Understanding Stock Price Volatility: The Role of Earnings

In an efficient capital market, asset prices vary when investors change their expectations about cash flows, discount rates, or both. Using dividends to measure cash flows, previous research shows that the aggregate dividend‐price ratio varies due to changes in expected discount rates (returns) rather than expected cash flows. In contrast, using accounting earnings instead of dividends as a measure of cash flows, this paper shows that as much as 70% of the variation in the dividend‐price ratio can be explained by changes in expected earnings. Moreover, the paper documents a significant negative correlation between expected returns and expected earnings, suggesting that variations in a common factor to both may generate significant price volatility. The results are consistent with the dividend‐policy irrelevance hypothesis.     

Consumption Volatility Risk

We show that time variation in macroeconomic uncertainty affects asset prices. Consumption volatility is a negatively priced source of risk for a wide variety of test portfolios. At the firm level, exposure to consumption volatility risk predicts future returns, generating a spread across quintile portfolios in excess of 7% annually. This premium is explained by cross‐sectional differences in the sensitivity of dividend volatility to consumption volatility. Stocks with volatile cash flows in uncertain aggregate times require higher expected returns.     

Volatility,the Macroeconomy,and Asset Prices

How important are volatility fluctuations for asset prices and the macroeconomy? We find that an increase in macroeconomic volatility is associated with an increase in discount rates and a decline in consumption. We develop a framework in which cash flow, discount rate, and volatility risks determine risk premia and show that volatility plays a significant role in explaining the joint dynamics of returns to human capital and equity. Volatility risk carries a sizable positive risk premium and helps account for the cross section of expected returns. Our evidence demonstrates that volatility is important for understanding expected returns and macroeconomic fluctuations.     

Earnings Volatility, Cash Flow Volatility, and Informed Trading

I examine whether earnings that are smoother or more volatile than cash flows provide or garble information. Consistent with theories that predict more informed trading when public information is less informative, I find that bid-ask spreads and the probability of informed trading are higher both when earnings are smoother than cash flows and also when earnings are more volatile than cash flows. Additional tests suggest that managers' discretionary choices that lead to smoother or more volatile earnings than cash flows garble information, on average. However, I find that informed trading is attenuated in settings in which theory suggests that discretionary smoothing or volatizing of earnings is likely to be informative.     

Cash Flow News and Stock Price Dynamics

We develop a new approach to modeling dynamics in cash flows extracted from daily firm-level dividend announcements. We decompose daily cash flow news into a persistent component, jumps, and temporary shocks. Empirically, we find that the persistent cash flow component is a highly significant predictor of future growth in dividends and consumption. Using a log-linearized present value model, we show that news about the persistent dividend growth component predicts stock returns consistent with asset pricing constraints implied by this model. News about the daily dividend growth process also helps explain concurrent return volatility and the probability of jumps in stock returns.     

Stock Price Volatility,Ordinary Dividends,and Other Cash Flows to Shareholders

This paper shows that the results of variance-bound tests depend on how cash distributions to shareholders are measured. As in prior studies, we find apparent evidence of excess volatility when a narrow definition of cash flow (dividends only) is applied. However, we are unable to reject the hypothesis of market efficiency when the cash flow measure also includes share repurchases and takeover distributions in addition to ordinary cash dividends.     

Preference heterogeneity and asset prices: An exact solution

We introduce a general equilibrium model of a multi-agent, pure-exchange economy and find a set of conditions that enable us to obtain explicit closed-form solutions to the equilibrium interest rate, stock price, risk premium and stock market volatility when investors have heterogenous risk aversions. Because the market is dynamically complete, full risk sharing obtains and a representative agent can be constructed, though the risk aversion of this agent fluctuates over time with the state of the economy, as the relative wealth distribution of the individual investors changes. We show that preference heterogeneity can cause asset prices to be significantly more volatile than the underlying dividends and that it can lead to leverage-like effects in volatility, in the sense that volatility increases after stock-market declines.     

Information,interest rates,and the volatility of asset prices

The volatility of an asset price is modelled as a function of the volatility of an information signal, real interest rates and inflation expectations. Volatility depends on the duration of cash flows, and the degree to which cash flows are indexed to real rates and inflation. The model is applied to determine asset betas, the volatility of the futures prices of assets and the volatility of equity prices.     

