Does alternative data reduce stock price crash risk? Evidence from third-party online sales disclosure in China

This study empirically investigates the effect of releasing alternative data on firm-specific price crash risk. Using the public launch of a firm's third-party online sales data in a well-known Chinese financial database as an exogenous shock, we find that stock price crash risk significantly decreases with the disclosure of third-party online sales data. The results are robust to a series of endogeneity corrections and robustness checks. We also find that the reduction of stock price crash risk is due to the decrease in managers' bad news withholdings and the increase in the accuracy of market expectations. In addition, the negative association between third-party online sales disclosure and crash risk is more pronounced for firms with weaker external governance, higher earnings volatility, greater likelihood of sales manipulation, and lower book-to-market ratio. Our findings yield important implications for a comprehensive understanding of the information disclosure effect of online sales data in the capital market and the mechanisms to reduce stock price crash risk.     

Local Polynomial Regressions versus OLS for Generating Location Value Estimates

We estimate location values for single family houses using a standard house price and characteristics dataset and local polynomial regressions (LPR), a procedure that allows for complex interactions between the values of structural characteristics and the value of land. We also compare LPR to additive OLS models in the Denver metropolitan area with out-of-sample methods. We determine that the LPR model is more efficient than OLS at predicting location values in counties with greater densities of sales. Also, LPR outperforms OLS in 2010 for all counties in our dataset. Our findings suggest that LPR is a preferable approach in areas with greater concentrations of sales and in periods of recovery following a financial crisis.     

List Price Information in the Negotiation of Commercial Real Estate Transactions: Is Silence Golden?

We examine the use (and non-use) of list price information in the process of marketing commercial real estate. While housing market research suggests that list prices can serve as a strong anchor and/or signal, list price information is included in less than one-third of the commercial property sales and is less likely to be included as part of the sellers’ offering information for larger and more complex properties. Given the potentially powerful effect of list prices (first offers) on outcomes, the non-use of list price information is a puzzle. We speculate that the limited use of list prices may be due to the sellers’ interests in both maintaining their informational advantage and not truncating higher than expected offers, especially during periods of economic growth or with more complex properties. Using a two-stage selection correction model, we find that office properties which provide list price information are, on average, associated with lower price outcomes (ceteris paribus) and that these outcomes vary by price cohort and economic condition. It is important to note, however, that while these findings identify a correlation, they do not necessarily imply causation. Our results support the notion that asymmetric information and information signaling play a dominant role in explaining the sellers’ strategic non-use of list price information in the commercial real estate market and that the signaling effect is more pronounced in higher priced properties and during periods of strong economic growth.     

The Dynamics of Location in Home Price

It is well established that house prices are dynamic. It is also axiomatic that location influences such selling prices, motivating our objective of incorporating spatial information in explaining the evolution of house prices over time. In this paper, we propose a rich class of spatio-temporal models under which each property is point referenced and its associated selling price modeled through a collection of temporally indexed spatial processes. Such modeling includes and extends all house price index models currently in the literature, and furthermore permits distinction between the effects of time and location. We study single family residential sales in two distinct submarkets of a metropolitan area and further categorize the data into single- and multiple-transaction observations. We find the spatial component is very important in explaining house price. Moreover, the relative homogeneity of homes within the submarket and the frequency with which homes sell affects the pattern of variation across space and time. Differences between single and repeat sale data are evident. The methodology is applicable to more general capital asset pricing when location is anticipated to be influential.     

Green Design and the Market for Commercial Office Space

This paper considers the relationship between energy-efficient design and the leasing/sales markets for commercial real estate. An economic model is provided that considers lease rates and occupancy in simultaneous equilibrium. The behavior of both is predicted to be influenced by efficient design attributes. Selling price is determined by both rents and occupancy; therefore the impact of efficient design on commercial sales activity should be distributed through the leasing market. The model is tested empirically using a national sample of sales and leasing data for class A office buildings. The evidence indicates that “green” buildings achieve superior rents and sustain significantly higher occupancy. The improved performance in the rental market is reflected in a significant premium for the selling price of Energy Star-labeled and LEED-certified properties.     

