Excess Returns Research Articles

Consensus credit ratings: a view from banks

While the production of credit ratings has long been limited mainly to rating agencies (CRAs), recent years have seen the growing popularity of consensus credit ratings crowdsourced from banks (i.e., bank ratings). We provide the first comprehensive examination of the properties and informativeness of bank ratings relative to CRA ratings. We find that bank ratings often deviate from CRA ratings, with over 60% of firm-months having different bank and CRA ratings. These deviations contain useful information. Bank ratings improve out-of-sample prediction of defaults and CRA rating revisions and explain the cross-section of credit spreads. However, bank ratings do not improve out-of-sample prediction of credit excess returns, indicating that current prices incorporate bank rating information. Overall our findings suggest that bank ratings are a useful supplement to traditional credit ratings.

Analysis of Investor Reactions to The Right Issue at The IDX Before and During Covid-19

The COVID-19 pandemic has resulted in fluctuations in movement stock prices, and greatly influenced economic conditions as well as reactions of investors regarding the rights issue policy which will influence buying and selling interest shares. The rights issue is an event where information is published as an announcement that can be used to see market reactions. This research was conducted to analyze investor reactions to the Rights Issue announcement. Using 50 samples of companies that carried out rights issues during 2017 - 2020. The inquiry about was carried out utilizing the occasion consider strategy with an advertising show approach. The ordinariness test is the Kolmogorov – Smirnov test. The statistical test used in this research is the difference between two means tests (Wilcoxon Signed Rank Test). The comes about of this inquiry appears that there's a noteworthy contrast in excess returns within the period sometime recently and after the declaration of the rights issue, be that as it may, this inquiry appears that there's no distinction in stock liquidity sometime recently and after the declaration of the rights issue. there's no distinction between the bid-ask spread sometime recently and after the declaration of the rights issue, both sometime recently and amid the widespread COVID-19 implies that the declaration of the rights issue cannot be utilized as a reference choice in an exertion to extend the liquidity of the company offers within the long-term brief, this can be since the declaration of the rights issue isn't sufficient contains data that can impact venture choices.

Old age care fund helps to manage their funds In easy way

Abstract: A Old Age Care Funds is a sort of investment vehicle in which the combined capital of several participants is used to purchase a diverse range of bonds, equities, and other securities. Funds, which are professionally managed, give people the chance to invest in a variety of assets without having to handle them themselves. The value of the Fund shares that investors purchase varies according to how well the underlying assets perform. Funds are well-liked by both rookie and seasoned investors because they offer diversity, liquidity, and expert management. Diverse mutual fund categories, such as stock, bond, money market, and hybrid funds, are available to suit varying investment goals and risk tolerances. Investors can select funds according to their risk tolerance, time horizon, and financial objectives. Securities regulators supervise mutual funds to maintain transparency and safeguard the interests of investors. They are now an essential instrument for people to develop a diversified investment portfolio and engage in the financial markets. Old Age Care Fund portfolio analysis is an important component of investment management that includes a variety of approaches for evaluating the performance, risk, and composition of investment portfolios. The efficient market hypothesis (EMH) states that markets are efficient and reflect all available information, making it difficult for fund managers to regularly outperform the market, according to Malkiel and Fame (1970). Jensen (1968) proposed the Capital Asset Pricing Model (CAPM), which emphasizes risk-adjusted returns and the identification of alpha, which indicates a fund manager’s capacity to create excess returns above the market. IndexTerms – Python, Powet-BI, Data Analysis.

Portfolio Analysis of Multi-Factor Stock Selection Model Based on Investor Sentiment

Since 1990, China's securities market has been gradually improved. Domestic and foreign scholars have proposed many theoretically perfect investment models, but not many of them have been applied in practice because the market cannot satisfy the strict assumption conditions of the models. Foreign quantitative investment models are not necessarily effective in the Chinese market. Based on this, this paper takes the portfolio theory as a guide, uses the scoring method to construct a multi-factor stock selection model, and selects the relevant factors in combination with the characteristics of the Chinese market. Through empirical analyses, the validity of the multi-factor stock selection model with equal weight allocation in China's market is verified. In addition, this paper innovatively constructs an investor sentiment factor, combines the sentiment timing with the multi-factor stock selection model, optimises the timing on the basis of the multi-factor stock selection model, and proposes an investment strategy to obtain excess returns stably.

