This paper analyzes the relationship between employee satisfaction and long-run stock returns. A value-weighted portfolio of the "100 Best Companies to Work For in America" earned an annual four-factor alpha of 3.5% from 1984-2009, and 2.1% above industry benchmarks. The results are robust to controls for firm characteristics, different weighting methodologies and the removal of outliers. The Best Companies also exhibited significantly more positive earnings surprises and announcement returns. These findings have three main implications. First, consistent with human capital-centered theories of the firm, employee satisfaction is positively correlated with shareholder returns and need not represent managerial slack. Second, the stock market does not fully value intangibles, even when independently verified by a highly public survey on large firms. Third, certain socially responsible investing ("SRI") screens may improve investment returns.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1448247_code393296.pdf?abstractid=985735&rulid=8649398&mirid=3

Does the Stock Market Fully Value Intangibles? Employee Satisfaction and Equity Prices

http://faculty.london.edu/aedmans/Rowe.pdf

An innovations state space modeling framework is introduced for forecasting complex seasonal time series such as those with multiple seasonal periods, high-frequency seasonality, non-integer seasonality, and dual-calendar effects. The new framework incorporates Box–Cox transformations, Fourier representations with time varying coefficients, and ARMA error correction. Likelihood evaluation and analytical expressions for point forecasts and interval predictions under the assumption of Gaussian errors are derived, leading to a simple, comprehensive approach to forecasting complex seasonal time series. A key feature of the framework is that it relies on a new method that greatly reduces the computational burden in the maximum likelihood estimation. The modeling framework is useful for a broad range of applications, its versatility being illustrated in three empirical studies. In addition, the proposed trigonometric formulation is presented as a means of decomposing complex seasonal time series, and it is show...

/pdf/forecasting-time-series-with-complex-seasonal-patterns-using-559fdkmheo.pdf

Forecasting time series with complex seasonal patterns using exponential smoothing

