11,161 research outputs found
Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey
Financial portfolio management is one of the problems that are most
frequently encountered in the investment industry. Nevertheless, it is not
widely recognized that both Kelly Criterion and Risk Parity collapse into Mean
Variance under some conditions, which implies that a universal solution to the
portfolio optimization problem could potentially exist. In fact, the process of
sequential computation of optimal component weights that maximize the
portfolio's expected return subject to a certain risk budget can be
reformulated as a discrete-time Markov Decision Process (MDP) and hence as a
stochastic optimal control, where the system being controlled is a portfolio
consisting of multiple investment components, and the control is its component
weights. Consequently, the problem could be solved using model-free
Reinforcement Learning (RL) without knowing specific component dynamics. By
examining existing methods of both value-based and policy-based model-free RL
for the portfolio optimization problem, we identify some of the key unresolved
questions and difficulties facing today's portfolio managers of applying
model-free RL to their investment portfolios
A stochastic reachability approach to portfolio construction in finance industry
In finance industry portfolio construction deals with how to divide the
investors' wealth across an asset-classes' menu in order to maximize the
investors' gain. Main approaches in use at the present are based on variations
of the classical Markowitz model. However, recent evolutions of the world
market showed limitations of this method and motivated many researchers and
practitioners to study alternative methodologies to portfolio construction. In
this paper we propose one approach to optimal portfolio construction based on
recent results on stochastic reachability, which overcome some of the limits of
current approaches. Given a sequence of target sets that the investors would
like their portfolio to stay within, the optimal portfolio allocation is
synthesized in order to maximize the joint probability for the portfolio value
to fulfill the target sets requirements. A case study in the US market is given
which shows benefits from the proposed methodology in portfolio construction. A
comparison with traditional approaches is included.Comment: 15 pages, 7 figure
Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning Framework
We approach the continuous-time mean-variance (MV) portfolio selection with
reinforcement learning (RL). The problem is to achieve the best tradeoff
between exploration and exploitation, and is formulated as an
entropy-regularized, relaxed stochastic control problem. We prove that the
optimal feedback policy for this problem must be Gaussian, with time-decaying
variance. We then establish connections between the entropy-regularized MV and
the classical MV, including the solvability equivalence and the convergence as
exploration weighting parameter decays to zero. Finally, we prove a policy
improvement theorem, based on which we devise an implementable RL algorithm. We
find that our algorithm outperforms both an adaptive control based method and a
deep neural networks based algorithm by a large margin in our simulations.Comment: 39 pages, 5 figure
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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Optimal funding and investment strategies in defined contribution pension plans under Epstein-Zin utility
A defined contribution pension plan allows consumption to be redistributed from the plan member’s working life to retirement in a manner that is consistent with the member’s personal preferences. The plan’s optimal funding and investment strategies therefore depend on the desired pattern of consumption over the lifetime of the member.
We investigate these strategies under the assumption that the member has an Epstein-Zin utility function, which allows a separation between risk aversion and the elasticity of intertemporal substitution, and we also take into account the member’s human capital.
We show that a stochastic lifestyling approach, with an initial high weight in equity-type investments and a gradual switch into bond-type investments as the retirement date approaches is an optimal investment strategy. In addition, the optimal contribution rate each year is not constant over the life of the plan but reflects trade-offs between the desire for current consumption, bequest and retirement savings motives at different stages in the life cycle, changes in human capital over the life cycle, and attitude to risk
Network models of financial systemic risk: A review
The global financial system can be represented as a large complex network in
which banks, hedge funds and other financial institutions are interconnected to
each other through visible and invisible financial linkages. Recently, a lot of
attention has been paid to the understanding of the mechanisms that can lead to
a breakdown of this network. This can happen when the existing financial links
turn from being a means of risk diversification to channels for the propagation
of risk across financial institutions. In this review article, we summarize
recent developments in the modeling of financial systemic risk. We focus in
particular on network approaches, such as models of default cascades due to
bilateral exposures or to overlapping portfolios, and we also report on recent
findings on the empirical structure of interbank networks. The current review
provides a landscape of the newly arising interdisciplinary field lying at the
intersection of several disciplines, such as network science, physics,
engineering, economics, and ecology.Comment: 33 pages, 6 figure
Practical Deep Reinforcement Learning Approach for Stock Trading
Stock trading strategy plays a crucial role in investment companies. However,
it is challenging to obtain optimal strategy in the complex and dynamic stock
market. We explore the potential of deep reinforcement learning to optimize
stock trading strategy and thus maximize investment return. 30 stocks are
selected as our trading stocks and their daily prices are used as the training
and trading market environment. We train a deep reinforcement learning agent
and obtain an adaptive trading strategy. The agent's performance is evaluated
and compared with Dow Jones Industrial Average and the traditional min-variance
portfolio allocation strategy. The proposed deep reinforcement learning
approach is shown to outperform the two baselines in terms of both the Sharpe
ratio and cumulative returns
Master’s Program in Money and Finance (MMF)
The Master’s program in Money and Finance (MMF) is an innovative joint venture of the Department of Money and Macroeconomics and of the Department of Finance, both located in the new House of Finance. The program offers promising students from all over the world an intellectually stimulating and challenging setting in which to prepare for their professional careers in central banking, commercial banking, insurance and other financial services. By being located in Frankfurt, one of the world's leading financial centers and the only city in the world with two central banks (the ECB and the German Bundesbank), it offers unique opportunities for interaction with practitioners. The program is taught exclusively in English; knowledge of German is not required for admission to, or completion of the program. It has been designed with a view to establishing itself as a leading Masters program integrating studies in monetary economics, macroeconomics and finance and a major gateway to high-profile jobs in the banking and financial sector
The History of the Quantitative Methods in Finance Conference Series. 1992-2007
This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.
A Novel Two-Stage Dynamic Decision Support based Optimal Threat Evaluation and Defensive Resource Scheduling Algorithm for Multi Air-borne threats
This paper presents a novel two-stage flexible dynamic decision support based
optimal threat evaluation and defensive resource scheduling algorithm for
multi-target air-borne threats. The algorithm provides flexibility and
optimality by swapping between two objective functions, i.e. the preferential
and subtractive defense strategies as and when required. To further enhance the
solution quality, it outlines and divides the critical parameters used in
Threat Evaluation and Weapon Assignment (TEWA) into three broad categories
(Triggering, Scheduling and Ranking parameters). Proposed algorithm uses a
variant of many-to-many Stable Marriage Algorithm (SMA) to solve Threat
Evaluation (TE) and Weapon Assignment (WA) problem. In TE stage, Threat Ranking
and Threat-Asset pairing is done. Stage two is based on a new flexible dynamic
weapon scheduling algorithm, allowing multiple engagements using
shoot-look-shoot strategy, to compute near-optimal solution for a range of
scenarios. Analysis part of this paper presents the strengths and weaknesses of
the proposed algorithm over an alternative greedy algorithm as applied to
different offline scenarios.Comment: 8 Pages, International Journal of Computer Science and Information
Security, IJCSI
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