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

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.

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.

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