Business cycles are both less volatile and more synchronized with the world cycle in rich countries than in poor ones. We develop two alternative explanations based on the idea that comparative advantage causes rich countries to specialize in industries that use new technologies operated by skilled workers, while poor countries specialize in industries that use traditional technologies operated by unskilled workers. Since new technologies are difficult to imitate, the industries of rich countries enjoy more market power and face more inelastic product demands than those of poor countries. Since skilled workers are less likely to exit employment as a result of changes in economic conditions, industries in rich countries face more inelastic labour supplies than those of poor countries. We show that either asymmetry in industry characteristics can generate cross-country differences in business cycles that resemble those we observe in the data. We are grateful to Daron Acemoglu and Fabrizio Perri for useful comments. The views expressed here are the authors' and do not necessarily reflect those of The World Bank. Business cycles are not the same in rich and poor countries. A first difference is that fluctuations in per capita income growth are smaller in rich countries than in poor ones, in the top panel of Figure 1 , we plot the standard deviation of per capita income growth against the level of (log) per capita income for a large sample of countries. We refer to this relationship as the volatility graph and note that it slopes downwards. A second difference is that fluctuations in per capita income growth are more synchronized with the world cycle in rich countries than in poor ones. In the bottom panel of Figure 1 , we plot the correlation of per capita income growth rates with world average per capita income growth, excluding the country in question, against the level of (log) per capita income for the same set of countries. We refer to this relationship as the comovement graph and note that it slopes upwards. Table 1 , which is self-explanatory, shows that these facts apply within different sub-samples of countries and years. 1 Why are business cycles less volatile and more synchronized with the world cycle in rich countries than in poor ones? Part of the answer must be that poor countries exhibit more political and policy instability, they are less open or more distant from the geographical center, and they also have a higher share of their economy devoted to the production of agricultural products and the extraction of minerals. Table 1 shows that, in a statistical sense, these factors explain a substantial fraction of the variation in the volatility of income growth, although they do not explain much of the variation in the comovement of income growth. More important for our purposes, the strong relationship between income and the properties of business cycles reported in Table 1 is still present after we control for these variables. In short, there must be other factors behind the strong patterns depicted in Figure 1 beyond differences in political instability, remoteness and the importance of natural resources. With the exception that the comovement graph seems to be driven by differences between rich and poor countries and not within each group. Acemoglu and Zilibotti (1997) also present the volatility graph. They provide an explanation for it based on the observation that rich countries have more diversified production structures. We are unaware of any previous reference to the comovement graph. In this paper, we develop two alternative but non-competing explanations for why business cycles are less volatile and more synchronized with the world in rich countries than in poor ones. Both explanations rely on the idea that comparative advantage causes rich countries to specialize in industries that require new technologies operated by skilled workers, while poor countries specialize in industries that require traditional technologies operated by unskilled workers. This pattern of specialization opens up the possibility that cross-country differences in business cycles are the result of asymmetries between these types of industries. In particular, both of the explanations advanced here predict that industries that use traditional technologies operated by unskilled workers will be more sensitive to country-specific shocks. Ceteris paribus, these industries will not only be more volatile but also less synchronized with the world cycle since the relative importance of global shocks is lower. To the extent that the business cycles of countries reflect those of their industries, differences in industrial structure could potentially explain the patterns in Figure 1 . One explanation of why industries react differently to shocks is based on the idea that firms using new technologies face more inelastic product demands than those using traditional technologies. New technologies are difficult to imitate quickly for technical reasons and also because of legal patents. This difficulty confers a cost advantage on technological leaders that shelters them from potential entrants and gives them monopoly power in world markets. Traditional technologies are easier to imitate because enough time has passed since their adoption and also because patents have expired or have been circumvented. This implies that incumbent firms face tough competition from potential entrants and enjoy little or no monopoly power in world markets. The price-elasticity of product demand affects how industries react to shocks. Consider, for instance, the effects of country-specific shocks that encourage production in all industries. In industries that use new technologies, firms have monopoly power and face inelastic demands for their products. As a result, fluctuations in supply lead to opposing changes in prices that tend to stabilize industry income. In industries that use traditional technologies, firms face stiff competition from abroad and therefore face elastic demands for their products. As a result, fluctuations in supply have little or no effect on their prices and industry income is more volatile. To the extent that this asymmetry in the degree of product-market competition is important, incomes of industries that use new technologies are likely to be less sensitive to country-specific shocks than those of industries that use traditional technologies. Another explanation for why industries react differently to shocks is based on the idea that the supply of unskilled workers is more elastic than the supply of skilled workers. A first reason for this asymmetry is that non-market activities are relatively more attractive to unskilled workers whose market wage is lower than that of skilled ones. Changes in labour demand might induce some unskilled workers to enter or abandon the labour force, but are not likely to affect the participation of skilled workers. A second reason for the asymmetry in labour supply across skill categories is the imposition of a minimum wage. Changes in labour demand might force some unskilled workers in and out of unemployment, but are not likely to affect the employment of skilled workers. The wage-elasticity of the labour supply also has implications for how industries react to shocks. Consider again the effects of country-specific shocks that encourage production in all industries and therefore raise the labour demand. Since the supply of unskilled workers is elastic, these shocks lead to large fluctuations in employment of unskilled workers. In industries that use them, fluctuations in supply are therefore magnified by increases in employment that make industry income more volatile. Since the supply of skilled workers is inelastic, the same shocks have little or no effects on the employment of skilled workers. In industries that use them, fluctuations in supply are not magnified and industry income is less volatile. To the extent that this asymmetry in the elasticity of labour supply is important, incomes of industries that use unskilled workers are likely to be more sensitive to country-specific shocks than those of industries that use skilled workers To study these hypotheses we construct a stylized world equilibrium model of the cross-section of business cycles. Inspired by the work of Davis (1995), we consider in section one a world in which differences in both factor endowments a la Heckscher-Ohlin and industry technologies a la Ricardo combine to determine a country's comparative advantage and, therefore, the patterns of specialization and trade. To generate business cycles, we subject this world economy to the sort of productivity fluctuations that have been emphasized by Kydland and Prescott (1982). 2 In section two, we characterize the cross-section of business cycles and show how asymmetries in the elasticity of product demand and/or labour supply can be used to explain the evidence in Figure 1 . Using available microeconomic estimates of the key parameters, we calibrate the model and find that: (i) The model exhibits slightly less than two-thirds and one-third of the observed cross-country variation in volatility and comovement, respectively; and (ii) The asymmetry in the elasticity of product demand seems to have a quantitatively stronger effect on the slopes of the volatility and comovement graphs, than the elasticity in the labour supply. We explore these results further in sections three and four. In section three, we extend the model to allow for monetary shocks that have real effects since firms face cash-in-advance constraints. We use the model to study how cross-country variation in monetary policy and financial development affect the cross-section of business cycles. Once these factors are considered, the calibrated version of the model exhibits roughly the same cross-country variation in volatility and about 40 percent of the variation in comovement as the data. In section four, we show th

