Single Asset Research Articles

Social stratification and housing wealth inequality in transitional urban China

ABSTRACT As the Chinese economy continues to transform, housing wealth has gradually become the single largest asset in most urban households. At the same time, housing wealth inequality has worsened, and housing stratification has become a major form of social stratification in the country. However, there is no empirical evidence of temporal changes in housing wealth inequality and their implications on social stratification. Pooling three waves of survey data from the 1995, 2008, and 2013 iterations of the Chinese Household Income Project, we present a dynamic image of housing wealth inequality in urban China. The results suggest that housing wealth inequality decreased initially and increased later. To further illustrate how housing wealth is stratified by social groups, we conducted regression analyses and structural tests, which revealed three main results: the education level of the household head has an increasingly positive impact on housing wealth inequality; a household head with superior occupational status generally has an advantage in terms of possession of housing wealth with no evidence that this advantage diminishes during the transitional period; and income has a significant, positive impact on housing wealth, and this effect exhibited an increasing trend over time. These results imply that the temporal changes in housing wealth inequality exacerbated the social stratification in transitional urban China. Highlights This study explores the temporal changes of housing wealth inequality in urban China. Housing inequality decreased and then increased during the transitional period. Both education and income have increasingly positive impacts on the housing inequality. The advantages of the superior occupational status haven’t diminished. These temporal changes exacerbated the social stratification in China.

Empirical Convergence Analysis of Federated Averaging for Failure Prognosis

Data driven prognosis involves machine learning algorithms to learn from previous failures and generate its prediction model. However, often a single asset does not fail so frequently to have enough training data in the form of historical failures. This problem can be addressed by learning from failures across a cluster of similar other assets, but often working in different environments. The algorithm therefore must learn from a distributed dataset which might be heterogenous but with underlying similarities. Federated Learning is an emerging technique that has recently also been proposed as a fitting solution for prognosis of industrial assets. However, even the most commonly used Federated Learning algorithms lack theoretical convergence guarantees, and therefore their convergence must be analysed empirically. This paper empirically analyses the convergence of the Federated Averaging (FedAvg) algorithm for a fleet of simulated turbofan engines. Results demonstrate that while FedAvg is applicable for prognosis, it cannot acknowledge the differences in asset failure mechanisms. As a result, the prognosis framework needs to be modified such that similar failures are clustered together before FedAvg can be implemented.

Discrete time portfolio optimisation managing value at risk under heavy tail return distribution

We consider an investor, whose portfolio consists of a single risky asset and a risk free asset, who wants to maximize his expected utility of the portfolio subject to the Value at Risk assuming a heavy tail distribution of the stock prices return. We use Markov Decision Process and dynamic programming principle to get the optimal strategies and the value function which maximize the expected utility for parametric as well as non parametric distributions. Due to lack of explicit solution in the non parametric case, we use numerical integration for optimization

Discrete time portfolio optimisation managing value at risk under heavy tail return distribution

We consider an investor, whose portfolio consists of a single risky asset and a risk free asset, who wants to maximise his expected utility of the portfolio subject to the value at risk assuming a heavy tail distribution of the stock price return. We use Markov decision process and dynamic programming principle to get the optimal strategies and the value function which maximise the expected utility for parametric as well as non-parametric distributions. Due to lack of explicit solution in the non-parametric case, we use numerical integration for optimisation. The highlights of the study are used Markov decision process and dynamic programming to get recursive relation for optimal strategy of known distribution. Once it approaches the terminal position, we will build-up more on the risky asset or will liquidate the risky asset. In the non-parametric case, we used numerical integration to find the recursive relation for optimal strategy. In this case, the value function we obtained has wide range of fluctuations and the same is also true for the portfolio wealth.

Deep Learning Profit & Loss

Building the future profit and loss (P&L) distribution of a portfolio holding, among other assets, highly non-linear and path-dependent derivatives is a challenging task. We provide a simple machinery where more and more assets could be accounted for in a simple and semi-automatic fashion. We resort to a variation of the Least Square Monte Carlo algorithm where interpolation of the continuation value of the portfolio is done with a feed forward neural network. This approach has several appealing features not all of them will be fully discussed in the paper. Neural networks are extremely flexible regressors. We do not need to worry about the fact that for multi assets payoff, the exercise surface could be non connected. Neither we have to search for smart regressors. The idea is to use, regardless of the complexity of the payoff, only the underlying processes. Neural networks with many outputs can interpolate every single assets in the portfolio generated by a single Monte Carlo simulation. This is an essential feature to account for the P&L distribution of the whole portfolio when the dependence structure between the different assets is very strong like the case where one has contingent claims written on the same underlying.

