Portfolio rebalancing based on time series momentum and downside risk

Time series momentum and macroeconomic risk

Optimality of Momentum and Reversal

Partial moment momentum

These days we are witnessing a deep change in the characteristics of the type of energy that our economies are supplied with. A clear trend is that sustainable and green energies are decisively replacing traditional fossil fuel-based sources of energy. For various reasons, this fundamental change implies an increasing risk in investments on portfolios heavily based on traditional energy industries. What is less known, is that these industries have returns that show a very low correlation with sustainable fossil fuel-free stock portfolios making them an appealing tool for portfolio managers to design properly diversified investments. In this study we examine this and related phenomena proposing statistical methods to implement the expected shortfall (ES), the challenging risk measure recently adopted by the financial regulator. We obtain evidence that a newly proposed backtesting procedure for the ES based on multinomial tests is an adequate and simple method to validate these risk measures when applied to a highly volatile stock index. Backtesting results of the ES show that flexible heavy-tailed distribution α–stable performs well for modelling the loss distribution. These results are even improved when the variances of fossil fuel price returns are included as external regressors in the GARCH model variance equation. In this case, the ES computed from the four considered loss distributions perform properly.

Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-Asset Multi-Factor Allocations

The purpose of this study is to analyze the determinants or factors that determine the decision to invest in Islamic Mutual Funds through online platforms, using the Theory of Planned Behavior (TPB). This study measures one dependent variable, namely the decision to invest in Islamic Mutual Funds through online platforms and six independent variables, namely Islamic financial literacy, Islamic mutual fund investment literacy, benefit perception, technological advancement, investment risk and investment return. The research is quantitative in nature, using primary data. Data collection was conducted with the dissemination of questionnaires to respondents in Greater Jakarta Area who have accessed online platforms to invest in Islamic Mutual Funds. This study then uses SEM-PLS (Structural Equation Modeling-Partial least Square) analysis with SmartPLS software to analyze the data. The study shows that Islamic Financial Literacy and Investment Literacy of Islamic Mutual Funds have a negative and significant influence on the decision to invest in Islamic Mutual Funds Online. While the variables of perception of benefits, technological advances, investment risk and investment return have no influence on the decision to invest in Islamic Mutual Funds Online. Thus, the better the level of literacy or understanding of Islamic finance and the investment literacy of Islamic mutual funds, the more people's decisions to invest in sharia-compliant mutual funds through online platforms.

Trend Following and Macroeconomic Risk

PurposeMany studies have shown that from a theoretical and empirical point of view, downside risk-based measures of risk are better than the traditional ones. Despite academic appeal and practical implications, downside risk has not been thoroughly examined in markets outside developed country markets. Using downside beta as a measure of downside risk, this study examines the relationship between downside beta and stock returns in Indian equity market, an emerging market with unique investor, asset and market characteristics.Design/methodology/approachThis is an empirical study done by using ranked portfolio return analysis and regression analysis methodologies.FindingsThe study results show that downside risk, as measured by downside beta, is distinctly priced in the Indian equity market. There is a direct positive relationship between downside beta and contemporaneous realized returns, indicating a premium for downside risk. Downside risk carries a higher weightage than upside potential in the aggregate return of the stock portfolios. Downside beta is a better measure of systematic risk than conventional market beta and downside coskewness.Practical implicationsThe empirical results support the adoption of downside beta in practice and provide a case for replacing traditional beta with downside beta in asset pricing applications, trading and investment strategies, and capital allocation decision-making.Originality/valueThis is one of the first in-depth studies examining downside beta in Indian equity markets using a broad sample of individual stock returns covering a wide time range of 22 years. To the best of our knowledge, this study is the first one to compare downside beta and downside coskewness using individual stock data from the Indian equity market.

The return and risk of stock investment have a high level of volatility because it depends on fundamental and technical conditions and the influence of micro and macro variables. This study aims to determine the risk and return on investment in insurance companies and analyze the effect of macroeconomic variables on the level of risk and return on investment in insurance companies. The sampling technique used was purposive sampling. Data analysis shows that for three years, Asuransi Jasa Tania Tbk has the highest level of conclusion, which is 22.3%, and Asuransi Harta Aman Pratama Tbk. has the lowest rate of -3.3%. For three years, the value of Gross Domestic Product has increased successively so that this will cause a stock return that is proportional to the level of risk that will be faced. Changes in the inflation rate up and down for three consecutive years have a different effect every year where when the inflation rate decreases, the rate of return on investment will be high. In addition, the interest rate decreased from 4.75% in 2016 to 4.25% in 2017. Then it increased to 6.00% in 2018. The Rupiah exchange rate against the dollar is getting weaker, indicating an increase in the exchange rate. Every year from 2016 to 2018. When the rupiah exchange rate weakens, people will choose to invest in foreign currencies because the value of these foreign currencies can determine the size of the risk of a business.

