The paper analyzes competition among fund managers using excess logarithmic returns and constructs games to find optimal allocations.
problem Optimal allocation strategies among fund managers considering excess logarithmic returns.
method Constructs both n-player and mean field games to address the competition problem. result The MFE of the MFG represents the limit of n-player game's equilibrium as n approaches infinity. Study excess logarithmic residues for foliations to bound invariant hypersurfaces and test log canonicity.
problem Bounding invariant hypersurfaces and testing log canonicity of singularities.
method Introduce excess logarithmic residues, prove residue formula, derive Poincaré-type bound, and use them to recover log discrepancies.
result Componentwise logarithmic residues of a lifted foliation along the exceptional divisor recover log discrepancies of singularities.
New distribution resolves excess volatility puzzle in finance.
problem Excess volatility in equity prices not explained by rational finance theory.
method Empirical analysis of historical returns using a new distribution.
result Volatility puzzle disappears when using a more appropriate return distribution.
Currency volatility shocks predict lower excess returns, and buying weak transmitters outperforms selling strong ones.
problem Predicting currency returns using volatility shocks.
method Constructed a dynamic, directed network of volatility connections using option-implied volatilities.
result Currencies that transmit more volatility shocks earn lower excess returns.
A financial model without short-selling shows deviations from normality.
problem Modeling financial asset prices with constraints on short selling.
method Developed a binomial model with two types of investors (bulls and bears) and a market maker, proving moments and fitting parameters.
result The model can approximate skewness and excess kurtosis, demonstrated with real data.
Mathematical study of excess growth rate connects info theory with finance.
problem Understanding the excess growth rate in portfolio theory.
method Axiomatic characterization theorems of excess growth rate in terms of relative entropy, Jensen's inequality gap, and logarithmic divergence.
result Established rich connections between information theory and finance.
We derive asset pricing formula for markets with incomplete information and subjective views.
problem Asset pricing in markets with informational imperfections and subjective investor beliefs.
method Closed-form market equilibrium formula based on Merton's model, non-linear system of equations, conditional posterior distribution.
result Derivation of market reference model for excess returns under random shadow-costs.
Study finds high cyber risk stocks generate significant excess returns.
problem Understanding and quantifying cyber risk's impact on stock returns.
method Machine learning algorithm measuring cyber risk proximity to a corpus.
result High cyber risk stocks generate an excess return of 18.72% p.a.
Investor optimizes worst-case portfolio in uncertain markets.
problem Optimizing investment in markets with potential crashes.
method Enhanced martingale approach via BSDEs and PDEs.
result Characterized indifference optimal strategies for various models.
AlphaMLDigger predicts excess returns in fluctuating markets.
problem Mining effective information for investment decisions in a volatile market.
method Two-phase approach using deep NLP for sentiment analysis and ensemble ML models.
result Ensemble models achieve 0.984 accuracy, significantly outperforming baseline.
Regression Trees analyze stock returns, revealing market excess return as the most informative factor.
problem Understanding informational content of three factors in stock returns.
method Joint regression tree analysis of daily stock return data for 5 major US corporations.
result The market excess return factor is always the most informative in all cases (solo and joint).
Study tests if equity factors explain Bitcoin's risk and returns.
problem Explaining Bitcoin's risk and return with equity factors.
method Applied statistical methods to test Fama-French factors on Bitcoin's excess returns.
result Fama-French factors have explanatory power on Bitcoin's risk and returns.
AlphaZeroBeta uses deep reinforcement learning for market-neutral portfolios, outperforming traditional methods.
problem Traditional portfolio management methods often fail during market regime shifts or when assumptions break down.
method Combines a composite reward function and CNN-GRU policy trained end-to-end via Recurrent PPO.
result Achieves higher Sharpe ratios than baselines while maintaining near-zero benchmark correlations.
Study finds significant premium for low-beta stocks in firm-level idiosyncratic return distributions.
problem Understanding the role of common idiosyncratic quantile factors in asset pricing.
method Quantile factor analysis to extract common idiosyncratic quantile factors with asymmetric pricing effects.
result Significant premium for innovations to the lower-tail factor: high-beta stocks outperform low-beta stocks by around 7-8% per year.
Simple model uses time series momentum to outperform benchmarks in equity and bond markets.
problem Finding systematic excess returns in various markets.
method Time series momentum applied to multiple investable indices without complex parameter estimation.
result Significant outperformance in equity and bond markets, nearly doubling returns.
