Articulation-based accruals

We build an articulating financial statements model in which the beginning and ending balance sheet amounts are explicitly linked to accruals. We distinguish accruals based on the source financial statement of the accruals, either the cash flow statement, balance sheet, or statement of owners’ equity. We then examine how the accrual-generating source affects the relations between accruals and future earnings and stock returns. While prior studies document a negative association between accruals and future earnings and returns, we find accruals relating to the current operating section of the balance sheet are positively associated with future earnings. Further, accruals originating from net financial asset via the statement of owners’ equity are positively associated with future returns. We also show that equity investment and discontinued asset accruals differ from operating asset accruals in their association with future earnings.     

线上销售与未来股票收益

本文通过分析相关上市公司在电商平台的线上销售数据,发现线上销售增长可以预测未来股票收益。根据线上销售增长率构建投资组合可以获得月均1.27%的超额收益,经三因子、五因子模型调整后收益率分别为1.40%和1.35%,并且该超额收益在较长时间内不会逆转。横截面回归结果显示,线上销售增长与未来股票收益显著正相关,并在控制其他市场异象因子后仍然显著。此外,本文还发现线上销售数据的预测能力主要集中在投资者关注有限、线上销售占比高以及套利成本高的公司,其投资价值来源于对公司未来基本面信息的预测能力。进一步研究表明,同时利用线上销售指标和营业收入指标进行投资可以获得更高的超额收益。在考虑业绩预告和业绩快报对线上销售指标预测能力的潜在影响后,结果依然稳健。     

财务分析师盈利预测的投资价值:来自深沪A股市场的证据

本文研究了中国股票市场上财务分析师的盈利预测信息对投资者是否有价值。我们发现,可以利用公开的盈利预测制定可获利的套头交易策略。套头交易的回报率不仅在统计意义上显著大于零,而且在经济意义上也是显著的。这些结果不受不同的检验方法影响,也无法为我们所考虑到的风险因素所解释。本文的发现说明了中国的股票市场尚未达到Fama(1970)意义上的半强式有效,投资者在投资决策时可以利用分析师的盈利预测以提高其投资的回报。此外,本文的结果也有助于回答中国的财务分析师是否具有专业胜任能力这一颇有争议的问题。     

Funding conditions and insurance stock returns: Do insurance stocks really benefit from rising interest rate regimes?

We examine funding conditions and U.S. insurance company stock returns. Although constrained funding conditions, signaled by restrictive Federal Reserve monetary policy, correspond with increases in the future payouts of fixed‐income securities held by insurance firms and potentially provide value through the liability side of insurer balance sheets, they also decrease the values of securities currently held in insurer portfolios. Prior research finds that restrictive policy has a negative effect on equity returns in general. Our results suggest the negative impacts of constrained funding environments outweigh the potential positives, as insurance company stock returns are significantly lower during periods of constrained funding. This effect varies within a given funding state and also across insurer type. The effect is strongest during the first 3 months of a constrained funding environment and for life and health insurers—insurer types with longer portfolio durations. For property and liability (P&L) insurers, lower stock return performance only exists in the first 3 months of a constrained funding environment. In the subsequent months, P&L insurers actually have higher stock returns during constrained periods, consistent with their typically shorter duration asset portfolios, which are more quickly rolled over into new higher‐yielding securities.     

The Variation of Economic Risk Premiums in Real Estate Returns

We examine the predictable components of returns on stocks, bonds, and real estate investment trusts (REITs). We employ a multiple-beta asset pricing model and find that there are varying degrees of predictability among stocks, bonds, and REITs. Furthermore, we find that most of the predictability of returns is associated with the economic variables employed in the asset pricing model. The stock market risk premium is highly important in capturing the predictable variation in stock portfolios, and the bond market risk premiums (term and risk structure of interest rates) are important in capturing the predictable variation in bond portfolios. For REITs, however, both the stock and bond market risk premiums capture the predictable variation in returns. REITs have comparable return predictability to stock portfolios. We conclude that there is an important economic risk premium for REITs that are not captured by traditional multiple-beta asset pricing models.     

