Future Returns Research Articles

The Effect of Earning Management, Information Asymmetry and Growth Asset on Future Stock Return with Dividend Policy as the Moderating Variable

This study aims to test and analyse the effect of effect of Eamings Management, Information Asymmetry and Asset Growth on the Future Stock Returns and whether the Dividend Policy could strengthen the relationship between the effects of Eamings Management, Information Asymmetry and Asset Growth on the Future Stock Returns in index LQ-45 companies listed on the Indonesia Stock Exchange from 2017 to 2022. This type of research is quantitative, using secondary data. The hypothesis testing used in the study was the Moderated Regression Analysis and Eviews 13 applications. The population in this study were all companies listed on index LQ-45. The data collection technique in this study was purposive sampling technique, from 45 research populations to 18 used as research samples. The results showed that Eamings Management, Information Asymmetry and Asset Growth simultaneously affect Future Stock Returns in index LQ-45 companies from 2017 to 2022. The research results obtained Risk Eamings Management affects Future Stock Returns, Information Asymmetry has no effect on Future Stock Returns, Asset Growth has no effect Future Stock Returns and the dividend policy could not strengthen the relationship between the effect of eamings management, information asymmetry and asset growth on the future stock retums.

Impact of OM Content in Earnings Calls on a Firm’s Stock Performance

We investigate whether and to what extent financial markets value and respond to operations management (OM)-related information in quarterly earnings calls directed toward financial market participants. We develop and use a novel construct called “stated OM focus” (SOMF) to mine the incidence of and emphasis on OM information in the quarterly earnings conference calls for a large cross-section of firms (S&P 1500) over a time frame of 15 years. We empirically establish the value-relevance of OM information as well as summarize and capture both explanatory and predictive aspects of the OM function’s contribution to firm value. We show that OM-related information disclosed by firms systematically, significantly, positively, and persistently affects abnormal stock returns after controlling for known covariates and controls (financial metrics, standard OM metrics, firm and time effects, and lexical-structure related) and after accounting for earnings surprise. Furthermore, we find evidence that firms’ stated OM focus bears significant predictive power in the short term. Long-short portfolios, created using a sorting signal derived solely from stated OM-focus information, predict future abnormal returns consistently over one, two and three months forward from the earnings call event. We show that the OM information metric is reliable and bears internal, external, and predictive validity. These findings offer useful implications for firms, financial market participants, and OM function stakeholders. This paper was accepted by Jayashankar Swaminathan, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02221 .

Macroeconomic Conditions, Speculation, and Commodity Futures Returns

Macroeconomic Conditions, Speculation, and Commodity Futures Returns

Mars Sample Return: From collection to curation of samples from a habitable world

NASA’s Mars 2020 mission has initiated collection of samples from Mars’ Jezero Crater, which has a wide range of ancient rocks and rock types from lavas to lacustrine sedimentary rocks. The Mars Sample Return (MSR) Campaign, a joint effort between NASA and ESA, aims to bring the Perseverance collection back to Earth for intense scientific investigation. As the first return of samples from a habitable world, there are important challenges to overcome for the successful implementation of the MSR Campaign from the point of sample collection on Mars to the long-term curation of the samples on Earth. In particular, the successful execution of planetary protection protocols adds well-warranted complexity to every step of the process from the two MSR Program flight elements to the ground element at the sample receiving facility (SRF). In this contribution, we describe the architecture of the MSR Campaign, with a focus on infrastructure needs for the curation (i.e., the clean storage, processing, and allocation) of pristine Martian samples. Curation is a science-enabling and planetary protection-enabling activity, and the curation practices described in this contribution for the SRF and any long-term curation facility will enable the sample safety assessment, initial scientific investigations of the samples, and establish the MSR collection as a scientific resource that will enable generations of science and discovery through studies of the returned Mars samples. The planetary protection and curation processes established for MSR will provide critical insights into potential future sample return missions from other habitable worlds like Enceladus and Europa.

Good idiosyncratic volatility, bad idiosyncratic volatility, and the cross-section of stock returns

We decompose the idiosyncratic volatility of stock returns into “good” and “bad” volatility components, which are associated with positive and negative returns, respectively. Using firm characteristics, we estimate a cross-sectional model for the expected idiosyncratic good minus bad volatility (EIGMB). The EIGMB outperforms expected idiosyncratic skewness (EISKEW) and standard time-series models in capturing conditional idiosyncratic return asymmetry. EIGMB is negatively and significantly associated with future stock returns, even after controlling for EIKSEW and exposure to systematic-skewness-related factors. Separating the role each specific characteristic plays in driving the predictive power of EIGMB for returns, we find that return on equity and momentum are two important elements of variation in EIGMB.