There are some statistical anomalies in the Chinese stock market, i.e., positive return skewness, anti-leverage effect (positive returns induce higher volatility than negative returns); and reverse volatility asymmetry (contemporaneous return-volatility correlation is positive). In this paper, we first confirm the exis…
We show that our generalization of the Black-Scholes partial differential equation (pde) for nontrivial diffusion coefficients is equivalent to a Martingale in the risk neutral discounted stock price. Previously, this was proven for the case of the Gaussian logarithmic returns model by Harrison and Kreps, but we prove …
Studying Binomial and Gaussian return dynamics in discrete time, we show how excess volatility can be traded to create growth. We test our results on real world data to confirm the observed model phenomena while also highlighting implicit risks.
This study uses NLP to predict stock performance based on analyst reports.
problem Predicting stock performance using textual information from analyst reports.
method Natural language processing (NLP) and a customized BERT deep learning model for Chinese text.
result Strong positive sentiment in analyst reports increases excess return and intraday volatility, while strong negative sentiment increases volatility and trading volume but decreases excess return.
Deep learning models improve stock market portfolio returns.
problem Optimizing portfolio returns using deep learning methods.
method Deep neural networks (feedforward and LSTM) applied to stock market excess returns forecasting.
result Deep learning models deliver significant gains in portfolio certainty equivalent returns and Sharpe ratios.
New method uses asymmetric Tsallis relative entropy for better risk assessment in financial portfolios.
problem Improving risk assessment for financial portfolios using asymmetric data.
method Generalized Tsallis relative entropy (ATRE) for asymmetric distributions of returns.
result ATRE shows better risk-return profiles, especially during market crashes.
Sparse portfolio strategy from mutual funds' favorite stocks in China A share market.
problem Building a sparse portfolio from mutual funds' favorite stocks in a market with limited fund information.
method Analyzed mutual fund favorite stocks, used portfolio optimizer with constraints, and compared different methods.
result Sparse portfolios consistently outperform the benchmark index 930950.CSI.
We decompose returns for portfolios of bottom-ranked, lower-priced assets relative to the market into rank crossovers and changes in the relative price of those bottom-ranked assets. This decomposition is general and consistent with virtually any asset pricing model. Crossovers measure changes in rank and are smoothly …
This study explains and mitigates inflated returns and turnover in SPO-based portfolio optimization.
problem Inflated returns and excessive turnover in SPO-based portfolio optimization.
method KKT-based interpretation of portfolio decisions as ranking over adjusted scores, empirical evaluation of stabilization mechanisms.
result Realistic output constraints and portfolio-level turnover control improve SPO-based strategies.
Crypto simulations show HODL strategy loads risk onto most investors, with macro-sentiment affecting returns.
problem Understanding real risk-return trade-offs and factors affecting crypto returns.
method Two independent analyses: 480 million Monte Carlo simulations and Bayesian multi-horizon local projection framework.
result HODL strategy exposes most investors to extreme downside risk, and macro-sentiment conditions are dominant indicators for future outcomes.
Deep neural network learns meaningful factors to predict stock returns.
problem Predicting excess returns of assets like Tesla stock.
method 5-layer deep neural network with gated activation layer to filter noise.
result Proposed model outperforms in predicting stock returns over 2,000 stocks.
Study finds key investing characteristics for success in equity markets.
problem Understanding what traits lead to financial success in equity markets.
method Exploratory factor analysis and multiple linear regression on 403 respondents' data.
result Investing characteristics significantly impact individual investors' excess return.
We forecast S&P 500 excess returns using a flexible Bayesian econometric state space model with non-Gaussian features at several levels. More precisely, we control for overparameterization via novel global-local shrinkage priors on the state innovation variances as well as the time-invariant part of the state space mod…
New EI strategies using OWA and SSD for excess return.
problem Selecting EI portfolios that stochastically dominate a benchmark.
method Proposes a new OWA-based EI model and introduces a new SSD criterion.
result OWA-based EI portfolios stochastically dominate a benchmark and generate excess return.
