Decomposes returns of bottom-ranked assets, finds excess returns.
problem Excess returns of bottom-ranked assets not explained by existing models.
method Decomposes returns into rank crossovers and relative price changes.
result Excess returns of bottom-ranked assets are driven by relative price changes, not rank.
Enhances currency strategy Sharpe ratio by 30% using context-aware Learning to Rank.
problem Sub-optimal ranking of assets during critical market periods.
method Context-aware Learning to Rank model based on Transformer architecture.
result Significantly improves Sharpe ratio and various performance metrics.
Paper proposes SaiyanH to learn BNs with full evidence propagation from dependent variables.
problem Learning disjoint subgraphs that prevent full evidence propagation.
method SaiyanH, a hybrid structure learning algorithm.
result SaiyanH outperforms state-of-the-art algorithms in reconstructing true DAGs.
AI predicts stock winners with 2.43 Sharpe ratio, but returns are highly concentrated.
problem Predicting stock returns with AI, focusing on identifying top winners.
method Deployed a state-of-the-art LLM to autonomously search the web for stock attractiveness, avoiding look-ahead bias.
result AI can generate alpha by identifying top winners, but returns are highly concentrated.
Using an artificial neural network (ANN), a fixed universe of approximately 1500 equities from the Value Line index are rank-ordered by their predicted price changes over the next quarter. Inputs to the network consist only of the ten prior quarterly percentage changes in price and in earnings for each equity (by quart…
Paper explores asset pricing dynamics in Bachelier model.
problem Understanding risky asset price dynamics in Bachelier model.
method Analyzes Bachelier market model to represent risky asset price dynamics.
result Defines riskless assets within the Bachelier model.
Enhances portfolio construction with tailored regime forecasts for individual assets.
problem Traditional portfolio construction methods fail to account for asset-specific market conditions.
method Hybrid framework combining unsupervised and supervised learning for regime identification and forecasting.
result Outperforms traditional portfolio models across various asset classes.
Predicts financial asset dependencies using spatiotemporal patterns.
problem Complex dependency structures in financial assets for risk mitigation.
method Proposes Asset Dependency Matrix (ADM) and Asset Dependency Neural Network (ADNN) with ConvLSTM for spatiotemporal asset dependency prediction.
result ADNN outperforms baselines in predicting asset dependencies and their applications.
IDA makes DFMM's asset tradeable, enhancing cross-chain finance efficiency.
problem Making DFMM's asset tradeable to improve cross-chain finance efficiency.
method Introducing IDA as a tradeable asset, leveraging DFMM's robust liquidity and dynamic AMM.
result IDA enhances cross-chain finance efficiency through tradeable asset and dynamic AMM.
Paper proposes a new approach to predict power system asset class failures.
problem Predicting failures for different asset classes in power systems is critical for cost-effective asset management.
method Combines unsupervised (K-means clustering) and supervised (logistic regression) learning methods using asset condition data.
result The proposed approach outperforms standard methods in predicting asset class failures.
Investors prioritize ESG in crypto-assets, showing higher exposure than traditional assets.
problem Understanding ESG preferences in crypto-assets and their investment behavior.
method A representative household finance survey in Austria to examine ESG preferences and crypto-investment exposure.
result ESG-conscious investors have higher exposure to crypto-assets compared to traditional asset classes.
How to price and hedge claims on nontraded assets are becoming increasingly important matters in option pricing theory today. The most common practice to deal with these issues is to use another similar or "closely related" asset or index which is traded, for hedging purposes. Implicitly, traders assume here that the h…
New heuristic selects fewer assets for efficient portfolios, reducing costs.
problem High transaction costs and fees from including many assets in portfolios.
method Surrogate formulation to select assets, re-optimizes portfolio with fewer assets.
result Effective in constructing portfolios with fewer assets, reducing costs.
Study examines hedging options on asset portfolios against one underlying asset with transaction costs.
problem Hedging options on asset portfolios when one underlying asset is expensive to trade.
method Simulated data analysis with varying trading intervals, correlation coefficients, and transaction costs.
result Trading the wrong asset can be beneficial when correlation is high and transaction costs are low.
A model for choosing crypto assets based on security and stability.
problem Optimal selection of crypto assets considering security and stability.
method A recommender app-like system that presents pairs of crypto assets and collects investor preferences.
result A variety of possible outcomes for crypto asset investments and adoption.
Dynamic model considers private asset markets' complexities.
problem Understanding and optimizing private asset allocation.
method State-of-the-art dynamic model with machine learning.
result Optimal investment policies quantified over fund life.
