Abstract

     This paper adds to the recent interest in the link between cash and firm performance, by studying how this relationship varies across boom- and bust cycles. We use data of Norwegian firms from a broad range of sectors in the period 2005–2015, and both replicate and extend previous findings on the relationship between cash and performance over the business cycle. We find that i) cash has a positive, but weakly diminishing effect on operational firm performance (ROA) throughout the entire sample period, and ii) that the curvilinear relationship between cash and firm performance is the most pronounced in the pre-recession years, while it is virtually linearly positive in recessions and post-recession periods. We conclude that cash indeed has an impact on firms’ operational performance, and especially so in recessionary times.

Introduction

     Traditionally, strategy scholars have disregarded cash as a strategic asset (e.g. Barney, 1986), while finance scholars have viewed cash holdings as a signal that a firm lacks profitable investment opportunities or have managerial issues (e.g. Jensen, 1986). Recent studies have questioned these views by documenting that cash may indeed be positively related to performance (Kim & Bettis, 2014), and that contextual factors such as state of the business cycle (Nason & Patel, 2016) and competitive dynamics (Deb et al., 2017, Jung et al., 2020, O’Brien and Folta, 2009) are important moderators for the strength of the cash-performance relationship.

     While this recent research stream has made important advancements to our understanding of the relationship between cash and firm performance, we know less about how these insights can be generalized to other contexts than large, listed (manufacturing) companies operating in relatively stable periods, and if relationships between cash and market-based measures of performance also holds for operational measures of performance. Currently, most studies on the cash-performance relationship use samples of large, listed, US, (manufacturing) firms (e.g. Deb et al., 2017; Jung et al., 2020; Kim & Bettis, 2014; O’Brien & Folta, 2009), study relatively stable periods and not more turbulent times such as economic downturns where financing constraints tends to increase (Forseth et al., 2015), and/or use market-based measures of firm performance such as Tobin’s Q as their dependent variables (e.g. Deb et al., 2017; Jung et al., 2020; Kim & Bettis, 2014; O’Brien & Folta, 2009).

Conclusion

     The purpose of this paper was to examine the relationship between cash and firm performance over the business cycle.

     Throughout our analysis we find that cash has a positive impact on firm performance across the different phases of the business cycle. The linear term of cash is highly significant in all our models and across all time periods. The quadratic term is less consistent; in some cases, we identify a curvilinear relationship, whilst sometimes the quadratic term is low and insignificant indicating a linear relationship between cash and firm performance.

     After running our full and expanded models, we conclude that the curvilinear relationship between cash and performance is weakly curvilinear for our sample of Norwegian firms, and that this curve-linearity is the most pronounced in the pre-recession period, and more or less linear during the recession and post-recession periods.

     In closing, we believe that our results add to an important stream of research at the intersection of strategy and finance. Mainstream theories, especially in strategy discard cash and other financial assets as less important drivers for heterogeneity and performance differences. Our findings, and the findings of the studies we build on, challenge these assumptions. It is, however, important to note that cash seems more likely as an indirect source of performance differences, through enabling firms to make investments and deploy strategies that in turn create performance heterogeneity. That is, cash appears to be less of a king, but more a key member of the court that pull some important strings.

Purpose – The purpose of this paper is to predict real gross domestic product (GDP) growth and business cycles by using information from both liquidity and volatility measures. Design/methodology/approach – The paper estimates liquidity and volatility measures from over 5,000 NYSE rms and extracts a common factor, which the paper calls uncertainty. In-sample and out-ofsample forecasting tests are used to determine the ability of the uncertainty factor to predict growth in real GDP, industrial production, consumer price index, real consumption and changes in real investment. Findings – The paper finds that on average, positive shocks to the uncertainty factor occur in the quarters preceding and at the beginning of a recession. During the quarters toward the end of recessions, there are negative shocks to uncertainty on average. Originality/value – Previous research has explored using either liquidity or volatility to forecast economic activity. The paper bridges the two branches of research and finds a link to real GDP growth and business cycles.

