Asset Returns Research Articles

Bayesian Reconciliation of Return Predictability

Abstract This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The bivariate VAR system comprises asset returns and a further prediction variable, such as the dividend-price ratio, and allows pinning down the question of return predictability to the value of one particular model parameter. We develop a new shrinkage type prior for this parameter and compare our Bayesian approach to ordinary least squares estimation and to the reduced-bias estimator proposed in Amihud and Hurvich (2004. “Predictive Regressions: A Reduced-Bias Estimation Method.” Journal of Financial and Quantitative Analysis 39: 813–41). A simulation study shows that the Bayesian approach dominates the reduced-bias estimator in terms of observed size (false positive) and power (false negative). We apply our methodology to a system comprising annual CRSP value-weighted returns running, respectively, from 1926 to 2004 and from 1953 to 2021, and the logarithmic dividend-price ratio. For the first sample, the Bayesian approach supports the hypothesis of no return predictability, while for the second data set weak evidence for predictability is observed. Then, instead of the dividend-price ratio, some prediction variables proposed in Welch and Goyal (2008. “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction.” Review of Financial Studies 21: 1455–508) are used. Also with these prediction variables, only weak evidence for return predictability is supported by Bayesian testing. These results are corroborated with an out-of-sample forecasting analysis.

Quantile coherency across bonds, commodities, currencies, and equities

This paper examines quantile coherency in bonds, commodities, currencies, and equities using a novel quantile coherency approach. While recent literature has explored single-frequency tail- and time-frequency dependence in asset returns, we provide fresh evidence on asset return dependence across quantiles (proxying business cycles or market conditions) at different frequencies (representing investment horizons). Considering sixty-seven individual asset return series in four asset classes, we observe that low frequency (yearly) dependence is stronger in the bond, foreign exchange, and equity markets. Specifically, we find strong dependence between the German and French bond markets, heating oil and crude oil, gold and silver, British Pound, and Euro, French and German and Canadian and US equities. As we report asset return interdependence in different business cycles and at different time horizons, these results have important implications for portfolio allocation and investment strategy formulation.

Credibilistic portfolio optimization with higher-order moments using coherent triangular fuzzy numbers

Financial portfolio formation is usually a multi-objective decision-making problem concerning return and risk on the investment. In this study, we make use of an extension of regular triangular fuzzy numbers, known as coherent triangular fuzzy numbers, to describe portfolio returns in a credibility-based framework. The paper’s novelty lies in proposing and deriving a crisp equivalent for computing the coherent triangular fuzzy number-based credibilistic semivariance, credibilistic skewness, and credibilistic semikurtosis. Utilizing these analytical expressions, along with additional formulations found in existing literature, we present three multi-objective portfolio optimization models involving the practical constraints related to investment decisions. All the proposed analytical expressions, when used with the returns of the portfolio as a whole, help to overcome the computationally expensive process of simulating results using the returns of individual assets. The proposed models differ with respect to different risk measures, viz. semivariance, Mean-Absolute-Semi-Deviation (MASD), and Conditional Value-at-Risk (CVaR). These models are solved using an adaptation of an efficient Multi-Objective Genetic Algorithm (MOGA) specifically designed to solve portfolio optimization problems with practical constraints. Data from the National Stock Exchange (NSE) in Mumbai, India and the New York Stock Exchange (NYSE) in New York, USA, are used to demonstrate the effectiveness of the proposed portfolio optimization models and solution methodology. All the proposed models are compared with respect to each other and the benchmarks considered in this study to bring out the performance stability.

Adaptive online mean-variance portfolio selection with transaction costs

Online portfolio selection is attracting increasing attention in both artificial intelligence and finance communities due to its efficiency and practicability in deriving optimal investment strategies in real investment activities where the market information is constantly renewed every second. The key issues in online portfolio selection include predicting the future returns of risky assets accurately given historical data and providing optimal investment strategies for investors in a short time. In the existing online portfolio selection studies, the historical return data of one risky asset is used to estimate its future return. In this paper, we incorporate the peer impact into the return prediction where the predicted return of one risky asset not only depends on its past return data but also the other risky assets in the financial market, which gives a more accurate prediction. An adaptive moving average method with peer impact (AOLPI) is proposed, in which the decaying factors can be adjusted automatically in the investment process. In addition, the adaptive mean-variance (AMV) model is firstly applied in online portfolio selection where the variance is employed to measure the investment risk and the covariance matrix can be linearly updated in the investment process. The adaptive online moving average mean-variance (AOLPIMV) algorithm is designed to provide flexible investment strategies for investors with different risk preferences. Finally, numerical experiments are presented to validate the effectiveness and advantages of AOLPIMV.