Digital Twin Enabled Asset Anomaly Detection for Building Facility Management

Assets play a significant role in building utilities by undertaking the majority of their service functionalities. However, a comprehensive facility management solution that can help to monitor, detect, record and communicate asset anomalous issues is till nowhere to be found. The digital twin concept is gaining increasing popularity in architecture, engineering and construction/facility management (AEC/FM) sector, and a digital twin enabled asset condition monitoring and anomaly detection framework is proposed in this paper. A Bayesian change point detection methodology is tentatively embedded to reveal the suspicious asset anomalies in a real time manner. A demonstrator on cooling pumps is developed and implemented based on Centre for Digital Built Britain (CDBB) West Cambridge Digital Twin Pilot. The results demonstrate that supported by the data management capability provided by digital twin, the proposed framework realizes a continuous condition monitoring and anomaly detection for single asset, which contributes to efficient and automated asset monitoring in O&M management.

Context Ontology Development for Connected Maintenance Services

Abstract The opportunity to shift from corrective and preventive to data-driven Predictive Maintenance has received a significant boost with the deeper penetration of Internet of Things (IoT) technologies in industrial environments. Processing IoT generated data nonetheless creates challenges for data management and actionable data processing. One way to handle such complexity is to introduce context information modelling and management, wherein data and service delivery are determined upon resolving the apparent context of a service or data request. In this paper, context information management is considered on the basis of a valid knowledge construct for reliability-oriented maintenance management. The aim is to produce a viable semantic organization of data for maintenance services. It is applied on an industrial case linked to maintenance of a distributed fleet of connected production grade industrial printers. The complexity of translating the data generated by such production assets to actionable information is significant, as the status of a single asset is characterised by several hundreds of failure modes and a multitude of event codes. To assess the viability of the ontology for the targeted application, a qualitative usability evaluation study of the ontology is performed.

Efektywność i stabilność systemów transakcyjnych opartych na zwykłych, wykładniczo i liniowo ważonych średnich ruchomych

<p><strong>Uzasadnienie teoretyczne:</strong> Większość artykułów dotyczy optymalizacji systemów transakcyjnych jedynie w odniesieniu do pojedynczego aktywu lub indeksu. W literaturze przedmiotu zauważalny jest brak opracowań kompleksowych dotyczących całej grupy aktywów.</p><p><strong>Cel artykułu:</strong> Optymalizacja systemów transakcyjnych bazujących na przecięciu średniej ruchomej i ceny zamknięcia (sygnał kupna i sprzedaży) dla 404 akcji notowanych na Giełdzie Papierów Wartościowych w Warszawie. Dla każdego waloru badaniem objętych zostało 5000 sesji lub mniej, jeśli akcje były notowane w krótszym horyzoncie czasowym. Użytymi w badaniu rodzajami średnich były: zwykła średnia ruchoma (SMA), ważona liniowo (WMA) i ważona wykładniczo (EMA).W kolejnych częściach artykułu został przeprowadzony ranking średnich ruchomych oraz dokonano oceny stabilności systemów transakcyjnych.</p><p><strong>Metody badawcze:</strong> W opracowaniu zastosowano następujące metody: 1) średnie ruchome optymalizujące system transakcyjny – analiza korelacji stóp zwrotu i długości średnich ruchomych, regresja liniowa, 2) ranking skuteczności systemów transakcyjnych – rankingowanie proste i ważone stopą zwrotu, 3) analiza stabilności systemów transakcyjnych – korelacja długości pierwszej i drugiej średniej ruchomej, przynoszących najwyższe stopy zwrotu, współczynnik determinacji dla par średnich ruchomych i stóp zwrotu, a także współczynnik WF (średni spadek efektywności 16 najlepszych systemów transakcyjnych przypadający na jednostkową stopę zwrotu najlepszego systemu transakcyjnego).</p><p><strong>Główne wnioski:</strong> Otrzymane rezultaty jednoznacznie wskazują, że w przypadku wszystkich rodzajów średnich systemy transakcyjne były optymalizowane w przeważającej większości przez średnie krótkoterminowe, co potwierdza behawioralną skłonność inwestorów do zawierania transakcji raczej o zabarwieniu spekulacyjnym niż inwestycyjnym. Przeprowadzony ranking skuteczności trzech rodzajów średnich ruchomych (WMA, SMA, EMA) jednoznacznie wskazał, że w przeważającej części najwyższe stopy zwrotu były uzyskiwane przy zastosowaniu WMA, przed SMA i EMA. Różnice skuteczności systemów opartych o WMA i SMA były niewielkie, za to systemy bazujące na tych dwóch średnich ruchomych okazały się zdecydowanie bardziej efektywne od systemów wykorzystujących EMA.</p>

