Study option pricing in sideways markets and target zones.
problem Option pricing in sideways markets and target zones.
method Closed-form option pricing formulas for sideways markets and target zones.
result Closed-form option pricing formulas for sideways markets and target zones.
Optimizes profit in targeted marketing across multiple markets with varying marketing expenditures.
problem Maximizing profit in a sequential marketing strategy with multiple markets and varying marketing costs.
method Near-optimal algorithms in an adversarial bandit setting, proving regret bounds for different demand curve types.
result Proved near-optimal regret bounds for the profit-maximization problem in targeted marketing.
Study bond market making with hit-ratio target using optimal control and HJB equations.
problem Optimizing bond market making with hit-ratio target in OTC markets.
method Stochastic optimal control approach, dualizing hit-ratio target, HJB equation, Riccati equation, linearization.
result Explicit quote decompositions into riskless spread, inventory-risk correction, and hit-ratio correction.
This paper studies a continuous-time market where an agent, having specified an investment horizon and a targeted terminal mean return, seeks to minimize the variance of the return. The optimal portfolio of such a problem is called mean-variance efficient à la Markowitz. It is shown that, when the market coefficients a…
Model predicts EU carbon prices using market and political factors.
problem Predict future carbon prices for EU market management.
method Support vector regression with grid search and cross validation.
result Model predicts carbon prices accurately for 2030.
Well begun is half done. In the crowdfunding market, the early fundraising performance of the project is a concerned issue for both creators and platforms. However, estimating the early fundraising performance before the project published is very challenging and still under-explored. To that end, in this paper, we pres…
This paper investigates the equilibrium interactions between trading targets and private information in a multi-period Kyle (1985) market. There are two investors who each follow dynamic trading strategies: A strategic portfolio rebalancer who engages in order splitting to reach a cumulative trading target and an uncon…
Leveraged ETFs can outperform their targets in certain market conditions, contrary to the volatility drag hypothesis.
problem The long-term performance decay of leveraged ETFs due to volatility drag.
method Unified framework incorporating AR(1) and AR-GARCH models, continuous-time regime switching, and flexible rebalancing frequencies.
result Return dynamics, including return autocorrelation, volatility clustering, and regime persistence, determine LETF performance.
Matched filters reveal optimal normalization methods for different market participants.
problem Optimizing signal extraction from order flow for market microstructure analysis.
method General matched filter principle applied to normalization strategies.
result Optimal normalization methods (e.g., SMC and STV) differ based on trader types. Uplift models support decision-making in marketing campaign planning. Estimating the causal effect of a marketing treatment, an uplift model facilitates targeting communication to responsive customers and efficient allocation of marketing budgets. Research into uplift models focuses on conversion models to maximize inc…
In rare disease physician targeting, a major challenge is how to identify physicians who are treating diagnosed or underdiagnosed rare diseases patients. Rare diseases have extremely low incidence rate. For a specified rare disease, only a small number of patients are affected and a fractional of physicians are involve…
Optimizes LightGBM for stock market forecasting with novel feature engineering and transformation methods.
problem Accurately forecasting stock market fluctuations to mitigate risks.
method Feature engineering and transformation methods for LightGBM optimization.
result Log Returns, Returns and EMA Difference Ratio are the most effective target variable transformations.
This paper improves bond market making by adjusting hit-ratios for client flow quality.
problem Economic misleading of raw hit-ratios in corporate bond market making.
method Stochastic-control framework with residual-quality-adjusted hit-ratio.
result Optimal quotes decompose into various components, improving service/economics frontier.
This paper improves traditional Markowitz optimization by considering variance at multiple time scales.
problem Traditional Markowitz optimization limits to a single time scale, ignoring variance across different frequencies.
method Introduces multifrequency optimization allowing specification of target Hurst exponents across multiple time scales.
result Effective risk management strategy that aligns with investor preferences at various time scales.
A novel approach using graph learning and synthetic long positions for statistical arbitrage in options markets.
problem Exploiting statistical arbitrage opportunities in options markets using machine learning.
method Two-stage graph learning approach: first stage defines a novel prediction target isolating pure arbitrages via synthetic bonds; second stage proposes SLSA positions.
result Statistically significant outperformance of GL baselines and consistent positive returns with an average P&L-contract information ratio of 0.1627.