Integrated prediction of green bond return under the dual risks of climate change and energy crisis

Prediction of bond return is a classic problem in financial area, providing an important basis for portfolio construction and risk management. The sustainable investment attribute of green bonds has been favored by investors, so that green bonds have become an important component for major asset allocation. However, due to the specific investment focus of green bonds, investors’ return expectations are influenced not only by traditional corporate bond factors, but also by related factors such as climate change and energy transition. Against the backdrop of increasingly severe climate risks and the global energy crisis, this paper analyses the volatility characteristics of China’s green bonds at multiple time scales, and introduces exogenous variables such as returns of the alternative financial assets, climate risks and returns of energy markets for prediction. Based on the LSTM model, the volatility of green bond yield at different time scales is separately predicted using optimal exogenous variable before integration. It is found that the new integrated prediction model can significantly improve the forecasting performance compared to traditional single LSTM models and simple decomposition-integrated models. Further, both climate risks and energy markets variables have a significant improvement effect on predicting green bond in low-frequency item, while energy markets variables also have a better predictive effect on trend items. Building on the use of only LSTM model, it could be further enhanced by integrating more algorithms to select the best single model for each component, further improve the prediction accuracy and provide a more effective quantitative tool for investment decision-making and risk management in related fields.

Stable paretian distribution, return generating processes and habit formation—The implication for equity premium puzzle

We study an asset pricing model with stable Paretian shocks to solve the equity premium puzzle. We extend the model with different return generating processes for consumption and dividends, relaxing the strong assumption made in previous studies that aggregate dividends are equal to consumption. The model derives solutions for asset prices and returns. Solutions are provided for the CRRA (constant relative risk aversion) utility function and habit formation utility function. We also prove the convergence of the solution under the maximally negative skewness condition and show that this new model can generate higher risk premia. With more realistic assumptions about the exogenous endowments and utility functions, our new model improves the explanatory power of fat-tailed models with stable shocks for the equity premium, thus providing an alternative approach to resolving the equity premium puzzle.

Robust and Sparse Portfolio: Optimization Models and Algorithms

The robust and sparse portfolio selection problem is one of the most-popular and -frequently studied problems in the optimization and financial literature. By considering the uncertainty of the parameters, the goal is to construct a sparse portfolio with low volatility and decent returns, subject to other investment constraints. In this paper, we propose a new portfolio selection model, which considers the perturbation in the asset return matrix and the parameter uncertainty in the expected asset return. We define three types of stationary points of the penalty problem: the Karush–Kuhn–Tucker point, the strong Karush–Kuhn–Tucker point, and the partial minimizer. We analyze the relationship between these stationary points and the local/global minimizer of the penalty model under mild conditions. We design a penalty alternating-direction method to obtain the solutions. Compared with several existing portfolio models on seven real-world datasets, extensive numerical experiments demonstrate the robustness and effectiveness of our model in generating lower volatility.

Reversal of Burden Proof as a Reform of Criminal Law Eradicate Money Laundering

One of the efforts of the Indonesian government in reducing money laundering is to pay special attention in terms of proof, namely with the reverse proof system. Over time, the reversed proof system in the Corruption Law is still regulated even though the Corruption Law has undergone many changes. Although it has been included in positive law, in practice this proof is not applied thoroughly and there are still obstacles in its application. This research seeks to answer in what cases the reverse burden of proof is applied and what are the obstacles in its application. This type of research is normative juridical. The results of the study resulted in the merging of corruption cases with money laundering can be considered to provide its own advantages in handling corruption cases. More actors are ensnared including corporations. Maximum punishment, streamlining the return of state assets can impoverish corruptors. Legal culture that has not adapted to the new system, reverse evidence is not clearly regulated in the law.

Do geopolitical risk, economic policy uncertainty, and oil implied volatility drive assets across quantiles and time-horizons?

