This paper proposes a two-stage scoring approach to help lenders decide their fund allocations in the peer-to-peer (P2P) lending market. The existing scoring approaches focus on only either probability of default (PD) prediction, known as credit scoring, or profitability prediction, known as profit scoring, to identify…
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48 results for “profit scoring”
A new framework integrates credit scoring into profit scoring for better P2P lending investments.
problem Maximizing profit while minimizing risk in P2P lending investments.
method Two-stage framework using Light Gradient Boosting Machine (lightGBM) to integrate credit scoring into profit scoring.
result The proposed framework identifies more profitable loans and provides better investment guidance.
The paper explores fairness in credit scoring using machine learning.
problem The lack of research on fair machine learning in credit scoring.
method Revisits statistical fairness criteria, catalogs algorithmic options, and empirically compares fairness processors.
result Multiple fairness criteria can be approximately satisfied at once, and fair processors deliver a good balance between profit and fairness.
Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a multitude of sophisticated classification techniques have been developed to improve the statistical performance of credit scoring models. Instead of focusing on the techniques themselves, this paper leverages alternative d…
The paper is aware of the importance of certain figures that are essential to an understanding of Credit Scoring models in credit acceptance process optimization, namely if the power of discrimination measured by Gini value is increased by 5% then the profit of the process can be increased monthly by about 1 500 kPLN (…
The real estate is a pillar industry of China's national economy. Due to changes in policy and market conditions, the real estate companies are facing greater pressures to survive in a competitive environment. They must improve their financial competitiveness. Based on the conceptual framework of financial competitiven…
This paper evaluates financial competitiveness of Indian real estate companies using entropy method.
problem Improving financial competitiveness of Indian real estate companies in a competitive market.
method Financial competitiveness evaluation index system using key financial ratios and a scoring system.
result Companies with high scores have strong profitability and operational capacity, while those with lower scores struggle with solvency and working capital.
The paper tackles sampling bias in credit scoring models and proposes methods to improve their training and evaluation.
problem Sampling bias in credit scoring models leads to an incomplete representation of the borrower population.
method Bias-aware self-learning framework and Bayesian evaluation method to correct for bias.
result Bayesian evaluation outperforms standard accuracy measures in predicting future performance.
The paper explores fair machine learning policies for balancing competing objectives in noisy data.
problem Balancing competing objectives in noisy data.
method Analyzes a class of policies that trace an empirical Pareto frontier based on learned scores.
result Characterizes optimal strategies and bounds Pareto errors due to score inaccuracies.
Study shows how financial report sentiment impacts bank profitability.
problem Understanding causal effects of financial report sentiment on bank profitability.
method Causal forest machine learning methodology, FinancialBERT sentiment scores, SHAP analysis, comprehensive dataset.
result Statistically significant causal associations between balance sheet and expense management variables and profitability.
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Modelcs.LG
Study optimizes scoring rules for incentivizing agent's information gathering in online settings.
problem Optimizing incentives for agents to acquire information in online settings.
method Designing a sample-efficient algorithm that tailors the UCB algorithm to the strategic agent's model.
result Achieves sublinear -regret after iterations, independent of the number of states.
This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using "private information" about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a…
The paper introduces ESE scores for farmers to assess climate change risks.
problem Assessing climate change risks in individual farmers' credit evaluations.
method Integrating ESG variables into joint liability models and using a mean-variance utility function.
result Optimal group sizes and individual-ESE score relationships under various climatic conditions.
Study finds CRPS learning doesn't improve day-ahead bidding profits despite better accuracy.
problem Improving day-ahead bidding profits through better probabilistic price forecasting.
method CRPS learning to minimize continuous ranked probability score (CRPS) for ensemble predictions.
result Higher diversity in ensemble predictions improves accuracy but doesn't lead to higher profits.
Decision makers increasingly rely on algorithmic risk scores to determine access to binary treatments including bail, loans, and medical interventions. In these settings, we reconcile two fairness criteria that were previously shown to be in conflict: calibration and error rate equality. In particular, we derive necess…
Scores political leanings in Web3 betting markets.
problem Understanding political motivations in decentralized prediction markets.
method Constructing PBLS from Polymarket data, analyzing 15k addresses, 4k events, 8k markets.
result Validated PBLS through internal and external comparisons, revealing political and profit motives.
Optimizes stock portfolios with profit, risk, and sustainability.
problem Balancing profit, risk, and sustainability in stock portfolio management.
method Developed a novel utility function combining Sharpe ratio and ESG scores; used genetic algorithm for optimization.
result System outperforms traditional reinforcement learning methods and improves on risk and sustainability metrics.
