Research

15 pages, 5601 KiB

Open AccessArticle

Cognitive User Interface for Portfolio Optimization

by Yuehuan He, Oleksandr Romanko, Alina Sienkiewicz, Robert Seidman and Roy Kwon

J. Risk Financial Manag. 2021, 14(4), 180; https://0-doi-org.brum.beds.ac.uk/10.3390/jrfm14040180 - 14 Apr 2021

Cited by 2 | Viewed by 2459

This paper describes the development of a chatbot as a cognitive user interface for portfolio optimization. The financial portfolio optimization chatbot is proposed to provide an easy-to-use interface for portfolio optimization, including a wide range of investment objectives and flexibility to include a [...] Read more.

This paper describes the development of a chatbot as a cognitive user interface for portfolio optimization. The financial portfolio optimization chatbot is proposed to provide an easy-to-use interface for portfolio optimization, including a wide range of investment objectives and flexibility to include a variety of constraints representing investment preferences when compared to existing online automated portfolio advisory services. Additionally, the use of a chatbot interface allows investors lacking a background in quantitative finance and optimization to utilize optimization services. The chatbot is capable of extracting investment preferences from natural text inputs, handling these inputs with a backend financial optimization solver, analyzing the results, and communicating the characteristics of the optimized portfolio back to the user. The architecture and design of the chatbot are presented, along with an implementation using the IBM Cloud, SS&C Algorithmics Portfolio Optimizer, and Slack as an example of this approach. The design and implementation using cloud applications provides scalability, potential performance improvements, and could inspire future applications for financial optimization services. Full article

(This article belongs to the Special Issue Financial Optimization and Risk Management)

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17 pages, 2677 KiB

Open AccessArticle

Credit Risk Model Based on Central Bank Credit Registry Data

by Fisnik Doko, Slobodan Kalajdziski and Igor Mishkovski

J. Risk Financial Manag. 2021, 14(3), 138; https://0-doi-org.brum.beds.ac.uk/10.3390/jrfm14030138 - 23 Mar 2021

Cited by 9 | Viewed by 5346

Abstract

Data science and machine-learning techniques help banks to optimize enterprise operations, enhance risk analyses and gain competitive advantage. There is a vast amount of research in credit risk, but to our knowledge, none of them uses credit registry as a data source to [...] Read more.

Data science and machine-learning techniques help banks to optimize enterprise operations, enhance risk analyses and gain competitive advantage. There is a vast amount of research in credit risk, but to our knowledge, none of them uses credit registry as a data source to model the probability of default for individual clients. The goal of this paper is to evaluate different machine-learning models to create accurate model for credit risk assessment using the data from the real credit registry dataset of the Central Bank of Republic of North Macedonia. We strongly believe that the model developed in this research will be an additional source of valuable information to commercial banks, by leveraging historical data for all the population of the country in all the commercial banks. Thus, in this research, we compare five machine-learning models to classify credit risk data, i.e., logistic regression, decision tree, random forest, support vector machines (SVM) and neural network. We evaluate the five models using different machine-learning metrics, and we propose a model based on credit registry data from the central bank with detailed methodology that can predict the credit risk based on credit history of the population in the country. Our results show that the best accuracy is achieved by using decision tree performing on imbalanced data with and without scaling, followed by random forest and linear regression. Full article

(This article belongs to the Special Issue Financial Optimization and Risk Management)

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27 pages, 426 KiB

Open AccessArticle

Time-Consistent Investment and Consumption Strategies under a General Discount Function

by Ishak Alia, Farid Chighoub, Nabil Khelfallah and Josep Vives

J. Risk Financial Manag. 2021, 14(2), 86; https://0-doi-org.brum.beds.ac.uk/10.3390/jrfm14020086 - 20 Feb 2021

Cited by 2 | Viewed by 1718

Abstract

In the present paper, we investigate the Merton portfolio management problem in the context of non-exponential discounting, a context that gives rise to time-inconsistency of the decision-maker. We consider equilibrium policies within the class of open-loop controls that are characterized, in our context, [...] Read more.

In the present paper, we investigate the Merton portfolio management problem in the context of non-exponential discounting, a context that gives rise to time-inconsistency of the decision-maker. We consider equilibrium policies within the class of open-loop controls that are characterized, in our context, by means of a variational method which leads to a stochastic system that consists of a flow of forward-backward stochastic differential equations and an equilibrium condition. An explicit representation of the equilibrium policies is provided for the special cases of power, logarithmic and exponential utility functions. Full article

(This article belongs to the Special Issue Financial Optimization and Risk Management)

13 pages, 433 KiB

Open AccessArticle

Market Graph Clustering via QUBO and Digital Annealing

by Seo Woo Hong, Pierre Miasnikof, Roy Kwon and Yuri Lawryshyn

J. Risk Financial Manag. 2021, 14(1), 34; https://0-doi-org.brum.beds.ac.uk/10.3390/jrfm14010034 - 12 Jan 2021

Cited by 9 | Viewed by 3687

Abstract

We present a novel technique for cardinality-constrained index-tracking, a common task in the financial industry. Our approach is based on market graph models. We model our reference indices as market graphs and express the index-tracking problem as a quadratic K-medoids clustering problem. We [...] Read more.

