Asset Returns Research Articles

Uncertain Regulations, Definite Impacts: The Impact of the U.S. Securities and Exchange Commission's Regulatory Interventions on Crypto Assets

This study employs an event study methodology to investigate the market impact of the U.S. Securities and Exchange Commission's (SEC) classification of crypto assets as securities. It explores how SEC interventions influence asset returns and trading volumes, focusing on explicitly named crypto assets. The empirical analysis highlights significant adverse market reactions, notably returns plummeting 12% over one week post-announcement, persisting for a month. We demonstrate that the severity of market reaction depends on sentiment and asset characteristics such as market size, age, volatility, and illiquidity. Further, we identify significant ex-ante trading volume effects indicative of pre-announcement informed trading.

Faktor-Faktor Yang Mempengaruhi Auditor Switching Pada Perusahaan Consumer Goods Industry Yang Terdaftar Di Bursa Efek Indonesia Periode 2018-2022

Companies use independent auditors to increase the credibility of financial statements in increasing public trust and reducing agency problems, so an auditor must maintain a professional attitude in auditing. The closer relationship between auditors and management can cause auditors to trust management clients more, which may manipulate financial statements or prioritise personal interests, in which case auditor switching is necessary. The purpose of this study is to determine the factors that influence auditor switching in consumer goods industry companies listed on the Indonesia Stock Exchange (IDX) for the period 2018-2022. The research method used is a quantitative approach with purposive sampling method and the analytical tool used in the research is logistic regression analysis. Data processing in this study using SPSS V.20 from secondary data. The population in this study were consumer goods industry companies listed on the IDX. This study used 44 samples of companies, during 5 years of observation 220 reports were analysed. The results of this study prove that simultaneously management change, institutional ownership, leverage, and Return of Assets (ROA) affect auditor switching. Partially, leverage affects auditor switching. Management turnover, institutional ownership, and ROA partially have no effect on auditor switching.

Risk parity portfolio optimization under heavy‐tailed returns and dynamic correlations

Risk parity portfolio optimization, using expected shortfall as the risk measure, is investigated when asset returns are fat‐tailed and heteroscedastic with regime switching dynamic correlations. The conditional return distribution is modeled by an elliptical multi‐variate generalized hyperbolic distribution, allowing for fast parameter estimation via an expectation‐maximization algorithm, and a semi‐closed form of the risk contributions. A new method for efficient computation of non‐Gaussian risk parity weights sidesteps the need for numerical simulations or Cornish–Fisher‐type approximations. Accounting for fat‐tailed returns, the risk parity allocation is less sensitive to volatility shocks, thereby generating lower portfolio turnover, in particular during market turmoils such as the global financial crisis or the COVID shock. While risk parity portfolios are rather robust to the misuse of the Gaussian distribution, a sophisticated time series model can improve risk‐adjusted returns, strongly reduces drawdowns during periods of market stress and enables to use a holistic risk model for portfolio and risk management.

Drivers of Portfolio Flows into Chinese Debt Securities Amidst China’s Bond Market Development

Abstract The paper focuses on China’s onshore bond market and the drivers of non-resident net portfolio flows into Chinese debt securities. Following a review of China’s bond market, a simple theoretical model of push and pull factors driving bond flows is built. It represents a foundation for the empirical analysis on drivers of bond flows into China. Static and time-varying models are estimated to explain the importance of push and pull factors in China’s bond market. While China-specific pull factors, such as domestic economic growth and asset returns, are important drivers of flows, the results reveal that global push factors, such as US interest rates and risk aversion, have recently gained significance as drivers of flows into China. The results confirm China’s continued bond market deepening and integration with the rest of the world, which may have financial stability implications. Therefore, increased awareness regarding bond market developments in China is warranted.

Modeling the distribution of jet fuel price returns based on fat-tail stable Paretian distribution.

