Tweet-level data from a social media platform reveals low average accuracy and high dispersion in the quality of advice by financial influencers, or “finfluencers”: 28% of finfluencers are skilled, generating 2.6% monthly abnormal returns, 16% are unskilled, and 56% have negative skill (“antiskill”) generating -2.3% monthly abnormal returns. Consistent with homophily shaping finfluencers’ social networks, antiskilled finfluencers have more followers and more influence on retail trading than skilled finfluencers. The advice by antiskilled finfluencers creates overly optimistic beliefs most times and persistent swings in followers’ beliefs. Consequently, finfluencers cause excessive trading and inefficient prices such that a contrarian strategy yields 1.2% monthly out-of-sample performance.

We identify a new dimension of cross-firm linkages by exploring the social connectedness between firms' geographical locations. Industry peers located in regions with strong social ties tend to adopt similar strategies and exhibit strong co-movements in both fundamentals and returns. However, this information is not immediately reflected in stock prices and can be exploited using information contained in social peer returns (SPFRET). The predictability of SPFRET lasts for up to a year and forecasts future earnings surprises, analysts' forecast errors, and returns around earnings announcements. The effect is particularly strong for low-visibility firms and those located outside of industry clusters.

Expert networks provide investors with in-depth discussions with subject matter experts. Expert call demand is higher for younger, technology-oriented firms and those with greater intangible assets, consistent with demand for information on hard-to-value firms. Expert calls are more (less) likely to emphasize technology and operational (financial) topics relative to earnings calls. We find that expert call volume is associated with hedge fund position changes and greater price efficiency. The relation is asymmetric, with call volume preceding hedge fund sales, greater short interest, and negative firm performance. The evidence suggests that expert networks help investors discern complicated bad news.

What are the real costs of reversals in international capital flows? In this paper, I exploit plausibly exogenous variation in firms’ exposure to rollover risk to identify a causal liquidity channel at play during sudden stop episodes. Using a panel of firms across 39 countries, I show that firms with higher exposure (as measured by the share of long-term debt maturing over the next year) reduce investment ten percentage points more than non-exposed firms following sudden stops in capital flows. The impact is persistent: exposed firms experience lower investment, lower employment and lower assets than non-exposed firms even three years after the initial shock. In robustness tests, I show that the results are specific to sudden stop episodes in that they do not hold in periods without sudden stops, and they hold across sudden stop episodes regardless of whether the sudden stop takes place during large economic contractions.

The corporate basis measures the price differences between bonds issued in dollars and foreign currencies by the same corporate entity. In this paper, we propose a novel method to decompose the corporate basis into three components: credit spread differential, convenience yield differential, and deviation from covered interest rate parity. With this decomposition, we document several stylized facts, and in particular, the substitution effect between safe and risky dollar assets. We provide further evidences on the substitution effect using the structural VAR analysis, which shows that a negative shock to financial intermediaries' balance sheets causes a tightening of credit spread differential, a demand shift toward safe assets, and an appreciation of the dollar. We also find consistent holdings-level evidences using foreign investors' aggregated holdings of safe and risky dollar assets. Lastly, we find spillover effects to the equity and commodity markets, as well as to the domestic and international economic activities. Our results highlight the important role of the dollar, which are further amplified by financial intermediaries, in the global financial markets.

This paper elicits subjective risk premia from an international survey dataset on interest rates and exchange rates. Survey implied risk premia are (i) unconditionally negative for bonds, positive for investment currencies and negative for funding currencies, (ii) correlated with (subjective) macro expectations, (iii) correlated with quantities of risk, (iv) mean-reverting, as opposed to extrapolative; and (v) predict future realised returns with a positive sign. Taking beliefs as given, we estimate a subjective asset pricing model with time-variation in economic uncertainty which supports these findings. This demonstrates that subjective risk premia respect a risk-return trade-off regardless of whether they are rational or not, suggesting that behavioural theories of belief formation can co-exist with rational theories of risk pricing.

This study documents how investors extrapolate from recent stock returns of locally headquartered firms when forming beliefs about aggregate stock market outcomes. Consistent with studies on the equity home bias, we find that the responsiveness to local information is a function of proximity. While investors may feel more comfortable interpreting local information, we find no evidence that these effects are sensitive to the informativeness of local returns for the aggregate outcome. Our findings suggest that differences in beliefs about information contained in public signals varies systematically with geography, which has been suggested as an important driver of the local bias in equity markets.

We study a macro-finance model with entrepreneurs who have diverse views about the likelihood that their ideas will lead to successful innovations. These views and the resulting experimentation stimulate economic growth and overcome market failures that would otherwise occur in an equilibrium without this diversity. The resulting benefits for future generations come at the cost of higher wealth and consumption inequality because a few entrepreneurs will ex-post be successful while most entrepreneurs will fail. Hence, our model provides a potential explanation for the “entrepreneurial puzzle” in which entrepreneurs choose to innovate despite taking on substantial idiosyncratic risk accompanied by low expected returns. Venture capital funds and taxes enhance risk sharing among entrepreneurs, stimulating innovation and growth unless high taxes deplete entrepreneurial capital. Redistribution via taxes reduces inequality and can raise interest rates. Nevertheless, a tradeoff exists between risk-sharing and the exertion of costly effort, giving rise to a hump-shaped economic growth curve when plotted against tax rates.