Introduction

Recently, there has been a large branch of literature that examines market liquidity. Pástor and Stambaugh (2003), Acharya and Pedersen (2005), Chen (2005) and Sadka (2006), all look into systematic liquidity risk. Additionally, as there are several different measures of liquidity, many studies have focused on identifying a common systematic liquidity factor (Chordia et al., 2000; Hasbrouck and Seppi, 2001; Eckbo and Norli, 2002; Korajczyk and Sadka, 2008). Amihud, Hameed, Kang and Zhang (2015) examine 45 countries and find evidence that the illiquidity premium exists in a number of international markets. With the most recent financial crisis, there has been an interest in the apparent causal link between a reduction in liquidity and an economic slowdown. Czauderna et al. (2015) find a bidirectional link between liquidity (as measured using exchange traded funds) and market returns in the German stock market between July 2006 and June 2010. Wagner and Winter (2013) include liquidity risk in models to evaluate daily mutual fund performance for funds focused on the European market. When using a six-factor model including liquidity and idiosyncratic risk, they find that a large number of funds have significant loadings on both the liquidity and idiosyncratic risk factors. In measuring exposure to the liquidity risk factor, they find that on average, fund managers prefer liquid stocks. In a paper by Næs et al. (2011), they show that this link between liquidity and recessions is not a recent phenomenon but has existed in past recessions as well. They find that, on average, there is an increase in illiquidity before a recession followed by an increase in liquidity during the tail end of the recession. Furthermore, measures of liquidity help in forecasting future real gross domestic product (GDP) growth. Switzer and Picard (2016) also examine measures of aggregate liquidity and their relation to economic cycles. Rather than using a linear model, as was done by Næs et al. (2011), Switzer and Picard (2016) use nonlinear models and find the relationship between aggregate liquidity measures and the real economy to be significantly less pronounced. In addition to linking liquidity to business cycles, several papers have explored stock market volatility and its relation to real macroeconomic variables and business cycles (Schwert, 1989; 1990; Hamilton and Gang, 1996). Hamilton and Gang (1996) find that stock volatility may be useful in forecasting economic activity. We merge these two trains of thought and check if there is any added benefit from considering liquidity and volatility jointly. In Carlston (2012), multiple daily liquidity and volatility measures are estimated from daily stock prices and returns. Common factors, what we will term as “uncertainty”, are extracted across all of the liquidity and volatility measures. He finds that this common risk factor carries a significant premium and helps explain the cross section of expected returns. In this paper, we follow the methodology of Næs et al. (2011) to explore a possible link between this common liquidity and volatility measure and the real economy and business cycles.

This paper measures the economic value of conditioning stock and bond return forecasts on recession dummies. Motivated by recent studies, I restrict stock returns to only be predictable in recessions and bond returns to only be predictable in expansions. These restrictions generate notable increases in risk-adjusted performance in a standard setup with mean-variance utility and a simple bivariate regression model. I also let regression coefficients switch across recessions and expansions without restricting them. This extension performs particularly well when combined with forecast averaging. The risk-adjusted returns are robust over time and prevail using real-time recession signals. However, high accuracy of business cycle turning points is essential and seemingly small tweaks to the classification rule can have large economic implications. Goyal and Welch (2008) forcefully argue that simple regression models cannot forecast stock returns out of sample. Thornton and Valente (2012) reach similar conclusions for bonds. However, these studies do not condition on the business cycle. Henkel et al. (2011) let coefficients be regime-dependent and document that stock returns are predictable but only in economic recessions.1 Andreasen et al. (2016) find the mirror image for bond returns as they are only forecastable in expansions. Thus, return predictability seems to be highly dependent on the business cycle and, moreover, it is asymmetric across stocks and bonds. This motivates why I extend standard regression models to take macroeconomic conditions into account. Markov switching models are one of the most popular approaches to capturing state-dependence in returns. These models let the return distribution depend on unobservable realizations of a Markov chain without imposing an economic interpretation. Examples of papers taking this approach are Guidolin and Timmermann (2007), Guidolin and Hyde (2012), and Henkel et al. (2011). Henkel et al. (2011) find a strong connection between the inferred states of their return forecasting model and the business cycle. On one hand, Markov switching models provide flexibility by not imposing that the regimes are related to specific economic events. On the other hand, they are highly nonlinear and rely on potentially unstable numerical estimation methods which may hamper their usefulness in out-of-sample forecasting. If the underlying states are related to recessions, which are short-lived and infrequent, more robust techniques could be useful. I therefore instead use observable recession dummies which allows me to estimate the models using simple linear regressions. This choice is natural since most of the studies that identify differences in predictability across states of the economy have done so using recession dummies (see citations above). I do also show results for Markov switching models but find that, in contrast to the dummy switching strategies, they do not improve on constant coefficient models for real-time trading. Further, tying financial markets explicitly to recessions is in the spirit of the macro-finance asset pricing literature as summarized in Cochrane (2017).