Jet fuel plays a crucial role as an essential energy source in aerospace and aviation operations. The recent increase in fuel prices has presented airlines with the new challenge of managing jet fuel costs to ensure consistent cash flow and minimize operational uncertainties. The conventional risk prediction models used by airlines often assume that risks are normally distributed according to the classical Central Limit Theorem, which can lead to under-hedging. This paper proposes an innovative approach using the stable Paretian model to analyze the price return of jet fuel in large samples. It comprehensively compares the fitting effect of the stable Paretian distribution with that of the normal distribution based on specific criteria and non-parametric significance tests. Furthermore, it investigates the accuracy of risk measures such as Value at Risk (VaR) and Conditional Value at Risk (CVaR) predicted by both models. In addition to comparing differences in VaR between predicted values and actual values, this paper provides a more comprehensive comparison of risk measures under rolling window forecast situation. Results suggest that despite indistinguishable results in VaR backtest, the stable Paretian distribution has a overall better fitting effect as well as a less biased predicted CVaR based on the AIC of -14099.46, BIC of -14110.98, p = 0.58 in Kolmogorov-Smirnov test and p = 0.46(0.92) in the 0.01(0.05) significance level of Expected Shortfall Regression Test. This might be explained by its ability to capture asset return dynamics while maintaining shape stability with few parameters. This research can provide valuable insights for guiding airlines' risk management decisions. its ability to capture asset return dynamics while maintaining shape stability with few parameters. This research can provide valuable insights for guiding airlines' risk management decisions.

DIVING DEEP INTO FIN- TECHS: AN EXAMINATION OF ONE 97 COMMUNICATIONS LTD. (PAYTM) FINANCIAL PERFORMANCE

India has seen rise in the number of the Fin -Techs as a part of Start Ups project coming up. Need to analysis the financial statements of such start ups becomes an important task as many stakeholders as being connected directly or indirectly with the company. In this study the financial statement analysis of ONE 97 COMMUNICATIONS Ltd. popularly well known as Paytm was done in the context of profitability and financial stability ratios using the data for the period of 2 years since its listing of the Stock Exchanges. Results showed that there are positive trends in the reduction of loss magnitudes and improvements in operational efficiency. It is crucial for the company to continue its efforts to enhance profitability, manage costs, and work towards achieving positive returns for both assets and equity. On side of financial stability the company has a healthy current and liquid ratio, indicating strong short-term liquidity but the position still needs to be continued for a long term to see a better and positive picture.

Equilibrium Data Mining and Data Abundance

ABSTRACTWe study theoretically how the proliferation of new data (“data abundance”) affects the allocation of capital between quantitative and nonquantitative asset managers (“data miners” and “experts”), their performance, and price informativeness. Data miners search for predictors of asset payoffs and select those with a sufficiently high precision. Data abundance raises the precision of the best predictors, but it can induce data miners to search less intensively for high‐precision signals. In this case, their performance becomes more dispersed and they receive less capital. Nevertheless, data abundance always raises price informativeness and can therefore reduce asset managers' average performance.

A Global-in-Time Neural Network Approach to Dynamic Portfolio Optimization

ABSTRACT We discuss a neural network approach, which does not rely on dynamic programming techniques, to solve dynamic portfolio optimization problems subject to multiple investment constraints. The approach allows for objectives of a very general form encompassing both time-consistent and time-inconsistent objectives, as well as objectives requiring multi-level optimization. The number of parameters of the neural network remains independent of the number of portfolio rebalancing events. Compared to reinforcement learning, this technique avoids the computation of high-dimensional conditional expectations. The approach remains practical when considering large numbers of underlying assets, long investment time horizons or very frequent rebalancing events. We prove convergence of the numerical solution to the theoretical optimal solution of a large class of problems under fairly general conditions, and present ground truth analyses for a number of popular formulations, including mean-variance, mean-semi-variance, and mean-conditional value-at-risk problems. Numerical experiments show that if the investment objective functional is separable in the sense of dynamic programming, the correct time-consistent optimal investment strategy is recovered, otherwise we obtain the correct pre-commitment (time-inconsistent) investment strategy. This method is agnostic as to the underlying data generating assumptions, and results are illustrated using (i) parametric models for underlying asset returns, (ii) stationary block bootstrap resampling of empirical returns, and (iii) generative adversarial network (GAN)-generated synthetic asset returns.

