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.
Diversification improves profits for heavy-tailed investments.
problem Investment portfolios of Pareto-distributed returns.
method Stochastic dominance and majorization order.
result Diversification increases first-order stochastic dominance for heavy-tailed returns.
Study optimal dividend policies for firms with random profitability.
problem Firms face a trade-off between bankruptcy and profit extraction.
method General cash flow drifts (Ornstein-Uhlenbeck, CIR) considered; rigorous proofs, numerical scheme provided.
result Optimal strategy includes barrier and band strategies, voluntary liquidation.
Study examines impact of capital structure on Indian auto companies' profitability.
problem Understanding the impact of capital structure on profitability of Indian auto companies.
method Used fixed and random effect models with 10 years of data from 17 companies.
result Optimal capital structure improves company performance and maintains capital adequacy.
This paper completes the analysis of Choulli et al. Non-Arbitrage up to Random Horizons and after Honest Times for Semimartingale Models and contains two principal contributions. The first contribution consists in providing and analysing many practical examples of market models that admit classical arbitrages while the…
The paper develops formulas for hedging and arbitrage in markets with random stopping times.
problem Developing pricing formulas for assets in markets with random stopping times.
method Modeling market with random stopping time, analyzing conditional essential supremum, and describing super-hedging prices.
result Explicit formulas for super-hedging prices and Immediate-Profit arbitrage are derived.
The study uses machine learning to predict cryptocurrency market trends and design profitable trading strategies.
problem Predicting cryptocurrency market trends for profitable trading.
method Applied k-Nearest Neighbours, eXtreme Gradient Boosting, and Random Forest classifiers to detect trends.
result High profit factor of 1.60 for unseen data, showing promising results.
Model assesses loan profitability under changing credit conditions.
problem Financial institutions face risks of default and prepayment.
method Develops a Random Net Present Value (RNPV) model to evaluate profitability.
result Mean and variance of RNPV calculated at individual and portfolio levels.
A version of indifference valuation of a European call option is proposed that includes statistical regularities of nonstochastic randomness. Classical relations (forward contract value and Black-Scholes formula) are obtained as particular cases. We show that in the general case of nonstochastic randomness the minimal …
We analyze wealth condensation for a wide class of stochastic economy models on the basis of the economic analog of thermodynamic potentials, termed transfer potentials. The economy model is based on three common transfers modes of wealth: random transfer, profit proportional to wealth and motivation of poor agents to …
Adaptive market-making strategy improves profit by adjusting to order flow.
problem Optimizing market-making profits in a dynamic market environment.
method Closed-form solutions for optimal bid-ask spreads, modeling demand randomness, and adapting to market order behavior.
result Adaptive strategies outperform fixed and non-adaptive strategies.
The profitable bandit problem aims to maximize earnings by choosing actions with uncertain rewards.
problem Maximizing earnings from uncertain rewards in a subset selection problem.
method Adapted and studied three strategies: kl-UCB, Bayes-UCB, and Thompson Sampling.
result Establishes asymptotic optimality for each strategy with finite time regret bounds.
Study analyzes factors affecting capital adequacy in Bangladesh's banks.
problem Factors influencing capital adequacy in commercial banks in Bangladesh.
method Fixed Effect, Random Effect, and Pooled Ordinary Least Square (POLS) methods.
result Several independent variables significantly affect capital adequacy, with specific relationships between leverage, liquidity risk, and other factors.
MACE optimizes stock portfolios for maximal predictability.
problem Maximizing risk-adjusted profitability through predictable stock returns.
method Developed a machine learning algorithm (MACE) using Random Forest and Ridge Regression.
result Significant increases in predictability and profitability with minimal conditioning information.
This paper identifies a negative profit effect in limit order fills.
problem Profit drag in limit order fills due to adverse price movements.
method Discrete market model, empirical simulation of US Treasury Bond futures.
result Existence of negative drift in limit order fills.
We investigate activities that have different periods of duration. We define the profit intensity as a measure of this economic category. The profit intensity in a repeated trading has a unique property of attaining its maximum at a fixed point regardless of the shape of demand curves for a wide class of probability di…
The paper analyzes optimal market making in latency-prone markets.
problem Optimal market making for large-tick assets in the presence of latency.
method Random walk model for asset price, Markov Decision Processes (MDP) formulation, explicit criteria for profitability.
result Market makers can earn positive expected profit under certain conditions.
Forecasting US stock market indices during COVID-19 using machine learning models.
problem Predicting stock market behavior during the pandemic.
method Used Random Forest and LSTM models on historical stock prices.
result Improved accuracy in forecasting stock market returns.