This paper examines the impact of three global risk factors (geopolitical risk (GPR), economic policy uncertainty (EPU), and crude oil volatility (OVX)) on the returns and variance of commodity, Islamic stock, and green bond markets across quantile distributions and various time horizons. To this end, Granger causality tests in quantiles and distributions along with wavelet-based correlation and causality approaches are applied to daily data from February 1, 2013 to June 30, 2023. The results of the Granger causality in quantiles tests show strong evidence that all three global risk factors Granger-cause returns across all quantiles, except the lowest and middle quantiles. The Granger causality is significant for both returns and variances, where GPR is the least predictor and OVX is the most predictor. Evidence of causation in risk spillovers is in the right tail and center of the distribution rather than the left tail, indicating no evidence of down-to-down risk spillover. The upside risk of OVX causes both the upside and downside risk of asset returns. The positive volatility of EPU and GPR drives the positive and negative volatility of the green bond and Islamic stock markets, respectively. Green bond markets are completely immune to risk spillover from geopolitical risks. The effects of risk factors are negligible at the lower and somewhat middle quantiles but strengthen with varying magnitude and significance for the remaining quantiles. The results of the wavelet analysis indicate that asset returns co-move with the global risk factors in the short term but decouple in the longer term. Risk factors exert short-lived causal impacts in the short term, but the duration of significant causal periods rises with time and the effect intensifies during crisis periods.

Robust signal dimension estimation via SURE

The estimation of signal dimension under heavy-tailed latent variable models is studied. As a primary contribution, robust extensions of an earlier estimator based on Gaussian Stein’s unbiased risk estimation are proposed. These novel extensions are based on the framework of elliptical distributions and robust scatter matrices. Extensive simulation studies are conducted in order to compare the novel methods with several well-known competitors in both estimation accuracy and computational speed. The novel methods are applied to a financial asset return data set.

Factor Sufficiency in Asset Pricing: An Application for the Brazilian Market

The multifactor asset pricing model derived from the Fama–French approach is extensively used in asset risk premium estimation procedures. Even including a considerable number of factors, it is still possible that omitted factors affect the estimation of this model. In this work, we compare estimators robust to the presence of omitted factors in estimating the risk premium in the Brazilian market. Initially, we analyze the panel of asset returns using the mean group and common correlated effect estimators to detect the presence of omitted factors. We then compare the results with those obtained by a estimator robust to omitted variables, which uses a principal components approach to correct the estimation in the case of the omission of latent factors. We conclude that there is evidence of omitted factors, and the best predictor for the expect returns is the common correlated effects estimator.

Measuring systemic risk contribution: A higher-order moment augmented approach

Marginal contributions of individual institutions to the systemic risk contain predictive power for potential future exposures and provide early warning signals to regulators and the public. In this paper, the higher-order co-skewness and co-kurtosis are used to construct systemic risk contribution measures, which allow us to identify and characterize the co-movement driving the asymmetry and tail behavior of the joint distribution of asset returns. We illustrate the usefulness of higher-order moment augmented approach by using 4868 stocks living in the Chinese market from June 2002 to March 2022. The empirical results show that these higher-order moment measures convey useful information for systemic risk contribution measurement and portfolio selection, complementary to the information extracted from a standard principal components analysis.

The Influence of Current Ratio (CR), Return of Assets (ROA), Company Size and Sales Growth on Capital Structure in Companies Registered on JII for the 2015-2021 Period

This study aims to analyze the effect of the Current Ratio (CR), Return Of Assets (ROA), Company Size (size) and Sales Growth (Growth) on the Capital Structure of companies listed on JII for the 2015-2021 period. The sample in this study were 10 companies. The data used in this study comes from secondary data obtained from the company's financial statements. Related to data analysis, this study uses linear regression analysis of panel data with the Random Effect Model (REM) model. It is known that the results obtained in this study are Current Ratio (CR) having a negative and significant effect on capital structure by proxies Debt to Equity Ratio (DER), Return Of Assets (ROA), and Firm Size having a positive and significant effect on capital structure by Debt to Equity Ratio (DER) proxy and Sales Growth has no effect on capital structure. Therefore, companies can increase their Current Ratio (CR), Return Of Assets (ROA), and Company Size so that the company's capital structure can be used optimally and efficiently.

Loss Aversion Robust Optimization Model Under Distribution and Mean Return Ambiguity

From the aspect of behavioral finance, which is an emerging area integrating human behavior into finance, this work studies a robust portfolio problem for loss-averse investors under distribution and mean return ambiguity. A loss-aversion distributionally-robust optimization model is constructed if the return distribution of risky assets is unknown. Then, under the premise that the mean returns of risky assets belong to an ellipsoidal uncertainty set, a model under joint ambiguity in distribution and mean returns is constructed. This study solves both robust models and derives their analytical solutions, respectively. Moreover, the effect of ambiguity aversion and loss aversion on robust optimal portfolio returns is studied. The results show that ambiguity-neutral investors who do not know the return distribution obtain more robust optimal portfolio returns than ambiguity-averse investors who are unaware of both the distribution and mean return. The difference between them decreases with the increase of loss aversion coefficients and increases with ambiguity aversion coefficients. Both loss aversion and ambiguity aversion play important roles in investors’ behavioral portfolio selection.

