This paper empirically investigates how exposures of an industry’s cash flows and stock returns to discount rate shocks are affected by the market concentration of its upstream and downstream industries. In the cross-section of U.S. industries, we find that industries’ cash flows and stock returns are more negatively exposed to fluctuations in the aggregate discount rate if their upstream or downstream industries are more concentrated. These industries also have higher expected stock returns and costs of capital. Our study highlights the role of vertical competition in determining firm risk exposure and expected returns. This paper was accepted by Lukas Schmid, finance. Funding: K. C. J. Wei received partial financial support from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project 15510222]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.04715 .

We examine the efficacy of machine learning in a central task of fundamental analysis: forecasting corporate earnings. We find that machine learning models not only generate significantly more accurate and informative out-of-sample forecasts than the state-of-the-art models in the literature but also perform better compared to analysts' consensus forecasts. This superior performance appears attributable to the ability of machine learning to uncover new information through identifying economically important predictors and capturing nonlinear relationships. The new information uncovered by machine learning models is of considerable economic value to investors. It has significant predictive power with respect to future stock returns, with stocks in the most favorable new information quintile outperforming those in the least favorable quintile by approximately 34 to 77 bps per month on a risk-adjusted basis.

Prior studies have challenged the practical usefulness of Markowitz portfolio optimization in improving the return–risk trade-off in portfolio management. The authors approach this question from a unique angle by examining whether one can improve the performance of a large sample of actual mutual fund portfolios by reoptimizing the holdings using simple mean–variance optimization methods. The analyses produce compelling evidence of the benefits of Markowitz optimization. Simple portfolio optimization improves mutual fund portfolios’ risk-adjusted performance despite noisy expected return estimates inferred from mutual fund portfolio weights. Several alternative optimization strategies, including the risk-parity portfolio, minimum variance portfolio, mean–variance portfolio, and Sharpe ratio maximization portfolio, all outperform actual mutual fund portfolios in terms of the Sharpe ratio and other risk-adjusted performance measures. Moreover, the results are robust to subsamples partitioned on various dimensions. In contrast to the findings of DeMiguel et al. (2009), the authors find that the 1/N portfolio performs the worst.

Haifeng You Hong Kong University of Science and Technology Email: achy@ust.hk Abstract We find evidence that investors categorize stocks into a new investment style based on the theme of disruption. We identify disruption style stocks by their extreme return sensitivity to Bitcoin returns during the 2010 to 2019 period. These stocks experience temporary overvaluation and subsequent return reversal that exceeds −1% per month. Additional tests indicate that this trading habitat is dominated by retail clientele. Our evidence suggests that investors evaluate these stocks in a way that is consistent with the probability weighting features of prospect theory.

Building on dynamic collusion theories, we predict that firms with less concentrated upstream or downstream industries have lower systematic risk because their supply chain partners tend to compete more aggressively during recessions, absorbing more of the adverse effect of aggregate shocks. Consistent with this prediction, we find that these firms experience a smaller decrease in fundamental performance during recessions, have significantly lower fundamental and capital market risks, and enjoy a significantly lower cost of equity capital. The overall results highlight the importance of vertical competition in determining a firm’s systematic risk and cost of equity capital.

We investigate the determinants of analysts’ target price implied returns and the implication of our findings for investment decision-making. We identify four broad sets of factors that help explain the cross-sectional variation in target price implied returns: future realized stock returns, errors in forecasting fundamentals, errors in forecasting the expected return to risk, and biases relating to analysts’ incentives. Our results suggest that all four sets help explain target price implied returns, with errors in forecasting the expected return to empirical risk proxies having the greatest impact. Collectively, these variables explain nearly a quarter of the cross-sectional variation in target price implied returns. We use our model to predict the optimistic bias in target price implied returns and evaluate whether investors correctly ignore the predictable bias. The results suggest that investors make similar valuation errors to analysts and/or do not perfectly back out the predicted bias in target prices.

We posit that firms with more competitive upstream or downstream industries tend to have greater bargaining power relative to their suppliers or customers. Hence they can pass on adverse economic shocks along the supply chain and reduce their risk exposures. Consistent with this prediction, we find that firms with more competitive upstream or downstream industries have significantly lower capital market and fundamental risk. Moreover, these firms also enjoy significantly lower implied cost of equity capital. The results are robust to alternative model specifications and remain intact after controlling for endogeneity bias. Overall, our results highlight the importance of vertical competition in determination of systematic risk and cost of equity capital.

To maximize firm value managers must efficiently invest new capital. This paper examines whether managers learn from analysts in making capital investment decisions. Using broker mergers and closures as exogenous shocks to the number of analysts covering a firm, we find that firm investment-Q sensitivity significantly decreases after losing an analyst. The impact is largest for firms with the fewest number of analysts. Moreover, the effect is mainly driven by the role analysts play in information acquisition and in price efficiency. We also find the spillover effect of analyst coverage using Reg FD as an alternative shock to analyst information production. The results suggest that the recent decline in analyst coverage may negatively impact firm investment and market-wide resource allocation.

