Portfolio Behavior Research Articles

Modelling common bubbles in cryptocurrency prices

Bubbles and spikes in cryptocurrency prices increase considerably the risk on investments in these assets. In the traditional time series literature bubbles are viewed as nonstationary and non-estimable components of a process. In this paper, we adopt a different approach and consider the bubbles as inherent features of a strictly stationary causal-noncausal (mixed) Vector Autoregressive (VAR) process. This approach allows us to model and estimate the common bubbles and spikes in cryptocurrency prices. It also provides us linear combinations of cryptocurrencies that eliminate common bubbles analogously to the cointegrating vectors eliminating common trends in unit root processes. They are used to build cryptocurrency portfolios immune to the risk of common bubbles that ensure stable investment strategies. The mixed VAR model is estimated from the US Dollar prices of Bitcoin, Ethereum, Ripple, and Stellar over the period 2017–2019. We document the common bubbles and illustrate the behaviour of bubble-free portfolios.

DivFolio: a Shiny application for portfolio divestment in green finance wealth management

Abstract This paper introduces DivFolio, a multiperiod portfolio selection and analytic software application that incorporates automated and user-determined divestment practices accommodating Environmental Social Governance (ESG) and portfolio carbon footprint considerations. This freely available portfolio analytics software tool is written in R with a GUI interface developed as an R Shiny application for ease of user experience. Users can utilize this software to dynamically assess the performance of asset selections from global equity, exchange-traded funds, exchange-traded notes, and depositary receipts markets over multiple time periods. This assessment is based on the impact of ESG investment and fossil-fuel divestment practices on portfolio behavior in terms of risk, return, stability, diversification, and climate mitigation credentials of associated investment decisions. We highlight two applications of DivFolio. The first revolves around using sector scanning to divest from a specialized portfolio featuring constituents of the FTSE 100. The second, rooted in actuarial considerations, focuses on divestment strategies informed by environmental risk assessments for mixed pension portfolios in the US and UK.

Analysis of Stock Portfolio with Global Economic Factors using Dynamic Data Modelling

This article analyzes stock portfolios and worldwide economic challenges. This study uses APIs to retrieve data and analyze correlations. To determine and measure how economic indicators affect stock performance. This work introduces an autoencoder-based model to better comprehend the complex relationship between economic conditions and stock portfolio dynamics. The analysis begins with an API retrieval of a wide range of macroeconomic information. These indicators include global economic metrics like GDP, unemployment, CPI, federal funds rates, and treasury bill rates. After collection, data is carefully curated and prepared for analysis. This study uses correlation analysis to understand economic variables and stock portfolio performance. This study explores how economic conditions affect stock prices and portfolio returns. This study seeks to discover trends, dependencies, and future issues that may affect investment decisions. This study also introduces an autoencoder-based neural network model to capture complex nonlinear relationships between economic variables and stock portfolio behavior. Deep learning improves interpretability and prediction, allowing a better understanding of the complex financial ecosystem dynamics. The inquiry provides valuable insights for investors, financial experts, and regulators. This study advances data-driven investment and risk management solutions. The autoencoder-based approach also reveals latent structures and hidden factors that affect stock portfolios. This novel approach opens new study options. In conclusion, this study provides a thorough stock portfolio analysis approach for global economic challenges. API data retrieval, correlation analysis, and a novel autoencoder model are used in this work to better understand the complicated relationships between economic indicators and financial markets. These insights can improve investment and policy decisions in a more integrated and dynamic global economy.

Currency portfolio behavior in seven major Asian markets

This study explores the returns and volatility patterns of currency portfolios (CP) in seven major Asian markets during noncrisis and crisis periods. The CP is developed by investing or divesting in the top three forward premiums or discount currencies in these markets and is rebalanced quarterly. The main findings are as follow: (1) Stochastic volatility models perform better than generalized autoregressive conditional heteroskedasticity models. (2) Spillover effects from the stock market on CP are observed for the examined periods. (3) The stochastic dominance analysis revealed that CP outperformed most stock markets.

Climate and environmental risk factors in the market risk field: An extended model

The extension of the risk management models to the broad sustainability concept is an open issue in both the academic and financial communities. The current state of the art for the risk measurement models is not satisfactory. There are many weaknesses in the data feasibility and the debate about what the new models should measure is still open. We propose a model that aims to improve the existing market risk models by capturing the sustainability risk sources. The starting point is the incremental risk charge (IRC) model, namely a 1 year 99.9 percent value at risk that covers default and migration risk. We extend the traditional model by defining the environmental incremental risk charge (E-IRC), with two enhancements: 1) by some data analysis and statistical techniques we introduce some new environmental, social, and governance (ESG) risk factors to better explain the portfolio behavior; 2) we adjust the default probabilities provided by the rating agencies by combining the green premium (lower spread) observed in the markets with the available ESG score for each obligor. The new model was tested on a real portfolio by a Montecarlo engine. The model does not affect too much the existing IRC results, so allowing continuity in the reporting process. The main advantage of E-IRC is the availability of a more effective risk decomposition process, where the ESG contributions can be properly highlighted.