Analysis of Stock Value in the Science and Technology Innovation Board Market: A Principal Component Analysis Approach

The factors influencing stock returns have been extensively studied in the current academic sphere. In this study, the factor analysis method is employed to assess the investment value of stocks in the Science and Technology Innovation Board market (STAR Market) using indicators such as asset-liability ratio, current ratio, quick ratio, gross profit amount, YoY growth rate of net assets, asset return rate A, return on equity ratio ROEA, operating net profit margin, earnings per share of net assets, sales operating profit margin, and YoY growth rate of total assets. Factor scores are derived using principal component analysis. The obtained factor scores are ranked in descending order, revealing that Juchen Corporation holds a factor score of 1.11, ranking first among all companies and demonstrating the highest investment value. Following closely are EastMicro Semiconductor and Baochu Electronics. Additionally, this paper summarizes the stock performance of 20 other companies, offering investors a reference for value-based investment decisions. The quantitative findings of this study facilitate informed decision-making for investors, serving as a valuable reference for their investment strategies.

A multi-agent incomplete equilibrium model and its applications to reinsurance pricing and life-cycle investment

This paper develops an incomplete equilibrium model with multi-agents' different risk attitudes and heterogeneous income/payout profiles. Particularly, we apply its concrete and computationally tractable model to reinsurance derivatives pricing and life-cycle investment, which are important for insurance and asset management companies in practice. In numerical experiments, we explicitly obtain endogenously determined expected returns of the risky asset in equilibrium, agents' specific reinsurance prices with their stochastic discount factors (SDF) and optimal life-cycle trading strategies. Moreover, we investigate how each agent's degree of risk aversion and income/payout profile, and correlations between an insurance or economic factor and the risky asset price affect reinsurance claims pricing and optimal portfolios in life-cycle investment.

Persistence of Green Asset Returns, Fossil Fuel Returns, and Climate Policy Uncertainty

The study uses a novel approach to test for the persistence of green asset returns, fossil fuel returns, and climate policy uncertainty. We find evidence of persistence in the series when asymmetries are considered in our test. The fossil fuel returns and climate policy uncertainty have no effect on the persistence of sustainable green asset returns. Policies around climate policy uncertainty and green asset returns should be long term.

Financial Report Analysis to Measure Financial Performance at PT. Adaro Energy Indonesia, TBK 2015 – 2021

Financial Report Analysis is an effort to find out the Performance Results and Health of the Entity, which is an important concern for each entity in making decisions and steps for the next year. The data used in this research is quantitative data and the author uses descriptive methods. The research objective is to look at Financial Performance and Health, using Liquidity Ratios, Solvency Ratios and Profitability Ratios. Information comes from the financial reports of PT Adaro Energy Indonesia, Tbk, for the 2015-2021 period which are listed on the Indonesian Stock Exchange. The research results show that the cash ratio, quick ratio and current ratio are all in good condition, so they are able to pay current debts if needed quickly. Solvency ratios for 2015-2021, from the Debt to Equity Ratio, Long-Term Debt to Equity Ratio and Debt to Asset Ratio, each below 1, indicating that the entity is still able to manage the Entity's Assets and Equity optimally. The 2015-2021 Profitability Ratio, Return on Assets Ratio and Return on Equity Ratio respectively with an average of 6.4% and 10.8% are still low but at the end of 2021 they increased and the Margin Ratio to Gross Profit, to Operational Profit and to Net Profit is still high and it is hoped that in the future the Entity will be able to control Cost of Goods Sold, General Expenses and Other Expenses. Thus, a conclusion can be made that the performance of PT Adaro Energy Tbk is good

Survival strategies in an evolutionary finance model with endogenous asset payoffs

Survival strategies in an evolutionary finance model with endogenous asset payoffs

What difference do new factor models make in portfolio allocation?

This paper compares the Hou-Xue-Zhang four-factor model with the Fama-French five-factor model from an investing perspective both in- and out-of-sample. Without margin requirements and model uncertainty, the Hou-Xue-Zhang model outperforms the Fama-French model. However, the outperformance could become negligible if an investor is subject to margin requirements and model uncertainty. The Hou-Xue-Zhang model shows similar power as the Fama-French model in describing the covariance matrix of asset returns. Overall, the two models do not make a difference for investing in a realistic setting.