The Roadmap of a Cash Dupont Analysis and Its Impact on Stock Valuation Strategy

It is a common belief that capital intensive corporations are associated with relatively low profit margins. They operate in environments where it may be a struggle to keep returns in excess of their capital costs. This further implies a possible decline in stock value because of these lower margins. This paper extends the financial literature on the effects of capital intensity on firm performance through the introduction of the Cash DuPont Model in conjunction with a Monte Carlo Simulation. The model’s use of the free cash flow to equity (FCFE), cash conversion factor (CCF), and resulting Cash ROE ultimately leads to the determination of the fair price to book value multiple. All of these data metrics are blended into a process of stock price valuation that explains both the possibility and probability of the mispricing of a stock. The results reveal that a capital-intensive firm can show undervalued tendencies despite low profit margins.

Shrinking the 13D disclosure window will benefit non-activist investors

PurposeThis study aims to examine the Securities and Exchange Commission’s final rule in Modernization of Beneficial Ownership Reporting, which reduced the time for significant public company shareholders to file Schedule 13D (effective February 5, 2024). The authors corroborate prior results under the historic 10-day maximum reporting regime and provide updated academic analysis regarding how the five-day deadline between the “triggering” event, accumulating 5% of the outstanding shares and public disclosure of that event will affect abnormal returns.Design/methodology/approachThis empirical archival study uses publicly available data.FindingsThe analyses show that changing from a 10-day to a 5-day Schedule 13 disclosure window will reduce activist investors’ opportunity to profit by legally delaying the filing of Schedule 13D. These excess returns for delay exist regardless of the profitability or size of the target firm or the shareholder’s disclosed reason for filing. The authors conclude that accelerating the timing of the disclosure window is an improvement that is in the best interest of the general investing public.Originality/valueTo the authors’ knowledge, this is the only academic study of Schedule 13D filings to include the postpandemic period. As such, the authors establish an updated “baseline projection” for expectations regarding how the Modernization final rule will impact activist investors and stock returns under a five-day reporting regime. In addition, the authors measure and test abnormal returns after considering differences between “triggering” events and filing dates of Schedule 13Ds in the sample rather than grouping all filings. This approach allows the authors to account for the time difference between the triggering event and the filing date.

Does Corporate Social Responsibility Create Value in Acquisitions? Evidence from the German Market

This paper examines the impact of a firm’s Corporate Social Responsibility (CSR) level on abnormal stock returns around merger and acquisitions (M&A) announcements. Using a sample of transactions announced by German DAX-listed acquirers from 2017 and 2022, the analysis assesses whether CSR creates value for acquiring firms’ shareholders and offers a comprehensive discussion of potential factors supporting or opposing this notion. Our study seeks to fill a notable gap in the German literature on the relationship between CSR performance and abnormal stock returns surrounding M&A announcements. Building upon prior research findings in the US and in an international sample, our investigation focuses on the German market. Employing event study methodology, our results indicate that M&A transactions of German-listed acquirers did not yield significant negative or positive cumulative abnormal returns for event windows of 3 and 11 days. Furthermore, based on multiple linear regression, no evidence was found that CSR positively or negatively influenced abnormal stock returns following M&A announcements, suggesting that positive and negative effects potentially offset each other. The outcomes of our research have important implications for investors, as CSR initiatives do not serve as a positive trading signal, guaranteeing excess returns, which contrasts findings from previous studies in other developed countries. For managers, it is essential to concentrate on factors beyond CSR performance, such as synergies and fit. Finally, both managers and investors should not view CSR as a shareholder value-enhancing short-term investment but as an integral component of fostering sustainable business development.