Is Information Risk Priced? New Evidence from Outer Space

Satellite images of the parking lots of U.S. retail firms provide information about the firms’ future earnings, and the limited access to these images produces information asymmetry between sophisticated and unsophisticated investors. We construct a cloud-based information risk (CIR) measure to capture this satellite information risk and investigate its asset pricing performance. We find that CIR positively predicts future stock returns of retail firms in the cross-section and that the predictability cannot be explained by weather reasons. We provide evidence that CIR indeed captures the information asymmetry among investors. The profitability of short selling is more pronounced on clear days. The return predictability of CIR is more pronounced in preannouncement periods. During cloudy days, parking lot traffic data from satellite images are harder to obtain, and the retail store sales estimated using parking lot data are noisier. We further show that high CIR is associated with low liquidity and that the decrease in liquidity purchases is greater than the decrease in liquidity sales. Our empirical analyses suggest that information asymmetry is priced and that CIR affects equity premiums through the liquidity channel, which are consistent with the theoretical predictions from a noisy rational expectations equilibrium model under imperfect competition. This paper was accepted by Kay Giesecke, finance. Funding: S. Wang acknowledges financial support from the National Natural Science Foundation of China [Grants 72373110 and 71902140] and Fundamental Research Funds for the Central Universities in China. Y. Wang acknowledges financial support from the National Natural Science Foundation of China [Grants 72342003 and 72071114]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00713 .

Monte Carlo Simulation Forecasting the Prices of Selected Stocks in the Automotive Sector

Investors are exposed to risk and uncertainty because of changes in financial markets’ prices. Investors perceive the risks associated with changes in the market prices as higher due to inaccuracy in predicting future returns because of fluctuations in prices. For this reason, they adopt different risk management methods that reduce or eliminate these risks. This research relies on Monte Carlo Simulation technique in predicting forthcoming yield rates from three companies operating under Turkish automotive segment namely, Dogus Automotive (DOAS), Tofas (TOASO) and Ford Otosan (FROTO). The simulation, which runs from January 1, 2023, to December 31, 2023, gives investors research-based insights that help them make strategic investment choices in times of high volatility in the market. According to the results, by modeling prospective future scenarios, MCS can be employed as a viable means of predicting stock prices in financial markets which subsequently helps people make rational investments thereby securing profitable ventures. Furthermore, this study offers practical suggestions in the form of MCS-generated volatility ranges. Investors can determine when it is advisable to buy or sell stocks in order to reduce potential losses and increase profits by setting realistic price objectives and allocating the portfolio differently in accordance with these calls.

Research on the Prediction of Yield and Portfolio Planning of Actively Managed Funds Based on Machine Learning

This paper takes 23 features of China's actively managed funds from 2015 to 2024 as samples, and predicts the future returns of the funds based on traditional linear regression models, Elastic Net models, Decision Tree models, Random Forest models, Ridge Regression models and Lasso Regression models, and constructs investment portfolios in groups according to the predicted return. The effectiveness of the investment portfolio construction is examined by analyzing the Sharpe ratio, information ratio, volatility and maximum drawdown rate of real data, and the predictive ability and stability of the features are examined by analyzing the value of Rank IC and ICIR of the features. The study found that the features used in this paper can provide rich information for the model, and most of the features have strong predictive ability and predictive stability; the performance of each investment portfolio is consistent with the real data; compared with the traditional linear model, the machine learning method has higher prediction accuracy, more flexibility and stability.

Advancing Momentum Strategies with Machine Learning

Momentum strategies, which predict future returns based on historical asset performance, have been widely applied across various asset classes, including stocks and bonds. However, traditional momentum strategies face limitations when addressing the complexity and dynamics of modern financial markets. In recent years, advancements in computational technology have enabled the integration of machine learning techniques into momentum strategy research, providing new methods for identifying complex market patterns and optimizing trading strategies. This study aims to enhance traditional momentum strategies using machine learning techniques and explores their applicability and performance in diverse market environments. By reviewing relevant literature and comparing traditional and machine learning-enhanced momentum strategies, the study highlights the potential of machine learning in improving prediction accuracy, enhancing risk-adjusted returns, and adapting to market volatility. This research not only contributes to the expansion of the theoretical basis of momentum strategies but also provides investors with more scientific and practical tools for investment decision-making.

Source of US market predictability in international equities: the investor attention perspective

US stock returns lead other countries due to delays in information transmission caused by limited investor attention. But two conditions must be held for this argument to be tenable. One is attention’s domestic return predictability, and the other is the US’s internationally central position among attention lead-lag relationships. While many recent studies suggest investors’ limited attention materially influences future asset returns, there lacks direct evidence of investors focusing on the US first and then gradually turning their attention to the rest of the world. This paper fills this gap by demonstrating the international leadership of investor attention on the US equity market.