European green policy announcements and sectoral stock returns

To fulfill the Paris Agreement commitments and stimulated by an unprecedented amount of public resources put in place to recover from the COVID-induced recession, European governments have recently announced sizable green policy plans. In this paper, we examine the behavior of green and brown portfolios around green policy-related announcements (GPAs) made by major European governments in 2020 via a standard event study analysis and the use of returns of stocks listed in the “STOXX 100 All Europe”. Our main empirical findings indicate the presence of positive cumulative abnormal returns (CARs) both in the green and brown sectors following GPAs. However, the estimated positive sentiment effect is stronger in the former sector. A size effect in terms of the amount of resources announced to be allocated for a specific category of policy is also observed. We find that the observed positive sentiment is mainly driven by announcements on climate change mitigation-related policies, which account for 70% of the total allocated funds. At the sector level, positive and significant CARs due to GPAs are found in the (i) energy, (ii) financial and (iii) industrial sectors. At the country level, GPAs are found to drive a significant positive sentiment effect in the following European countries: Switzerland, Spain, UK, Ireland and Italy. Sector- and country-level analyses confirm the presence of larger benefits from GPAs among more sustainable portfolios.

Permutation-weighted portfolios and the efficiency of commodity futures markets

We study the behavior of permutation-weighted portfolios, portfolios with weights that are proportional to a permutation of the current market weights. For markets with more than two assets, these portfolios are not functionally generated (except for the identity permutation), so we use rank-based methods to analyze their behavior. The reverse-wighted portfolio is the permutation-weighted portfolio with weights proportional to the market weights, but reversed by rank. We show that in a market represented by a first-order model with rank-symmetric variance parameters, the reverse-weighted portfolio will outperform the market portfolio over the long term. This result carries over to a commodity futures market with rank-based parameters similar to those of such a first-order model. In this market we find that the reverse-weighted portfolio outperforms the price-weighted market portfolio from 1977–2018.

The Characteristics and Portfolio Behavior of Bitcoin Investors: Evidence from Indirect Cryptocurrency Investments

Abstract Cryptocurrencies have received growing attention from individuals, the media, and regulators. However, little is known about the investors whom these financial instruments attract. Using administrative data, we describe the investment behavior of individuals who invest in cryptocurrencies with structured retail products. We find that cryptocurrency investors are active traders who are prone to investment biases and hold risky portfolios. Cryptocurrency investors are more likely to invest in stocks with high media sentiment and more likely to employ heuristics from technical analysis. In line with attention effects and anticipatory utility, we find that the average cryptocurrency investor substantially increases account logins and trading activity after his or her first cryptocurrency purchase. Furthermore, cryptocurrency investors tend to tilt their portfolios toward even more risky securities after cryptocurrency adoption. Our results document which investors are more likely to adopt new financial products and help inform regulators about investors’ vulnerability to cryptocurrency investments.

Performance measurement for option portfolios in a stochastic volatility framework

Measuring the performance of stock portfolios that include options is challenging due to options' nonlinearity in the underlying, their exposure to volatility risk, and their time decay. Our contribution to the literature is twofold: First, we provide a theoretically rigorous derivation of the time-variable factor loadings in a two-factor model under stochastic volatility according to [Heston, S.L., A closed-form solution for options with stochastic volatility with applications to bond and currency options. Rev. Financ. Stud., 1993, 6, 327–343.]. Within this setting, the portfolio returns are explained by the market and an additional option factor, i.e. a portfolio of standard options exposed to volatility risk. We show that (i) any option factor is suitable to perfectly explain the portfolio behavior if simple returns are considered in instantaneous time and that (ii) the option factor's loading equals the fraction of the volatility elasticities of the portfolio and of the option factor while the option factor's underlying elasticity enters the factor loading of the underlying. Second, we analyze the behavior of option factors in practical applications, where time is discrete and factor loadings are estimated in a single regression over a certain time horizon. We show how the bias of (Jensen) alpha and its sign depend on the skewness of market returns. For several option factors from the literature, we conduct a simulation study to analyze their suitability to reduce this bias. As the results are disappointing, we propose a two-step procedure for choosing an adequate factor.