Analisis Tingkat Kebangkrutan pada PT Patra Trading Jakarta

This research aims to determine the prediction of the bankruptcy level of PT. Patra Trading uses financial report data from 2021 to 2023. The method used in this research is Quantitative Descriptive non-Statistical. This research uses secondary data originating from the company PT Patra Trading in the form of the company's annual financial report. The population used is a subsidiary of PT Pertamina Patra Niaga. The sampling technique used in this research is Purposive Sampling for taking samples from the population based on certain criteria. The benchmarks for this bankruptcy prediction analysis consist of several ratios; (1) working capital to total asset; (2) rentaint to total asset; (3) profit before interest and tax to total assets; (4) book value equity to book value liquiditas; (5) profit before tax on current liabilities; (6) sales to total assets: (7) return of assets; (8) debt ratio; (9) current ratio. The results of the analysis using the Altman Z-Score, Springate, Zmijewski, Grover model on PT Patra Trading, provide different research results, where two of them say that PT Patra Trading is in the healthy category, namely in the Zmijewski model the final figure for this model is below 0 and the Grover model final figure is above 0.001. The Springate model says that PT. Patra Trading is still in the bankruptcy category where the final figure is below the standard, namely 0.862. Finally using the Altman Z Score model at PT. Patra Trading is in the gray area.

Probabilities of transitions among endogenous regimes in asset returns and Environmental, Social and Governance scores

AbstractAssets' returns can be efficiently clustered in regimes, that are suitably defined non‐overlapping intervals creating a partition of the real numbers. This paper explores the relationship between the transition probabilities from one regime to another in assets' returns and the assets' MSCI Environmental, Social and Governance (ESG) scores. We apply the proposed methodology to the relevant empirical instance of the assets in the STOXX® Global 1800 Index. We consider three regimes—low, medium and high, on the basis of the variation range of the considered returns. Regimes are endogenous, in that their identification comes out from an entropy‐based optimization problem over the possible ranges of variation of the returns. We specifically investigate the possible linear relationship between transition probabilities among regimes and the ESG scores for different geographic regions, namely, America, Europe and Asia Pacific. The reference empirical period is the quadrennium 2018–2021. Results suggest that assets that are low ranked in ESG tend to remain in the low state of returns, if they are in the low state, while they tend to switch from higher to lower return states when the initial state is higher. On the other hand, assets that are highly ranked in the ESG dimensions, are likely to switch from a lower to a higher return state, when they are in a lower state or to remain in the same state when they are in a higher state. Results are more evident for America and Asia Pacific regions rather than Europe where regulation on ESG integration is at a more developed stage with respect to the other regions.

Asset Returns: Reimagining Generative ESG Indexes and Market Interconnectedness

Financial economists have long studied factors related to risk premiums, pricing biases, and diversification impediments. This study examines the relationship between a firm’s commitment to environmental, social, and governance principles (ESGs) and asset market returns. We incorporate an algorithmic protocol to identify three nonobservable but pervasive E, S, and G time-series factors to meet the study’s objectives. The novel factors were tested for information content by constructing a six-factor Fama and French model following the imposition of the isolation and disentanglement algorithm. Realizing that nonlinear relationships characterize models incorporating both observable and nonobservable factors, the Fama and French model statement was estimated using an enhanced shallow-learning neural network. Finally, as a post hoc measure, we integrated explainable AI (XAI) to simplify the machine learning outputs. Our study extends the literature on the disentanglement of investment factors across two dimensions. We first identify new time-series-based E, S, and G factors. Second, we demonstrate how machine learning can be used to model asset returns, considering the complex interconnectedness of sustainability factors. Our approach is further supported by comparing neural-network-estimated E, S, and G weights with London Stock Exchange ESG ratings.

Comparative Analysis on the Application of Markowitz Model and Index Model in Portfolios

This article presents a comparison between the Markowitz model and the index model, focusing on their applications in portfolio management. The Markowitz model, also known as the mean-variance model, revolutionized portfolio theory by introducing a quantitative method for balancing risk and return. This model constructs optimal portfolios that maximize expected return for a given level of risk or minimize risk for a given expected return, using a covariance matrix to assess relationships between asset returns. In contrast, index models popularized by William Sharpe offer a simplified approach focused on the relationship between individual asset returns and a common benchmark (usually a market portfolio). The article explores the theoretical foundations of the two models, highlighting their mathematical formulations and assumptions. It also discusses the computational methodologies involved in implementing these models and examines the challenges and advances in optimization techniques. It evaluates how these technological innovations have enhanced portfolio optimization, enabling the handling of larger datasets and more complex market dynamics. In addition to comparing these two models, the article delves deeper into the concepts of efficient frontier, inefficient frontier and minimum variance frontier. The efficient frontier is the portfolios that offer the highest expected return for target risk, while the inefficient frontier includes portfolios that do not offer the best return for the level of risk. Minimum variance frontier focuses on portfolios that minimize risk for target expected return, emphasizing the importance of risk management in investment decisions.

