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Do lottery characteristics matter for analysts’ forecast behavior?

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Abstract

This study examines the effect of lottery characteristics on analysts’ earnings forecasts. We find that analysts are more optimistic for lottery firms. Compensation incentives and access to private information from management are key drivers for analysts’ optimistic forecasts. The optimism of earnings forecast for lottery stocks decreases with analysts’ general and firm-specific experience. Our findings suggest that both behavioral biases and rational incentives help explain analysts’ optimism toward lottery stocks. Moreover, investor sentiment positively affects the effect of lottery characteristics on analyst optimism. Firm-specific risk and market uncertainty enhance the effect of lottery characteristics on analysts’ earnings forecast bias. The level of optimism on lottery stocks increases during up markets and economic expansion.

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Notes

  1. Kumar (2009) method was widely adopted in the literature. For example, Meng and Pantzalis (2018) classify lottery-type stocks as those in the lowest 50th stock price percentile, the highest 50th idiosyncratic volatility percentile, and the highest 50th idiosyncratic skewness percentile. Kumar et al. (2016), Chen et al. (2021a, b), Gong et al. (2021), and Kwon et al. (2022) use Kumar’s (2009) composite lottery index based on idiosyncratic volatility, idiosyncratic skewness, and stock price to proxy for stocks’ lottery-like characteristics. Moreover, some researches use the maximum daily return (MAX) as a proxy for lottery-like features (e.g., Bali et al., 2011, 2017; Agarwal et al. 2022). Gould et al. (2023) measure a stock’s lottery-like characteristics in terms of idiosyncratic volatility, idiosyncratic skewness, and stock price (Kumar 2009), a stock’s maximum single-day return during a month (Bali et al. 2011) as well as a low return for the past month. To the extent that maximum daily return and a low return for the past month are a short-term (one month) proxy for lottery-like characteristics, this study follows Kumar (2009), Kumar et al. (2016), Chen et al. (2021a, b), Gong et al. (2021), and Kwon et al. (2022) and use stock price, idiosyncratic volatility, and idiosyncratic skewness to measure longer period (previous 12 months) lottery-like characteristics.

  2. Chu and Zhai (2021) posit that analysts make more optimistic earnings forecasts of high distress risk because they underestimate the implication of their poor performance.

  3. Our results remain qualitatively unchanged when the volatility of earnings is measured by the standard deviation of earnings per share (VAREARN) in the preceding four quarters.

  4. The exogenous rise in the minimum tick size increases analyst coverage of small stocks (Chen et al. 2021a, b).

  5. We first classify lottery and non-lottery stocks, and then exclude firms without complete data. Therefore, the proportion of lottery observations in our sample is not 12.5% (0.5*0.5*0.5).

  6. The marginally negative coefficient of ROA is consistent with Brown (2001), who argues that losses make analysts’ forecast less accurate. Higher number of analyst following (COVERAGE) indicates more intense competition among analysts, and thereby provides more incentives for analysts to make more optimistic forecasts to cater to managers.

  7. The VIX index is often referred to as an ‘‘investor fear gauge” (Whaley 2000), and can also reflect negative investor sentiment in addition to fundamental market volatility (Bekaert et al. 2013).

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Funding

This study supported by Ministry of Science and Technology, Taiwan (MOST 108-2410-H-305-021).

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Authors and Affiliations

  1. Department of Business Management, National Taipei University, New Taipei City, Taiwan

    Mei-Chen Lin

  2. College of Business, Oregon State University, Corvallis, USA

    J. Jimmy Yang

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Appendix 1: variable definitions

Appendix 1: variable definitions

This appendix shows the definitions of all variables used in this study. Accounting data are from Compustat, stock return data are from CRSP, analyst recommendation data are from I/B/E/S, and institutional ownership data are from Thomson 13F.

Variables

Definition

LOTTERY

Lottery stocks: stocks jointly below the median price, and above the median idiosyncratic volatility and skewness

OPTIMISM

The difference between forecast and actual values, divided by the absolute value of actual values

AVOL

Abnormal trading volume, denoted as the normalized difference between the dollar volume on day j and the average dollar volume over days (− 41, − 11) of the announcement

Firm-related variables

SIZE

The logarithm of market value of equity

MB

The ratio of market value of equity to book value of equity

ROA

Return of assets

INST

Institutional ownership: a firm’s shares held by institutions scaled by shares outstanding

ADV

Advertising expenses divided by net sales

RD

Research and development expenditure divided by net sales

COVERAGE

The number of analysts providing earnings forecasts in the previous year

AGE

The number of years since the firm was first covered by CRSP

RSQ

RSQ measures the extent to which stock returns are affected by market- and industry-wide information by estimating the following modified market model:\({Ret}_{i,w}={\alpha }_{0}+{\alpha }_{1}{Mkret}_{w-1}+{\alpha }_{2}{Mkret}_{w}+{\alpha }_{3}{Indret}_{w-1}+{\alpha }_{4}{Indret}_{w}+{\varepsilon }_{i,w}\)

Earnings-related variables

VAREARN

Variance of earnings per share

LOSS

An indicator variable, with the value 1 if ROA is smaller than zero, and 0 otherwise

Forecast-related variables

HORIZON

The numbers of days between the forecast issue date and the earnings announcement date

NCOS

The number of firms for which the analyst supplied at least one forecast during the past 12 months

NSIC2

The number of two-digit SICs of firms for which the analyst supplied at least one forecast during the past 12 months

Analyst-related variables

GEXP

General experience: the number of years between the analyst’s first earnings forecast in I/B/E/S and the current forecast date

FEXP

Firm-specific experience: the number of years from the analyst’s first earnings forecast on the specific firm in I/B/E/S to the current forecast date

Market- and macroeconomic-related variables

SENTIMENT

Baker and Wurgler (2006) sentiment index, which is constructed from several underlying sentiment proxies, each orthogonalized for observable economic fundamentals

VIX

A measure of market volatility, which is constructed from implied volatilities of S&P 500 index options

UP

A dummy variable, which is one if market return is positive, and zero otherwise

EXPANSION

A dummy variable, which equals one during an expansion, and zero otherwise. The expansion begins the first day of the period following a trough and ends on the last day of the period of the peak

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Lin, MC., Yang, J.J. Do lottery characteristics matter for analysts’ forecast behavior?. Rev Quant Finan Acc 61, 1057–1091 (2023). https://doi.org/10.1007/s11156-023-01176-x

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