We consider the problem of dynamic buying and selling of shares from a collection of N stocks with random price fluctuations. To limit investment risk, we place an upper bound on the total number of shares kept at any time. Assuming that prices evolve according to an ergodic process with a mild decaying memory proper…
The paper combines supervised and unsupervised learning to predict financial market movements.
problem Predicting profitable opportunities in financial markets using machine learning.
method The paper uses linear models and Gaussian Mixture Models (GMM) to extract features from Bitcoin, Pepecoin, and Nasdaq markets.
result GMM filtering improved the performance of KNN and RF algorithms, leading to higher average returns.
One of the problems faced by a firm that sells certain commodities is to determine the number of products that it must supply in order to maximize its profit. In this article, the authors give an answer to this problem of economic interest. The proposed problem is a generalization of the results obtained by Stirzaker (…
We check the claims that data from Google Trends contain enough data to predict future financial index returns. We first discuss the many subtle (and less subtle) biases that may affect the backtest of a trading strategy, particularly when based on such data. Expectedly, the choice of keywords is crucial: by using an i…
Endogenous randomness emerges from adversarial market learning.
problem Market randomness
method Deterministic adversarial market model
result Out-of-sample profitability collapses to zero.
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.
We study arbitrage opportunities, market viability and utility maximization in market models with an insider. Assuming that an economic agent possesses from the beginning an additional information in the form of a random variable G, which only becomes known to the ordinary agents at date T, we give criteria for the No …
We use a continuous-time random walk (CTRW) to model market fluctuation data from times when traders experience excessive losses or excessive profits. We analytically derive "superstatistics" that accurately model empirical market activity data (supplied by Bogachev, Ludescher, Tsallis, and Bunde)that exhibit transitio…
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.
Paper uses conformal prediction for solar power forecasting in electricity markets.
problem Enhancing participation in electricity markets through accurate day-ahead PV power predictions.
method Combines machine learning for point predictions and conformal prediction for uncertainty quantification.
result CP with k-nearest neighbors and Mondrian binning outperforms linear quantile regressors in predicting PV power.
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.
Study shows Bitcoin mining with surplus electricity can boost KEPCO's financial stability.
problem Improving energy resource efficiency and reducing KEPCO's debt.
method Utilized surplus electricity for Bitcoin mining using Antminer S21 XP Hyd, analyzed with Random Forest Regressor and Long Short-Term Memory models.
result Bitcoin mining with surplus electricity generates economic revenue, minimizes energy loss, and resolves payment issues for KEPCO.
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.
Bayesian classifier uses stock price as ground truth for profitable trading signals.
problem Using sentiment analysis to predict stock prices based on social media data.
method Bayesian classifier trained on stock-specific dictionaries generated from tweets labeled as Buy or Sell based on stock price movement.
result 5.18% return rate over 1 month, annualizing to 83% per annum.
This paper addresses the question of how an arbitrage-free semimartingale model is affected when stopped at a random horizon. We focus on No-Unbounded-Profit-with-Bounded-Risk (called NUPBR hereafter) concept, which is also known in the literature as the first kind of non-arbitrage. For this non-arbitrage notion, we ob…
Two-stage scoring approach for P2P lending improves loan profitability prediction.
problem Class imbalance and lack of profitability prediction in existing scoring methods.
method Integrates credit scoring and profit scoring using wide and deep learning.
result Two-stage scoring approach outperforms existing methods in loan profitability prediction.
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.
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. …
Machine learning struggles to predict binary options movements due to randomness.
problem Predicting binary options movements using machine learning.
method Tested multiple machine learning models (RF, LR, GB, kNN) and neural networks (MLP, LSTM) on EUR/USD currency pairs.
result None of the models surpassed the ZeroR baseline accuracy, indicating randomness in binary options.
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.
The paper fits cash management models to data using stochastic and linear programming.
problem Cash flow probability distribution assumptions in cash management models are relaxed.
method Stochastic and linear programming to fit models to data.
result A small random sample of data is sufficient to fit bound-based models.
The prospects of Kahneman and Tversky, Mega Million and Powerball lotteries, St. Petersburg paradox, premature profits and growing losses criticized by Livermore are reviewed under an angle of view comparing mathematical expectations with awards received. Original prospects have been formulated as a one time opportunit…
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.
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.
Graph theory applied to stock market investing for risk reduction and diversification.
problem Risk reduction and diversification in stock market investments.
method Transformed correlation matrix into a graph, identified largest complete graph for diversified portfolio, used stock price data for predicting optimal investment times.
result Diversified portfolios consistently outperform the market, while undiversified portfolios are riskier.
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.
We present a broad agenda for meaningful banking regulation reform aiming the creation of evolutive competitive environment to maximize the effectiveness of international financial system through the introduction of fair competition process among the banks in free market capitalism. We assume that the international fin…