The Choice of Methodology for Computing Housing Price Indexes: Comparisons of Temporal Aggregation and Sample Definition

Housing transactions are executed and recorded daily, but are routinely pooled into longer time periods for the measurement and analysis of housing price trends. We utilize an unusually rich data set, covering essentially all arm's length housing sales in Sweden for a dozen years, in an attempt to understand the effect of temporal aggregation upon estimates of housing prices and their volatilities. This rich data set also provides a unique opportunity to compare the results using the conventional weighted repeat sales model (WRS) to those based on a research strategy which incorporates all available information on house sales. The results indicate the clear importance of temporal disaggregation in the estimation of housing prices and volatilities—regardless of the model employed.The appropriately disaggregated model is then used as a benchmark to compare estimates of the course of housing prices produced by the two models during the twelve year period 1981–1993. These results indicate that much of the difference between estimates of price movements can be attributed to the data limitations which are inherent in the repeat sales approach. The results, thus, suggest caution in the interpretation of government-produced price indices or those produced by private firms based on the repeated sales model.     

Contract expiration and sales price

This article represents the first empirical attempt to detect the relationship between sales price and listing (or contract) period. Specifically, we examine the relationship between sales price and contract expiration days. Our hypothesized positive relationship between sales price and contract expiration days is borne out by the results of this study. These results show that the home seller is able to exact a price premium of 0.04% per contract day that he/she is able to preserve. Alternatively stated, he/she will concede a price discount of 0.04% per day, on average, as the sales contract approaches its expiration. Simple analyses of time on the market (TOM) without controlling for listing period may yield misleading signals.     

Selectivity,Quality Adjustment and Mean Reversion in the Measurement of House Values*

This paper develops a model of price formation in the housing market which accounts for the non-random selection of those dwellings sold on the market from the stock of existing houses. The model we develop also accounts for changes in the quality of dwellings themselves and tests for mean reversion in individual house prices. The model is applied to a unique body of data representing all dwellings sold in Sweden's largest metropolitan area during the period 1982–1999. The analysis compares house price indices that account for selectivity, quality change and mean reversion with the conventional repeat sales models used to describe the course of metropolitan housing prices. We find that the repeat sales method yields systematically large biased estimates of the value of the housing stock. Our comparison suggests that the more general approach to the estimation of housing prices or housing wealth yields substantially improved estimates of the course of housing prices and housing wealth.     

Estimating price trends for residential property: A comparison of repeat sales and assessed value methods

The repeat sales methodology for estimating residential price indices is based on actual appreciation of individual properties. On the other hand, the repeat sales method wastes data, typically discarding a large percentage of all sales. This article explores two issues related to the subsample of repeat sales. First, are paired sales representative of the entire population of properties that sold? Second, is there evidence that sample selectivity biases the price trend estimates? Evidence from five metropolitan areas supports a negative answer to the first question and the second question. It appears that a “lemon” or “starter home” effect causes repeat residential sales to be a biased subsample of all transactions. Cumulative price trends for the repeat subsamples can differ from the full samples over periods ranging from two to ten quarters. While short-term price trends can differ widely, there are no systematic differences among the samples over long periods of time (e.g., three years or more).     

Collusion with intertemporal price dispersion

We develop a theory of optimal collusive intertemporal price dispersion. Dispersion clouds consumer price awareness, encouraging firms to coordinate on dispersed prices. Our theory generates a collusive rationale for price cycles and sales. Patient firms can support optimal collusion at the monopoly price. For less patient firms, monopoly prices must be punctuated with fleeting sales. The most robust structure involves price cycles that resemble Edgeworth cycles. Low consumer attentiveness enhances the effectiveness of price dispersion by reducing the payoff to deviations involving price reductions. However, for sufficiently low attentiveness, price rises are also a concern.     

Flood Hazards, Insurance Rates, and Amenities: Evidence From the Coastal Housing Market

This study employs the hedonic property price method to examine the effects of flood hazard on coastal property values. We utilize Geographic Information System data on National Flood Insurance Program flood zones and residential property sales from Carteret County, North Carolina. Our results indicate that location within a flood zone lowers property value. Price differentials for flood risk and the capitalized value of flood insurance premiums are roughly equivalent—both exhibiting a nonlinear relationship in flood probability. Our results support the conclusion that flood zone designation and insurance premiums convey risk information to potential buyers in the coastal housing market.     