We examine how companies voluntarily change their financial reporting conservatism in response to an exogenous decrease in analyst coverage. We hypothesize that more severe information asymmetry and weaker external monitoring associated with such a decrease in analyst coverage exacerbate agency conflicts between contracting parties, which in turn creates a greater demand for conservative accounting. Consistent with this prediction, we document a significant increase in accounting conservatism following an exogenous drop in analyst coverage. Furthermore, the effect is stronger when the dropped analyst is more informed and when the affected firm has a higher financial leverage ratio, less favorable credit ratings, and a higher proportion of cash-based CEO compensation. The overall evidence is consistent with the notion that accounting conservatism arises as part of the efficient technology employed by firms to address agency problems between contracting parties. JEL classification: G17; M41

This paper examines the effect of tournament incentives on financial reporting. We hypothesize that the CEO promotion tournament increases the costs of opportunistic reporting. Consistent with this prediction, we find that the size of the tournament prize is negatively associated with the level of earnings management. Furthermore, this association is more pronounced for firms in more heterogeneous industries and in the period immediately preceding a CEO succession, but is weaker immediately after the appointment of a new CEO. Overall, our findings suggest that promotion-based tournament incentives mitigate the agency problem of opportunistic reporting.

Previous research indicates that firms issue shares when their stock is overpriced and repurchase shares when their stock is underpriced. Such transactions transfer wealth from transacting stockholders to ongoing stockholders. We quantify the magnitude of these wealth transfers and analyze their implications. Strikingly, we find that for the average firm-year, these wealth transfers approximate 40% of net income. We also find that these wealth transfers can be predicted using a variety of firm characteristics and that future wealth transfers are an important determinant of current stock prices.

We investigate three potential channels of analyst value creation: improving fundamental performance through monitoring, reducing information asymmetry, and increasing investor recognition. We show that changes in investor recognition have consistent explanatory power for the market reaction to coverage initiations and terminations but find mixed evidence for changes in information asymmetry and no evidence for changes in fundamental performance as determinants of the market reaction. These results suggest that analysts create value for firms under their coverage by improving their investor recognition and not by monitoring or reducing information asymmetry.

This paper investigates whether the desire to achieve higher equity valuations induces conglomerates to manipulate their segment earnings. I extend the Stein (Q J Econ 104:655–669, 1989 ) model to a multi-segment setting and show that conglomerates have incentives to transfer profits from segments operating in industries with lower valuation multiples to those with higher multiples, even if the market is not fooled in equilibrium. If companies engage in such manipulation, segments with relatively high (low) valuations should report abnormally high (low) profits. The empirical tests confirm this prediction and further show that the relation is stronger for firms with more dispersed segment valuations. This paper also demonstrates that the simple sum-of-the-parts valuation with multiples tends to overestimate the enterprise values for conglomerates and that the measurement errors increase with segment valuation dispersion.

We investigate the determinants of analysts' target price forecasts and evaluate their relative importance for explaining the cross-sectional variation in target price implied returns. We identify four broad determinants: the informational component predictive of future stock returns, errors in forecasting fundamentals, errors in forecasting the expected return to risk, and biases relating to analysts' incentives. Our findings indicate that analysts have a limited ability to predict short-term future returns, and incorrect fundamental forecasts marginally impact target price valuations. Errors in forecasting the expected return to empirical risk proxies such as beta and idiosyncratic volatility have the greatest impact and induce significant noise and optimism into target prices. Job-related incentives induce incremental optimism in target prices. We use our target price determinants model to predict the optimistic bias in target price forecasts and evaluate whether investors correctly ignore the predictable bias. The results suggest that investors make similar valuation errors to analysts and/or do not perfectly back out the predicted bias in target prices.

This study examines whether firms surrounding the Sarbanes–Oxley Section 404 market value compliance threshold behave opportunistically to reduce their market value to avoid compliance with Section 404. We find evidence that those firms reduce their market value temporarily during threshold measurement quarters, whereas control firms experience increasing market value. We find strong evidence of dampened stock returns and some evidence of insider trading as means to reduce the float. Additionally, we find that downward earnings management is used as a mechanism to alter investors’ expectations of firm value in order to temporarily reduce stock prices. We consider this opportunistic evidence of regulatory avoidance. Finally, we find that the likelihood of avoidance increases with the power of the CEO and decreases with the strength of the monitoring of the CEO, which suggest that avoidance is more likely to happen in firms with poor corporate governance mechanisms.

ABSTRACTWe examine how the mandatory adoption of International Financial Reporting Standards (IFRS) in continental Europe affects the contractual usefulness of accounting information in executive compensation, as reflected in pay‐performance sensitivity (PPS) and relative performance evaluation (RPE). The empirical evidence indicates a weak increase in accounting‐based PPS in the post‐adoption period, primarily driven by countries with large differences between IFRS and their previously adopted local accounting standards. We also document a significant increase in accounting‐based RPE using foreign peers after the adoption. Additional analysis shows that the increase in RPE is greater for firms with more foreign sales, and for those with lower availability of domestic peers of comparable size. The overall results are consistent with the compensation committees in those countries perceiving earnings after IFRS adoption to be of higher quality and comparability. Our paper highlights an important benefit of IFRS largely ignored by the literature, that is, the higher earnings quality and comparability brought by the adoption of IFRS facilitate executive compensation contracting.

We investigate analysts’ motives for rounding annual EPS forecasts (placing a zero or five in the penny location of the forecast). We first show that an intuitive reason for analysts to engage in rounding is in circumstances where the penny location of the forecast is of less economic significance. By rounding, analysts reveal that their forecasts are not intended to be precise to the penny. We also show that analyst incentives impact the likelihood of rounding. Specifically, we predict that analysts will exert less effort forecasting earnings for firms that generate less brokerage or investment banking business since such firms create less value for the analysts’ employers. As a consequence of this lack of effort and attention, the analyst will be more uncertain about the penny digit of the forecast and so will round. Our results are consistent with this prediction. One implication of our findings is that a rounded forecast is a simple and easily observable proxy for a more noisy measure of the market’s expectation of earnings. Consistent with this implication, we show that rounded forecasts bias down earnings response coefficients at earnings announcements.