When exuberance rules, investors tend to extrapolate the good times and expect high returns. However, if returns remain high or even increase, the growth of earnings or the expansion in the price-earnings ratio required to sustain high returns become increasingly unlikely. Based on a simple decomposition of stock returns, this article discusses the bullish environment at the end of the 1990s, relates it to the environment in the summer of 2025, and draws some relevant conclusions for expected stock returns.

This study examines the value relevance of R&D intensity and development costs’ capitalization intensity under IFRS, by gauging their contribution to the informational components of the volatility of unexpected returns. We use a multivariate time-series approach that can be reconciled to a log-linear valuation model by also accommodating time-varying discount rates. Our results show that R&D intensity has a significant positive impact upon the variance contribution of cash flow and accrual news to the variance of unexpected returns. Thus, R&D intensity indeed conveys value relevant information about shocks to both future operating cash flows and accruals. We also show that this association is stronger as the capitalization intensity of development costs increases, but only for accrual news. Overall, the return decomposition that we employ shows the channel through which the return variation related to R&D and development costs’ capitalization intensity occurs. Additional analysis shows that our results are not driven by country level growth option risk but are weaker in countries with higher uncertainty avoidance. This study contributes to the R&D and return variance decomposition strands of the literature and raises policy implications by providing evidence in favor of development costs’ capitalization.

ABSTRACTWe examine the contribution of a shock to climate concern to the observed outperformance of a portfolio of European green stocks relative to a brown benchmark. We show, first, that an information set given by 1‐month stock return and realized volatility of each stock constituent (and their cross‐sectional averages) improves the (in‐sample) forecasting performance for the return series relative to the traditional market risk factors proxied by Fama–French portfolios. Moreover, the identification of the shock to climate concern occurs in two stages: First, we compute the historical decomposition based on a Panel SVAR fitted to the return and volatility of each green and brown portfolio constituent. Then, the contribution of the first common shock to the historical decomposition of returns is purged of macroeconomic forecast errors, and the residual is interpreted as the innovation to climate concern. The empirical evidence is robust to a number of different selections of stocks entering the green and brown portfolio.

ABSTRACTThis paper proposes that the tail risk associated with commodity futures returns performs well at predicting the S&P 500 index futures returns in‐ and out‐of‐sample, even after controlling business cycles, economic factors, investor sentiment factors, other forms of tail risk factors, and macroeconomic conditions. Following Kelly and Jiang (2014), we directly estimate the commodity tail risk factor from the cross‐section of commodity futures returns, which can efficiently capture the prevailing level of tail risk in the cross‐sectional distribution. Our empirical analysis involves forecasting regressions, which aim to predict index futures returns using lagged up‐tail risk, down‐tail risk, and overall tail risk. We uncover asymmetric forecasting power between up‐tail risk and down‐tail risk, highlighting their distinct influences. Notably, our return decomposition analysis shows that the commodity tail risk factors primarily drive index futures returns through the discount rate channel.

We propose a new method for decomposing realized stock market capital gains into contributions from changes to the real yield curve, equity premia, and expected dividends. The method centers on changes to observable inputs of the present value formula and requires no regressions or log-linearization. In S&P500 data for 2005-2023, changes to expected dividends dominated the cumulative capital gain. Changes to the real yield curve and equity premia contributed more to capital gain fluctuations. A mix of higher equity premia and lower expected earnings drove the 2008 and 2020 market declines, while higher real yields drove the 2022 market drop.

This paper proposes an interest rate model of dynamic risk factors and examines the returns of inter–temporal capital assets. The nominal pricing kernel is built on a yield-surface of federal bonds, inflation rates and market indexes. The beta values of derivative or portfolio in the system are determined by finding correlations between asset returns and pricing kernel innovations from data. We present the calibrations of capital asset returns to the market data with the numerical fitting. The model is tested using market return, short rate, inflation, term spread and produces the cross-sectional evidence that the value stocks have higher loadings on the cash-flow and discount-rate shocks than do growth stocks. We infer the discount-rate shocks and the cash-flow shocks, and find that the value stocks are riskier than the growth ones on the measurement of volatility shocks, which verifies and supports the asset return decomposition.

