The 20/60/20 rule improves risk management and portfolio optimization in finance.
problem Understanding and managing financial data with heavy tails.
method Application of the 20/60/20 rule to stock market data, development of new measures for tail heaviness, and integration into portfolio optimization.
result The 20/60/20 rule enhances robustness and performance in portfolio optimization.
ARMS automates fraud detection rules to improve efficiency and accuracy.
problem Maintaining effective fraud detection rules over time and reducing manual review.
method ARMS uses heuristic search and a user-defined loss-function to optimize and manage rules.
result ARMS can maintain original systems' performance with only a fraction of the rules.
Dynamic rule-based investment strategies outperform static ones in pension schemes.
problem Managing retirement income with dynamic investment strategies.
method Rule-based investment strategies compared to dynamic programming.
result Rule-based strategies achieve higher probability of meeting retirement income targets.
Enhances multi-project scheduling with multiple priority rules.
problem Resource allocation in multi-project scheduling with limited time and resources.
method Simulation-based approach using composite priority rules.
result Increased probability of finding schedules with shortest duration.
Optimizes forecast distributions for financial risk management.
problem Improving risk management through better forecast distributions.
method Optimizes forecast distributions using scoring rules relevant to financial risk management.
result Tail-focused predictive distributions yield better outcomes in hedging strategies involving VIX futures.
In this paper, we study the problem of learning probabilistic logical rules for inductive and interpretable link prediction. Despite the importance of inductive link prediction, most previous works focused on transductive link prediction and cannot manage previously unseen entities. Moreover, they are black-box models …
Context-awareness in smart mobile applications is a growing area of study, because of it's intelligence in the applications. In order to build context-aware intelligent applications, mining contextual behavioral rules of individual smartphone users utilizing their phone log data is the key. However, to mine these rules…
New rule reduces exploration regret to logarithmic, improving bad episode handling.
problem Improving exploration regret in average reward MDPs.
method Replacing Doubling Trick with Vanishing Multiplicative rule in EVI-based algorithms.
result Regret is logarithmic under the new rule, significantly better than linear.
LI-ITR combines flexible ML with interpretable approximations for personalized treatment rules.
problem Combining flexibility and interpretability in personalized treatment rules.
method Uses variational autoencoders and a mixture of interpretable experts.
result Accurately recovers true local coefficients and optimal treatment strategies.
The decision to rollout a vehicle is critical to fleet management companies as wrong decisions can lead to additional cost of maintenance and failures during journey. With the availability of large amount of data and advancement of machine learning techniques, the rollout decisions of a supervisor can be effectively au…
The study develops a logic reasoner to verify MS case management specifications.
problem Ensuring correctness and solvability of abstract clinical work products.
method Developed a logic reasoner using UML class and state diagrams, SPIN, and ODM.
result Formal representations of UML diagrams proved consistent for MS case management.
VMAT strategy improves multivariate pair trading performance.
problem Leveraging multivariate time series for profitable portfolio management.
method Volatility & Model Adaption Trade-off (VMAT) strategy.
result VMAT strategy outperforms baseline strategies.
Paper proposes a new approach to GDPR compliance using data protection analytics.
problem Lack of research on data protection risk management and difficulty in GDPR compliance.
method Quantitative approach to data protection risk-based compliance.
result Improves data protection impact assessments by integrating analytics and expert opinions.
Approximate dynamic programming (ADP) has proven itself in a wide range of applications spanning large-scale transportation problems, health care, revenue management, and energy systems. The design of effective ADP algorithms has many dimensions, but one crucial factor is the stepsize rule used to update a value functi…
DRL optimizes asset managers' hedging timing based on market conditions.
problem Optimal timing for hedging strategies given market conditions.
method Deep Reinforcement Learning framework with contextual information, lagged observations, and robust testing.
result Our approach achieves superior returns and lower risk compared to standard methods.
ANADDH uses deep learning to improve volatility risk management.
problem Traditional Vega hedging strategies are inadequate for rapidly changing markets.
method Combines distributional reinforcement learning with adaptive Nesterov acceleration.
result Significant performance gains over existing hedging techniques.
New model incorporates long-range dependence in mortality rates for better valuation and risk management.
problem Lack of appropriate models for valuing and managing mortality securities with long-range dependence.
method Proposes a novel class of Volterra mortality models that incorporate LRD, derived in closed-form solution.
result Models provide flexibility and tractability for valuing and hedging mortality-related products.
