Funds investing in illiquid assets report returns with spurious autocorrelation. Consequently, investors need to unsmooth these funds’ returns when evaluating their risk exposures. We show that funds with similar investments share a common source of spurious autocorrelation not fully resolved by traditional unsmoothing methods and thereby leading to underestimation of systematic risk. Thus, we propose a generalized unsmoothing technique and apply it to hedge funds and private commercial real estate funds. Our method significantly improves the measurement of funds’ risk exposures and risk-adjusted performance, especially for highly illiquid funds. Overall, the average illiquid fund alpha is lower than previously thought.

We find evidence of selective exposure to confirmatory information among 400,000 users on the investor social network StockTwits. Self-described bulls are five times more likely to follow a user with a bullish view of the same stock than are self-described bears. Consequently, bulls see 62 more bullish messages and 24 fewer bearish messages than bears do over the same 50-day period. These “echo chambers” exist even among professional investors and are strongest for investors who trade on their beliefs. Finally, beliefs formed in echo chambers are associated with lower ex post returns, more siloing of information, and more trading volume.

Cryptocurrencies are among the largest unregulated markets in the world. We find that approximately one-quarter of bitcoin users are involved in illegal activity. We estimate that around $76 billion of illegal activity per year involve bitcoin (46% of bitcoin transactions), which is close to the scale of the U.S. and European markets for illegal drugs. The illegal share of bitcoin activity declines with mainstream interest in bitcoin and with the emergence of more opaque cryptocurrencies. The techniques developed in this paper have applications in cryptocurrency surveillance. Our findings suggest that cryptocurrencies are transforming the black markets by enabling “black e-commerce.” Received June 1, 2017; editorial decision December 8, 2018 by Editor Andrew Karolyi.

We show that a global imbalance risk factor that captures the spread in countries’ external imbalances and their propensity to issue external liabilities in foreign currency explains the cross-sectional variation in currency excess returns. The economic intuition is simple: net debtor countries offer a currency risk premium to compensate investors willing to finance negative external imbalances because their currencies depreciate in bad times. This mechanism is consistent with exchange rate theory based on capital flows in imperfect financial markets. We also find that the global imbalance factor is priced in cross-sections of other major asset markets.

This paper uses a novel dataset of commodity-linked notes (CLNs) to examine the impact of the flows of financial investors on commodity futures prices. Investor flows into and out of CLNs are passed to and withdrawn from the futures markets via issuers' trades to hedge their CLN liabilities. The flows are not based on information about futures price movements but nonetheless cause increases and decreases in commodity futures prices when they are passed through to and withdrawn from the futures markets. These finding are consistent with the hypothesis that non-information-based financial investments have important impacts on commodity prices.

We use the Longitudinal Research Database (LRD) of the U.S. Census Bureau, which covers the entire universe of private and public U.S. manufacturing firms, to study several related questions regarding the efficiency gains generated by venture capital (VC) investment in private firms. First, do VCs indeed improve the efficiency (total factor productivity, TFP) of private firms, and if so, are certain kinds of VCs (high reputation vs. low reputation) better at generating such efficiency gains than others? Second, do VCs invest in more efficient firms to begin with (screening), or do they improve efficiency after investment (monitoring)? Third, do efficiency improvements due to VC backing arise from increases in sales or reductions in costs? Fourth, do VC backing and the associated efficiency gains affect the probability of a successful exit (IPO or acquisition)? Our analysis shows that the overall efficiency of VC-backed firms is higher than that of non-VC-backed firms at every point in time. This efficiency advantage of VC-backed firms arises from both screening and monitoring: The efficiency of VC-backed firms prior to receiving financing is higher than that of non-VC-backed firms, and further, the growth in efficiency subsequent to VC financing is greater for such firms. The above increases in efficiency of VC-backed firms are spread over the first two rounds of VC financing after which the TFP of such firms remains constant until exit. Additionally, we show that while the TFP of firms prior to receiving financing is lower for high-reputation VC-backed firms, the increase in TFP subsequent to financing is significantly greater for these firms, consistent with high-reputation VCs having greater monitoring ability. We disentangle the screening and monitoring effects of VC backing using three different methodologies: switching regression with endogenous switching, regression discontinuity analysis, and propensity score matching. We show that while overall efficiency gains generated by VC backing arise primarily from improvements in sales, the efficiency gains of high-reputation VC-backed firms arise also from lower increases in production costs. Finally, we show that VC backing and the associated efficiency gains positively affect the probability of a successful exit.

At what point in a firm's life should it go public? How do a firm's ex ante product market characteristics relate to its going-public decision? Further, what are the implications of a firm going public on its post-IPO operating and product market performance? In this article, we answer the above questions by conducting the first large sample study of the going-public decisions of U.S. firms in the literature. We use the Longitudinal Research Database (LRD) of the U.S. Census Bureau, which covers the entire universe of private and public U.S. manufacturing firms. Our findings can be summarized as follows. First, a private firm's product market characteristics (total factor productivity [TFP], size, sales growth, market share, industry competitiveness, capital intensity, and cash flow riskiness) significantly affect its likelihood of going public after controlling for its access to private financing (venture capital or bank loans). Second, private firms facing less information asymmetry and those with projects that are cheaper for outsiders to evaluate are more likely to go public. Third, as more firms in an industry go public, the concentration of that industry increases in subsequent years. The above results are robust to controlling for the interactions between various product market and firm-specific variables. Fourth, IPOs of firms occur at the peak of their productivity cycle: the dynamics of TFP and sales growth exhibit an inverted U-shaped pattern, both in our univariate analysis and in our multivariate analysis using firms that remained private throughout as a benchmark. Finally, sales, capital expenditures, and other performance variables exhibit a consistently increasing pattern over the years before and after the IPO. The last two findings are consistent with the view that the widely documented post-IPO operating underperformance of firms is due to the real investment effects of going public rather than being due to earnings management immediately prior to the IPO.

We provide experimental evidence that relaxing margin restrictions to allow more short selling can exacerbate overpricing, even though it reduces equilibrium price levels. This is because smart-money traders initially profit more by front-running optimistic investor sentiment than by disciplining prices. When short selling is not possible, competitive pressures among arbitrageurs rapidly drive prices to the equilibrium. However, the risk of margin calls slows the convergence process, because arbitrageurs who sell short too early face substantial losses if they are unable to synchronize their trades with other arbitrageurs (as in Abreu and Brunnermeier. 2002. Journal of Financial Economics 66(2--3):341--60; 2003. Econometrica 71(1):173--204). The Author 2008. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected]., Oxford University Press.

This article shows how to evaluate the performance of managed portfolios using stochastic discount factors (SDFs) from continuous-time term structure models. These models imply empirical factors that include time averages of the underlying state variables. The approach addresses a performance measurement bias, described by Goetzmann, Ingersoll, and Ivkovic (2000) and Ferson and Khang (2002), arising because fund managers may trade within the return measurement interval or hold positions in replicable options. The empirical factors contribute explanatory power in factor model regressions and reduce model pricing errors. We illustrate the approach on US government bond funds during 1986–2000.

We quantify the real implications of trade-offs between firm information disclosure and long-term investment efficiency. We estimate a dynamic equilibrium model in which firm managers confront realistic incentives to misreport earnings and distort their real investment choices. The model implies a socially optimal level of disclosure regulation that exceeds the estimated value. Counterfactual analysis reveals that eliminating earnings misreporting completely through disclosure regulation incentivizes managers to distort real investment. Lower earnings informativeness raises the cost of capital, which results in a 5.7% drop in average firm value, but more modest effects on social welfare and aggregate growth.