Phase Transition in Global Financial Markets: Empirical Evidence, Risk Measure, and Portfolio Selection

2017 ◽  
Author(s):  
Qi Zhou
Keyword(s):  
Phase Transition ◽  
Financial Markets ◽  
Empirical Evidence ◽  
Portfolio Selection ◽  
Risk Measure

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.

Trust and Household Portfolios

2021 ◽  
pp. 78-97
Author(s):  
Diego Lubian
Keyword(s):  
Financial Markets ◽  
Empirical Evidence ◽  
Financial Literacy ◽  
Risky Assets ◽  
Financial Decisions ◽  
Portfolio Decisions ◽  
Financial Decision ◽  
Financial Portfolio ◽  
Socio Demographic Factors ◽  
The Relationship

This article provides empirical evidence on the existence and the extent of the influence of trust in financial decisions using individual data on Italian households from the Survey on Household Income and Wealth, 2010. This article studies the relationship between, trust in people, trust in banks and more detailed previously unexplored dimensions of trust, and household financial portfolio decisions. The article provides empirical evidence that trust in people and trust in banks affect both participation in financial markets, the share of risky assets and the diversification of the financial portfolio, controlling socio-demographic factors, risk aversion, and financial literacy as well. The article finds that trust is important for individuals with a lower level of education who have limited possibilities to acquire and process information on financial markets need to rely in trustworthy relationship to define their financial portfolio. Further, we present evidence that the main channel by which trust affects financial decision making and determines too little participation, a lower share of risky assets in the financial wealth and poorly diversified portfolios is trust in family and friends.

Portfolio Selection in a Noisy Environment Using Absolute Deviation as a Risk Measure

2006 ◽  
pp. 220-225 ◽  
Author(s):  
Imre Kondor ◽  
Szilárd Pafka ◽  
Richárd Karádi ◽  
Gábor Nagy
Keyword(s):  
Portfolio Selection ◽  
Risk Measure ◽  
Noisy Environment ◽  
Absolute Deviation

Excess demand and equilibration in multi-security financial markets: the empirical evidence

2003 ◽  
Vol 6 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Elena Asparouhova ◽  
Peter Bossaerts ◽  
Charles Plott
Keyword(s):  
Financial Markets ◽  
Empirical Evidence ◽  
Excess Demand

scholarly journals Risk-aware multi-armed bandit problem with application to portfolio selection

2017 ◽  
Vol 4 (11) ◽  
pp. 171377 ◽  
Author(s):  
Xiaoguang Huo ◽  
Feng Fu
Keyword(s):  
Portfolio Selection ◽  
Risk Measure ◽  
Mathematical Framework ◽  
Risk Awareness ◽  
Bandit Problem ◽  
Sequential Decision ◽  
Quantitative Finance ◽  
Coherent Risk ◽  
Natural Connection ◽  
Learning Policies

Sequential portfolio selection has attracted increasing interest in the machine learning and quantitative finance communities in recent years. As a mathematical framework for reinforcement learning policies, the stochastic multi-armed bandit problem addresses the primary difficulty in sequential decision-making under uncertainty, namely the exploration versus exploitation dilemma, and therefore provides a natural connection to portfolio selection. In this paper, we incorporate risk awareness into the classic multi-armed bandit setting and introduce an algorithm to construct portfolio. Through filtering assets based on the topological structure of the financial market and combining the optimal multi-armed bandit policy with the minimization of a coherent risk measure, we achieve a balance between risk and return.

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