Market research is generally performed by surveying a representative sample of customers with questions that includes contexts such as psycho-graphics, demographics, attitude and product preferences. Survey responses are used to segment the customers into various groups that are useful for targeted marketing and commun…
Using virtual stock markets with artificial interacting software investors, aka agent-based models (ABMs), we present a method to reverse engineer real-world financial time series. We model financial markets as made of a large number of interacting boundedly rational agents. By optimizing the similarity between the act…
This study uses complex networks to analyze influential spreaders and their effects on different market sectors.
problem Existing methods failed to distinguish between positive and negative influences of market sectors.
method LIEST (Local Influential Effects for Specific Target) method using complex network analysis.
result LIEST effectively distinguishes positive and negative influences of market sectors during different periods.
This work presents the results of an empirical research with the target of modeling the stylized facts of the daily expost System Marginal Price (SMP) of the Greek wholesale electricity market, using data from January 2004 to December of 2011. SMP is considered here as the footprint of an underline stochastic and nonli…
Study addresses RTB model performance drops due to distribution shifts.
problem Distribution shifts between training and target environments in RTB markets.
method Applies Exponential Tilt Reweighting Alignment (ExTRA) algorithm to estimate and correct model weights.
result Demonstrates improved RTB model performance using ExTRA algorithm.
Insurance firms use RL to optimize customer offers for desired target portfolios.
problem Optimizing insurance offers to achieve a desired customer portfolio.
method Developed a novel reinforcement learning algorithm.
result The RL algorithm outperforms traditional methods in a synthetic market.
The authors seek financial datasets to benchmark feature engineering methods on US market data.
problem Improving predictive models for financial data science competitions.
method Feature engineering methods applied to multivariate time-series data from the US market.
result Predictive power of models tested against Numerai-Signals targets.
Study minimizes market inefficiency in systemic economies.
problem Minimizing deviations of market prices from fundamental values.
method Characterized market inefficiency and developed a matrix of holdings to minimize it.
result Portfolio holdings should deviate more from diversification if banks have similar systemic significance.
We present a reactive beta model that includes the leverage effect to allow hedge fund managers to target a near-zero beta for market neutral strategies. For this purpose, we derive a metric of correlation with leverage effect to identify the relation between the market beta and volatility changes. An empirical test ba…
The autonomous trading agent is one of the most actively studied areas of artificial intelligence to solve the capital market portfolio management problem. The two primary goals of the portfolio management problem are maximizing profit and restrainting risk. However, most approaches to this problem solely take account …
Efficiently calibrates SABR/LIBOR models to real market caplets and swaptions data.
problem Calibration of stochastic volatility models to real market data.
method Proposes a parallelized simulated annealing algorithm for multi-GPUs.
result Numerical results show advantages of using multi-GPUs for SABR/LIBOR model calibration.
Central bank strategy to maintain currency exchange rate within limits.
problem Maintaining a currency exchange rate within a target zone despite adverse economic trends.
method Modeling the problem with a continuous-time market impact model and solving it as a stochastic control problem.
result Optimal strategy minimizes accumulated inventory of foreign currency.
A new uplift modeling approach uses binary treatment indicators more efficiently.
problem Lack of full value utilization in binary uplift modeling.
method Design a novel transformed outcome for binary target variables.
result Our new approach outperforms traditional methods in synthetic and real-world datasets.
This paper presents a continuous-time model of intraday trading, pricing, and liquidity with dynamic TWAP and VWAP benchmarks. The model is solved in closed-form for the competitive equilibrium and also for non-price-taking equilibria. The intraday trajectories of TWAP trading targets cause predictable intraday pattern…
Method improves volatility targeting for index construction.
problem High turnover, leverage spikes, and sensitivity to estimation error in existing volatility-targeting strategies.
method Proportional-control approach for setting index weights that corrects tracking error through feedback.
result The proportional-control approach achieves the target volatility more effectively than open-loop alternatives.
Quantile regression using random forest proximities improves prediction and uncertainty quantification.
problem Forecasting corporate bond volume with uncertainty quantification.
method Introduced a novel approach to compute quantile regressions from random forests using proximity metrics.
result Superior performance in approximating conditional target distributions and prediction intervals.
Method identifies potential customers from limited data.
problem Efficiently market products to interested but non-loyal customers.
method Double Positive and Unlabeled (PU) learning approach.
result Proposed algorithm achieves efficient marketing.