We present a novel technique for cardinality-constrained index-tracking, a common task in the financial industry. Our approach is based on market graph models. We model our reference indices as market graphs and express the index-tracking problem as a quadratic K-medoids clustering problem. We take advantage of a purpose-built hardware architecture to circumvent the NP-hard nature of the problem and solve our formulation efficiently. The main contributions of this article are bridging three separate areas of the literature, market graph models, K-medoid clustering and quadratic binary optimization modeling, to formulate the index-tracking problem as a binary quadratic K-medoid graph-clustering problem. Our initial results show we accurately replicate the returns of various market indices, using only a small subset of their constituent assets. Moreover, our binary quadratic formulation allows us to take advantage of recent hardware advances to overcome the NP-hard nature of the problem and obtain solutions faster than with traditional architectures and solvers. Full article

(This article belongs to the Special Issue Financial Optimization and Risk Management)

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31 pages, 1332 KiB

Open AccessArticle

Multi-Period Portfolio Optimization with Investor Views under Regime Switching

by Razvan Oprisor and Roy Kwon

J. Risk Financial Manag. 2021, 14(1), 3; https://0-doi-org.brum.beds.ac.uk/10.3390/jrfm14010003 - 23 Dec 2020

Cited by 9 | Viewed by 5235

Abstract

We propose a novel multi-period trading model that allows portfolio managers to perform optimal portfolio allocation while incorporating their interpretable investment views. This model’s significant advantage is its intuitive and reactive design that incorporates the latest asset return regimes to quantitatively solve managers’ [...] Read more.

We propose a novel multi-period trading model that allows portfolio managers to perform optimal portfolio allocation while incorporating their interpretable investment views. This model’s significant advantage is its intuitive and reactive design that incorporates the latest asset return regimes to quantitatively solve managers’ question: how certain should one be that a given investment view is occurring? First, we describe a framework for multi-period portfolio allocation formulated as a convex optimization problem that trades off expected return, risk and transaction costs. Using a framework borrowed from model predictive control introduced by Boyd et al., we employ optimization to plan a sequence of trades using forecasts of future quantities, only the first set being executed. Multi-period trading lends itself to dynamic readjustment of the portfolio when gaining new information. Second, we use the Black-Litterman model to combine investment views specified in a simple linear combination based format with the market portfolio. A data-driven method to adjust the confidence in the manager’s views by comparing them to dynamically updated regime-switching forecasts is proposed. Our contribution is to incorporate both multi-period trading and interpretable investment views into one framework and offer a novel method of using regime-switching to determine each view’s confidence. This method replaces portfolio managers’ need to provide estimated confidence levels for their views, substituting them with a dynamic quantitative approach. The framework is reactive, tractable and tested on 15 years of daily historical data. In a numerical example, this method’s benefits are found to deliver higher excess returns for the same degree of risk in both the case when an investment view proves to be correct, but, more notably, also the case when a view proves to be incorrect. To facilitate ease of use and future research, we also developed an open-source software library that replicates our results. Full article

(This article belongs to the Special Issue Financial Optimization and Risk Management)

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28 pages, 1192 KiB

Open AccessFeature PaperArticle

Risk Return Trade-Off in Relaxed Risk Parity Portfolio Optimization

by Vaughn Gambeta and Roy Kwon

J. Risk Financial Manag. 2020, 13(10), 237; https://0-doi-org.brum.beds.ac.uk/10.3390/jrfm13100237 - 04 Oct 2020

Cited by 7 | Viewed by 5808

Abstract

This paper formulates a relaxed risk parity optimization model to control the balance of risk parity violation against the total portfolio performance. Risk parity has been criticized as being overly conservative and it is improved by re-introducing the asset expected returns into the [...] Read more.