مقاله سال 2016

Two key issues motivate this paper: do business cycles affect patenting behavior and if so, is patenting pro-cyclical or countercyclical? Earlier, Griliches (1990) had observed the effects of oil shocks during the 1970s on patent applications. Likewise, the Great Recession of 2008 and 2009 has spurred interest in the impact of cyclical shocks on patenting.1 The relationship matters to institutional organizations that operate the global patenting system, such as national and regional patent offices, and to industries that provide complementary services to the patenting community; for example, legal, translation, and consulting services. Cyclical shocks affect the ability of these organizations to forecast accurately for purposes of planning and budgeting. Such shocks can therefore affect the supply of services and resources for patent procurement. Cyclical shocks can * Corresponding author. E-mail addresses: phingley@epo.org (P. Hingley), wgp@american.edu (W. Park). 1 See, for example, Bertenrath et al. (2011), European Commission (2011), Guellec and Wunsch-Vincent (2009), OECD (2009), and World Intellectual Property Organization (2010). also affect the demand side by influencing the investment and marketing decisions of firms and other potential patentees. The resulting imbalances in supply and demand could thus have repercussions for the nature and direction of innovation and commercialization.2

Much of the existing literature studying the determinants of patents and patenting propensity has not taken into account the role of cyclical shocks.3 Patents and innovation are often studied under the branch of economic growth theory, as drivers of long run productivity and technological change, where the emphasis is more on structural determinants and trend factors than on cyclical influences. Furthermore, innovation is viewed by many as being driven by longer term considerations, given that the duration of innovation projects is often longer than that of market cycles (see Heger, 2004). On the other hand, short run resource constraints may be binding for some innovators as patenting is costly, and the costs of procurement are often incurred upfront before a patented invention is exploited commercially.

The literature on the relationship between innovation and business cycles is very limited, as will be surveyed in the next section. Few academic studies exist, focusing mostly on the input side of innovation – namely, research and development (R&D) expenditures. Research on the effects of business cycles on patenting is rarer. Furthermore, previous work has not formally derived and used a measure of business cycles, and instead has employed proxy variables, such as credit constraints or sales declines (although business cycles also include sales booms), or has compared conditions before and/or after an event, such as the Great Recession of 2008–2009. In this paper, we derive business shocks using standard filtering methods, and allow them to vary by country. A key novelty of our paper is that we apply our business cycle model to forecasting patent filings. This would especially be useful to the supply side we mentioned earlier. Failure to anticipate fluctuations leads these institutions and service providers to allocate resources poorly. Improved forecasting can lead to more accurate budgeting and greater cost effectiveness in services, and these increased efficiencies should potentially raise social welfare.

مقاله سال 2016

 مجله مدیریت مالی چند ملیتی – Journal of Multinational Financial Management

A firm’s managers have more information about the expected profitability of the firm than investors. When that profitability falls short of analysts’ expectations, managers can voluntarily choose to issue a profit warning (PW), typically about 3–4 weeks ahead of the formal earnings announcement. A PW differs from an earnings announcement in the sense that it occurs irregularly and unpredictably across firms and time. Kothari, Shu, and Wysocki (2009) argue that the large negative return generated from bad news that is unanticipated gives management an incentive to withhold bad news. PWs exhibit this negative reaction and the firm suffers a substantial decline in the stock price (Bulkley & Herrerias, 2005; Kasznik & Lev, 1995; Spohr, 2014; Tucker, 2007; Xu, 2008).

Mendenhall and Nichols (1988) and Chen and Mohan (1994) argue that when managers issue PWs they are striving to time the bad news release in order to minimize the negative market reaction. Previous research has also indicated that the interpretation of bad news is different over the business cycle (DeStefano, 2004). So some environments might be better than others for a voluntary disclosure. 3Com Corp issued a profit warning during the evening of December 4, 2000, near the top of a long bull market. When the market opened the next day, its price fell nearly 30 percent, an abnormal return of −32.5%. 3Com warned again nearly three months later. The market reaction in early March 2001 to this PW was a stock price decline of 21%, an abnormal return of −4.7%. While there are important differences between these two warnings themselves, the state of the market may have also been an important factor in the market reaction. The Dow Jones Industrial Average was still near its high during the first warning, which was at the end of the economic expansion period. The economy had just entered a recession during the second warning and the NASDAQ (where 3Com stock traded) was collapsing.

Anasymmetricmarket response to badnews is predicted by severaltheoreticalmodels. For example,the regime switching model of Veronesi (1999) explains the asymmetric impacts of bad news throughout the business cycle in terms of changing an investor’s belief system. During good times, investors do not expect bad news. If bad news occurs during good times, investors are surprised and must drastically change their belief system (posterior). For scheduled disclosures, Conrad, Cornell, and Landsman (2002) examine the price response to bad and good earnings shocks. They find that the stock market response to negative earnings surprises increases as the relative level of the market rises. Our research builds on the study by Conrad et al. (2002) by examining the PW, a voluntary bad news disclosure, over the business cycle. We compare this impact to those firms that have negative earnings surprises, but choose not to warn shareholders.