Peer performance and stock market entry

Peer performance can influence the adoption of financial innovations and investment styles. We present evidence of this type of social influence: recent stock returns that local peers experience affect an individual's stock market entry decision, particularly in areas with better opportunities for social learning. The likelihood of entry does not decrease as returns fall below zero, consistent with people not talking about decisions that have produced inferior outcomes. Market returns, media coverage, local stocks, omitted local variables, short sales constraints, and stock purchases within households do not seem to explain these results.     

The economic value of VIX ETPs

The fairly new VIX ETPs have been promoted for providing effective and easily accessible diversification, while at the same time having large negative returns. We examine the economic value of using VIX ETPs for diversification of stock–bond portfolios. Our analysis begins in 2009, when the first VIX ETPs are introduced, and therefore only considers the period after the recent financial crisis. For investors with a constant allocation strategy, the diversification benefits of the VIX ETPs do not offset their negative returns. This implies negative economic value of a constant allocation. For a dynamic allocation strategy, including short VIX ETPs in the investment opportunity set can have substantial positive economic value.     

The complementary relationship between financial and non-financial information in the biotechnology industry and the degree of investor sophistication

We examine whether financial and non-financial variables, separately and in tandem, are value relevant in explaining market returns, equity values and the degree of investment by sophisticated investors for a sample of drug development companies. Patent counts, number of collaborations and probability-adjusted portfolios of drugs under development are the non-financial information metrics used in this study. Earnings are the main financial information variable. We show that news about these non-financial measures is significantly associated with abnormal returns. We also find that earnings are value relevant in explaining cumulative abnormal returns and equity prices around earnings announcement dates despite the fact that R&D expenditures are large and usually expensed as incurred. We further show that non-financial information is value relevant in explaining annual returns, equity prices and degree of investment by (long-horizon) sophisticated investors. Moreover, non-financial variables are value relevant after controlling for financial variables suggesting that the two types of variables are complements.     

The efficiency of the buy-write strategy: Evidence from Australia

We examine the performance of the buy-write option strategy (BWS) on the Australian Stock Exchange and analyse whether such an investment opportunity violates the efficient market hypothesis on the basis of its risk and returns. This study investigates the relationship between buy-write portfolios returns and past trading volume and other fundamental financial factors including dividend yield, firm size, book to market ratio, earnings per share (EPS), price earnings ratio and value stocks within these portfolios. We also test the profitability of the buy-write strategy during bull and bear markets. Consistent with the literature, it is observed that BWS offers superior risk adjusted returns for low levels of out-of-moneyness and contrary evidence is observed for deeper out-of-money portfolios. Consistent with a preference for options with a maturity of around 3 months in Australia, this research shows that quarterly rebalancing periods offer better returns for the BWS.     

Intraday online investor sentiment and return patterns in the U.S. stock market

We implement a novel approach to derive investor sentiment from messages posted on social media before we explore the relation between online investor sentiment and intraday stock returns. Using an extensive dataset of messages posted on the microblogging platform StockTwits, we construct a lexicon of words used by online investors when they share opinions and ideas about the bullishness or the bearishness of the stock market. We demonstrate that a transparent and replicable approach significantly outperforms standard dictionary-based methods used in the literature while remaining competitive with more complex machine learning algorithms. Aggregating individual message sentiment at half-hour intervals, we provide empirical evidence that online investor sentiment helps forecast intraday stock index returns. After controlling for past market returns, we find that the first half-hour change in investor sentiment predicts the last half-hour S&P 500 index ETF return. Examining users’ self-reported investment approach, holding period and experience level, we find that the intraday sentiment effect is driven by the shift in the sentiment of novice traders. Overall, our results provide direct empirical evidence of sentiment-driven noise trading at the intraday level.     