Recent findings on the term structure of equity and bond yields pose severe challenges to existing equilibrium asset pricing models. This paper presents a new equilibrium model of subjective expectations to explain the joint historical dynamics of equity and bond yields (and their yield spreads). Equity/bond yields movements are mainly driven by subjective dividend/GDP growth expectations. Yields on short-term dividend claims are more volatile because short-term dividend growth expectation mean-reverts to its less volatile long-run counterpart. Procyclical slope of equity yields is due to the counter-cyclical slope of dividend growth expectations. The correlation between equity returns/yields and nominal bond returns/yields switched from positive to negative after the late 1990s, mainly owing to a shift in correlation between real GDP growth and real dividend growth expectations from negative to positive, and only partially due to procyclical inflation. Dividend strip returns are predictable and the strength of predictability decreases with maturity due to underreaction to dividend news and hence predictable dividend forecast revisions. The model is also consistent with the data in generating persistent and volatile price-dividend ratios, excess return volatility, and momentum.

Asset pricing theory predicts high expected returns are a compensation for risk. However, high expected returns might also represent anomalies due to frictions or behavioral biases. We propose two complementary tests to assess whether risk alone can explain differences in expected returns, provide general-equilibrium foundations, and study their properties in simulations. The tests account for any risk disliked by risk-averse individuals, including high-order moments and tail risks. The tests do not rely on the validity of a factor model or other parametric statistical models. Empirically, we find risk cannot explain a large majority of differences in expected returns of characteristic-sorted portfolios.

We propose a method to estimate latent asset-pricing factors that incorporates economically motivated targets for both cross-sectional and time-series properties of the factors. Cross-sectional targets may capture the shape of loadings (monotonicity of expected returns across characteristic-sorted portfolios) or the pricing span of exogenous state variables (macroeconomic innovations or intermediary-based risk factors). Time-series targets may capture overall expected returns or mispricing relative to a benchmark reduced-form model. Using a large-scale set of assets, we show that these targets nudge risk factors to better span the pricing kernel, leading to substantially higher Sharpe ratios and lower pricing errors than conventional approaches.

U.S. stocks' response to inflation surprises is, on average, robustly negative. Stocks' response to positive inflation surprises shows much more pronounced time-series variability than their response to negative inflation surprises. In our sample, stocks react significantly to positive inflation surprises only when there is a contemporaneous change in monetary policy expectations. In the cross-section, firms with low net leverage, large market capitalization, high market beta, low book-to-market, and low market power (i.e. low markups) are especially susceptible to inflation surprises.

This paper studies the desirability of programmable payments where transfers are automatically executed conditional upon preset objective criteria. We do so by studying optimal payment arrangements in a framework that captures a wide range of economic relationships between two parties. Our framework stacks the cards in favor of programmable payments by considering an environment without legal recourse. The results show that the optimal payment arrangements for long-term economic relationships consist predominantly of simple direct payments. Direct payments increase the surplus by avoiding the liquidity cost of locking-up funds from the moment where the payer commits the funds in a programmable payment until the moment where the conditions are satisfied to release those funds to the payee. Programmable payments will be desirable, and may in fact be the only viable payment arrangement, in situations where economic relationships are of a short duration. Our results identify a limit to the growth in the demand for payments as their cost decreases: While the number of feasible trading relationships will increase, existing trading relationships will optimally rely on fewer payments.

Stablecoins - a category of crypto-assets designed to keep their value stable - have grown rapidly since 2020. The largest stablecoins hold short-term dollar-denominated assets to manage their peg against the dollar. This paper documents one implication of this pegging mechanism for the short-term funding markets. To this aim, we identify changes in the stablecoin demand for commercial papers (CP) by tracking the stablecoin tokens in circulation and by exploiting cross-sectional and time-varying heterogeneity in reserve assets policy of the main stablecoin issuers. We show that CP issuers catered to the additional demand from stablecoins by issuing more, highlighting a new connection between crypto-assets and conventional markets.

We show that by lending to merchants and monitoring them, an e-commerce platform can price-discriminate between merchants with high and low financial constraints: the platform offers credit priced below market rates and designed to select merchants with lower capital or collateral while simultaneously increasing the platform's access fees. The credit market then becomes endogenously segmented with banks focusing on less financially constrained borrowers. Lending by the platform expands with its monitoring efficiency but can arise even when the platform is less efficient than banks at monitoring. Platform credit benefits more financially constrained merchants as well as buyers, but can hurt less financially constrained merchants if cross-side network effects with buyers are too small. The platform's propensity to offer credit and the financial inclusion of more constrained merchants depends on the platform's market power in its core business.