The Hierarchical Repeat Sales Model for Granular Commercial Real Estate and Residential Price Indices

This paper concerns the estimation of granular property price indices in commercial real estate and residential markets. We specify and apply a repeat sales model with multiple stochastic log price trends having a hierarchical additive structure: One common log price trend and cluster specific log price trends in deviation from the common trend. Moreover, we assume that the error terms potentially have a heavy tailed (t) distribution to effectively deal with outliers. We apply the hierarchical repeat sales model on commercial properties in the Philadelphia/Baltimore region and on residential properties in a small part of Amsterdam. The results show that the hierarchical repeat sales model provides reliable indices on a very detailed level based on a small number of observations. The estimated degrees of freedom for the t-distribution is small, largely rejecting the commonly made assumption of normality of the error term.     

Repeat Sales Index for Thin Markets

The repeat sales model is commonly used to construct reliable house price indices in absence of individual characteristics of the real estate. Several adaptations of the original model by Bailey et al. (J Am Stat Assoc 58:933–942, 1963) are proposed in literature. They all have in common using a dummy variable approach for measuring price indices. In order to reduce the impact of transaction price noise on the estimates of price indices, Goetzmann (J Real Estate Finance Econ 5:5–53, 1992) used a random walk with drift process for the log price levels instead of the dummy variable approach. The model that is proposed in this article can be interpreted as a generalization of the Goetzmann methodology. We replace the random walk with drift model by a structural time series model, in particular by a local linear trend model in which both the level and the drift parameter can vary over time. An additional variable—the reciprocal of the time between sales—is included in the repeat sales model to deal with the effect of the time between sales on the estimated returns. This approach is robust can be applied in thin markets where relatively few selling prices are available. Contrary to the dummy variable approach, the structural time series model enables prediction of the price level based on preceding and subsequent information, implying that even for particular time periods where no observations are available an estimate of the price level can be provided. Conditional on the variance parameters, an estimate of the price level can be obtained by applying regression in the general linear model with a prior for the price level, generated by the local linear trend model. The variance parameters can be estimated by maximum likelihood. The model is applied to several subsets of selling prices in the Netherlands. Results are compared to standard repeat sales models, including the Goetzmann model.     

Intraday Behavior of Stock Prices and Trades around Insider Trading

Our evidence indicates that insiders' trades provide significant new information to market participants and they are incorporated more fully in stock prices as compared to noninsiders' trades. We find that market professionals do not front-run insiders' trades. Both insiders' purchases and sales result in significant contemporaneous and subsequent price impact, while sales by large shareholders result in a contemporaneous stock price decline that is subsequently reversed. The arrival of insider purchases reverse the prevailing negative order imbalances from third party trades and lead to piggy-backing by market professionals resulting in subsequent market purchase orders as well as stock price increases.     

Dissecting Earnings Recognition Timeliness

We dissect the portion of stock price change of the fiscal year that is recognized in reported accounting earnings of the year. We call this portion earnings recognition timeliness (ERT). The emphasis in our dissection is on empirical identification of two fundamental precepts of financial accounting: (1) the matching principle, which is manifested in the recognition of expenses in the same period as the related benefits (i.e., sales revenue) accrue; and (2) recognition of expenses in the current period due to changes in expectations regarding earnings of future periods (we refer to these expenses as the expectations element of expenses). Although the expectations element has implicitly been at the core of much of the recent empirical literature on asymmetry in the earnings/return relation, it has not been explicitly identified. This recent literature is based on the premise that bad news about the future leads to more recognition of expenses in the current period (such as write‐downs) whereas good news about the future tends to have a much lesser effect on expenses of the current period; asymmetry in the expenses/return relation is captured implicitly via the observation of asymmetry in the earnings/return relation (i.e., asymmetry in ERT). Since the ERT reflects the relation between sales revenue and returns, matched expenses and returns, as well as the relation between the expectations element of expenses and returns, a focus on the expectations element may lead to sharper inferences. Our straightforward empirical procedure permits a focus on this element.     