Location-specific stock market indices: an exploration

This article develops an alternative location-specific stock market index driven by investors’ ‘attachment’ towards investment at a specific location. We evaluate the performance of hypothetical stock market indices that track companies based on their state of registration, taking the US stock market as our case. Using annual data since 1980 we present raw, risk-adjusted and value-weighted state portfolios’ returns to study the extent to which stock market performance varies by state-level demographics and economic factors. A dynamic panel data estimation – with and without spatial spillover effects – is employed to establish a strong association between stock price performance and the state-level (or geography-weighted) factors. We find that spatial effects are strong and that the ‘spatial attachment’ of companies in interaction with the various location-specific variables imparts an overarching influence on stock-price performance. Comparison of model performances further supports our claims.     

Dynamic mode decomposition for financial trading strategies

We demonstrate the application of an algorithmic trading strategy based upon the recently developed dynamic mode decomposition on portfolios of financial data. The method is capable of characterizing complex dynamical systems, in this case financial market dynamics, in an equation-free manner by decomposing the state of the system into low-rank terms whose temporal coefficients in time are known. By extracting key temporal coherent structures (portfolios) in its sampling window, it provides a regression to a best fit linear dynamical system, allowing for a predictive assessment of the market dynamics and informing an investment strategy. The data-driven analytics capitalizes on stock market patterns, either real or perceived, to inform buy/sell/hold investment decisions. Critical to the method is an associated learning algorithm that optimizes the sampling and prediction windows of the algorithm by discovering trading hot-spots. The underlying mathematical structure of the algorithms is rooted in methods from nonlinear dynamical systems and shows that the decomposition is an effective mathematical tool for data-driven discovery of market patterns.     

REIT Characteristics and the Sensitivity of REIT Returns

Previous research on the returns to real estate investment trusts (REITs) has considered whether REITs are systematically exposed to general stock-market risk and interest-rate risk. This study examines how the sensitivity of REIT returns to these factors may be influenced by various REIT characteristics. Using a sample of publicly traded REITs, we estimate the sensitivity of REIT returns to stock market and interest-rate changes. We then propose and implement a model for testing whether differences in asset structure, financial leverage, management strategy, and degree of specialization in the REIT portfolios are related to their sensitivity to interest rate and market risk. Our results permit us to offer some inferences about how REITs can alter their risk exposure by managing these characteristics.     

The Effect of Socially Responsible Investing on Portfolio Performance

More and more investors apply socially responsible screens when building their stock portfolios. This raises the question whether these investors can increase their performance by incorporating such screens into their investment process. To answer this question we implement a simple trading strategy based on socially responsible ratings from the KLD Research & Analytics: Buy stocks with high socially responsible ratings and sell stocks with low socially responsible ratings. We find that this strategy leads to high abnormal returns of up to 8.7% per year. The maximum abnormal returns are reached when investors employ the best‐in‐class screening approach, use a combination of several socially responsible screens at the same time, and restrict themselves to stocks with extreme socially responsible ratings. The abnormal returns remain significant even after taking into account reasonable transaction costs.     

Long-term adjusted volatility: Powerful capability in forecasting stock market returns

We develop the long-term adjusted volatility (LV_ADJ) by removing the interference information of short-term volatility from the simple long-term volatility and examine the role of LV_ADJ in the predictability of stock market returns. Using a sample from January 2000 to December 2019 and considering 19 popular predictors, LV_ADJ positively predicts the next-month returns of S&P 500 and the univariate model with LV_ADJ has the best forecasting performance with adjusted in-sample r-squared of 3.825%, out-of-sample r-squared of 3.356%, return gains of 5.976%, CER gains of 4.708 and Sharpe ratio gains of 0.394. The predictive information of LV_ADJ is independent of that obtained from the 19 popular predictors. Furthermore, we find that LV_ADJ also has predictive power for long-term (3–12 months) stock returns, and can forecast returns of industry portfolios and characteristic portfolios.     

