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Firm-level stock returns exhibit comovement above that in fundamentals, and the gap tends to be higher in developing countries. We investigate whether correlated beliefs among sophisticated, but imperfectly informed, traders can account for the patterns of return correlations across countries. We take a unique approach by turning to direct data on market participants' information - namely, real-time firm-level earnings forecasts made by equity market analysts. The correlations of firm-level forecasts exceed those of fundamentals and are strongly related to return correlations across countries. A calibrated information-based model demonstrates that the correlation of beliefs implied by analyst forecasts leads to return correlations broadly in line with the data, both in levels and across countries - the correlation between predicted and actual is 0.63. Our findings have implications for market-wide volatility - the model-implied correlations alone can explain 44% of the cross-section of aggregate volatility. The results are robust to controlling for a number of alternative factors put forth by the existing literature.
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We study a model of investment in which both technological and informational frictions as well as institutional/policy distortions lead to capital misallocation, i.e., static marginal products are not equalized. We devise an empirical strategy to disentangle these forces using readily observable moments in firm-level data. Applying this methodology to manufacturing firms in China reveals that adjustment costs and uncertainty have significant aggregate consequences but account for only a modest share of the observed dispersion in the marginal product of capital. A substantial fraction of misallocation stems from firm-specific distortions, both productivity/size-dependent as well as permanent. For large US firms, adjustment costs are relatively more salient, though permanent firm-level factors remain important. These results are robust to the presence of liquidity/financial constraints.
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Emerging markets exhibit high returns to capital, the 'Lucas Paradox,' alongside volatile growth rate regimes. We investigate the role of long-run risks, i.e., risk due to fluctuations in economic growth rates, in leading to return differentials across countries. We take the perspective of a US investor and outline an empirical strategy to identify risky growth shocks and quantify their implications. Long-run risks account for 60-70% of the observed return disparity between the US and a group of the poorest countries. At the individual country level, our model predicts average returns that are highly correlated with those in the data (0.61).
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We propose a theory linking imperfect information to resource misallocation and hence to aggregate productivity and output. In our setup, firms look to a variety of noisy information sources when making input decisions. We devise a novel empirical strategy that uses a combination of firm-level production and stock market data to pin down the information structure in the economy. Even when only capital is chosen under imperfect information, applying this methodology to data from the US, China, and India reveals substantial losses in productivity and output due to the informational friction. Our estimates for these losses range from 7-10% for productivity and 10-14% for output in China and India, and are smaller, though still significant, in the US. Losses are substantially higher when labor decisions are also made under imperfect information. We find that firms turn primarily to internal sources for information; learning from financial markets contributes little, even in the US.
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Ever since Keynes' famous quote about animal spirits, there has been an interest in linking firms' expectations and actions. However, empirical evidence has been limited due to a lack of firm-level panel data on expectations and outcomes. In this paper, we build such a dataset by combining a unique survey of Japanese firms' GDP forecasts with company accounting data for 25 years for over 1,000 large Japanese firms. We find four main results. First, firms' GDP forecasts are positively associated with their employment, investment, and output growth in the subsequent year. Second, both optimistic and pessimistic forecast errors lower profitability. Third, while over-optimistic forecasts lower measured productivity, over-pessimistic forecasts do not tend to have an effect on productivity. Overall, these results are stronger for firms whose performance is more sensitive to the state of macroeconomy. We show that a simple model of firm input choice under uncertainty and costly adjustment can rationalize there results. Finally, larger and more cyclically sensitive firms make more accurate forecasts, presumably reflecting a higher return to accuracy for these firms. More productive, older, and bank-owned firms also make more accurate forecasts, suggesting that forecasting ability is also linked to management ability, experience, and governance. Collectively, our results highlight the importance of firms' forecasting ability for micro and macro performance.
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