Asymmetry, Tail Risk and Time Series Momentum

Downside risk in Dow Jones Islamic equity indices: Precious metals and portfolio diversification before and after the COVID-19 bear market

Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, the downside risks are important to consider and model. As a result, the profitability of crypto market operations depends on the predictability of price volatility. Predictive models that can successfully explain volatility help to reduce downside risk. In this paper, we investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregressive score (GAS) models. We apply these models to five of some of the largest market capitalization cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Steller, respectively). The forecasts are evaluated using various backtesting and model confidence set (MCS) techniques. To create the best VaR forecast model, a weighted aggregative technique is used. The findings demonstrate that the quantile-based models using a weighted average method have the best ability to anticipate the negative risks of cryptocurrencies.

Despite the fact any investor prefers lower risk and higher return, investors may have different preferences about what would be an acceptable risk or a minimal return. For instance, some investors prefer to have a lower bound risk rather than gaining a higher return. In portfolio theory, it is commonly assumed the existence of one risk free asset that offers a positive return. This theoretical risk free asset combined with a risky portfolio creates a new portfolio that presents a linear relation between risk and return as the risk free asset weight (w f ) changes. Hence, any level of risk or of return is easy to achieve separately, just by changing w f . However, in a world without any risk free assets, the combination between assets creates nonlinear portfolios. Achieving a specific level of risk or return is not a trivial task. In this paper, we assume a risky world rather than the existence of a risk free asset, in order to model an automated asset management system. Furthermore, some automated asset managers give very different results when evolving in different contexts: hence, a very profitable manager can have very bad results in other market situations. This paper presents a multiagent architecture, aiming to tackle these problems. The architecture, named COAST (COmpetitive Agent SocieTy), is based on competitive agents that act autonomously on behalf of an investor in financial asset management. It allows the simultaneous and competitive use of several asset analysis techniques currently applied in the finance field. Some dedicated agents, called advisors, apply a particular technique to a single asset. The results provided by these advisors are then submitted to and analyzed by a special agent called coach, who evaluates its advisors' performance and defines an expectation about the future price of one specific asset. Within COAST, several coaches negotiate to define the best money allocation among different assets, by using a negotiation protocol defined in this paper. We also propose an investor description model that is able to represent different investors' preferences with defined acceptable limits of risk and/or return. The COAST architecture was designed to operate adequately with any possible investor's preference. It was implemented using a financial market simulator called AgEx and tested using real data from the Nasdaq stock exchange. The test results show that the architecture performed well when compared to an adjusted market index.

The purpose of this study was to determine whether financial literacy, investment risk and investment motivation simultaneously and partially affect student interest in investing in the capital market. This research design is quantitative. The population in this study were students of the Management and Accounting Study Program at Universitas Perjuangan Tasikmalaya. The sample in this study were 100 students. Data collection in this study using the questionnaire method. Then test the data with multiple linear regression analysis with SPSS Version 25. The results showed that Simultaneously Financial Literacy, Investment Risk and Investment Motivation have a significant effect on Investment Interest in Students of the Faculty of Economics and Business, Universitas Perjuangan Tasikmalaya. Partially Financial Literacy, and Investment Motivation have a significant influence on Investment Interest in Students of the Faculty of Economics and Business, Universitas Perjuangan Tasikmalaya. Meanwhile, Investment Risk does not have a significant influence on Investment Interest in Students of the Faculty of Economics and Business, Universitas Perjuangan Tasikmalaya.

With the penetration of new energy, electric vehicles and others, distribution networks (DN) nowadays are facing the increasing uncertainties. investment competition in the DN is intensifying. Investment efficiency and investment benefits have become the focus of consideration for power grid companies. A proper investment portfolio model is required for the investor to choose better candidate between different functional and cost candidates. Hence, this paper proposes a two-tiered index system by integrating the development diagnosis index of DN and assessment of the investment efficiency of an individual project. Moreover, the distribution characteristics of the project yield into investment risk caused by project uncertainties are considered by conditional value at risk (CVaR). So, a multi objective optimization model of DN portfolio investment solved by fuzzy satisfaction to get precise investment strategies is established to cooptimize the trade-off between efficiency and risk. The proposed model is validated using 20 projects in a package of investment plan of a power grid company as case study to verify the effectiveness of the proposed approach.