Dynamics of financial markets and transaction costs: A graph-based study

2016 ◽  
Vol 38 ◽  
pp. 455-465 ◽  
Author(s):  
Felipe Lillo ◽  
Rodrigo Valdés
Keyword(s):  
Financial Markets ◽  
Transaction Costs

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.

Economic Complexity and Trade Diversification in the Western Hemisphere Between 1962 and 2017

2021 ◽  
pp. 115-147
Author(s):  
Jorge Mauricio Falcón Gómez ◽  
Fernando Martín Mayoral
Keyword(s):  
Financial Markets ◽  
Transaction Costs ◽  
Trade Openness ◽  
Terms Of Trade ◽  
Western Hemisphere ◽  
Short Term ◽  
Economic Complexity ◽  
Comparative Advantages ◽  
Negative Effect

Trade diversification patterns help explain the level of utilization of trade opportunities by countries, mainly the least developed. Empirical analyses show an inverse U relationship between trade diversification and level of development. Trade diversification measures used do not take into account differences in complexity of exports, and complexity indices only consider products with comparative advantages. This study seeks to cover both gaps by analyzing the differences in the determinants of trade diversification, considering the complexity of products exported by 19 Western Hemisphere countries from 1962 to 2017. The results show that after controlling for economic complexity, the inverted U relationship disappears. Development of financial markets positively affects the complexity of trade diversification in the long term, while the terms of trade that have a negative effect on trade diversification does not affect the complexity-corrected indices. In the short term, transaction costs and trade openness appear to have a significant effect.

scholarly journals Reinforcement Learning in Financial Markets

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 110 ◽  
Author(s):  
Terry Lingze Meng ◽  
Matloob Khushi
Keyword(s):  
Reinforcement Learning ◽  
Financial Markets ◽  
Transaction Costs ◽  
Machine Learning Method ◽  
Board Game ◽  
Bid Ask Spread ◽  
Exponential Increase ◽  
Testing Data ◽  
The Impact ◽  
Forex Prediction

Recently there has been an exponential increase in the use of artificial intelligence for trading in financial markets such as stock and forex. Reinforcement learning has become of particular interest to financial traders ever since the program AlphaGo defeated the strongest human contemporary Go board game player Lee Sedol in 2016. We systematically reviewed all recent stock/forex prediction or trading articles that used reinforcement learning as their primary machine learning method. All reviewed articles had some unrealistic assumptions such as no transaction costs, no liquidity issues and no bid or ask spread issues. Transaction costs had significant impacts on the profitability of the reinforcement learning algorithms compared with the baseline algorithms tested. Despite showing statistically significant profitability when reinforcement learning was used in comparison with baseline models in many studies, some showed no meaningful level of profitability, in particular with large changes in the price pattern between the system training and testing data. Furthermore, few performance comparisons between reinforcement learning and other sophisticated machine/deep learning models were provided. The impact of transaction costs, including the bid/ask spread on profitability has also been assessed. In conclusion, reinforcement learning in stock/forex trading is still in its early development and further research is needed to make it a reliable method in this domain.

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