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An earnings surprise, or unexpected earnings, in accounting, is the difference between the reported earnings and the expected earnings of an entity. [1] Measures of a firm's expected earnings, in turn, include analysts' forecasts of the firm's profit [2] [3] and mathematical models of expected earnings based on the earnings of previous accounting periods. [4] [5]
Stock markets tend to react in the same direction as earnings surprises—positively to positive earnings surprises and negatively to negative earnings surprises—although a significant proportion of earnings surprises result in stock markets reacting in the opposite direction, which may be a reaction to other relevant information released with the earnings announcement or inaccurate measurement of the earnings surprise. [6]
The market, however, may not correctly estimate the implications of earnings surprises when it revises its expectations of future earnings, which will decrease the change in stock prices associated with the change in earnings. In fact, many studies in accounting research have documented that the market takes up to a year to adjust to earnings announcements, a phenomenon known as the post-earnings announcement drift. [7]
Large negative earnings surprises may have legal and reputational costs to managers. Firstly, managers can be held personally liable if shareholders sue the firm for failing to disclose negative earnings news promptly. Secondly, money managers may choose not to hold, and analysts may choose not to follow, the stocks of firms whose managers have reputations for withholding bad news. This may contribute to managers' voluntary disclosure of information related to negative earnings surprises: quarterly earnings announcements containing large negative earnings surprises are preempted by voluntary disclosures more frequently than are other earnings announcements. [8]
Earnings surprises can be measured using historical earnings or analysts' forecasts. [9]
In accounting research, a measure that uses historical earnings is standardized unexpected earnings (SUE). SUE is the standardized difference between reported earnings and expected earnings, where expected earnings is modelled based on the assumption that earnings follows a seasonal random walk with a specific trend. In other words, in the case of quarterly earnings the SUE for quarter t is
where σ(X) is the standard deviation of X, and the expected earnings, E(Qt), is calculated using prior reported earnings:
where Qt-4 is the reported earnings for quarter t-4 and δ is the average trend. [4]
An alternative measure of SUE that uses analysts' forecasts is
where EPS is a firm's earnings per share, and Forecast is analysts' consensus forecast of its earnings per share. [9]
The Black–Scholes or Black–Scholes–Merton model is a mathematical model for the dynamics of a financial market containing derivative investment instruments. From the parabolic partial differential equation in the model, known as the Black–Scholes equation, one can deduce the Black–Scholes formula, which gives a theoretical estimate of the price of European-style options and shows that the option has a unique price given the risk of the security and its expected return. The equation and model are named after economists Fischer Black and Myron Scholes. Robert C. Merton, who first wrote an academic paper on the subject, is sometimes also credited.
In mathematical finance, a risk-neutral measure is a probability measure such that each share price is exactly equal to the discounted expectation of the share price under this measure. This is heavily used in the pricing of financial derivatives due to the fundamental theorem of asset pricing, which implies that in a complete market, a derivative's price is the discounted expected value of the future payoff under the unique risk-neutral measure. Such a measure exists if and only if the market is arbitrage-free.
Stock valuation is the method of calculating theoretical values of companies and their stocks. The main use of these methods is to predict future market prices, or more generally, potential market prices, and thus to profit from price movement – stocks that are judged undervalued are bought, while stocks that are judged overvalued are sold, in the expectation that undervalued stocks will overall rise in value, while overvalued stocks will generally decrease in value. A target price is a price at which an analyst believes a stock to be fairly valued relative to its projected and historical earnings.
In finance, the beta is a statistic that measures the expected increase or decrease of an individual stock price in proportion to movements of the stock market as a whole. Beta can be used to indicate the contribution of an individual asset to the market risk of a portfolio when it is added in small quantity. It refers to an asset's non-diversifiable risk, systematic risk, or market risk. Beta is not a measure of idiosyncratic risk.
In finance, the Sharpe ratio measures the performance of an investment such as a security or portfolio compared to a risk-free asset, after adjusting for its risk. It is defined as the difference between the returns of the investment and the risk-free return, divided by the standard deviation of the investment returns. It represents the additional amount of return that an investor receives per unit of increase in risk.
The 'PEG ratio' is a valuation metric for determining the relative trade-off between the price of a stock, the earnings generated per share (EPS), and the company's expected growth.