Dynamic behaviour of optimal portfolio with stochastic volatility

In the existing literature, little is known about the dynamic behaviour of the optimal portfolio in terms of market inputs and arbitrary stochastic factor dynamics in an incomplete market with a stochastic volatility. In this paper, to study optimal portfolio behaviour, we compute and analyze the mean and the variance of the optimal portfolio and of their adjustment speed in terms of market inputs in an incomplete market. The incompleteness arises from the additional source of uncertainty of the volatility in Heston’s stochastic volatility model. Conducting sensitivity analysis for the mean and the variance of the optimal portfolio process as well as its adjustment speed to the market parameters, we find several interesting behavioural patterns of investors towards asset price and its volatility shocks. Our results are robust and convergent by the agreement from two simulation methods for different time step increments and the number of Monte Carlo simulation paths.

The Effect of the 2008 Global Financial Crisis on the Efficiency of Large U.S. Commercial Banks

Abstract The 2008 financial crisis, originated by securitization of sub-prime mortgage loans, had a huge impact on U.S. financial institutions and markets. We hypothesize that due to this crisis, the commercial banking industry has changed their portfolio structures and risk-taking behavior. To shed light on the response of U.S. banks to the 2008 financial crisis, we use the non-parametric approach to measure and compare the overall efficiency of large U.S. banks pre- and post-2008 financial crisis. We then decompose the overall measure of efficiency into allocative, overall technical, pure technical, and scale efficiency measures to better understand the sources of banking inefficiencies. The results indicate that large U.S. banks indeed changed their portfolios structure, and the efficiency of large commercial banks in the United States declined substantially during the financial crisis. Although it has been recovering since then, it still has not reached to the pre-crisis efficiency level.

Uso da técnica DEA-Sharpe na análise do comportamento de carteiras de investimento

O risco inerente ao mercado de ações permeia o processo de tomada de decisão no instante em que se deve decidir quais ativos farão parte do portfólio para formar uma carteira de investimentos eficiente. A busca por modelos de otimização que fornecem uma melhor forma de dirimir e minimizar os riscos atraem o olhar de investidores e pesquisadores. O objetivo deste artigo é analisar o comportamento de carteiras de investimento formadas por ativos selecionados por meio da ferramenta Análise Envoltória de Dados [DEA] e utilizando o Índice de Sharpe como indicador, para assim, compará-las com carteiras sem a utilização do mesmo. Ao decorrer da análise, foi possivel observar que a aplicação do DEA demostrou-se exequível e oportunizou que a descriminação das unidades de análise transcorresse de forma coesa e coerente.

Multimedia Portfolio Behavior Analysis Based on Bayesian Game Algorithm

A portfolio prediction model based on Bayesian game algorithm is proposed to improve the accuracy of correlation analysis model in portfolio application. Firstly, the portfolio problem is analyzed. The market value constraint and the upper bound constraint are combined considering the general portfolio model according to Markowitz theory, it improves the portfolio model and obtains the portfolio model with mixed constraints. Secondly, a probabilistic portfolio monitoring strategy based on incomplete information non-cooperative Bayesian game is proposed. The framework combines the enterprise investment behavior detection module with the specified rules to identify the portfolio pattern, the interaction between portfolio selection and monitored investment behavior is modeled as a two-person non-cooperative Bayesian game, which allows portfolio selection to adopt a probability monitoring strategy based on game Bayesian Nash equilibrium, thereby reducing the computational complexity of the portfolio approach introduced into the game algorithm. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.

Impact of Capital Structure on the Portfolio Behaviour of Commercial Banks in Tanzania

This study examined the impact of bank capital structure measured by leverage ratio as defined in Based III based on tier 1 capital to total assets, on the portfolio behaviour of commercial banks in Tanzania by using fixed balance panel of annual financial statements data of 20 banks from 2002 to 2017. The studied banks had more than 89 percent of the total commercial banks’ assets in 2017 and 79 percent of total loans. Consideration of portfolio behaviour was explained based on choice assets (loans, investment in government securities and interbank loans) and non-choice items (non-earning assets including cash, required reserved and investment in fixed assets). The study examined the impact of capital structure on the bank portfolio holding with respect to the size of the bank and ownership. Findings revealed that only 35% of the banks are undercapitalized as at the end of 2017. The variables tested were significant at p <0.01 and p <0.001 except for the bank size measure. This means size of the bank is not a measure for its leverage structure, but with positive relationship.

