Working Papers

While there is no apparent reason for loan spreads to cluster at certain numbers, we find that around 70% of loans have round-yard spreads (i.e., multiples of 25 basis points). We hypothesize that dominant banks implicitly collude by using the round-yards as focal pricing points when negotiating with their borrowers. The tacit collusion leads to higher spreads and total costs of the round-yard-priced loans than non-round-yard-priced loans. Consistent with our tacit collusion hypothesis, dominant banks round up rather than round down loan spreads to the multiples of yards. Moreover, round-yard pricing is more prevalent among lower-quality and non-repeat borrowers. Overall, we provide the first evidence that dominant banks use round-yard pricing as an effective tool for tacit collusion in the loan market.

The first twenty years of the 21st century were a period of transformation and change in the development models of the financial market. One of the strongest in the history world financial crises of 2007-2008 ended the post-deregulation model. Transition to the new financial market model turned out to be largely unpredictable, complex, and spontaneous, unlike the previous periods, without the purposeful participation of state regulators. An objective but random reason for this course of events was the COVID-19 pandemic. Pandemic has distorted the effect of the loose monetary policy, which caused building the grounds for a new financial market model. The post-pandemic model of the financial market is still in the early stages of formation, and it is too early to talk about all its properties and elements. However, as seen from current events and processes, the essential factor of the new financial market model formation is a gamification of investors' behavior. The author believes this behavioral model requires much more attention of researches than that in nowadays scientific literature.

Hedge funds can subsequently amend their originally reported 13F quarterly holdings using restatements. We conduct the first systematic analysis of such filings, which are as common as confidential filings (used by funds to delay holdings disclosures), but affect four times as many stocks. Restated holdings are associated with significant abnormal returns, suggesting that some original holdings are strategically misreported to hide funds???????�???? trading intentions. We construct a return gap measure to gauge the value added by such restatements and find that it predicts future fund performance. Finally, commonly used databases such as Thomson Reuters do not fully adjust for restatements.

We exploit variation in the ancestries of U.S. equity mutual fund managers and show that ancestry affects portfolio decisions. Controlling for fund firm location, we find that funds overweight stocks from their managers' ancestral home countries in their non-U.S. portfolio by 132 bps or 20.34% compared with their peers. Similarly, funds overweight industries that are comparatively large in their manager's ancestral home countries. The documented ancestral biases are pervasive across fund styles and across different manager ancestries. The effect is more pronounced for funds that are less resource-constrained and for managers whose connection to their ancestral home country is more recent. Stocks linked to managers' ancestry do not outperform stocks in the same countries and industries but held by managers of other ancestry, confirming that ancestry-linked investments are not informed.

'Epidemiological' models of belief formation put social interactions at their core; such models are widely used by scholars who are not economists to study the dynamics of beliefs in populations. We survey the literature in which economists attempting to model the consequences of beliefs about the future -'expectations'- have employed a full-fledged epidemiological approach to explore an economic question. We draw connections to related work on 'contagion,' narrative economics, news/rumor spreading, and the spread of internet memes. A main theme of the paper is that a number of independent developments have recently converged to make epidemiological expectations ('EE') modeling more feasible and appealing than in the past.

Forecasters predicting how people change their behavior in response to a treatment or intervention often consider a set of alternatives. In contrast, those who are treated are typically exposed to only one of the treatment alternatives. For example, managers selecting a wage schedule consider a set of alternative wages while employees are hired at a given rate. We show that forecasts made in joint-prediction mode - which considers a set of alternatives -generate predictions that expect substantially larger behavioral responses than those made in separate-prediction mode - which considers the response to only one treatment realization in isolation. Results show the latter to be more accurate in matching people's actual responses to interventions and treatment changes. We present applications to managerial decision-making and forecasting of scientific results.

This paper uses machine learning to infer nonprofessional social media investment analysts' (SMAs) beliefs from their opinions on individual stocks. SMAs' average beliefs predict future abnormal returns and earnings surprises. However, there exists substantial heterogeneity in SMAs' ability to form beliefs that yield investment value. Some 13% high-skilled SMAs form beliefs that yield a sizeable one-week three-factor alpha of 61 bps, while the remaining 87% low-skilled SMAs generate only 6 bps. Firm and industry specializations are the most distinctive characteristics of high-skilled SMAs. When forming beliefs, SMAs extrapolate from past returns and herd on the consensus view of their peers. However, these seemingly behavioral biases do not result in systematically wrong beliefs.