http://link.springer.com/content/pdf/10.1007/BF01322868.pdf

Business cycles: A methodological approach

We construct a framework for measuring economic activity at high frequency, potentially in real time. We use a variety of stock and flow data observed at mixed frequencies (including very high frequencies), and we use a dynamic factor model that permits exact filtering. We illustrate the framework in a prototype empirical example and a simulation study calibrated to the example.

/pdf/real-time-measurement-of-business-conditions-22hf8s1pt1.pdf

Real-Time Measurement of Business Conditions

https://www.nber.org/system/files/working_papers/w8736/w8736.pdf

Time series decomposition and measurement of business cycles, trends and growth cycles

We show that U.S. stock and Treasury futures prices respond sharply to recurring stale information releases. In particular, we identify a unique macroeconomic series---the U.S. Leading Economic Index® (LEI)---which is released monthly and constructed as a summary statistic of previously released inputs. We show that a front-running strategy that trades S&P 500 futures in the direction of the announcement a day before its release and then trades in the opposite direction of the announcement following its release generates an average annual return of close to 8%. These patterns are more pronounced for high beta stocks, for stocks that are more difficult to arbitrage, and during times when investors' sensitivity to firm-specific stale information is high. Treasury futures exhibit similar, albeit less pronounced, price patterns. Other measures of information arrival, such as price volatility and volume, spike following the release. These empirical findings suggest that some investors are inattentive to the stale nature of the information included in the LEI releases, instead interpreting it as new information, and thereby causing temporary yet significant mispricing.

This paper was accepted by Brad Barber, Teck Ho, and Terrance Odean, special issue editors.

/pdf/investor-inattention-and-the-market-impact-of-summary-1icbquwxga.pdf

Investor Inattention and the Market Impact of Summary Statistics

Using financial indicators to predict turning points in the business cycle: The case of the leading economic index for the United States

Effectively predicting cyclical movements in the economy is a major challenge. While there are other approaches to forecasting, the U.S. leading index has long been used to analyze and predict economic fluctuations. We describe and test a new procedure for making the index more timely. The new index significantly outperforms its less timely counterpart and offers substantial gains in real-time out-of-sample forecasts of changes in aggregate economic activity and industrial production. It provides timely and accurate ex-ante information for predicting, not only the business cycle turning points, but the monthly changes in the economy.

A More Timely and Useful Index of Leading Indicators

Clusters of cyclical turning points in the coincident indicators help us identify and date euro area recessions and recoveries in the past several decades. In the USA and some other countries, composite indexes of coincident indicators (CEI) are used to date classical business cycle turning points; also indexes of leading indicators (LEI) are used to help in the difficult task of predicting these turning points. This paper reviews a selection of the available data for monthly and quarterly euro area coincident and leading indicators. From these data, we develop composite indexes using methods analogous to those tested in the US CEI and LEI published by The Conference Board. We compare the resulting business cycle chronology with the existing alternatives and evaluate our selection of leading indicators in the context of how well they predict current economic activity and its major fluctuations for the euro area. Copyright © 2009 John Wiley & Sons, Ltd.

https://www.conference-board.org/pdf_free/workingpapers/E-0031-08-WP.pdf

Ataman Ozyildirim

Papers

Time series decomposition and measurement of business cycles, trends and growth cycles

Investor Inattention and the Market Impact of Summary Statistics

Using financial indicators to predict turning points in the business cycle: The case of the leading economic index for the United States

A More Timely and Useful Index of Leading Indicators

Business cycles in the euro area defined with coincident economic indicators and predicted with leading economic indicators