A feature-rich Bitcoin trading assistant using reinforcement learning.
problem Predicting and analyzing Bitcoin market trends for long-term profits.
method Reinforcement learning with technical indicators and Twitter sentiment scores.
result Average profit of 69% over 685 days, including the Covid-19 period.
New model fusion method improves Bitcoin price prediction accuracy.
problem Improving robustness in financial price prediction models.
method Combinatorial Fusion Analysis (CFA) combining score and rank combinations.
result Significantly improved MAPE performance of 0.19\%.
The paper compares ML models for credit scoring and investment decisions using explainable AI.
problem The opacity of machine learning models in financial services.
method Comparison of various machine learning models (single classifiers, ensembles, neural networks) and explainability techniques (LIME, SHAP).
result Ensemble classifiers and neural networks outperform in credit scoring models.
Deep Learning-Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Marketcs.LG
Transformer model forecasts electricity price spread for virtual bidding.
problem Volatility in renewable energy causes price forecasting challenges.
method Transformer-based deep learning model using various time-series features.
result Trading strategy at peak hour yields nearly consistent profit.
NoxTrader predicts stock returns using LSTM for profitable trading.
problem Predicting profitable stock returns for quantitative trading.
method LSTM model for time-series analysis of stock data.
result Improved investment return from -60% to 325%.
The study compares profitability of conventional and Islamic banks in Bangladesh.
problem Evaluating profitability of commercial banks in Bangladesh.
method Examined bank-specific, industry-specific, and banking system factors on profitability.
result Islamic banks consistently outperform conventional banks in profitability.
Paper proposes hybrid approach for transparent credit scoring models.
problem Lack of transparency in machine learning models limits their use in regulated environments.
method Post-hoc interpretation of black-box models guides feature selection, followed by training glass-box models.
result Reduces feature usage from 106 to 10 while maintaining comparable performance.
The profitability of CPMMs is significantly impacted by mint and burn fees.
problem Understanding the profitability of decentralized exchanges.
method Formalized liquidity providers' profitability conditions, studied the effect of mint and burn fees, and compiled a large data set from Uniswap V2 transactions.
result The profitability of liquidity provision is severely affected by mint and burn costs.
Fossil power firms have recently profited more than renewables, but this may be a temporary phenomenon.
problem The profitability gap between renewable and fossil power firms in Europe.
method Machine-learning clustering and Bayesian model averaging.
result Renewable power firms are becoming more profitable, while fossil power firms are becoming less so.
This paper introduces strategies to maximize arbitrage profits in decentralized exchanges.
problem Maximizing profits from arbitrage loops in decentralized exchanges.
method Three strategies: MaxPrice, MaxMax, and Convex Optimization.
result The Convex Optimization strategy yields the highest monetized arbitrage profit in theory and practice.
Marxism's rate of profit thesis rejected as illogical.
problem Marxism's rate of profit thesis is challenged as illogical.
method Rejects Marx's thesis on the long-term tendency of the rate of profit to fall.
result Marxism's rate of profit thesis is rejected as illogical.
Equations track profits and losses in trading algorithms.
problem Evaluating the performance of trading algorithms.
method Formal equations incorporating spread.
result Evaluate trading model algorithms' performance.
Fluctuations of company yearly profits versus scaled revenue: Fat tail distribution of Levy typeq-fin.GN
We analyze annual revenues and earnings data for the 500 largest-revenue U.S. companies during the period 1954-2007. We find that mean year profits are proportional to mean year revenues, exception made for few anomalous years, from which we postulate a linear relation between company expected mean profit and revenue. …
Study analyzes factors affecting profits in crypto liquidity provision.
problem Liquidity providers lack guidance for developing profitable strategies.
method Developed a measurement model based on impermanent loss to analyze key parameters.
result Uncovered influences of key parameters on LPs' profits.
Is an investor stolen their profits by mimic investors? Investigated by an agent-based modelq-fin.CP
Study shows adding similar investors can either increase or decrease profits, depending on their strategy.
problem Investors argue conflictingly about the impact of adding similar investors on their profits.
method Built an agent-based financial market model with additional agents and investigated their earnings.
result Adding similar fundamental agents stabilizes market prices and decreases profits, while adding similar technical agents makes prices unstable and increases profits.
Study shows environmental spending positively impacts company profitability.
problem Impact of environmental spending on company profitability.
method Panel data regression analysis using E-Views.
result Environmental spending positively impacts profitability metrics.