Firm-specific information and systemic risk

Although there is substantial literature linking news to the asset return volatility of a single asset, little attention has been paid to how news influences the relationships between firms. This paper addresses this issue by examining how firm-specific scheduled and unscheduled news arrivals influence the systemic risk of individual firms based on a sample of 47 US financial institutions. Whereas negative surprises from scheduled news announcements and a higher rate of unscheduled news both increase the systemic risk of a firm, positive news surprises decrease this systemic risk. In addition, negative scheduled news and a higher rate of unscheduled news across the sector increases the total connectedness or systemic risk across the sector as a whole. These effects are magnified when the market is already in distress. The results indicate that regulators should consider more than volatility and pay attention to the news flow when monitoring systemic risk.

Portfolio Selection in Quantile Utility Models

This paper develops a model for optimal portfolio allocation for an investor with quantile preferences, i.e., who maximizes the τ-quantile of the portfolio return, for τ ∈ (0,1). Quantile preferences allow to study heterogeneity in individuals' portfolio choice by varying the quantiles, and have a solid axiomatic foundation. Their associated risk attitude is captured entirely by a single dimensional parameter (the quantile τ), instead of the utility function. We formally establish the properties of the quantile model. The presence of a risk-free asset in the portfolio produces an all-or-nothing optimal response to the risk-free asset that depends on investors' quantile preference. In addition, when both assets are risky, we derive conditions under which the optimal portfolio decision has an interior solution that guarantees diversification vis-\`a-vis fully investing in a single risky asset. We also derive conditions under which the optimal portfolio decision is characterized by two regions: full diversification for quantiles below the median and no diversification for upper quantiles. These results are illustrated in an exhaustive simulation study and an empirical application using a tactical portfolio of stocks, bonds and a risk-free asset. The results show heterogeneity in portfolio diversification across risk attitudes.

Flood risk assessment of cultural heritage at large spatial scales: Framework and application to mainland Portugal

Abstract In a global context of increasing flood risk, the development of risk assessments that can effectively support disaster risk management strategies and policies is crucial. This is particularly relevant in the case of cultural heritage, given its socioeconomic value. Although there is a substantial body of literature on the topics of cultural heritage preservation and natural hazard impact mitigation in cultural heritage, the applicability of such studies is usually limited to single assets or sites. The assessment of cultural heritage flood risk at large spatial scales has received little research attention, which contrasts with the importance of the topic. This paper proposes a novel framework to perform semi-quantitative flood risk assessments to immovable cultural heritage assets in a large region, such as a country. The proposed framework employs various scientifically established concepts from the field of natural hazard risk in order to support the computation of coherent and comparable flood risk indices for each exposed asset. This framework can be used to provide a preliminary assessment of risk for a large number of cultural heritage assets with limited resources, as well as to identify those that warrant a more detailed evaluation of risk. The framework is illustrated through an application to immovable cultural heritage assets exposed to fluvial floods in mainland Portugal. This case study is used to discuss various issues related with data requirements, availability and reliability.

SDG Bonds: An Exploration, via a Case Study, of the Key Attributes of a Novel Financial Instrument That May Help Achieve Agenda 2030’s Sustainable Development Goals