Price Elasticity of Demand and an Optimal Cash Discount Rate in Credit Policy

While the provision of a cash discount is equivalent to a reduction in price, the role of price elasticity of demand in determining credit terms has been neglected in the extant literature. In this paper, this role is investigated and it is shown that the optimal cash discount rate is affected by the price elasticity of demand for the firm's product. The comparative effects on the optimal cash discount rate with respect to exogenous changes in the fraction of credit sales paid after taking cash discount, the cost of short-term funds and the bad debt loss ratio are investigated. A trade-off between the time value gain and the price elasticity of demand is established. We find that firms which sell in locations having different price elasticities for their products, and/or which face various costs of short-term funds in different locations, should vary their cash discount terms accordingly.     

Does short selling affect a firm's financial constraints?

Using a sample of Chinese A-share listed firms from 2007‐2017, we examine the impact of short sales on a firm's financial constraints. We develop three conceptual frameworks, the negative information effect, the undervaluation effect, and the deterrent effect, based on the prevailing theories and conduct an in-depth empirical analysis using the difference-in-differences, propensity score matching, and instrumental variable methods. Our findings suggest that: (1) Short sales generally worsen a firm's financial constraints by reducing its ability of raising cheap and overvalued external capital. (2) A shortable firm's financial constraints deteriorate more seriously in the case of higher credit risk or information asymmetry. (3) When a firm becomes shortable, its negative media coverage increases, external financing cost rises, and the amount of new external financing decreases. (4) The adverse impact of short sales on financial constraints is more pronounced for inefficient state-owned firms and mainly concentrates in the short term. Collectively, these results support the underlying logic of the negative information effect. However, further analysis shows that: (1) The deterrent effect also exists but is much weaker than the negative information effect. (2) The strength of the two effects will “wan and wax” with time or circumstances. Thus, the deterrent effect may outweigh the negative information effect by easing a firm's financial constraints in some cases, such as in the long term after short sales deregulation and when short sales magnitude is low or the managers are more sensitive to the decline of stock price. Our paper provides new insights into the impact of shorts sales on financial constraints, revealing some unique Chinese features compared to the US market and offering valuable lessons to other emerging markets.     

The impact of auctions on residential property prices

This study assesses whether the sale method in residential real estate markets – auction versus private treaty – is a determinant of sale price. Utilising a larger and richer dataset than previous research, we test for a price effect in auction sales in Sydney and Christchurch. When self‐selection biases are corrected for, using two‐stage hedonic regression analysis and a matched sampling procedure, we find no significant difference between prices of properties sold at auction to those sold by private treaty. This conflicts with the conclusions of previous research in the Australian and New Zealand housing markets, which have documented a price premium associated with auction sales.     

Equity sales in Belgian corporate groups: expropriation of minority shareholders? A clinical study

In Belgian corporate groups, complex pyramidal structures and interlocking ownership lead to separation of ownership and control. This may generate incentives for the controlling shareholder to divert resources within the group through intragroup equity sales. This in turn could lead to significant private benefits at the expense of the minority shareholders. We test this hypothesis by investigating the stock price reaction to the announcement of equity sales in Belgian groups. Our results suggest that intragroup equity sales create value for minority shareholders. Equity sales between group members and non-group members do not seem to affect the value for minority shareholders in Belgian groups.     

卖空机制能够约束内部人减持吗？——基于融资融券制度的经验证据

本文以我国放松卖空管制为视角,探究其对内部人减持的影响。研究表明,卖空机制能够抑制企业内部人减持行为。机制分析发现,卖空对内部人减持的抑制作用是通过缓解股权高溢价实现的。进一步研究表明,卖空能够抑制大股东、董事以及管理层减持,但对监事减持无影响;卖空能够降低内部人减持的获利程度,并且在内部人减持动机更大时,对内部人减持的抑制作用更强;卖空通过约束内部人减持提升了股票定价效率,还有助于降低内部人增持行为。本文的研究结论丰富了卖空和内部人减持领域的文献,并对政府部门完善制度设计具有启示意义。