Optimizes cryptocurrency exchanges' risk management by reducing positions based on leverage.
problem Managing risk in cryptocurrency futures exchanges during large price moves.
method Formulates ADL as an optimization problem to minimize risk of loss, using a water-filling rule to equalize leverage.
result The optimal ADL policy minimizes maximum leverage among participants, providing a transparent and implementable benchmark.
The 1/3 Financial Rule helps prevent household bankruptcy through balanced spending, savings, and debt repayment.
problem Reducing household bankruptcy risk through effective financial planning.
method Mathematical modeling, game theory, behavioral finance, and technological analysis.
result The 1/3 Financial Rule emerges as a robust solution for supporting household financial stability.
Researchers simulate and estimate a market model with a matching engine to understand its impact on order submission and management.
problem The impact of a matching engine on the modeling of order submission and management in financial markets.
method Simulation of a 10-variate Hawkes process with rules for different order types, including limit orders, to compare model parameters with the original order generating process.
result Practical considerations, not directly related to model specification, can significantly distort the true model specification in an asynchronous trading environment.
Proposes PRMs for interpreting financial risk concept drift.
problem Concept drift in high-stakes predictions like credit risk.
method Probabilistic Rule Models (PRMs) using Markov Logic Networks.
result Interpretable rules explain borrower risk changes.
One of the key elements in the banking industry rely on the appropriate selection of customers. In order to manage credit risk, banks dedicate special efforts in order to classify customers according to their risk. The usual decision making process consists in gathering personal and financial information about the borr…
OpenAlpha validates decentralized capital strategies using game theory and market aggregation.
problem Decentralized capital management's lack of trust-minimised, adaptive deployment.
method Game-theoretic validation, adversarial auditing, market-based belief aggregation.
result Confidence scores from validation phases inform capital allocation rules.
Study optimizes pension scheme risk-sharing for longevity bonds.
problem Managing longevity basis risk in pension schemes with income-drawdown guarantees.
method Stochastic optimal control, dynamic programming, HJB equations.
result Sharing longevity risk increases both manager and member utilities.
Suppose you have one unit of stock, currently worth 1, which you must sell before time T. The Optional Sampling Theorem tells us that whatever stopping time we choose to sell, the expected discounted value we get when we sell will be 1. Suppose however that we are able to see a units of time into the future, and ba…
AI models outperform simple rules in cross-asset futures timing, especially with lower transaction costs.
problem Optimizing cross-asset portfolio weights using traditional forecasting and optimization methods.
method End-to-end AI policies that map market states directly to portfolio weights, trained on CME futures using a differentiable Sharpe ratio loss function.
result Transformer-based AI policies outperform simple rules and equal weighting, trading less and matching or exceeding equal weighting through moderate transaction costs.
A dealer manages quotes and rejection rules to control slippage risk in FX markets.
problem Managing inventory risk and latency risk in OTC FX market making.
method Dynamic programming and adiabatic-quadratic approximation to optimize quotes and rejection rules.
result Developed a method to optimize quotes and rejection rules for managing slippage risk.
Unified stopping rules ensure accurate policies in contextual learning.
problem Stopping data collection to ensure accurate policies in personalized decision problems.
method Developed unified stopping rules based on GLR statistics for pairwise action comparisons.
result Unified stopping rules achieve target precision with fewer samples than benchmarks.
The paper develops a new framework for managing asymmetric volatility.
problem Managing asymmetric volatility to improve recovery and participation.
method Path-dependent framework for asymmetric volatility management.
result Skew engineering reduces harmful downside participation more than productive upside participation.
Unified AI system for data quality control and governance in regulated environments.
problem Isolated data quality control steps in existing systems.
method AI-driven framework integrating rule-based, statistical, and AI methods.
result Empirical gains in anomaly detection, reduced manual remediation, improved auditability.
Model analyzes how firms balance full disclosure with selective disclosure to maintain a good reputation.
problem Managing reputation in financial markets through voluntary disclosure.
method Developed a dynamic model with two disclosure strategies: candid and sparing, using a piecewise-deterministic model.
result Firms are rewarded for full disclosure but may switch to selective disclosure to avoid potential downgrades.
Investors adjust spending based on a social norm, spending less during losses and more during gains.
problem Managing spending and portfolio decisions while adhering to a social norm.
method Formulated a preference ordering with two CRRA preference orderings, solved analytically and numerically.
result Annual spending should be lower than expected financial return and procyclical, with spending cuts following losses.