The well-documented rise in political polarization among the U.S. electorate over the past 20 years has been accompanied by a substantial increase in the effect of partisan bias on survey-based measures of economic expectations. Individuals have a more optimistic view on future economic conditions when they are more closely affiliated with the party that controls the White House, and this tendency has increased significantly over time. Individuals report a large shift in economic expectations based on partisan affiliation after the 2008 and 2016 elections, but administrative data on spending shows no effect of these shifts on actual household spending.

We investigate whether there are temporary valuation impacts on stocks that media outlets list as involved in a major sporting event (the summer Olympics). We examine five summer Olympics and identify stocks that media outlets hype as benefiting from the Olympics (Olympic stocks). We find that Olympic stocks exhibit increases in comovement of returns after the announcement of the winning bid and declines in comovements after the games are played, consistent with the Olympics being used by investors as a category for investment. Furthermore, Olympic stock returns outperform their matched counterparts over this time period. If the comovement and valuation benefits are due to changes in underlying economics then we expect to observe corresponding increases in comovements of fundamentals and improvements in profitability. However, we find no observable changes in fundamental comovements or profitability. Consistent with investor sentiment driving the categorization, we find that Olympic firms with a greater retail investor presence have stronger comovements effects; and trading volume and volatility are abnormally high for Olympic firms on days where media outlets have stories linking the firm to the Olympic games. To clarify event-based categorization occurs in other settings where media outlets classify stocks for investment, we show comovement increases for stocks classified as "Stay-at-Home" by analysts and the media and "Meme"???� by retail investors on the Reddit social media platform.

We show that firms anticipate information spillover from peers' investment decisions and delay project exercise to learn from them. While this information improves project selection, the cost of waiting erodes these gains. To establish causality, we exploit local exogenous variation from the 1800s that shapes the number of peers that a firm can learn from today. The effect is most salient when information is scarce, costs of waiting are low, projects have low expected profitability, and the source information is more relevant. Finally, the anticipation of spillovers dampens aggregate investment, suggesting a role for this mechanism in macro-investment models.

Using 2020-2021 data from social media platform Reddit, we examine connections among stock prices, retail trading, short-selling and social media activity. Higher Reddit traffic, more positive tone, and higher Reddit connectedness predict higher returns, greater and more positive retail order flow, and lower shorting flows the next day. Social media information content is distinct from retail order and shorting information content. Higher Reddit traffic, more positive tone, more disagreement and higher Reddit connectedness increase shorting flow???�??s information content. Robinhood 50 stocks are more affected by social media activity, with stronger links among retail order flow, shorting flows and future returns.

We document that the recent house price experiences within an individual's social network affect her perceptions of the attractiveness of property investments, and through this channel have large effects on her housing market activity. Our data combine anonymized social network information from Facebook with housing transaction data and a survey. We first show that in the survey, individuals whose geographically-distant friends experienced larger recent house price increases consider local property a more attractive investment, with bigger effects for individuals who regularly discuss such investments with their friends. Based on these findings, we introduce a new methodology to document large effects of housing market expectations on individual housing investment decisions and aggregate housing market outcomes. Our approach exploits plausibly-exogenous variation in the recent house price experiences of individuals' geographically-distant friends as shifters of those individuals' local housing market expectations. Individuals whose friends experienced a 5 percentage points larger house price increase over the previous 24 months (i) are 3.1 percentage points more likely to transition from renting to owning over a two-year period, (ii) buy a 1.7 percent larger house, (iii) pay 3.3 percent more for a given house, and (iv) make a 7% larger downpayment. Similarly, when homeowners' friends experience less positive house price changes, these homeowners are more likely to become renters, and more likely to sell their property at a lower price. We also find that when individuals observe a higher dispersion of house price experiences across their friends, this has a negative effect on their housing investments. Finally, we show that these individual-level responses aggregate up to affect county-level house prices and trading volume. Our findings suggest that the house price experiences of geographically-distant friends might provide a valid instrument for local house price growth.