximising bookmaker profit and eliciting information are fundamentally incompatible. A key insight is that profit hinges on the deviation between (the distribution of) bettor and true beliefs, and that heavier tails in bettor belief distribution imply higher profit. Our algorithmic contribution is to introduce online learning methods for price-setting. Traditionally bookmakers update their prices rather infrequently, we present two algorithms that guide price updates upon seeing each bet, assuming very little of bettor belief distributions. The online pricing algorithm achieves stochastic regret of $\mathcal{O}(\sqrt{T})$ against the worst local maximum, or $ \mathcal{O}(\sqrt{T \log T}) $ with high probability against the global maximum under fair odds. More broadly, the inherent trade-off between profit and information-seeking in binary betting may inspire new understandings of large-scale multi-agent
The fine-tuning of pre-trained models has become ubiquitous in generative AI, computer vision, and robotics. Although much attention has been paid to improving the efficiency of fine-tuning model, there has been less scholarship around fine-tuning specifically for improved model performance. To remedy this gap, we present PROFIT, one of the first optimizers designed to incrementally fine-tune converged models on new tasks and/or datasets. Unlike traditional optimizers such as SGD or Adam, which make minimal assumptions due to random initializations, PROFIT takes the properties of a converged model into account explicitly to regularize the optimization process. Employing a temporal gradient-orthogonalization process, PROFIT outperforms fine-tuning methods in various tasks, from image classification to multimodal language model training to large-scale motion prediction. Moreover, PROFIT is encapsulated as a modular optimizer, which makes it easy to integrate directly into any training pipeline with minimal engineering effort.
To examine the relation between profitability and business models (BMs) across bank sizes, the paper proposes a research strategy based on machine learning techniques. This strategy allows for analyzing whether size and profit performance underlie BM heterogeneity, with BM identification being based on how the components of the bank portfolio contribute to profitability. The empirical exercise focuses on the European Union banking system. Our results suggest that banks with analogous levels of performance and different sizes share strategic features. Additionally, high capital ratios seem compatible with high profitability if banks, relative to their size peers, adopt a standard retail BM.
We study the coordination of actions and the allocation of profit in supply chains under decentralized control in which a single supplier supplies several retailers with goods for replenishment of stocks. The goal of the supplier and the retailers is to maximize their individual profits. Since the outcome under decentralized control is inefficient, cooperation among firms by means of coordination of actions may improve the individual profits. Cooperation is studied by means of cooperative game theory. Among others we show that the corresponding games are balanced and we propose a stable solution concept for these games.
This study analyses the tax-induced profit shifting behaviour of firms and the impact of governments' anti-shifting rules. We derive a model of a firm that combines internal sales and internal debt in a full profit shifting strategy, and which is required to apply the arm's length principle and a general thin capitalisation rule. We find several cases where the firm may shift profits to low-tax countries while satisfying the usual arm's length conditions in all countries. Internal sales and internal debt may be regarded either as complementary or as substitute shifting channels, depending on how the implicit concealment costs vary after changes in all transactions. We show that the cross-effect between the shifting channels facilitates profit shifting by means of accepted transfer prices and interest rates.
In a ride-pooling system, travellers experience discomfort associated with a detour and a longer travel time, which is compensated with a sharing discount. Most studies assume travellers receive either a flat discount or, in rare cases, a proportional to the inconvenience. We show the system benefits from individually tailored fares. We argue that fares that optimise an expected profit of an operator also improve system-wide performance if they include travellers' acceptance. Our pricing method is set in a heterogeneous population, where travellers have varying levels of value-of-time and willingness-to-share, unknown to the operator. A high fare discourages clients from the service, while a low fare reduces the profit margin. Notably, a shared ride is only realised if accepted by all co-travellers (decision is driven by the latent behavioural factors). Our method reveals intriguing properties of the shareability topology. Not only identifies rides efficient for the system and supports them with reduced fares (to increase their realisation probability), but also identifies travellers unattractive for the system (e.g. due to incompatibility with other travellers) and effectively shi
How much has market power increased in the United States in the last fifty years? And how did the rise in market power affect aggregate profits? Using micro-level data from U.S. Compustat, we find that several indicators of market power have steadily increased since 1970. In particular, the aggregate markup has gone up from 10% of price over marginal cost in 1970 to 23% in 2020, and aggregate returns to scale have risen from 1.00 to 1.13. We connect these market-power indicators to profitability by showing that the aggregate profit share can be expressed in terms of the aggregate markup, aggregate returns to scale, and a sufficient statistic for production networks that captures double marginalization in the economy. We find that despite the rise in market power, the profit share has been constant at 18% of GDP because the increase in monopoly rents has been completely offset by rising fixed costs and changes in technology. Our empirical results have subtle implications for policymakers: overly aggressive enforcement of antitrust law could decrease firm dynamism and paradoxically lead to lower competition and higher market power.
International taxation rules are outdated, allowing multinationals to shift profits to tax havens. This paper examines how tax reforms affect profit shifting and cross-country welfare. We propose a model that separates real economic profits from paper profits, introducing 'triangle identities' to estimate bilateral profit-shifting flows. Using macro- and firm-level data, paper profits' elasticity is three times that of the tax base. Global minimum tax reforms improve welfare by increasing public goods funding and reducing tax competition. We also identify optimal minimum rates under various taxing-right scenarios and demonstrate that unilateral destination-based-cash-flow-tax reforms' welfare effects depend highly on trade imbalances.
Most of the research in the recommender systems domain is focused on the optimization of the metrics based on historical data such as Mean Average Precision (MAP) or Recall. However, there is a gap between the research and industry since the leading Key Performance Indicators (KPIs) for businesses are revenue and profit. In this paper, we explore the impact of manipulating the profit awareness of a recommender system. An average e-commerce business does not usually use a complicated recommender algorithm. We propose an adjustment of a predicted ranking for score-based recommender systems and explore the effect of the profit and customers' price preferences on two industry datasets from the fashion domain. In the experiments, we show the ability to improve both the precision and the generated recommendations' profit. Such an outcome represents a win-win situation when e-commerce increases the profit and customers get more valuable recommendations.