Sustainable investing with ESG ambiguous information

This study introduces a novel model that incorporates ambiguous ESG information into the asset pricing framework, examining its impact on the optimal demand, equilibrium expected excess return, and welfare of an investor. Our analysis reveals that an investor’s response to insights into ESG performance significantly depends on the nature of the information received. When confronted with negative ESG information and lower excess return, the investor opts for high-precision information, resulting in a lower optimal asset demand with a steeper growth slope in demand. Conversely, with higher excess return, the investor chooses low-precision information, leading to a higher optimal asset demand with a gentler slope of demand growth. Furthermore, we identify a unique “ESG excess return region” where the investor’s optimal demand remains unchanged despite ambiguous ESG information. Moreover, the study finds that asset yield a higher equilibrium expected excess return with high-precision ESG signals than with low-precision signals when the ESG information is negative. Lastly, the research explores investor welfare, indicating that the welfare under positive information is significantly higher than under negative information. This study sheds light on the subtle strategies that an investor employs when facing uncertainties related to ESG information.

Optimal Portfolio Analysis on Stocks Listed in Lq45

The purpose of this study is to determine the optimal portfolio of stocks that are listed in the LQ-45 period (January 2023 – January 2024) and compare the return and risk in stocks that are included in the LQ-45 but not included in the optimal portfolio. The method used is the Single Index Method, which uses the ERB (Excess Return to Beta) assessment reference. The results showed that out of 45 stocks there were 10 stocks that had large ERB, which were included in the optimal portfolio were GGRM, BBTN, KLBF, EXCL, ICBP, MAPI, UNVR, CPIN, INDF, and TBIG, which provided a return of 6.61% per year and a risk of 0.08% per year. Meanwhile, the remaining thirty-five stocks were made up as well as 10 other portfolios. Each of these portfolios consists of 10 randomly selected stocks. These ten portfolios yield higher returns than optimal portfolios. However, they also have a higher risk. The results of the comparison of the coefficient of variation between the optimal portfolio and the other 10 portfolios show that the optimal portfolio is the best portfolio

Improving credit risk assessment in P2P lending with explainable machine learning survival analysis

Recent research using explainable machine learning survival analysis demonstrated its ability to identify new risk factors in the medical field. In this study, we adapted this methodology to credit risk assessment. We used a comprehensive dataset from the Estonian P2P lending platform Bondora, consisting of over 350,000 loans and 112 features with a loan volume of 915 million euros. First, we applied classical (linear) and machine learning (extreme gradient-boosted) Cox models to estimate the risk of these loans and then risk-rated them using risk stratification. For each rating category we calculated default rates, rates of return, and plotted Kaplan–Meier curves. These performance criteria revealed that the boosted Cox model outperformed both the classical Cox model and the platform’s rating. For instance, the boosted model’s highest rating category had an annual excess return of 18% and a lower default rate compared to the platform’s best rating. Second, we explained the machine learning model’s output using Shapley Additive Explanations. This analysis revealed novel nonlinear relationships (e.g., higher risk for borrowers over age 55) and interaction effects (e.g., between age and housing situation) that provide promising avenues for future research. The machine-learning model also found feature contributions aligning with existing research, such as lower default risk associated with older borrowers, females, individuals with mortgages, or those with higher education. Overall, our results reveal that explainable machine learning survival analysis excels at risk rating, profit scoring, and risk factor analysis, facilitating more precise and transparent credit risk assessments.

Deep learning for enhanced index tracking

We develop a novel deep learning method for the enhanced index tracking problem, which aims to outperform an index while effectively controlling the tracking error. We generate a dynamic trading policy from a neural network that accepts a set of features as inputs. We design four blocks in the neural network architecture to handle different types of features, including regimes of the index and stocks, their short-term characteristics, and the current allocation. Outputs from the blocks are integrated into the final output that changes the portfolio allocation. We test our model on several indexes in empirical studies based on real market data. Out-of-sample results reveal the importance of different features and demonstrate the ability of our method in obtaining excess returns while effectively controlling the tracking error, downside risk, and transaction costs.