UAMM uses external market prices to improve AMM efficiency and reduce liquidity provider risk.
problem Traditional AMMs lack consideration of external markets and risk management.
method UAMM calculates prices by incorporating external market prices and impermanent loss, maintaining constant product curve properties.
result UAMM eliminates arbitrage opportunities when external market prices are efficient, reducing liquidity provider risk.
AI agents in experimental markets exhibit behavioral patterns that aggregate into market dynamics.
problem Understanding AI trading behavior and its impact on market dynamics.
method Experimental asset markets populated by AI agents trained on Large Language Models (LLMs).
result AI agents' behavior leads to market dynamics similar to human traders, including bubbles.
After the shocking series of bankruptcies started in 2008, the public does not trust anymore the classical methods of assessing business risks. The global economic severe downturn caused demand for both developed and emerging economies' exports to drop and the crisis became truly global. However, this current crisis of…
Adversarial attacks can fool algorithmic trading systems.
problem Adversarial perturbations can manipulate algorithmic trading models.
method Real-time adversarial attacks on trading algorithms using universal perturbations.
result Perturbations can fool trading algorithms at unseen data points.
EarnMore uses masked stock representations to train RL agents for customizable stock pools efficiently.
problem Training RL agents for customizable stock pools (CSPs) is computationally expensive and unstable.
method EarnMore introduces a mechanism to mask out stocks outside the target pool, learns meaningful stock representations, and uses a re-weighting mechanism to focus on favorable stocks.
result EarnMore significantly outperforms state-of-the-art baselines in profit metrics with over 40% improvement.
Haircutting non-cash collateral has become a key element of the post-crisis reform of the shadow banking system and OTC derivatives markets. This article develops a parametric haircut model by expanding haircut definitions beyond the traditional value-at-risk measure and employing a double-exponential jump-diffusion mo…
A Python approach minimizes risk in decentralized exchanges.
problem Minimizing risk in decentralized exchanges.
method Three-step approach: Kernel Ridge Regression, function minimization, and algorithmic trick.
result Reduced computational load and increased solution accuracy.
Recently, prediction markets have shown considerable promise for developing flexible mechanisms for machine learning. In this paper, agents with isoelastic utilities are considered. It is shown that the costs associated with homogeneous markets of agents with isoelastic utilities produce equilibrium prices correspondin…
Proposes a model to estimate effects of multiple related treatments.
problem Estimating effects of many related treatments in observational data.
method Customized ridge regression to reduce noise and MSE.
result Significantly reduces MSE for individual sub-treatments while allowing reconstruction of aggregated treatment effects.
Pharmaceutical targeting is one of key inputs for making sales and marketing strategy planning. Targeting list is built on predicting physician's sales potential of certain type of patient. In this paper, we present a time-sensitive targeting framework leveraging time series model to predict patient's disease and treat…
Combines structured inference and targeted learning to tackle causal inference challenges.
problem Treatment assignment heterogeneity and lack of counterfactual data.
method Factorizes joint distribution into risk, confounding, instrumental, and miscellaneous factors; applies regularizer derived from influence curve.
result TVAE demonstrates competitive and state-of-the-art performance on benchmark datasets.
This paper measures financial market resilience in China and identifies key uncertainties.
problem Measuring financial market resilience in China.
method Quantitative analysis of total financial market and sub-markets, Diebold-Yilmaz connectedness approach.
result Financial market resilience in China is event-driven and influenced by geopolitical risks, economic and trade policy uncertainty, and U.S.-China tensions.
Financial exchanges provide incentives for limit order book (LOB) liquidity provision to certain market participants, termed designated market makers or designated sponsors. While quoting requirements typically enforce the activity of these participants for a certain portion of the day, we argue that liquidity demand t…
New method improves conditional covariance estimation using targeted groups of assets.
problem Improving conditional covariance estimation in financial time series.
method Introduces targeting in BEKK and DCC models for financial time series analysis.
result Encouraging results from empirical case study, especially with fewer assets.
In the current environment of financial distress, many governments are likely to soon become major holders of financial assets, but the policy debate focuses only on the likelihood and extent of short-term market stabilization. This paper shows that government intervention and propping up are likely to lead to long-ter…
Transfer learning improves electricity price forecasting accuracy.
problem Accurate day-ahead electricity price prediction using available data.
method Pre-train a neural network on source markets and fine-tune for target market.
result Transfer learning significantly improves forecasting performance.