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Found 17 papers, 2 papers with code
CFTM: Continuous time fractional topic model
This approach incorporates fractional Brownian motion~(fBm) to effectively identify positive or negative correlations in topic and word distribution over time, revealing long-term dependency or roughness.
Doubly Robust Mean-CVaR Portfolio
However, the instability associated with the input parameter changes and estimation errors can deteriorate portfolio performance.
Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty
Machine learning-based stock prediction methods including the TC method have been concentrating on point prediction.
Schrödinger Risk Diversification Portfolio
However, since it is difficult to estimate the expected return and the out-of-sample performance of the mean-variance portfolio is poor, risk-based portfolio construction methods focusing only on risk have been proposed, and are attracting attention mainly in practice.
Fractional SDE-Net: Generation of Time Series Data with Long-term Memory
It generalizes the neural stochastic differential equation model by using fractional Brownian motion with a Hurst index larger than half, which exhibits the LRD property.
Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction
For correcting the asymptotic bias with fewer observations, this paper proposes a \emph{local radial regression (LRR)} and its logistic regression variant called \emph{local radial logistic regression~(LRLR)}, by combining the advantages of LPoR and MS-$k$-NN.
No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging
Deep hedging (Buehler et al. 2019) is a versatile framework to compute the optimal hedging strategy of derivatives in incomplete markets.
Controlling False Discovery Rates under Cross-Sectional Correlations
We achieve superior statistical power to existing methods and prove that the false discovery rate is controlled.
Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction
We show the effectiveness of our method by conducting experiments on real market data.
Deep Portfolio Optimization via Distributional Prediction of Residual Factors
In this study, we propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors, which is known to be generally useful for hedging the risk exposure to common market factors.
Mean-Variance Efficient Reinforcement Learning by Expected Quadratic Utility Maximization
To achieve this purpose, we train an agent to maximize the expected quadratic utility function, a common objective of risk management in finance and economics.
Policy Gradient with Expected Quadratic Utility Maximization: A New Mean-Variance Approach in Reinforcement Learning
In this paper, we suggest expected quadratic utility maximization (EQUM) as a new framework for policy gradient style reinforcement learning (RL) algorithms with mean-variance control.
RM-CVaR: Regularized Multiple $β$-CVaR Portfolio
In order to improve this problem, we propose RM-CVaR: Regularized Multiple $\beta$-CVaR Portfolio.
Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management
We perform empirical analysis in the Japanese stock market and confirm the profitability of our framework.
TPLVM: Portfolio Construction by Student's $t$-process Latent Variable Model
By comparing these portfolios, we confirm the proposed portfolio outperforms that of the existing Gaussian process latent variable model.
A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy
Stock return predictability is an important research theme as it reflects our economic and social organization, and significant efforts are made to explain the dynamism therein.
Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model
Specifically, we extend the linear multi-factor model to be non-linear and time-varying with LSTM.