The role of “other information” in analysts’ forecasts in understanding stock return volatility

This study identifies “other information” in analysts’ forecasts as a legitimate proxy for future cash flows and examines its incremental role in explaining stock return volatility. We suggest that “other information” contains information about fundamentals beyond that reflected in current financial statements and reflects firms’ fundamentals on a more timely basis than dividends or earnings. Using standardized regressions, we find volatility increases when current “other information” is more uncertain and increases more in response to unfavorable news compared to favorable news. Variance decomposition analysis shows that the variance contribution of “other information” dominates that of expected-return news. The incremental role of “other information” is at least half of the effect of earnings in explaining future volatility. The results are more pronounced for firms with poor information environments. Overall, our results highlight the importance of including “other information” as an additional cash-flow proxy in future studies of stock prices and volatility.     

Feedback Effects and Asset Prices

Feedback effects from asset prices to firm cash flows have been empirically documented. This finding raises a question for asset pricing: How are asset prices determined if price affects fundamental value, which in turn affects price? In this environment, by buying assets that others are buying, investors ensure high future cash flows for the firm and subsequent high returns for themselves. Hence, investors have an incentive to coordinate, which may generate self‐fulfilling beliefs and multiple equilibria. Using insights from global games, we pin down investors' beliefs, analyze equilibrium prices, and show that strong feedback leads to higher excess volatility.     

Fundamentalists,chartists and asset pricing anomalies

This paper investigates whether excess volatility of asset prices and serial correlations of stock monthly returns may be explained by the interactions between fundamentalists and chartists. Fundamentalists forecast future prices cum dividends through an adaptive learning rule. In contrast, chartists forecast future prices based on the observation of past price movements. Numerical simulations reveal that the interplay of fundamentalists and chartists robustly generates excess volatility of asset prices, volatility clustering, trends in prices (i.e. positive serial correlations of returns) over short horizons and oscillations in prices (i.e. negative serial correlations of returns) over long horizons, often observed in financial data. Moreover, we find that the memory of the learning rule plays a key role in explaining the above-mentioned stylized facts. In particular, we establish that excess volatility of asset prices; volatility clustering and autocorrelation of returns at different horizons emerge when fundamentalists have short memory. However, volatility clustering as well as short-run and long-run dependencies, observed in financial time series, are more pronounced when fundamentalists have longer memory.     

Ambiguous Information,Portfolio Inertia,and Excess Volatility

I study the effects of risk and ambiguity (Knightian uncertainty) on optimal portfolios and equilibrium asset prices when investors receive information that is difficult to link to fundamentals. I show that the desire of investors to hedge ambiguity leads to portfolio inertia and excess volatility. Specifically, when news is surprising, investors may not react to price changes even if there are no transaction costs or other market frictions. Moreover, I show that small shocks to cash flow news, asset betas, or market risk premia may lead to drastic changes in the stock price and hence to excess volatility.     

Geometric Brownian Motion Models for Assets and Liabilities: From Pension Funding to Optimal Dividends

Abstract

In this paper asset and liability values are modeled by geometric Brownian motions. In the first part of the paper we consider a pension plan sponsor with the funding objective that the pension asset value is to be within a band that is proportional to the pension liability value. Whenever the asset value is about to fall below the lower barrier or boundary of the band, the sponsor will provide sufficient funds to prevent this from happening. If, on the other hand, the asset value is about to exceed the upper barrier of the band, the assets are reduced by the potential overflow and returned to the sponsor. This paper calculates the expected present value of the payments to be made by the sponsor as well as that of the refunds to the sponsor. In particular we are interested in situations where these two expected values are equal. In the second part of the paper the refunds at the upper barrier are interpreted as the dividends paid to the shareholders of a company according to a barrier strategy. However, if the (modified) asset value ever falls to the liability value, which is the lower barrier, “ruin” takes place, and no more dividends can be paid. We derive an explicit expression for the expected discounted dividends before ruin. From this we find an explicit expression for the proportionality constant of the upper barrier that maximizes the expected discounted dividends. If the initial asset value is the optimal upper barrier, there is a particularly simple and intriguing expression for the expected discounted dividends, which can be interpreted as the present value of a deterministic perpetuity with exponentially growing payments.     