Greenhouse gas emissions and mitigation potential of crop production in Northeast China

Out-of-sample equity premium predictability: An EMD-denoising based model

Separating the predictable part of returns with CNN-GRU-attention from inputs to predict stock returns

Context-aware reinforcement learning for cooling operation of data centers with an Aquifer Thermal Energy Storage

Not a one-trick pony: Price impact of rating agency information

Real-world decision-making problems are usually accompanied by delayed rewards, which affects the sample efficiency of Reinforcement Learning, especially in the extremely delayed case where the only feedback is the episodic reward obtained at the end of an episode. Episodic return decomposition is a promising way to deal with the episodic-reward setting. Several corresponding algorithms have shown remarkable effectiveness of the learned step-wise proxy rewards from return decomposition. However, these existing methods lack either attribution or representation capacity, leading to inefficient decomposition in the case of long-term episodes. In this paper, we propose a novel episodic return decomposition method called Diaster (Difference of implicitly assigned sub-trajectory reward). Diaster decomposes any episodic reward into credits of two divided sub-trajectories at any cut point, and the step-wise proxy rewards come from differences in expectation. We theoretically and empirically verify that the decomposed proxy reward function can guide the policy to be nearly optimal. Experimental results show that our method outperforms previous state-of-the-art methods in terms of both sample efficiency and performance. The code is available at https://github.com/HxLyn3/Diaster.

Is it alpha or beta? Decomposing hedge fund returns when models are misspecified

The impact of revenue diversification on profitability, capital, and risk in US banks by size

Investor sentiment and global economic conditions

Straw return benefits soil nutrient circulation and avoids the environmental pollution caused by incineration. The straw return effect is determined by many factors, such as the returning mode and tillage method. To find the most suitable straw return mode in the hilly areas of south China, we conducted experiments with preceding maize straw in Nanchong (Sichuan Province, China) for three years. Five treatments were tested: (A) rotary tillage without straw return (RT), (B) crushed straw return with rotary tillage (CRT), (C) crushed straw return without rotary tillage (CSR), (D) whole straw return with rotary tillage (WRT), and (E) whole straw return without rotary tillage (WSR). The results indicated that CRT had the fastest decomposition rate, followed by CSR. Moreover, CRT had the fastest nutrient release rates for nitrogen, phosphorus, potassium, cellulose, hemicellulose, and lignin, as well as the highest maize yield (6.62% higher than RT). CRT increased the content of organic matter, total nitrogen, total phosphorus, and total potassium in the soil, as well as improved the soil pH. Furthermore, the numbers of bacteria, Actinomycetes, and fungi in the soil under CRT, CSR, and WSR treatments were higher than those under the other two treatments. This study has important reference value for exploring the most favourable straw return method for improving farmland fertility.

This paper analyzes cash flow and cost of capital dynamics around share repurchase announcements of publicly traded US firms by decomposing stock returns into news related to cash flows and discount rates. After repurchase announcements, the cost of capital decreases significantly, while cash flows do not change. The decrease in the cost of capital is largest for firms that appear underpriced. These firms also experience the highest long-term returns after repurchase announcements. The findings suggest that market participants learn about a temporary overestimation of the cost of capital when firms announce share repurchases.

This paper derives a new decomposition of stock returns using price extremes and proposes a conditional autoregressive shape (CARS) model with beta density to predict the direction of stock returns. The CARS model is continuously valued, which makes it different from binary classification models. An empirical study is performed on the US stock market, and the results show that the predicting power of the CARS model is not only statistically significant but also economically valuable. We also compare the CARS model with the probit model, and the results demonstrate that the proposed CARS model outperforms the probit model for return direction forecasting. The CARS model provides a new framework for return direction forecasting.