This paper formulates a relaxed risk parity optimization model to control the balance of risk parity violation against the total portfolio performance. Risk parity has been criticized as being overly conservative and it is improved by re-introducing the asset expected returns into the model and permitting the portfolio to violate the risk parity condition. This paper proposes the incorporation of an explicit target return goal with an intuitive target return approach into a second-order-cone model of a risk parity optimization. When the target return is greater than risk parity return, a violation to risk parity allocations occurs that is controlled using a computational construct to obtain near-risk parity portfolios to retain as much risk parity-like traits as possible. This model is used to demonstrate empirically that higher returns can be achieved than risk parity without the risk contributions deviating dramatically from the risk parity allocations. Furthermore, this study reveals that the relaxed risk parity model exhibits advantageous traits of robustness to expected returns, which should not deter the use of expected returns in risk parity model. Full article

(This article belongs to the Special Issue Financial Optimization and Risk Management)

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6 pages, 1607 KiB

Open AccessCommunication

Volatility-Adjusted 60/40 versus 100—New Risk Investing Paradigm

by Jim Kyung-Soo Liew and Ahmad Ajakh

J. Risk Financial Manag. 2020, 13(9), 190; https://0-doi-org.brum.beds.ac.uk/10.3390/jrfm13090190 - 20 Aug 2020

Viewed by 2993

Abstract

In this study we examine the volatility-adjusted 60/40 rule at the individual company level. We document that strong diversification benefits exist over the long-term, and that both the equity and corporate bonds exhibit positive expected drifts. For our sample of 30 large-cap companies, [...] Read more.

In this study we examine the volatility-adjusted 60/40 rule at the individual company level. We document that strong diversification benefits exist over the long-term, and that both the equity and corporate bonds exhibit positive expected drifts. For our sample of 30 large-cap companies, given that corporate bond positions have shown less volatility than the equity position, we leveraged the resultant portfolio of 60/40 to match that of the equity position. When we compare the two investments, we document an outperformance of 100 to 200 bps per year, even after we account for the leverage costs of 100 bps. We believe our work will open up a new risk investing paradigm for those seeking long-term advantages. Full article

(This article belongs to the Special Issue Financial Optimization and Risk Management)

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44 pages, 3560 KiB

Open AccessArticle

Stochastic Optimization System for Bank Reverse Stress Testing

by Giuseppe Montesi, Giovanni Papiro, Massimiliano Fazzini and Alessandro Ronga

J. Risk Financial Manag. 2020, 13(8), 174; https://0-doi-org.brum.beds.ac.uk/10.3390/jrfm13080174 - 06 Aug 2020

Cited by 7 | Viewed by 3076

Abstract

The recent evolution of prudential regulation establishes a new requirement for banks and supervisors to perform reverse stress test exercises in their risk assessment processes, aimed at detecting default or near-default scenarios. We propose a reverse stress test methodology based on a stochastic [...] Read more.

The recent evolution of prudential regulation establishes a new requirement for banks and supervisors to perform reverse stress test exercises in their risk assessment processes, aimed at detecting default or near-default scenarios. We propose a reverse stress test methodology based on a stochastic simulation optimization system. This methodology enables users to derive the critical combination of risk factors that, by triggering a preset key capital indicator threshold, causes the bank’s default, thus detecting the set of assumptions that defines the reverse stress test scenario. This article presents a theoretical presentation of the approach, providing a general description of the stochastic framework and, for illustrative purposes, an example of the application of the proposed methodology to the Italian banking sector, in order to illustrate the possible advantages of the approach in a simplified framework, which highlights the basic functioning of the model. In the paper, we also show how to take into account some relevant risk factor interactions and second round effects such as liquidity–solvency interlinkage and modeling of Pillar 2 risks including interest rate risk, sovereign risk, and reputational risk. The reverse stress test technique presented is a practical and manageable risk assessment approach, suitable for both micro- and macro-prudential analysis. Full article

(This article belongs to the Special Issue Financial Optimization and Risk Management)

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19 pages, 1557 KiB

Open AccessArticle

Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds

by Doureige J. Jurdi

J. Risk Financial Manag. 2020, 13(6), 118; https://0-doi-org.brum.beds.ac.uk/10.3390/jrfm13060118 - 05 Jun 2020

Cited by 1 | Viewed by 3920

Abstract

This paper uses two highly liquid S&P 500 and gold exchange-traded funds (ETFs) to evaluate the impact of liquidity and macroeconomic news surprises on the frequency of observing intraday jumps. It explicitly addresses market microstructure noise-induced biases in realized estimators used in jump [...] Read more.

This paper uses two highly liquid S&P 500 and gold exchange-traded funds (ETFs) to evaluate the impact of liquidity and macroeconomic news surprises on the frequency of observing intraday jumps. It explicitly addresses market microstructure noise-induced biases in realized estimators used in jump detection tests and applies non-parametric intraday jump detection tests. The results show a significant increase in trading costs and elevated levels of information asymmetry before observing jumps. Depth, resiliency, and trading activity are associated with the frequency of observing intraday jumps and cojumps. The ability of liquidity variables to predict intraday jumps persists after controlling for news surprises. Results show that intraday jump realizations affect the price discovery of ETFs. Full article

(This article belongs to the Special Issue Financial Optimization and Risk Management)

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