Life Settlement Value Profiles across Different Death Rate Distributions

The life settlement asset class is a recognized part of the alternative investment market. Our article offers insight into the behavior of portfolios of life settlement contracts. We focus on the value profiles of pools of life settlement contracts in the value/cost of capital space. A pool of life settlement contracts has a death rate distribution that will determine the amount of premiums that must be paid each period and the amount of death benefits that will be collected each period. Because value is determined by the timing, magnitude, and certainty of the cash flows generated by an asset, it is inexorably linked to the timing of the life settlers’ deaths. We show that when certain conditions are met, pools with fundamentally different underlying mortality rates and risk profiles, but with the same cost of capital, can have the same value (pivot point in the value/cost of capital space). This pivot point can be useful for fund managers seeking to modify a portfolio of life settlements under a specific cost-of-capital constraint.

Does low leverage minimise the impact of financial shocks? New optimisation strategies using Islamic stock screening for European portfolios

This study embodies a preliminary endeavour at analysing the impact of leverage on portfolio behaviour, with specific reference to return and volatility, in the European stock markets, using the debt ratio as one of the important benchmarks for Islamic stock screening. Given the focus of Islamic stock screening on the debt ratio, we use data from 320 firms for eight European countries which were classified according to their level of debt and size. For this, the portfolio optimisation based Mean-Variance Efficient Frontier (MVEF), the Sharpe Ratio and the Capital Market Line (CML) were employed. Our findings tend to demonstrate that, under shocks, high leverage worsens the portfolio return, volatility, and value at risk. The results further point out that optimal portfolio composition is obtained through a high proportion of low debt funds in the case of two separate equity funds, of low debt and high debt portfolios respectively. The systematic risk of several portfolio strategies is further explored with regards to a benchmark of European index and market-wide, return and volatility shocks.

Low Interest Rates and Household Portfolio Behaviour in Euro Area Countries

This paper examines whether household portfolio structures change in favour of riskier assets when interest rates fall. Using euro area financial accounts data from the financial crisis through the present day, it is shown that the current low interest rate environment has not, up to this point, induced euro area households to add further risky assets to their investment portfolios.

ETFs, ETNs and Statistical Anomalies

We overview exchange-traded funds and notes mechanism and market. We consider different classes of ETFs: long, short, unleveraged, leveraged. We show tables and graphs of leveraged and short ETF behaviors for up and down moves for educational purposes. We describe historical behavior of several benchmark portfolios constructed using leveraged ETFs and describe their behaviors in different bull and bear markets. We describe implementation of constant proportion portfolio insurance strategy of Black, Jones, Perold using leveraged ETFs. Finally we describe different statistical anomalies: equal weighted ETFs anomaly and mispriced factors anomaly.

Performance Measurement for Option Portfolios in a Stochastic Volatility Framework

Measuring the performance of stock portfolios that include options is challenging due to options' nonlinearity in the underlying, their exposure to volatility risk, and their time decay. Our contribution to the literature is twofold: First, we provide a theoretically rigorous derivation of the time-variable factor loadings in a two-factor model under stochastic volatility according to Heston (1993). Within this setting, the portfolio returns are explained by the market and an additional option factor, i.e., a portfolio of standard options exposed to volatility risk. We show that (i) any option factor is suitable to perfectly explain the portfolio behavior if simple returns are considered in instantaneous time and that (ii) the option factor's loading equals the fraction of the volatility elasticities of the portfolio and of the option factor while the option factor's underlying elasticity enters the factor loading of the underlying. Second, in applications however, time has to be discretized and factor loadings are usually estimated in a single regression over a certain time horizon, which regularly leads to a bias in performance measurement. We analytically show how the discretization error arises through nonlinearity related to both risk factors, the underlying and the volatility. We run a simulation analysis to analyze the size of this bias when different option factors from the common literature are used and propose a two-step procedure to keep the bias small.

Central Bank Reserve Management and International Financial Stability-Some Post-Crisis Reflections

Motivated by the tension first revealed during the global financial crisis between the domestic and international financial stability obligations of central bank reserve managers, this paper offers some reflections along four main lines. First, the paper highlights how official reserve management has evolved to mirror important aspects of private institutional investor behavior over time, and addresses the policy relevance of this convergence. Second, evidence is documented of procyclical portfolio behavior by reserve managers during the crisis, which added to the stabilization burden shouldered by central banks in reserve currency-issuing countries. Third, in appraising the evolution of related vulnerabilities since the crisis, the paper finds grounds for both cautious optimism and lingering concern, the balance of which points to an uncertain future resolution. Fourth, some potential remedies are presented to help dampen the procyclical impulses of reserve managers in future periods of international financial turbulence.