Purpose This paper aims to study the underpricing phenomenon of initial public offerings (IPOs) of 355 Indian companies issued from 2007 to 2019. The research question this paper empirically examines is whether Indian corporate executives deliberately underprice IPOs from its fair value to attract investors, thereby causing an abnormal spike in the prices on the listing day. The findings of this study challenge a commonly held notion of leaving money on the table by IPO issuing companies. Of the overall average listing day returns of 17%, the deliberate premarket underpricing component is found to be mere 5.3%, while the remaining price fluctuation is, inter alia, a result of market momentum along with the unmet demands of impatient investors. Design/methodology/approach Following Koop and Li (2001), this study uses Stochastic frontier model (SFM) to study a routine anomaly of disparity between the primary market price (i.e. IPO issue price) and the secondary market price (listing price). The jump in the issue price observed on a listing day is decomposed into deliberate premarket underpricing component that reflects the extent of managerial manipulation and the after-market misvaluation component attributable to information asymmetry and prevailing market volatility. Findings This paper uses SFM to bifurcate initial returns into deliberate underpricing by managers and after-market mispricing by noise traders. This study finds that a significant part of the initial return is explained through after-market mispricing. This study finds that average initial returns are 17%, deliberate premarket underpricing is 5.3% and after-market mispricing averages 11.9%. Research limitations/implications This study can isolate underpricing done at the premarket by estimating a systematic one-sided error term that measures the maximum predicted issue price deviation from the offered price. Consequentially, the disaggregation of initial returns may be especially informative for retail investors in planning their exit strategy from an IPO by separating the strength of the firm's fundamentals and its causal relationship with the initial returns. Substantial proportion of after-market mispricing implies that future research should focus on factors causing after-market mispricing. As underlying causes are identified, tailor-made policy responses can be formulated to benefit investors. Practical implications This paper has empirically validated that initial return is a mix of both components, i.e. deliberate underpricing and aftermarket mispricing. This disaggregation of initial returns can prove helpful for investors in planning their exit strategy. This study can help investors to become more aware of the importance of the fundamentals of the firm and its causal relation with the initial returns. This information in turn can help reduce the information asymmetry amongst investors and help them lessen the costs of adverse selection. Originality/value A large number of research studies on IPO pricing find overwhelming evidence of underpricing in public issues. This research attempts to decompose the extent of underpricing into deliberate underpricing and after-market mispricing, thereby supplementing the existing literature on the IPO pricing puzzle. To the best of the authors’ knowledge, this study is the first contribution to the literature on initial return decomposition for the Indian capital markets.

PurposeThe purpose of this study is to measure mutual funds' manager performance by attributing it to their abilities to choose better securities (selectivity effect) and to allocate these securities better than their benchmarks (allocation effect). The study enables the authors to examine the relative contributions of the commonly known asset-pricing factors in mutual funds' performance.Design/methodology/approachTo examine managers' ability to steer funds' returns, the authors conduct a two-dimensional holdings-based analysis using factor-specific decomposition of funds' excess returns into their ability to select and allocate securities better than their benchmarks. Subsequently, the authors conduct an analysis of the covariance (ANCOVA) due to these factors in explaining funds' excess returns over time.FindingsWhile managers' ability to choose better securities than the benchmarks (the selectivity effect) appears modest, some funds (especially the winners) allocate securities in their portfolios better than their benchmarks (the allocation effect) based on their exposures to certain factors (e.g. the momentum factor for the winner funds). However, although funds consistently gain through their ability to predict the size and value factors well, they do not consistently possess the skills to predict the momentum factor.Research limitations/implicationsAlthough the paper analyzes all the available diversified funds, the sample excludes several other categories, such as thematic and international funds. Further, the analysis is based on equity-oriented Indian funds. Broader studies of changes in factor exposures and the inclusion of more factors apart from those conventionally used may shed more light on the managers' ability to maneuver these factors.Practical implicationsThe results show that mutual fund managers lack persistence in their performance, even though some of them could predict specific factors well. Since the activity in active mutual funds could not lead to superior performance over time, investors could be better off by selecting cheaper passive funds for their long-term investments.Originality/valueThe paper presents a novel approach to studying funds' performance by conducting a two-dimensional holdings-based analysis to capture the relative contributions of common asset-pricing factors in the cross-section as well as over time.