The launch by the United Nations of Agenda 2030 with its 17 Sustainable Development Goals (SDGs) is creating new paradigms for business, capital markets, society, and many other fields of human endeavor. Financing the SDGs is an important part of the solution. While Green bonds have taken a prominent role over the last decade in financing sustainability projects, their application remains a capital markets niche. The SDGs, because of their multiplicity, shift the paradigm from a focus on single “green” projects to a holistic concept of sustainability which goes beyond the single project or asset typically financed with Green bonds. As a result of the growing evidence of the significant business opportunities the SDGs offer, corporations are now increasingly embracing this holistic approach to sustainability and are deeply embedding the SDGs into their long term corporate strategies. Consequently corporations have been looking for a financial instrument that better caters to this holistic, rather than ad-hoc, approach to sustainability. SDG Bonds have been proposed as an answer to this need. The general-purpose SDG Bonds in particular offer the required flexibility to achieve broader goals beyond single project financing and importantly, provide an unambigous signal of this intention to investors who are increasingly seeking opportunities in this space. There are three cornerstones that underpin SDG Bond transactions: 1. A clear link to the sustainability strategy of the issuer, through pre-determined sustainability targets 2. An upfront discount in the pricing of the SDG Bond issue, that reflects the economic value of the sustainability strategy of the issuer 3. The assurance provided by external auditors on the achievement of the predetermined sustainability targets. The successful launch in September 2019 of the first ever general-purpose SDG Bond by ENEL, a multinational corporation, is a milestone in the financing of Agenda 2030. Potentially this expands opportunities to finance Agenda 2030, and indeed could be the beginning of a paradigm shift in debt and, perhaps in future, equity capital markets as holistic approaches to sustainability increasingly define corporate missions and investors’ perspectives. This behavioral shift by corporations and investors, and the creation by capital markets of new financial instruments to accommodate these aspirations may have another consequence. They may also represent further evidence of the declining relevance of economic theories based on “rational economic man”, and much of the microeconomic foundations of neo-classical economics. This paper examines the rationale that has led a major global corporation to issue SDG Bonds and how these differentiate themselves from Green bonds in significant ways. There are some, unexpected, consequences stemming from this event which are also briefly explored in this paper. Market pricing of SDG Bonds is analysed; it points to potential market failures in a number of areas. This, as well as other themes explored here, suggest several avenues of further research in what promises to become a rich new segment of study both for academics and practitioners in the capital markets.

Filter‐based portfolio strategies in an HMM setting with varying correlation parametrizations

AbstractWe consider portfolio optimization in a regime‐switching market. The assets of the portfolio are modeled through a hidden Markov model (HMM) in discrete time, where drift and volatility of the single assets are allowed to switch between different states. We consider different parametrizations of the involved asset covariances: statewise uncorrelated assets (though linked through the common Markov chain), assets correlated in a state‐independent way, and assets where the correlation varies from state to state. As a benchmark, we also consider a model without regime switches. We utilize a filter‐based expectation‐maximization (EM) algorithm to obtain optimal parameter estimates within this multivariate HMM and present parameter estimators in all three HMM settings. We discuss the impact of these different models on the performance of several portfolio strategies. Our findings show that for simulated returns, our strategies in many settings outperform naïve investment strategies, like the equal weights strategy. Information criteria can be used to detect the best model for estimation as well as for portfolio optimization. A second study using real data confirms these findings.

Assessing Protection Strategies for Urban Rail Transit Systems: A Case-Study on the Central London Underground

Urban rail transit systems are highly prone to disruptions of various nature (e.g., accidental, environmental, man-made). Railway networks are deemed as critical infrastructures given that a service interruption can prompt adverse consequences on entire communities and lead to potential far-reaching effects. Hence, the identification of optimal strategies to mitigate the negative impact of disruptive events is paramount to increase railway systems’ resilience. In this paper, we investigate several protection strategies deriving from the application of either single asset vulnerability metrics or systemic optimization models. The contribution of this paper is threefold. Firstly, a single asset metric combining connectivity, path length and flow is defined, namely the Weighted Node Importance Evaluation Index (WI). Secondly, a novel bi-level multi-criteria optimisation model, called the Railway Fortification Problem (RFP), is introduced. RFP identifies protection strategies based on stations connectivity, path length, or travel demand, considered as either individual or combined objectives. Finally, two different protection strategy approaches are applied to a Central London Underground case study: a sequential approach based on single-asset metrics and an integrated approach based on RFP. Results indicate that the integrated approach outperforms the sequential approach and identifies more robust protection plans with respect to different vulnerability criteria.

The multiple meanings of prosperity and poverty: a cross-site comparison from Tanzania

ABSTRACT Assets are important to local definitions of poverty and wealth in rural Africa. Yet their use in asset indices can miss locally valued change. We present data from 17 villages across Tanzania to explore differences in the meaning of wealth and poverty across the country. Despite limitations in our site selection we found considerable diversity that makes a single asset index difficult to compile. Current abbreviated asset indices risk counting assets that do not matter locally.