Enhanced synthetic dataset improves asset allocation analysis.
problem Lack of realistic synthetic data for fixed income portfolio construction.
method Improved CorrGAN model for synthetic correlation matrices and Encoder-Decoder model for additional data conditioning.
result Synthetic dataset enhances portfolio construction and asset allocation analysis.
We consider a portfolio allocation problem for trend following (TF) strategies on multiple correlated assets. Under simplifying assumptions of a Gaussian market and linear TF strategies, we derive analytical formulas for the mean and variance of the portfolio return. We construct then the optimal portfolio that maximiz…
Paper studies estimating asset correlations across sectors.
problem Estimating correlations between different asset sectors.
method Separates cross-sectional and time dimensions for estimation.
result Developed method for better asset correlation estimation.
Develops a dynamic latent-factor model for high-dimensional asset characteristics.
problem Estimating asset pricing tests with high-dimensional data.
method Dynamic latent-factor model with Double Selection Lasso regularization.
result The inflation-mimicking portfolio in the crypto asset class has positive risk compensation.
Game theory model shows optimal investment strategy for wealth growth.
problem Minimizing time to reach large wealth in a stochastic asset market.
method Proved strategy of proportional asset investment minimizes expected time.
result Proportional investment strategy asymptotically minimizes time to large wealth.
Research proposes a model to estimate transaction costs and assess asset liquidity risk.
problem Lack of standardized models for asset liquidity risk in asset management.
method Develops a market impact model and a two-regime model based on power-law property.
result Defines liquidity measures and applies model to stocks and bonds.
Efficient method for pricing multi-asset options with local volatility.
problem Pricing options on multiple assets with varying volatility.
method Generic hybrid numerical method for efficient pricing.
result Efficient pricing of multi-asset options with local volatility.
Given a new candidate asset represented as a time series of returns, how should a quantitative investment manager be thinking about assessing its usefulness? This is a key qualitative question inherent to the investment process which we aim to make precise. We argue that the usefulness of an asset can only be determine…
Clusters of highly correlated stocks are identified for better asset selection.
problem Identifying a small set of stocks to approximate the diversification of the whole stock universe.
method Data-driven correlation blockmodel clustering approach.
result The algorithm effectively detects clusters of highly correlated stocks.
Optimizes asset allocation with illiquid assets using MPC.
problem Strategic asset allocation with illiquid alternative asset classes.
method Formulates illiquid dynamics as a random linear system and proposes a convex optimization based MPC policy.
result Performance close to a fully liquid scenario, despite time delay and uncertainty.
This paper addresses AMMs for expiring assets, ensuring liquidity and risk management.
problem AMMs struggle with assets that expire, leading to liquidity issues and risk exposure.
method Combines AMM and limit-order book features, ensuring liveness and dynamic price adjustment.
result A DEX for expiring assets maintains liquidity and risk management.
A possible data source for the estimation of asset correlations is default time series. This study investigates the systematic error that is made if the exposure pool underlying a default time series is assumed to be homogeneous when in reality it is not. We find that the asset correlation will always be underestimated…
This paper optimizes crypto portfolios and valuates crypto options.
problem High volatility and lack of standard pricing models for crypto assets.
method Optimization techniques to minimize tail risk, dynamic pricing model for crypto assets, Esscher transform for fair valuation.
result Optimized crypto portfolios outperform major stock indices.
The paper revisits and applies FTAP to life insurance and annuities pricing.
problem Non-arbitrage pricing of life contingent assets in dynamic markets.
method Revisit FTAP, use martingale theory, apply FTAP to life insurance and annuities, clarify assumptions.
result Valuation formula for life contingent assets including life insurance policies and annuities.
Deep learning improves asset pricing models.
problem Estimating asset pricing models with limited data.
method Used deep neural networks, fundamental no-arbitrage condition, adversarial approach, and macroeconomic time series.
result Deep learning asset pricing model outperforms benchmarks.
We design an optimal strategy for investment in a portfolio of assets subject to a multiplicative Brownian motion. The strategy provides the maximal typical long-term growth rate of investor's capital. We determine the optimal fraction of capital that an investor should keep in risky assets as well as weights of differ…
Paper proposes a comprehensive taxonomy for crypto assets.
problem Lack of a holistic classification framework for crypto assets.
method Identified 14 attributes for classification, tested framework with cash and bitcoin.
result Proposes a structured classification framework for all types of assets.