How should an investor value financial data? The answer is complicated because it depends on the characteristics of all investors. We develop a sufficient statistics approach that uses equilibrium asset return moments to summarize all relevant information about others' characteristics. It can value data that is public or private, about one or many assets, relevant for dividends or for sentiment. While different data types, of course, have different valuations, heterogeneous investors also value the same data very differently, which suggests a low price elasticity for data demand. Heterogeneous investors' data valuations are also affected very differentially by market illiquidity.

We propose a dynamic theory of financial market concentration in settings where some investors trade strategically because of price impact. The distribution of risk and wealth determines market power, and wealth evolves over time given strategic portfolio choices. In equilibrium, the most well-capitalized investors remain under-diversified to capture rents, generating concentration and volatility in the wealth distribution. Conversely, wealth concentration leads to inflated asset prices, unequal returns to wealth, and poor liquidity that further exacerbates the distortions from market power. We discuss applications of our framework, and derive implications for evaluating welfare using asset pricing data.

We show that an increase in stock return exposure to media attention to narratives, measured with standard methods for extracting topic attention from news text, leads to a lower stock price informativeness about future fundamentals. Empirically, narrative exposure explains over 86% of idiosyncratic variance in the cross-section, and both narrative exposure and non-systematic information channels—idiosyncratic variance and variance related to public information—decrease stock price informativeness. Moreover, stocks with high narrative exposure demonstrate elevated trading volume. To rationalize the empirical results, we suggest a mechanism based on disagreement among investors arising due to the differential processing of information in media narratives.

We show that during the period of the Public Sector Purchase Program (PSPP) implemented by the Eurosystem, the prices of German sovereign bonds targeted by the PSPP increase predictably towards month-end and drop subsequently. We propose a sequential search-bargaining model that captures salient features of the implementation of the PSPP such as the commitment to buy within an explicit time horizon. The model can explain the predictable pattern as a result of imperfect competition between dealers that are counterparties to the Eurosystem. Motivated

by the model's predictions, we show that the price pattern is more pronounced (a) for bonds that are targeted by the PSPP, (b) for monthly windows where the Eurosystem has fewer counterparties, and (c) for monthly windows where the Eurosystem targets a larger amount of purchases. We discuss the implications of our analysis for future purchase programs.

It is common practice in finance to use difference-in-differences analysis to examine fixed-income pricing data. This paper uses simulations to show that this method applied to pricing variables that exhibit a term structure, such as yields or credit spreads, systematically produces false and mismeasured treatment effects. This holds true even if the treatment is randomly assigned. False and mismeasured treatment effects result from heterogeneous effects in different parts of the term structure in combination with unequal distributions of residual maturities in the treated and control bond samples. Neither bond fixed effects nor explicit yield-curve control in the specification resolve the issues.

We develop a conditional factor model for the term structure of Treasury bonds, which unifies non-parametric curve estimation with cross-sectional asset pricing. Our factors are investable portfolios and estimated with cross-sectional ridge regressions. They correspond to the optimal non-parametric basis functions that span the discount curve and are based on economic first principles. Cash flows are covariances, which fully explain the factor exposure of coupon bonds. Empirically, we show that four factors explain the discount bond excess return curve and term structure premium, which depends on the market complexity measured by the time-varying importance of higher order factors. The fourth term structure factor capturing complex shapes of the term structure premium is a hedge for bad economic times and pays off during recessions.

We show that firm default risk is the primary predictor of the correlation between corporate bond and stock returns, both in the cross-section and over time. Bonds of less creditworthy firms behave more like the issuing firms’ stocks, resulting in higher future comovement. As a direct implication, investing in bonds and stocks of the most creditworthy firms significantly enhances diversification benefits and Sharpe ratios out-of-sample. We develop a structural model with stochastic asset variance that rationalizes these findings, whereby time-variation in asset variance plays a critical role for breaking down the perfect stock-bond correlation implied by the Merton model.

We examine the role of institutional investors in monetary policy transmission to asset markets by exploiting a discontinuous threshold in the tax treatment of municipal bonds. As bonds approach the threshold, mutual funds, the primary institutional traders in the market, dispose of the bonds at significant risk of falling below the threshold. This is driven by mutual funds anticipating future illiquidity. Once bonds cross the threshold, their liquidity declines and illiquidity-induced yield spreads increase substantially as retail investors become more important in price formation. Unexpected monetary policy tightening sharply reduces trading activity, amplifying the path to illiquidity in the market.

We investigate the scope for active investing in corporate bonds by estimating an optimal portfolio using asset characteristics. Our portfolio weights are modeled to account for the severe trading frictions present in OTC bond markets. A portfolio based on maturity, rating, coupon, and size outperforms passive benchmarks and univariate sorts after transaction costs in and out of sample. Further, it predicts macroeconomic activity, suggesting bond characteristics provide hedging against macro-fluctuations. Active funds appear constrained by narrow investment mandates from holding the optimal portfolio. Overall, while active corporate bond portfolios are feasible, institutional constraints might limit their accessibility.