ESG combined score effects on stock performance of S&P 500-listed firms

The influence of ESG combined scores on the stock performance (excess return, volatility, and liquidity) of S&P 500-listed firms was examined from June 2013 to May 2023. Ordinary least squares and heterogeneity analyses were used, with robustness checks performed through the use of endogenous treatment and fixed-effects models. Results indicated that ESG combined scores are negatively correlated with returns and liquidity but positively correlated with volatility. The stocks of low-ESG firms were more ESG-sensitive than those of high-ESG firms. The impact of COVID-19 on stock price volatility was also considerable. The results contradicted the view that ESG enhances market performance, highlighting the need for active, sustained stakeholder engagement concerning ESG practices.

Stock return prediction with multiple measures using neural network models

In the field of empirical asset pricing, the challenges of high dimensionality, non-linear relationships, and interaction effects have led to the increasing popularity of machine learning (ML) methods. This study investigates the performance of ML methods when predicting different measures of stock returns from various factor models and investigates the feature importance and interaction effects among firm-specific variables and macroeconomic factors in this context. Our findings reveal that neural network models exhibit consistent performance across different stock return measures when they rely solely on firm-specific characteristic variables. However, the inclusion of macroeconomic factors from the financial market, real economic activities, and investor sentiment leads to substantial improvements in the model performance. Notably, the degree of improvement varies with the specific measures of stock returns under consideration. Furthermore, our analysis indicates that, after the inclusion of macroeconomic factors, there is a dissimilarity in model performance, variable importance, and interaction effects among macroeconomic and firm-specific variables, particularly concerning abnormal returns derived from the Fama–French three- and five-factor models compared with excess returns. This divergence is primarily attributed to the extent to which these factor models remove the variance associated with the macroeconomic variables. These findings collectively offer valuable insights into the efficacy of neural network models for stock return predictions and contribute to a deeper understanding of the intricate relationship between factor models, stock returns, and macroeconomic conditions in the domain of empirical asset pricing.

The Predictive Ability of U.S. Stock Market Skewness on Indonesian Stock Market Returns

The three-moment capital asset pricing model (three-moment CAPM) suggests that the expected excess return on stocks should include compensation for skewness risk. This study aims to investigate the ability of U.S. stock market skewness to predict Indonesian stock market returns. The data used in this research includes the S&P500 Index, JCI, JII, and LQ45 from January 2001 to December 2022. The results of this study indicate that U.S. stock market skewness can predict future excess returns of the Indonesian stock market. Additionally, when the estimation model incorporates alternative variables from both the U.S. and Indonesian stock markets, the predictive ability of U.S. stock market skewness remains significant and outperforms these alternative variables. The findings of this research can be used as a strategy for investors when trading in the Indonesian stock market. When the skewness of the U.S. stock market increases, the return of the Indonesian stock market is expected to decrease in the following month.

Optimization of investment portfolios of Chinese commodity futures market based on complex networks

Futures trading in developing countries is now attracting more attention since investors may easily generate more excess return compared to the markets in developed countries, especially in Chinese market. In this paper, we analyzed the relationship between the centrality of commodity in the Chinese commodity futures market network and the optimal weight of each commodity in a portfolio, empirically examined the market systemic factors and commodity idiosyncratic factors that affect the centrality of commodity, and evaluated the effect of network structure on the optimization of commodity portfolio selection under the mean-variance framework. We found that the commodities with high network centrality are often related to industrial products with high volatility and small portfolio weights. We put forward a kind of commodity futures investment strategy based on this network and results showed that cumulative yield is better than other benchmark portfolios. The main contribution of this paper is to apply complex network theory to optimize futures portfolio selection by establishing the relationship between portfolio weight and commodity centrality in Chinese market, which is still under explored.