In financial economics and accounting research, post–earnings-announcement drift or PEAD is the tendency for a stock’s cumulative abnormal returns to drift in the direction of an earnings surprise for several weeks following an earnings announcement.
In decision theory, a scoring rule provides evaluation metrics for probabilistic predictions or forecasts. While "regular" loss functions assign a goodness-of-fit score to a predicted value and an observed value, scoring rules assign such a score to a predicted probability distribution and an observed value. On the other hand, a scoring function provides a summary measure for the evaluation of point predictions, i.e. one predicts a property or functional , like the expectation or the median.
In finance, diversification is the process of allocating capital in a way that reduces the exposure to any one particular asset or risk. A common path towards diversification is to reduce risk or volatility by investing in a variety of assets. If asset prices do not change in perfect synchrony, a diversified portfolio will have less variance than the weighted average variance of its constituent assets, and often less volatility than the least volatile of its constituents.
A preannouncement occurs when a company or individual announces something either prior to the time that they do it or prior to the time that they would normally announce it. Preannouncements can take the form of a press release, filing a form with the government, a conference call, or a webcast.
In financial economics, finance, and accounting, the earnings response coefficient, or ERC, is the estimated relationship between equity returns and the unexpected portion of companies' earnings announcements.
An automated trading system (ATS), a subset of algorithmic trading, uses a computer program to create buy and sell orders and automatically submits the orders to a market center or exchange. The computer program will automatically generate orders based on predefined set of rules using a trading strategy which is based on technical analysis, advanced statistical and mathematical computations or input from other electronic sources.
In finance, volatility is the degree of variation of a trading price series over time, usually measured by the standard deviation of logarithmic returns.
Demand forecasting, also known as demand planning and sales forecasting (DP&SF), involves the prediction of the quantity of goods and services that will be demanded by consumers or business customers at a future point in time. More specifically, the methods of demand forecasting entail using predictive analytics to estimate customer demand in consideration of key economic conditions. This is an important tool in optimizing business profitability through efficient supply chain management. Demand forecasting methods are divided into two major categories, qualitative and quantitative methods:
In finance, the T-model is a formula that states the returns earned by holders of a company's stock in terms of accounting variables obtainable from its financial statements. The T-model connects fundamentals with investment return, allowing an analyst to make projections of financial performance and turn those projections into a required return that can be used in investment selection.
Fossil Fuel Beta (FFß) measures the percent change in excess (market-adjusted) stock returns for every 1 percent increase in fossil fuel prices. For example, if a company has an FFß of –0.20, then a 1 percent increase in fossil fuel prices should produce, on average, a 0.2% decline in the firm's stock price over and above the impact arising from fossil fuel price swing on the stock market as a whole.
In finance, the capital structure substitution theory (CSS) describes the relationship between earnings, stock price and capital structure of public companies. The CSS theory hypothesizes that managements of public companies manipulate capital structure such that earnings per share (EPS) are maximized. Managements have an incentive to do so because shareholders and analysts value EPS growth. The theory is used to explain trends in capital structure, stock market valuation, dividend policy, the monetary transmission mechanism, and stock volatility, and provides an alternative to the Modigliani–Miller theorem that has limited descriptive validity in real markets. The CSS theory is only applicable in markets where share repurchases are allowed. Investors can use the CSS theory to identify undervalued stocks.
Dividend policy, in financial management and corporate finance, is concerned with the policies regarding dividends; more specifically paying a cash dividend in the present, as opposed to, presumably, paying an increased dividend at a later stage. Practical and theoretical considerations will inform this thinking.
In portfolio management, the Carhart four-factor model is an extra factor addition in the Fama–French three-factor model, proposed by Mark Carhart. The Fama-French model, developed in the 1990, argued most stock market returns are explained by three factors: risk, price and company size. Carhart added a momentum factor for asset pricing of stocks. The Four Factor Model is also known in the industry as the Monthly Momentum Factor (MOM). Momentum is the speed or velocity of price changes in a stock, security, or tradable instrument.
Paul A. Griffin is an accountant, academic, and author. He is Distinguished Professor Emeritus at the Graduate School of Management, University of California, Davis.