Investing in niche marketplaces: value for leading companies

The role of strategic investors in the evolution of niche marketplaces, particularly in Ukraine, has been analyzed in the research. Automation and digitalization have become the key factors for efficiency in modern business. An important element of this process is a marketplace. They force traditional markets to adapt to new conditions. It is hypothesized that strategic investments in niche marketplaces contribute to long-term market development, leading to sustainable economic growth. The research was carried out through the analysis of literary sources, comparative analysis and the case study method. The fact that venture capital funds usually invest in marketplaces targeting high future returns was shown as a result of our investigation. However, strategic investors have a different approach. They direct their investments not only towards making a profit but also towards market development, creating long-term value, implementing innovations, advancing industry growth, building communication channels with customers, and obtaining valuable data for a better understanding of customer needs and market trends, as well as attracting organic traffic to meet specific market demands. Niche platforms among all variations of marketplaces deserve special attention, as they often represent unique opportunities. Such companies as EpicentrK.ua and Cropwise Operations are among examples of strategic investments in these marketplaces. They demonstrate how technological solutions can boost efficiency and productivity, ultimately leading to sustainable economic growth and development. These examples demonstrate that strategic investors prioritize developing market structures and creating high value for consumers

Shaking the coffee market

Cryptocurrencies can be negotiated during trading and non-trading days. Such characteristic enables some innovative analysis between contemporary financial resources and the traditional market assets. In this manuscript, we focus on two assets that are related to the same commodity: coffee. Therefore, the main purpose of this paper was to analyze the impact of returns-based information during non-trading days from a coffee-related cryptocurrency (namely: Coffee Coin® - CFCN) on the returns of coffee futures returns after these non-trading days. The dataset comprises historical prices of BM&F Arabica Coffee Futures, collected from Refinitiv Eikon (Thomson Reuters). Based on time series analysis, the main results suggest that CFCN volatility on non-trading days (particularly, holidays) can be used as a relevant indicator related to coffee futures contract. This evidence can also complement the literature on market anomalies, since we address two calendar patterns: the Monday and the Holiday effects.

The Test of CAPM and Fama-Franch Three-factor Model on China A-shares

William Sharpe et al. proposed the CAPM model in 1964, but subsequent empirical studies showed that the CAPM model still had limitations in practical application. In 1993, Fama and Franch proposed the three-factor model, which can explain the future return of the stock portfolio through the company size, the BM ratio of the companies, and express the excess return of a portfolio in the form of three factors. Based on the CAMP and Fama-French three-factor model, this study will use daily trading data of the constituents of the SSE 50 Index to analyze the A-share market by employing the market value factor, company size factor, and BM factor as explaining variables, so as to verify whether the CAPM and Fama-Franch three-factor model is suitable for China A-shares market. The final empirical results show that the CAPM model is applicable to China A-share market, but the validity test of the Fama-Franch three-factor model has not passed completely, that is to say, only in some industries, the Fama-Franch three-factors model can be effectively used.

Multiple Reputations: Selective Attention to Competence and Character.

Reputation is multidimensional, with some traits being more relevant than others in particular contexts. Can people selectively respond to reputational cues relevant to the task at hand? Across three studies, we examined how people weigh cues about helpfulness and competence when forming expectations about strangers' behavior. Using adapted investment games, we varied whether a stranger's helpfulness or competence predicted participants' future payoffs. We found that when helpfulness is task-relevant (Experiments 1 and 2), participants correctly use this cue in investment decisions. When competence matters most (Experiment 3), participants use it as the primary cue. Overall, a high reputation for outcome-irrelevant characteristics did not compensate for a low reputation for the outcome-relevant reputational cue. However, we also find an asymmetric spillover: Decision-makers prefer cooperating with others who are highly competent and highly helpful, regardless of task demands. We discuss our results within the theoretical framework of person perception and theories of reputation.

Dynamic Comovement between Indian Commodity Futures, Economic Policy Uncertainty and Geopolitical Risk: Evidence from Wavelet Analysis

The present study examines the dynamic comovements between two global risk factors and commodity futures returns. The study considers the daily futures price of nine commodities spanning from January 4th, 2012 to September 29th, 2023. The study employs wavelet analysis and wavelet- based Granger causality tests to analyze dynamic comovement and causal relationship between global risk factors and commodity futures return at different time horizons. The study results show a strong comovement between the US economic policy uncertainty (USEPU) and commodity futures return except for silver and mentha oil. On the other hand, the geopolitical risk (GPR) exhibits a weak relationship with gold, lead, zinc, and energy commodities across all-time frequencies. Further, the Wavelet Granger causality test results provide strong evidence that commodities futures return cause the USEPU in all the time horizons. Followed by the geopolitical risk reports significant evidence that commodities futures return causes GPR in all time horizons.