مقاله سال 2016

Business cycle and growth expectations play a major role in understanding macroeconomic relationships. They also determine the extent to which economic policy agents, including central banks, can influence macroeconomic outcomes. One way to deal with forecast uncertainty is to pool the expectations of professional forecasters (Zarnowitz, 1984) in order to hedge against the errors of individual forecasters, thus improving the forecast quality. Such surveys of professional forecasters are provided by either central banks or private companies. The idea of these consensus forecasts is that, although individual forecasters may outperform the average of a group of forecasters in certain cases, an individual forecaster rarely outperforms others systematically. Zarnowitz and Lambros (1987) find that the forecast errors of consensus forecasts are smaller than those of most individual forecasters. Batchelor (2001) shows that consensus forecasts are more accurate than the projections published by the OECD or the IMF.

The reliability and superiority of consensus forecasts depends crucially on whether the forecasters actually reveal their own best forecast or behave strategically, i.e., show herding or anti-herding tendencies. Forecaster herding arises if the forecasters ignore their private information and instead follow the forecasts of others (Scharfstein & Stein, 1990). For example, Bewley and Fiebig (2002) show that interest rate forecasters tend to indicate values in the safe consensus range, in order to avoid sticking their neck out with ‘‘extreme’’ forecasts. This is because a poor forecast may not damage a forecaster’s reputation if other forecasters also delivered poor forecasts. Thus, herding behavior biases the distribution towards the mean. Forecaster herding should not be confused with forecast clustering, where similar forecasts may be observed because all forecasters have access to the same set of economic data and similar forecast techniques. Herding behavior, on the other hand, refers to forecasters deliberately deviating from their best private forecasts for strategic reasons.

Forecaster anti-herding may arise if forecasters, for strategic or other reasons, deliberately scatter their forecasts away from the forecasts of others. This may arise when a forecaster’s income (or reputation) depends not only on the accuracy of their own forecasts, but also on their relative performances. If some of the customers of professional forecasters buy forecasts only occasionally,

مقاله سال 2016

Short-term analyses in central banks and other policy institutions are intended to provide policy makers, and possibly larger audiences, with assessments of the recent past and current business cycle. There is a long tradition in business cycle analysis of separating periods of contraction from periods of expansion (see Schumpeter, 1954). Policy decisions vary depending on whether the economy is in an expansionary or a recessionary period. Most of the research to date has focused on US data, where the cycle defined by the Business Cycle Dating Committee of the National Bureau of Economic Research (NBER) cycle is regarded as the official reference cycle.

There is no authoritative dating of classical business cycles for the Norwegian economy. Norway is characterized by being a small open economy with large exports of energy (gas and oil) goods, and it is not obvious whether Norwegian business cycles are synchronized fully with the cycles of other Scandinavian countries, or with the European or US cycles.

The contributions of this paper are twofold. First, we define classical business cycle turning points for the Norwegian economy for the period 1978Q1–2011Q4, exploring a widely used set of methods. Second, in a real-time out-of-sample forecasting exercise, we study the timeliness and accuracy of the different methods in order to predict the peak and trough of the last recession.

A traditional view of the US business cycle is that of alternating phases of expansion and recession, where an expansion corresponds to widespread, persistent growth in economic activity, and a recession consists of a widespread, relatively rapid, decline in economic activity. The timely identification of the turning points between these phases, or peaks and troughs, is of considerable importance to policymakers, financial markets, firms, and individuals. A substantial body of literature has focused on the prediction of future turning points using a variety of leading economic and financial time series, with some limited success.1 A smaller body of literature has focusedon the identification of turning points that have already occurred, using economic variables that are coincident with the business cycle.

The problem of the ex-post identification of turning points is of particular interest, because there are many examples of turning points that have not been predicted exante. This spotty forecasting record means that economic agents are left trying to determine whether a new business cycle phase has already begun. Even this is a difficult task, with new turning points usually not being identified until many months after they occur. The official chronology of business cycle turning points in the United States is provided by the National Bureau of Economic Research’s (NBER) Business Cycle Dating Committee, which has historically announced new turning points with a lag of between four and 21 months. Statistical models improve on the NBER’s timeliness considerably, with little difference in the timing of the turning point dates established.2

However, in general these models don’t identify turning points until several months after they occurred. As a recent example of this, Hamilton (2011) surveys a wide range of statistical models that were in place to identify business cycle turning points in real time, and finds that such models did not send definitive signals regarding the December 2007 NBER peak until late 2008.3 One important reason for these identification lags is data reporting lags, as many key coincident indicators are released only with a lag of one to two months. Another factor is the need for several months of negative or positive data to accumulate before a definitive turning point signal can be uncovered.