Option Pricing Based on Neural Stochastic Differential Equations

Firstly, based on the Black-Scholes stock price model, the neural stochastic differential equation (NSDE) model was established by parameterizing the asset return rate and volatility as a drift network and a diffusion network, respectively. Secondly, in the empirical analysis, the underlying asset as a single stock option was used as the research object, and real stock data was used for the network training and testing. The experimental results show that the NSDE model can overcome the defects of the constant assumption of the Black-Scholes model. Finally, for the case where the price of the underlying asset of the option was unobservable, we proposed that the price of any target option and the price of a known option could be constrained within the Wasserstein distance of their risk-neutral equivalent martingale measure, and theoretically proved the method.

THE INFLUENCE OF CURRENT RATIO AND DEBT TO EQUITY RATIO ON RETURN ON ASSETS AT PT HANJAYA MANDALA SAMPOERNA TBK FOR THE PERIOD 2013 - 2022

This study examines the influence of Current Ratio (CR) and Debt to Equity Ratio (DER) on Return on Assets (ROA) at PT Hanjaya Mandala Sampoerna Tbk over the period from 2013 to 2022. Using a descriptive quantitative approach, the research aims to clarify the relationship between CR, DER, and ROA in evaluating the company’s financial health and profitability. The findings reveal that CR does not have a significant individual impact on ROA, indicating that the company’s short-term liquidity does not directly affect its asset profitability. Conversely, DER has a significant negative impact on ROA, suggesting that higher debt relative to equity reduces asset returns, primarily due to increased interest burden. Simultaneously, CR and DER together have a significant impact on ROA, highlighting the need for balanced management of liquidity and leverage to maximize profitability. This study provides insights for company management and investors in assessing the financial strategies related to liquidity and debt structure to improve asset returns.

PENGARUH KECUKUPAN MODAL, PROFITABILITAS, DAN EFISIENSI TERHADAP ZAKAT PERUSAHAAN DENGAN UKURAN PERUSAHAAN SEBAGAI VARIABEL MODERATING

This research is motivated by the lack of financial performance discipline in paying zakat in Islamic banking. Not all Islamic banks in Indonesia fulfill their obligations in issuing corporate zakat. This is judged by the very essential position of Islamic banking, and is a good example for companies in issuing zakat in order to help the welfare of the community. The purpose of this study is to examine the effect of financial performance ratios, namely capital as proxied by Capital Adequacy Ratio (CAR), profitability as proxied by Return Of Assets (ROA), efficiency as proxied by Expenses/Operational Income to Islamic banking zakat. The approach in this study uses quantitative methods, with this type of research using associative. The sampling technique used purposive sampling, with secondary data. The data source uses the company's annual report recorded at the Financial Services Authority for the 2011-2020 period. The analytical methods used in this study include (1) Descriptive Statistics, (2) Test Models; (3) Classical Assumption Test, (4) Hypothesis Testing; (5) Moderation Regression Analysis. The analytical tool used in this research is EViews 10. The results showed that (1) CAR had no significant effect on corporate zakat (2) ROA had a significant effect on corporate zakat (3) BOPO had no significant effect on corporate zakat (4) company size was unable to moderate CAR on corporate zakat (5) company size unable to moderate ROA on corporate zakat; (6) the size of the company is able to moderate the BOPO against corporate zakat.