Adapting to uncertainty: A quantitative investment decision model with investor sentiment and attention analysis

In the face of global uncertainties, including pandemics, economic fluctuations, disruptions in supply chains, major disasters, wars, and impending economic crises, the financial landscape and the impact of investor sentiment on the return of stock index futures can be significantly altered. Understanding the relationship between investor sentiment, attention, and stock index futures returns in the face of these diverse challenges has become particularly critical. However, existing research does not adequately consider the effect of these unexpected events on the market and the shifts in investor attention. Using the COVID-19 pandemic as a case study, this research proposes a dynamic quantitative investment decision-making model that considers the influence of investors’ attention and emotional characteristics, aiming to adapt to the financial market under these global changes and improve the accuracy of quantitative investment forecasting. Initially, the Bidirectional Encoder Representations from Transformers model is employed to analyze investor comment data, extract information on investor attention and emotional characteristics, and construct investor sentiment indicators. Subsequently, a stock index futures forecasting method based on Variational Mode Decomposition algorithm and Support Vector Regression (SVR) model is constructed, and the grey wolf optimization algorithm is introduced to optimize the parameters of the SVR model. Guided by investor sentiment indicators, different market states are further distinguished, and appropriate investment strategies are implemented to effectively enhance the returns of quantitative investment. When compared with models that neglect investor attention and emotional characteristics, the results show that considering investor sentiment indicators not only improves the predictive ability of the model, but also reduces cognitive bias and market risk.

Migrant Content Creators as Actors of Transnational Education on Social Media

This paper discusses the role and motivations of migrants who create content about educational opportunities abroad and distribute it on social media platforms. Following a digital ethnography, this exploratory case study focuses on three young Brazilian women from lower socioeconomic classes who migrated to Germany to pursue formal education. Having identified a demand for information about such pathways, they started systematically to compile and circulate information about educational opportunities in Germany and gathered thousands of followers.Our analysis shows that their engagement in information sharing is based on solidarity and mutual support, paired with a personal enjoyment of social media use as well as expectations of future payoffs. We argue that their focus on vocational education and training highlights that the concept of transnational education is applicable beyond university degrees and that by circulating information about how to access those levels of education, these content creators contribute to the transnationalisation of education.

Retail investors and the behavioral component of idiosyncratic volatility

Utilizing the unique institutions and datasets in China, we find that the idiosyncratic volatility (IVOL) of stock returns correlates positively with retail investor activities at both the market aggregate and individual stock levels. Further analysis indicates that this effect is more pronounced in small-cap, high-priced stocks, and those with low institutional ownership. However, this relationship does not extend to the STAR Board, which is largely inaccessible to most retail investors. Additionally, employing the exit of Google from China as a quasi-natural experiment, we demonstrate that the relationship between retail investor activities and IVOL is likely causal. Lastly, we find that retail investor activities in China do not provide informative signals about future returns, and our findings are not attributable to information incorporation. Overall, our study suggests that IVOL contains an important behavioral component, to which noisy retail investor activities contribute.

Advanced trading strategies based on the sobering-up effect

PurposeThis study investigates analysts’ sobering-up effect on future stock returns and further verifies whether analysts’ familiarity with the forecasted firms and market sentiment affect the relationship between the magnitude of the sobering-up effect on analysts’ earnings forecasts and long-term future stock performance.Design/methodology/approachUsing stocks listed on the Taiwan Stock Exchange (TWSE) and the Taipei Exchange (TPEx) from 2000 to 2021, we propose a trading sobering-up strategy which involves taking a long position in the portfolio with a smaller magnitude of the sobering-up effect and a short position in the portfolio with a greater magnitude of the sobering-up effect.FindingsWe find that an advanced trading strategy which involves taking a long (short) position in the portfolio with a smaller (greater) magnitude of sobering-up effect during the bullish market period, is superior to the sobering-up trading strategy as well as the sobering-up plus familiarity trading strategy and can generate a significantly positive long-term abnormal stock return.Originality/valueThis study explores the dynamic psychological factors behind the magnitude of the sobering-up effect on analysts’ earnings forecasts’ influences. Employing familiarity and market sentiment verify the relationship between the magnitude of the sobering-up effect on analysts’ earnings forecasts and future stock returns. The results give a reference for investors when investing and offer the potential investors significant trading gains.

Do financial markets value corporate culture?

This paper examines how financial market participants incorporate corporate culture, an important value-relevant information, into their investment decisions. Utilizing firm-level corporate culture measures derived from the earnings conference call transcripts, we find that firms with stronger cultural values are associated with higher stock liquidity. We identify three channels through which corporate culture affects stock liquidity: reducing information risk, enhancing trust, and increasing investor recognition. In addition, we find that stronger corporate culture is significantly associated with higher stock price informativeness and future stock returns, and lower level of default risk and informed trading. Overall, our findings highlight the importance of corporate culture in enhancing financial market quality.