Predicting Stock Returns Using Financial Statement Information

We examine the profitability of the Ou and Penman (1989a) Pr trading strategy and the Holthausen and Larcker (1992) Prob trading strategy over the period 1980–1992 in the UK. This is a test of whether an investor can earn abnormal returns by exploiting fundamental accounting data. We employ alternative abnormal return metrics and research designs to control for risk. Using a UK dataset offers an independent test because the UK differs from the US in its formal and informal financial reporting environment, its structure of share ownership, and the behaviour of its economy over the study period. We find consistent evidence that an investor could have used a summary measure of financial statement information to predict future abnormal returns by indirectly predicting one-year-ahead earnings changes, but only weak and inconsistent evidence that an investor could have used a summary measure of financial statement information to predict one-year-ahead stock returns directly. We offer some thoughts on the reasons for these different results.     

Is it costly to be both shariah compliant and socially responsible?

Positive ethics associated with socially responsible investments (SRI) is challenging the limits of Islamic investments' conservative approach to promote corporate social responsibility. In this study, we test the integration of social performance measures (companies the most virtuous or high-rated in terms of environmental, social, and governance (ESG) issues) in Islamic portfolios using KLD social ratings. We seek to determine the financial price of complying both to Islamic investment and SRI principles. To do so, we measure the financial performance of self-composed Islamic portfolios with varying ESG scores. The results indicate no adverse effects on returns due to the application of ESG screens on shariah-compliant stocks during the 2007–2011 periods while reporting substantially higher performance for the portfolios with good records in governance, products, diversity, and environment issues. On the opposite, a negative performance is associated with an SRI strategy of disengagement from shariah-compliant stocks with community and human rights controversies. Our performance measures are controlled for market sensitivity, investment style, momentum factor, and sector exposure.     

Can financial statement analysis beat consensus analysts’ recommendations?

We examine whether investors can exploit financial statement information to identify companies with a greater likelihood of future earnings increases and whether stocks of those companies generate 1-year abnormal returns that exceed the abnormal returns from following analysts’ consensus recommendations. Our approach summarizes financial statement information into a “predicted earnings increase score,” which captures the likelihood of 1-year-ahead earnings increases. We find that, within our sample of consensus recommendations, stocks with high scores are much more likely to experience future earnings increases than stocks with low scores. A hedge portfolio strategy that utilizes our approach within each consensus recommendation level generates average annual abnormal returns of 10.9 percent over our 12-year sample period, after controlling for previously identified risk factors. These abnormal returns exceed those available from following analysts’ consensus recommendations. Our results show that share prices and consensus recommendations fail to impound financial statement information that helps predict future earnings changes.     

A study of cross-industry return predictability in the Chinese stock market

We investigate cross-industry return predictability for the Shanghai and Shenzhen stock exchanges, by constructing 6- and 26- industry portfolios. The dominance of retail investors in these markets, in conjunction with the gradual diffusion of information hypothesis provide the theoretical background that allows us to employ machine learning methods to test for cross-industry predictability. We find that Oil, Telecommunications and Finance industry portfolio returns are significant predictors of other industries. Our out-of-sample forecasting exercise shows that the OLS post-LASSO estimation outperforms a variety of benchmarks and a long–short trading strategy generates an average annual excess return of 13%.     

Predicting Japanese bank stock performance with a composite relative efficiency metric: A new investment tool

The paper's main objective is to predict bank stock performance one year ahead with a composite efficiency metric from relative contextual financial analysis. We bring together financial ratios, generalized data envelopment analysis and simulated annealing to rank Japanese banks on stock performance predicted from relative efficiency scores. An application of this ranking in a profitable investment strategy by designating long and short portfolios underscores the potential commercial value of the method. The method can also be used to monitor the effectiveness of ratios in forecasting stock performance and it is conducive to selecting predictive ratios when markets are changing rapidly.