scholarly journals Vector Autoregressive Weighting Reversion Strategy for Online Portfolio Selection

2020 ◽  
Author(s):  
Xia Cai
Keyword(s):  
Portfolio Selection ◽  
Moving Average ◽  
Time Series Prediction ◽  
Vector Autoregressive ◽  
Practical Applications ◽  
Public Datasets ◽  
Dynamic Relationships ◽  
Online Portfolio ◽  
Online Portfolio Selection ◽  
Sharp Ratio

Aiming to improve the performance of existing reversion based online portfolio selection strategies, we propose a novel multi-period strategy named “Vector Autoregressive Weighting Reversion” (VAWR). Firstly, vector autoregressive moving-average algorithm used in time series prediction is transformed into exploring the dynamic relationships between different assets for more accurate price prediction. Secondly, we design the modified online passive aggressive technique and advance a scheme to weigh investment risk and cumulative experience to update the closed-form of portfolio. Theoretical analysis and experimental results confirm the effectiveness and robustness of our strategy. Compared with the state-of-the-art strategies, VAWR greatly increases cumulative wealth, and it obtains the highest annualized percentage yield and sharp ratio on various public datasets. These improvements and easy implementation support the practical applications of VAWR.

Efficient online portfolio simulation using dynamic moving average model and benchmark index

2021 ◽  
Author(s):  
Amril Nazir
Keyword(s):  
Portfolio Selection ◽  
Moving Average ◽  
Empirical Studies ◽  
Contemporary Literature ◽  
Selection Strategy ◽  
Selection Strategies ◽  
Engineering Statistics ◽  
Moving Average Model ◽  
Online Portfolio ◽  
Online Portfolio Selection

Online portfolio selection and simulation are some of the most important problems in several research communities, including finance, engineering, statistics, artificial intelligence, machine learning, etc. The primary aim of online portfolio selection is to determine portfolio weights in every investment period (i.e., daily, weekly, monthly, etc.) to maximize the investor’s final wealth after the end of investment period (e.g., 1 year or longer). In this paper, we present an efficient online portfolio selection strategy that makes use of market indices and benchmark indices to take advantage of the mean reversal phenomena at minimal risks. Based on empirical studies conducted on recent historical datasets for the period 2000 to 2015 on four different stock markets (i.e., NYSE, S&P500, DJIA, and TSX), the proposed strategy has been shown to outperform both Anticor and OLMAR — the two most prominent portfolio selection strategies in contemporary literature.

scholarly journals A Novel Online Portfolio Selection Strategy with Multiperiodical Asymmetric Mean Reversion

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zijin Peng ◽  
Weijun Xu ◽  
Hongyi Li
Keyword(s):  
Portfolio Selection ◽  
Large Scale ◽  
Linear Time ◽  
Moving Average ◽  
Mean Reversion ◽  
Selection Strategy ◽  
Contrarian Strategies ◽  
Real Market ◽  
Online Portfolio ◽  
Online Portfolio Selection

Mean reversion is an important property when constructing efficient contrarian strategies. Researchers observe that mean reversion has multiperiodical and asymmetric nature simultaneously in real market. To better utilize mean reversion and improve the existing online portfolio selection strategies, we propose a new online strategy named multiperiodical asymmetric mean reversion (MAMR). The MAMR strategy incorporates a multipiecewise loss function with the moving average method and then imitates the passive-aggressive algorithm. We further provide a solution via convex optimization. This strategy runs in linear time and thus is suitable for large-scale trading applications. Our empirical results testing six real market datasets show that this strategy can achieve better results in bearing higher transaction cost.

scholarly journals Online Portfolio Selection with Cardinality Constraint and Transaction Costs based on Contextual Bandit

2020 ◽  
Author(s):  
Mengying Zhu ◽  
Xiaolin Zheng ◽  
Yan Wang ◽  
Qianqiao Liang ◽  
Wenfang Zhang
Keyword(s):  
Financial Markets ◽  
Transaction Costs ◽  
Portfolio Selection ◽  
Financial Engineering ◽  
Side Information ◽  
Cardinality Constraint ◽  
Real World Datasets ◽  
Online Portfolio ◽  
Online Portfolio Selection ◽  
Practical Constraints

Online portfolio selection (OLPS) is a fundamental and challenging problem in financial engineering, which faces two practical constraints during the real trading, i.e., cardinality constraint and non-zero transaction costs. In order to achieve greater feasibility in financial markets, in this paper, we propose a novel online portfolio selection method named LExp4.TCGP with theoretical guarantee of sublinear regret to address the OLPS problem with the two constraints. In addition, we incorporate side information into our method based on contextual bandit, which further improves the effectiveness of our method. Extensive experiments conducted on four representative real-world datasets demonstrate that our method significantly outperforms the state-of-the-art methods when cardinality constraint and non-zero transaction costs co-exist.

scholarly journals Combination Forecasting Reversion Strategy for Online Portfolio Selection

2018 ◽  
Vol 9 (5) ◽  
pp. 1-22 ◽  
Author(s):  
Dingjiang Huang ◽  
Shunchang Yu ◽  
Bin Li ◽  
Steven C. H. Hoi ◽  
Shuigeng Zhou
Keyword(s):  
Portfolio Selection ◽  
Combination Forecasting ◽  
Online Portfolio ◽  
Online Portfolio Selection
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