New models reduce regional inequality by adjusting exchange range and asset distribution bias.
problem Reduction of regional inequality in economic systems.
method Proposed new asset exchange models with spatial exchange range and local support bias to adjust asset distribution and circulation rates.
result Achieved asset distribution from over-concentration to exponential and eventually normal, reducing Gini coefficient.
Investigates price dynamics of two assets with and without bubbles, deriving conditions for equilibrium prices.
problem Understanding price dynamics and bubbles in multi-asset markets.
method Derives sufficient and necessary conditions for average equilibrium price dynamics in a two-asset model.
result Assets with positive average dividends display hump-shaped bubbles, while those with constant fundamental values show misvaluation effects.
Study asset price bubbles with proportional transaction costs.
problem Impact of transaction costs on asset price bubbles.
method Define fundamental value, use super-replication theorem, investigate bubbles intrinsically.
result Model intrinsically includes the birth of a bubble.
The CAPM's market returns are endogenously determined, affecting all assets' expected returns.
problem The standard CAPM's market return assumption is not endogenously consistent.
method Demonstrates the impact of endogenously determined market returns on asset returns and the range of feasible market returns.
result Expected returns are influenced by all assets' risks, and market returns are limited by asset distribution.
Decomposes portfolio returns into drift and asset price distribution changes.
problem Understanding efficient markets through portfolio returns and asset price distributions.
method Continuous semimartingale price representations and accounting identity.
result Existence of an asset pricing factor emerges from an accounting identity across various economic and financial environments.
Quantum assets are priced using a new theorem, extending classical asset pricing.
problem Quantum properties in financial markets and assets.
method Developed a new definition of arbitrage for quantum assets and proved a quantum version of the first fundamental theorem of asset pricing.
result There exists a risk-free density operator under which all quantum assets are martingales if no arbitrage exists.
Develops a climate risk model for asset managers.
problem Climate-related risks affecting asset performance and productivity.
method Uses the Vasicek model with downward jumps to represent climate impacts on asset dynamics.
result Expected losses increase over time due to climate-related extreme events.
The study assesses music as an investment asset class using discounted cashflow models.
problem Quantifying the risk and return characteristics of music royalty assets.
method Fitting three discounted cashflow models to Royalty Exchange platform transactions and backtesting performance.
result Life of Rights music assets had risk and return characteristics comparable to stocks in the S\&P500 over 5 years.
Improved asset pricing using uncertainty-adjusted sorting in machine learning models.
problem Ignoring asset-specific estimation uncertainty in portfolio construction.
method Uncertainty-adjusted prediction bounds for sorting assets.
result Improves portfolio performance across various ML models and equity panels.
Improved price bounds for multi-asset derivatives using market option data.
problem Creating robust price bounds for multi-asset derivatives under market-implied dependence.
method Extracting inter-asset dependence information from market option prices and applying modified martingale optimal transport.
result Improved price bounds for multi-asset derivatives, demonstrating relevance and tractability.
The study examines relationships between assets in foreign exchange markets using new measures.
problem Quantifying relationships between assets in non-stationary markets.
method Developed transformation equations for means and covariances under changing numeraire.
result Partial correlations between assets remain invariant under numeraire change.
Intuitively, the default risk of a single borrower is higher when her or his assets and debt are denominated in different currencies. Additionally, the default dependence of borrowers with assets and debt in different currencies should be stronger than in the one-currency case. By combining well-known models by Merton …
Study on asset price dynamics in OLG economies with and without a bubbly asset.
problem Analyzing asset price dynamics and optimality in OLG economies with an asset that yields dividends.
method Deriving conditions for three scenarios of equilibrium existence, providing a complete characterization of the equilibrium set, and investigating the relationship between asset price behaviors and optimality.
result A bubbly equilibrium exists if and only if the interest rate is lower than the population growth rate and the sum of per capita dividends is finite.
In this article we study a multi-asset version of the Merton investment and consumption problem with proportional transaction costs. In general it is difficult to make analytical progress towards a solution in such problems, but we specialise to a case where transaction costs are zero except for sales and purchases of …
The paper develops methods for conditional inference on the asset with the highest Sharpe ratio.
problem Performing inference on the asset with the highest Sharpe ratio among correlated assets.
method Conditional inference procedure using multivariate Sharpe ratio standard error, alternative tests, and asymptotic adjustments.
result The conditional inference procedure achieves nominal type I rate and maintains near-nominal rejection rates under the conditional null.