Applying Behavioral Finance to Investments: The Existence and Importance of 'Investment Tribes'

Investment organizations are complex to understand because decisions are the aggregation of multiple individuals who influence the process. This applies to organizations that apply both quantitative and qualitative investment approaches because the first step in a quantitative approach is still a qualitative statement of the investment hypothesis that is then formalized by the models. Increasingly, finance practice is starting to incorporate behavioral finance insights and recognize that individuals are not “rational utility maximizers”, but rather complex psychological beings that can exhibit non-traditional behaviors. Kahneman-Tversky (KT) introduced this notion of individuals being “humans” and not “econs” but made broad generalizations about behaviorally affected decisions (BADs) and did not provide a methodology to examine individual biases. A technique has been proposed to use KT’s questions to provide individual behavioral diagnostics and previous research demonstrated dramatic differences within and across groups based on age, gender and financial literacy, especially for gambles based on prospective gains and losses. This paper is a case study of a single asset management company that uses a mix of quantitative and qualitative investment techniques, driven by five decision makers with equally weighted votes in the final decision. In addition to demonstrating that each individual in the investment committee is unique and providing individual diagnostics that show that age, gender and education need not lead to a particular behavior, this paper highlights the fact that organizations have “investment tribes”; namely, that there are groups of individuals who appear to exhibit similar risk tendencies for gambles involving gains or losses. These tribes can influence and impact decision-making. Quantifying and making transparent the existence of these tribes could improve decision-making. This case study can be helpful for investment firms allowing them to become aware of potential biases, and also for asset owners that delegate decisions to third parties, as it allows them to understand how the investment firms they delegate to might behave when they experience drawdowns.

The impact of liquidity on portfolio value-at-risk forecasts

ABSTRACTHistorical crisis events have highlighted the insufficiency of Value-at-Risk (VaR) as a measure of market risk because such metric does not take liquidity into account. Unlike previous studies analyzing with only a single asset, we examine the impact of liquidity on computing VaR forecasts from a portfolio level. To this end, we use multivariate GARCH-t and GJR-GARCH-t models, as compared with univariate models, to seize the liquidity property embedded in individual stock returns and evaluate their accuracy and efficiency in computing VaR forecasts for portfolios with different liquidity levels.The empirical results indicate that computing portfolio VaR forecasts with multivariate models outperform the univariate models for full and subsample periods in terms of accuracy and efficiency evaluations, in particular for less-liquid portfolios. These results suggest the importance of liquidity in computing portfolio VaR forecasts. Ignorance of the impact of liquidity in computing portfolio VaR forecasts might result in inadequate coverage and insufficient market risk capital requirements.

Bitcoin and Venezuela's Unofficial Exchange Rate

Using Bitcoin trading data in Venezuelan bolivars from the LocalBitcoins peer-to-peer market place and using the theory of Purchasing Power Parity (PPP), Bitcoin, as a single universal asset, is substituted for the ‘basket of goods’ normally used in the PPP, allowing the estimation of the relationship between the Venezuelan bolivar and the United States dollar. In this analysis Bitcoin is used as a tool to enable the calculation of the bolivars to dollars unofficial exchange rate and consequently the implied inflation rate. Using Bitcoin’s publicly available prices in this way enables a government’s economic mismanagement to be identified more quickly than the typical approach of measuring changes in the Consumer Price Index. Venezuela is currently in crisis, which this approach identifies as a problem as far back as 2014, as official and unofficial exchange rates diverge and inflation rates increase yearly reaching an unbelievable 70,000% in 2018 alone.

A Review of Forward Starting Options Under Stochastic Numeraire

The purpose of this note is to demonstrate the impact of stochastic rates when pricing forward starting options on single asset equity derivatives. We will assume Hull-White one factor short rate model for rates with constant volatility and time dependent instantaneous volatility for the underlying.

Responsible or Thematic? The True Nature of Sustainability-Themed Mutual Funds

The aim of the paper is to compare the risk-adjusted performance of sustainability-themed funds with other categories of mutual funds: sustainable and responsible mutual funds that implement different approaches in portfolio selection and management, and thematic funds not committed to responsible investments. The study analyses a sample of about 1000 European mutual open-end funds where 302 are sustainability-themed funds, 358 are other responsible funds, and 341 other thematic funds. Risk-adjusted performance is analyzed for the period 2007–2017 using different methodologies: a single factor Capital Asset Pricing Model (CAPM), a Fama and French (1993) 3-factor model, and a Fama and French (2015) 5-factor model. Our main findings demonstrate that the risk-adjusted performance of ST funds is more closely related to their responsible nature than to their thematic approach. Sustainability-themed mutual funds are more similar to other socially responsible funds than to other thematic funds, as confirmed by performance analysis over time. They are also better than other thematic funds in overcoming financially turbulent periods and currently benefit from SRI regulation and disclosure.