How firms manage their cash flows: an examination of diversification’s effect

We extend recently documented evidence that diversified firms hold significantly less cash than specialized firms to consider differences in how diversified and specialized firms adjust their cash flows to achieve their target cash balance. We find that diversified firms have higher free cash flows as a result of equal operating cash flows and lower investment in comparison to specialized firms. Diversified firms save less cash by placing less reliance on external financing; by issuing less debt and equity, and distributing higher cash dividends. Our findings support the hypothesis that diversified firms are able to hold less precautionary cash as they are in better position to finance investment opportunities internally from operating cash flows.     

Asset Price Dynamics and Infrequent Feedback Trades

This article combines the continuous arrival of information with the infrequency of trades, and investigates the effects on asset price dynamics of positive and negative-feedback trading. Specifically, we model an economy where stocks and bonds are traded by two types of agents: speculators who maximize expected utility, and feedback traders who mechanically respond to price changes and infrequently submit market orders. We show that positive-feedback strategies increase the volatility of stock returns, and the response of stock prices to dividend news. Conversely, the presence of negative-feedback traders makes stock returns less volatile, and prices less responsive to dividends.     

Price Volatility and Investor Behavior in an Overlapping Generations Model with Information Asymmetry

This paper studies an overlapping generations model with multiple securities and heterogeneously informed agents. The model produces multiple equilibria, including highly volatile equilibria that can exhibit strong or weak correlations between asset returns—even when asset supplies and future dividends are uncorrelated across assets. Less informed agents rationally behave like trend‐followers, while better informed agents follow contrarian strategies. Trading volume has a hump‐shaped relation with information precision and is positively correlated with absolute price changes. Finally, accurate information increases the volatility and correlation of stock returns in the highly volatile, strongly correlated equilibrium.     

Disagreement and asset prices

How do differences of opinion affect asset prices? Do investors earn a risk premium when disagreement arises in the market? Despite their fundamental importance, these questions are among the most controversial issues in finance. In this paper, we use a novel data set that allows us to directly measure the level of disagreement among Wall Street mortgage dealers about prepayment speeds. We examine how disagreement evolves over time and study its effects on expected returns, return volatility, and trading volume in the mortgage-backed security market. We find that increased disagreement is associated with higher expected returns, higher return volatility, and larger trading volume. These results imply that there is a positive risk premium for disagreement in asset prices. We also show that volatility in and of itself does not lead to higher trading volume. Instead, only when disagreement arises in the market is higher uncertainty associated with more trading. Finally, we are able to distinguish empirically between two competing hypotheses regarding how information in markets gets incorporated into asset prices. We find that sophisticated investors appear to update their beliefs through a rational expectations mechanism when disagreement arises.     

Informed traders and limit order markets

We consider a dynamic limit order market in which traders optimally choose whether to acquire information about the asset and the type of order to submit. We numerically solve for the equilibrium and demonstrate that the market is a “volatility multiplier”: prices are more volatile than the fundamental value of the asset. This effect increases when the fundamental value has high volatility and with asymmetric information across traders. Changes in the microstructure noise are negatively correlated with changes in the estimated fundamental value, implying that asset betas estimated from high-frequency data will be incorrect.     

Taxes, Financing Decisions, and Firm Value

We use cross-sectional regressions to study how a firm's value is related to dividends and debt. With a good control for profitability, the regressions can measure how the taxation of dividends and debt affects firm value. Simple tax hypotheses say that value is negatively related to dividends and positively related to debt. We find the opposite. We infer that dividends and debt convey information about profitability (expected net cash flows) missed by a wide range of control variables. This information about profitability obscures any tax effects of financing decisions.     

A new dividend forecasting procedure that rejects bubbles in asset prices: the case of 1929's stock crash

We develop a new procedure to forecast future cash flows froma financial asset and then use the present value of our cashflow forecasts to calculate the asset's fundamental price. Asan example, we construct a nonlinear ARMA-ARCH-Artificial NeuralNetwork model to obtain out-of-sample dividend forecasts for1920 and beyond, using only in-sample dividend data. The presentvalue of our forecasted dividends yield fundamental prices thatreproduce the magnitude, timing, and time-series behavior ofthe boom and crash in 1929 stock prices. We therefore rejectthe popular claim that the 1920s stock market contained a bubble.