Study examines downsizing impact on Indian construction firms' profitability.
problem Impact of downsizing layoffs on construction firms' profitability in India.
method Used Co-integration test, OLS, and VAR models on secondary data of 15 companies.
result Employee Expenses and Number of Employees have significant impact on profitability.
Expands robust profit opportunities to include distributional uncertainty.
problem Distributional uncertainty in financial markets.
method Formulates infinite dimensional primal problems, simplifies to finite dimensional dual problems using Wasserstein distance.
result Distributional uncertainty can enhance robustness of profit opportunities.
A conjecture on the distribution of firm profitcond-mat.other
A common assumption of political economy is that profit rates across firms or sectors tend to uniformity, and often models are formulated in which this tendency is assumed to have been realised. But in reality this tendency is never realised and the distribution of firm profits is not degenerate but skewed to the right…
Study increasing profits in a flexible financial market model.
problem Characterize increasing profits in a 1D diffusion market with interest rates.
method Characterize increasing profits using an auxiliary deterministic signed measure and a canonical trading strategy.
result Existence and characterization of increasing profits in terms of and .
We investigate the structure of the profit landscape obtained from the most basic, fluctuation based, trading strategy applied for the daily stock price data. The strategy is parameterized by only two variables, p and q. Stocks are sold and bought if the log return is bigger than p and less than -q, respectively. Repet…
The paper defines fair profit sharing ratios in Islamic PL contracts.
problem Determining fair profit sharing ratios in Islamic PL contracts.
method Introduces -fair profit sharing ratios and uses econometrics models to compute or approximate them.
result Elucidates the relation between profit sharing ratios and economic factors.
This paper finds a new method for decomposing insurer profits and losses.
problem Nonlinear balance sheets make it hard to attribute changes to risk factors.
method An axiomatic approach leading to infinitesimal sequential updating (ISU) decompositions.
result ISU decompositions are more general and applicable beyond insurance.
Automated Market Makers: A Stochastic Optimization Approach for Profitable Liquidity Concentrationq-fin.TR
Optimizes liquidity provision intervals for profitable AMM participation.
problem Financial losses from poor liquidity provision intervals and reallocation costs.
method Developed a tractable stochastic optimization problem.
result Computes optimal liquidity provision intervals for profitable liquidity concentration.
Proposes a framework to reconcile policy learning and profit maximization in CATE estimation.
problem Aligning CATE estimation with profit maximization for optimal customer treatment decisions.
method Optimizes a novel objective function that concentrates learning capacity near the decision boundary, ensuring consistency with the original profit function.
result Consistent CATE estimates can be recovered from existing profit-maximization pipelines, allowing firms to navigate the trade-off between accuracy and profit.
Study examines the impact of employment benefit costs on firm profitability.
problem Impact of employment benefit costs on firm profitability.
method Panel data regression analysis using E-Views.
result There is a significant positive relationship between employment benefit costs and firm profitability.
Limited profit in predictable stock marketscond-mat.stat-mech
It has been assumed that arbitrage profits are not possible in efficient markets, because future prices are not predictable. Here we show that predictability alone is not a sufficient measure of market efficiency. We instead propose to measure inefficiencies of markets in terms of the maximal profit an ideal trader can…
The paper limits the profitability of technical trading rules and finds they are not better than random trading.
problem The profitability of technical trading rules in stock markets is controversial.
method Proves the upper bound of cumulative return and investigates the profitability of technical trading rules using bootstrap methodology.
result Technical trading rules are not better than random trading and less profitable than the market.
Study optimal portfolio selection using average and current profitability of risky assets.
problem Continuous-time mean-variance portfolio selection in time-varying financial markets.
method Introduced AP and CP indexes; estimated AP and CP using second-order variation of an auxiliary wealth process.
result Estimations of AP and CP are more accurate than traditional MLE.
Current study aims to provide new empirical evidence on the impact of debt on corporate profitability. This impact can be explained by three essential theories: signaling theory, tax theory and the agency cost theory. Using panel data sample of 2240 French non listed companies of service sector during 1999-2006. By uti…
Predicting risk/reward ratio in financial markets for asset management using machine learningq-fin.CP
Study improves trading decisions by predicting profit and loss outcomes.
problem Inconsistent profitability of machine learning forecasts in financial markets.
method Developed a novel algorithm for forecasting profit and loss outcomes, integrating with market trend predictions.
result Significantly improved performance of trading strategies, including traditional and algorithmic trading.