Study compares machine learning and process-based models for predicting rice blast disease.
problem Predicting rice blast disease to support rice growers in controlling the disease.
method Compared four models: two process-based (Yoshino and WARM) and two machine learning (M5Rules and RNN).
result Machine learning models outperformed process-based models in predicting rice blast disease.
Hybrid AI and rule-based framework de-identifies medical imaging data.
problem De-identifying medical imaging data to protect PHI and PII.
method Combines rule-based and AI techniques with uncertainty quantification.
result Robust performance across benchmark datasets and regulatory standards.
New algorithm solves complex stopping problems with robust optimization.
problem Solving complex stochastic optimal stopping problems.
method Simulation-based robust optimization with exact reformulation as a zero-one bilinear program.
result Developed polynomial-time heuristics and algorithms for practical solution.
Paper derives policy rules from observational data for hepatitis C treatment.
problem Improving treatment guidelines for HIV/HCV co-infected patients.
method Weighted K-means algorithm for estimating CATEs, decision tree implementation.
result Identifies a subgroup with high spontaneous HCV clearance rate.
Predictions are issued on the basis of certain information. If the forecasting mechanisms are correctly specified, a larger amount of available information should lead to better forecasts. For point forecasts, we show how the effect of increasing the information set can be quantified by using strictly consistent scorin…
Tree-based algorithm for functional data analysis reduces generalization error.
problem Classification and regression problems with functional data.
method Constrained convex optimization for weighted functional L2 space, multiple splitting rules, and weighted integral features. result Reduces generalization error while maintaining interpretability.
Sophisticated volatility models outperform naive portfolio strategies.
problem Improving mean-variance portfolio performance over the naive 1/N strategy.
method Investigated various econometric and portfolio models across multiple datasets.
result Most models achieve higher Sharpe ratios and lower portfolio volatility than the naive rule.
The Australian Government uses the means-test as a way of managing the pension budget. Changes in Age Pension policy impose difficulties in retirement modelling due to policy risk, but any major changes tend to be `grandfathered' meaning that current retirees are exempt from the new changes. In 2015, two important chan…
We study the Merton problem of optimal consumption-investment for the case of two investors sharing a final wealth. The typical example would be a husband and wife sharing a portfolio looking to optimize the expected utility of consumption and final wealth. Each agent has different utility function and discount factor.…
Investing in cryptocurrencies can improve portfolio risk-return profile, especially with diversification strategies.
problem Investing in cryptocurrencies and evaluating their potential for portfolio allocation strategies.
method Investigated different types of investors, various portfolio construction rules, and incorporated liquidity constraints.
result Cryptocurrencies can improve the risk-return profile of portfolios, especially with diversification strategies.
On June 26th, 2004, Central bank governors and the heads of bank supervisory authorities in the Group of Ten (G10) countries issued a press release and endorsed the publication of "International Convergence of Capital Measurement and Capital Standards: a Revised Framework", the new capital adequacy framework commonly k…
The paper defines the time function of stock prices using a mathematical model.
problem Understanding the movement and predictability of stock prices over time.
method Empirical evidence and mathematical modeling of white noise.
result Derives auto-correlation function, displacement formula, and power spectral density of stock price movement.
QTMRL uses RL with multi-indicators to improve trading adaptability.
problem Traditional trading models fail in volatile markets due to rigid assumptions.
method Combines multi-indicators with RL for adaptive portfolio management.
result QTMRL outperforms baselines in profitability and risk control.
Study nonconcave portfolio choice with smooth ambiguity and Bayesian learning.
problem Nonconcave portfolio choice under smooth ambiguity and Bayesian learning.
method Developed a general framework for dynamic, non-concave asset allocation.
result Dynamic consistency achieved through a robust representation.
This paper uses DRL for long-short portfolio optimization, improving risk-adjusted returns.
problem Traditional portfolio optimization limits diversification by excluding short-selling.
method Developed a DRL framework with a short-selling mechanism for continuous trading.
result DRL model with short-selling achieves superior risk-adjusted returns.
This study uses HMM and RL to dynamically allocate equities, Treasuries, and gold based on market regimes.
problem Developing a dynamic portfolio allocation strategy for different market conditions.
method Characterizes market regimes using Markov switching models and HMM, then applies RL for allocation decisions.
result RL-based allocation outperforms passive strategies, providing lower drawdowns and higher Sharpe ratios.