The geometric Lévy model (GLM) is a natural generalisation of the geometric Brownian motion model (GBM) used in the derivation of the Black-Scholes formula. The theory of such models simplifies considerably if one takes a pricing kernel approach. In one dimension, once the underlying Lévy process has been specified, th…
Earlier studies have shown that stock market distributions can be well described by distributions derived from Tsallis entropy, which is a generalization of Shannon entropy to non-extensive systems. In this paper, Tsallis relative entropy (TRE), which is the generalization of Kullback-Leibler relative entropy (KLRE) to…
The main objective is to present a some variant of the Black - Litterman model. We consider the canonical case when priori return is determined by means such excess return from the CAPM market portfolio which is derived using reverse optimization method. Then the a priori return is at risk quantified uncertainty. On th…
We present extensive evidence that ``risk premium'' is strongly correlated with tail-risk skewness but very little with volatility. We introduce a new, intuitive definition of skewness and elicit an approximately linear relation between the Sharpe ratio of various risk premium strategies (Equity, Fama-French, FX Carry,…
Pairs trading strategy fails to outperform market benchmarks, but performs well during bear markets.
problem The validity of pairs trading as a profitable strategy in modern markets.
method Used common distance and cointegration methods on US equities from 1990 to 2020, including the Covid-19 crisis.
result The pairs trading strategy does not consistently outperform market benchmarks, but performs well during bear markets.
Gradient boosted trees outperform other models in predicting corporate bankruptcy.
problem Predicting financial distress of publicly traded U.S. firms.
method Benchmarked various machine learning models using a comprehensive sample of bankruptcies.
result Gradient boosted trees outperform other models in one-year-ahead forecasts.
New method for unbiased regression reduces excess risk.
problem Least squares regression with optimal solution and Hessian matrix.
method Averaged stochastic gradient descent with time-average estimator.
result Unbiased estimator with O(1/k) expected excess risk.
LLMs overestimate stock returns and are less accurate at predicting extreme outcomes.
problem Behavioral biases in LLMs' stock return forecasts.
method Comparison of LLM forecasts with crowd-sourced estimates and historical data.
result LLMs overestimate stock returns and are less accurate at predicting extreme outcomes.
Novel ML approach optimizes large portfolios without covariance matrix issues.
problem Static and dynamic portfolio optimization for many assets.
method Machine learning for constrained optimization, avoiding covariance matrix computation.
result Significant excess returns in U.S. and China equity markets.
We study differentially private (DP) algorithms for stochastic convex optimization (SCO). In this problem the goal is to approximately minimize the population loss given i.i.d. samples from a distribution over convex and Lipschitz loss functions. A long line of existing work on private convex optimization focuses on th…
A guide to AI+ML for portfolio weight formation.
problem Optimizing portfolio weights using AI and ML techniques.
method Analysis of machine learning tools and their performance in portfolio weight formation.
result Nodewise regression with Global Minimum Variance portfolio weights deliver high Sharpe Ratios and returns.
How an investor invests in the market is largely influenced by the market efficiency because if a market is efficient, it is extremely difficult to make excessive returns because in an efficient market there will be no undervalued securities i.e. securities whose value is less than its assumed intrinsic value, which of…
We study a phenomenological model for the continuous double auction, equivalent to two independent M/M/1 queues. The continuous double auction defines a continuous-time random walk for trade prices. The conditions for ergodicity of the auction are derived and, as a consequence, three possible regimes in the behavior …
Unified approach to trend-following systems, deriving exact relationships and expected returns.
problem Designing and understanding trend-following systems in financial markets.
method Derive exact relationships, analyze expected returns, and use fractional ARFIMA processes.
result Profitability of trend-following systems depends on positive long-term autocorrelation and excess spectral mass at low frequencies.
We discuss the finding that cross-sectional characteristic based models have yielded portfolios with higher excess monthly returns but lower risk than their arbitrage pricing theory counterparts in an analysis of equity returns of stocks listed on the JSE. Under the assumption of general no-arbitrage conditions, we arg…
We present a simple dynamical model of stock index returns which is grounded on the ability of the Cyclically Adjusted Price Earning (CAPE) valuation ratio devised by Robert Shiller to predict long-horizon performances of the market. More precisely, we discuss a discrete time dynamics in which the return growth depends…
The Kelly Criterion is applied to prediction markets to analyze risk and return.
problem Mean beliefs in prediction markets often differ from actual prices.
method Logarithmic utility and Kullback-Leibler divergence are used to study risk and return adjustments.
result Misjudgment of bias and investment fraction affect portfolio growth rate.
A new method models financial returns by separating sign and magnitude, improving forecasting accuracy.
problem Capturing nonlinear predictability in financial return dynamics.
method Decomposes returns into sign and magnitude components, using a joint distribution model.
result Significantly outperforms traditional linear models in forecasting U.S. stock market returns.
The study approximates option prices using Hermite polynomials without assuming a specific distribution.
problem Approximating option prices without assuming a specific distribution of returns.
method Approximating the logarithmic return's density by a linear combination of rescaled Hermite polynomials.
result Empirical results suggest reasonable performance for options with moderate strike prices.