scholarly journals Arbitrage in Stationary Markets

2007 ◽  
Author(s):  
Igor V. Evstigneev ◽  
Dhruv Kapoor
Keyword(s):  
Stationary Markets

scholarly journals An Adaptive News-Driven Method for CVaR-sensitive Online Portfolio Selection in Non-Stationary Financial Markets

2021 ◽  
Author(s):  
Qianqiao Liang ◽  
Mengying Zhu ◽  
Xiaolin Zheng ◽  
Yan Wang
Keyword(s):  
Financial Markets ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Conditional Value At Risk ◽  
Art Portfolio ◽  
Stationary Markets ◽  
Real World Datasets ◽  
Online Portfolio ◽  
And Control ◽  
Online Portfolio Selection

CVaR-sensitive online portfolio selection (CS-OLPS) becomes increasingly important for investors because of its effectiveness to minimize conditional value at risk (CVaR) and control extreme losses. However, the non-stationary nature of financial markets makes it very difficult to address the CS-OLPS problem effectively. To address the CS-OLPS problem in non-stationary markets, we propose an effective news-driven method, named CAND, which adaptively exploits news to determine the adjustment tendency and adjustment scale for tracking the dynamic optimal portfolio with minimal CVaR in each trading round. In addition, we devise a filtering mechanism to reduce the errors caused by the noisy news for further improving CAND's effectiveness. We rigorously prove a sub-linear regret of CAND. Extensive experiments on three real-world datasets demonstrate CAND’s superiority over the state-of-the-art portfolio methods in terms of returns and risks.

scholarly journals Arbitrage in stationary markets

2008 ◽  
Vol 32 (1) ◽  
pp. 5-12
Author(s):  
Igor Evstigneev ◽  
Dhruv Kapoor
Keyword(s):  
Stationary Markets

Portfolio selection in non-stationary markets

2021 ◽  
pp. 1-13
Author(s):  
Eyal Kenig
Keyword(s):  
Stochastic Process ◽  
Portfolio Selection ◽  
Time Series Prediction ◽  
Stationary Stochastic Process ◽  
Learning Approach ◽  
Generalization Error ◽  
Time Step ◽  
Large Numbers ◽  
Stationary Markets ◽  
Short Time

We consider the task of portfolio selection as a time series prediction problem. At each time-step we obtain prices of a universe of assets and are required to allocate our wealth across them with the goal of maximizing it, based on the historic price returns. We assume these returns are realizations of a general non-stationary stochastic process, and only assume they do not change significantly over short time scales. We follow a statistical learning approach, in which we bound the generalization error of a non-stationary stochastic process, using analogues of uniform laws of large numbers for non-i.i.d. random variables. We use the learning bounds to formulate an optimization algorithm for portfolio selection, and present favorable numerical results with financial data.

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