Impact of Bank-Specific Factors on Capital Adequacy in the Nigerian Banking Sector: An Empirical Analysis

The study investigates the divergence between the capital adequacy of banks in Nigeria and the Bank for International Settlements' (BIS) global standards for bank capital adequacy. It specifically examines the factors affecting capital adequacy in Nigerian banks. The main objective is to evaluate how specific factors impact capital adequacy within this context. Data spanning from 2012 to 2022 from the Central Bank of Nigeria's Statistical Bulletin was collected for 13 selected deposit money banks in Nigeria. Econometric methods were employed to analyse this data. The findings indicate that asset quality and return on equity (ROE) adversely affect the capital adequacy of Nigerian deposit money banks during the study period. Conversely, bank liquidity, bank size, management efficiency, non-performing loans, and return on assets positively influence bank capital adequacy in Nigeria. It is recommended that particular attention be given to bank liquidity and bank size, as these factors significantly enhance the capital adequacy of Nigerian banks within the reviewed of period

Modeling dynamic higher-order comoments for portfolio selection based on copula approach

This paper introduces a novel approach to estimating time-varying higher-order comoments from a theoretical standpoint. We present how to estimate the dynamic higher-order comoments of asset returns under the copula framework, which builds on the ARCD and copula-DCC models to capture the time variation in higher-order moments and correlations of asset returns. Additionally, the elements in the coskewness and cokurtosis matrices are calculated by the double, triple, and quadruple integrals associated with the joint density function of asset returns. The empirical application to five international market indexes shows that the portfolio with time-varying higher-order comoments estimated by the copula approach significantly outperforms equally weighted and mean–variance strategies in economic performance. The robustness analysis verifies the validity and robustness of the proposed estimation method. Our research offers fresh insights for portfolio analysis and risk management decision-making in practical scenarios.

THE IMPACT OF INTERNAL FACTORS ON RETURN ON ASSETS RATIO IN DEPOSIT BANKS

This study examines the impact of endogenous factors on the return on assets ratio in publicly owned deposit banks in Turkey. The sample of the study covers 44 quarters of data of publicly owned deposit banks between 2013:1-2023:4 and the data are obtained from the website of the Banks Association of Turkey. In the study, linear regression analysis is applied. In the findings, the null hypotheses regarding the autocorrelation and variance problems and the specification error of the model are rejected since these problems are not present in the model. The results indicate statistically significant and positive association between return on assets ratio and return on equity ratio and fixed assets/total assets. However, there is a statistically insignificant and negative correlation between return on assets ratio and capital adequacy ratio and total loans/total assets.

Risks of heterogeneously persistent higher moments

Using intraday data for the cross-section of individual stocks, we show that both transitory and persistent fluctuations in realized market and average idiosyncratic volatility, skewness and kurtosis are differentially priced in the cross-section of asset returns, implying a heterogeneous persistence structure of different sources of higher moment risks. In particular, we find that both idiosyncratic transitory shocks to volatility and idiosyncratic persistent shocks to skewness share strong commonalities that are relevant to investors.

Hybrid retirement strategy in South Africa

Background: Many retirees in South Africa face the challenge of either outliving their retirement savings or living below their means. Studies suggest a ‘safe’ withdrawal rate of between 4% and 5%, which is below the average fund size-weighted drawdown rate of approximately 6.66%.Aim: To provide a scientific basis for the success rate of a ‘hybrid’ retirement strategy, whereby a retiree invests a proportion of their savings in a life annuity and the remaining proportion in a living annuity, to increase the success rate for South African retirees.Setting: Historical asset class returns (equities, bonds and inflation) for South Africa were sourced for the period 1900–2020.Method: Bootstrap sampling of historical asset returns was employed to simulate 10 000 random scenarios to investigate the success rate of various compositions of the ‘hybrid’ retirement strategy.Results: The success rate of all ‘hybrid’ portfolio compositions is significantly greater than the success rate of a pure living annuity when the withdrawal rate is less than 8%.Conclusion: In a South African context, a ‘hybrid’ retirement portfolio increases the probability of success for retirees withdrawing less than 8% from their portfolio – which constitutes approximately 50% of the current annuatised population – and may increase the inheritance of a retiree’s heir.Contribution: Where other studies have focussed solely on the success rate of a living annuity, we have shown that a ‘hybrid’ retirement strategy increases a South African retiree’s likelihood of retiring successfully when the withdrawal rate is less than 8%, which is approximately 50% of the annuatised population.