THE HOLIDAY EFFECTS ON AMUSEMENT AND THEME PARK COMPANIES’ STOCK PRICES

This study offers further evidence of the holiday effect on excess stock returns and volatility, and additional insights into its impact on the amusement park and attractions industry in the United States. The generalized auto-regressive conditional heteroscedasticity (GARCH) model and dummy variables are adopted to investigate pre- and post-holiday excess returns and volatility. Empirical results suggest the following: (1) both excess returns and excess volatility happen more frequently in pre-holidays than post-holidays; (2) the frequency of excess volatility is higher than excess returns for the holiday effect; and (3) nearly all significant excess volatilities are negative. We, therefore, conclude that the holiday effect exists in publicly traded amusement and theme parks.

A Comparative Study of Stock Selection Models Based on Decision Tree Algorithms

In this paper, the decision tree model in data mining is applied to select stock characteristics that can be effectively used for stock selection by using the C4.5 algorithm and the CART algorithm, respectively, in combination with the strategies of fundamental analysis and technical analysis. The paper concludes that the decision tree models constructed by the C4.5 and CART algorithms both have better classification ability for stock selection and portfolio construction, but the decision tree model constructed by the C4.5 algorithm is simpler. The stock portfolios determined by the decision tree model are able to achieve an excess return of 13.4% relative to the CSI 300 index, thus proving that the decision tree model is effective in stock selection and stock portfolio construction.

Wealthy individual investors and stock markets' tail risk.

This paper employs a unique data set to analyze the trading behavior of wealthy individual investors across Mainland China and their impact on Chinese stock markets' tail risk. Results show that the wealthy individual investors' trading behavior can explain Chinese stock markets' tail risk, and the daily investment portfolios based on the network density of wealthy individual investors have significant excess returns. This paper also investigates the determinants of wealthy individual investors' trading behavior with the social network method and the spatial econometric model, and reveals that wealthy individuals benefit from the spillover effect of their trading behavior through the investor networks. The results of this paper not only reveal micro evidence for the formation mechanism of asset prices, but also provide insight into the behavior of wealthy individual investors.

Examining the Adaptation of the Fama-French Three-Factor and Five-Factor Models in the US Market

The adaptability of the Fama-French three-factor and five-factor models in distinct markets is currently a key research topic. Some researchers are investigating whether the FF3FM and FF5FM are reasonable frameworks for explaining excess returns in stock markets by testing them with data from different markets. This paper mainly explores the adaptability of FF3FM and FF5FM in the US stock market, and adds CAPM to the comparison. This article uses the daily factors’ data and the daily average value weighted returns in 6 portfolios formed on size and book-to-market in the Kenneth French database website to construct a linear regression model to study the explanatory power of FF3FM and FF5FM in the US stock market. In order to determine whether these factors are parts of an effective asset pricing model, this paper assesses the efficacy, robustness, and reliability of factors in explaining asset returns using the Durbin-Watson test, the correlation matrix, and the sub-period analysis, which also confirm whether these factors have statistical significance, economic significance, and stability. It is found that the explanatory power of FF5FM is stronger than that of FF3FM in the US stock market. While both profitability and investment factors have relatively modest explanatory power, size and value factors offer significant explanatory power for stock returns.

Optimal taxation of normal and excess returns to risky assets

AbstractWe examine optimal linear taxes on normal and excess capital income with heterogeneous rates of return, alongside an optimal nonlinear earnings tax. Households optimize a portfolio containing three types of assets: risk‐free, diversifiable risky, and private investment with idiosyncratic risk and heterogeneous expected returns. We define normal capital income as the risk‐free rate times the size of the portfolio, and excess returns as any deviations from it. In this setting, taxing excess returns is ineffective for redistribution due to a Domar–Musgrave effect and only generates revenue, to be balanced against the cost of revenue uncertainty. Taxing normal returns does serve redistribution, as they reveal information about the investors' types beyond what the earnings tax base reveals.