تاثیر دینامیک موجودی بر روی بازده سهام در بلند مدت - تحقیقات تجربی از شرکت های تولیدی آمریکا
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|20785||2013||12 صفحه PDF||سفارش دهید||11112 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Operations Management, Volume 31, Issue 5, July 2013, Pages 250–261
This paper investigates the relationship between the inventory dynamics and long-term stock returns of a large panel of U.S. manufacturing firms over the time period from 1991 to 2010. We propose two measures of inventory dynamics: one metric to assess the fluctuations of quarterly inventories within the year and a second metric to quantify relative year-over-year inventory growth. Our results indicate that within-year inventory volatility (IV) and abnormal year-over-year inventory growth (ABI) are associated with abnormal stock returns. Both metrics cannot be entirely explained by common risk factors. We find that firms with high IV and low ABI have the best long-term stock returns, and that stock performance decreases monotonically with higher ABI values. Our results are robust to various control variables including size, book-to-market value, industry and prior performance. We therefore conclude that changes in inventory levels provide valuable insights into the risks and opportunities faced by a company.
Given the important role that inventory plays within manufacturing firms, it is natural to conjecture that inventory mismatches will have adverse financial consequences for companies. On the one hand inventory shortages and their resultant poor customer service will have negative implications for current and future sales (Hendricks and Singhal, 2005a). On the other hand, Hendricks and Singhal (2009) find that excess inventories are costly and bear the risk of obsolescence, particularly in fast-paced sectors such as apparel manufacturing, computer hardware and electronic components manufacturing (O’Glove, 1987). Accordingly, stakeholders focus on firm-level inventories, and inventories are frequently reported on in the business news. • Dealers Balk as Chrysler strains to cut inventory[…] DaimlerChrysler AG's Chrysler Group is running into resistance from some big dealers to its latest efforts to shrink its bloated inventory of unsold vehicles, a development that could make it harder for Chrysler to bounce back from an expected loss in the third quarter. – Wall Street Journal, October 16, 2006, Boudette (2006) • Sony to Corral TV inventory – finance chief says firm is willing to forgo some sales to rein in supply chain[…] Sony and its TV rivals, Samsung Electronics Co. and LG Electronics Inc., have already said they expect price competition to be especially fierce this holiday season because manufacturers are sitting on excess inventory from aggressive growth targets. […] Following the global financial crisis, Sony implemented tighter inventory controls to prevent the type of punishing losses from slow demand that forced deep discounting or write-downs for unsold goods. – Wall Street Journal, November 02, 2010, Wakabayashi (2010) • Intel cuts production as PC makers’ orders drop[…] Its fourth-quarter outlook predicted stagnating revenues and margin erosion from underutilized plants and inventory write-offs, with PC makers keeping inventories lean as they wait to gauge consumer demand for the new Windows 8 operating system. – Financial Times, October 17, 2012, Nuttall (2012) Given these examples, it is not surprising that there has been increasing interest in investigating firm-level inventories by leveraging secondary empirical data in the field of operations and supply chain management (Roth, 2007 and Fisher, 2007). Most of the contemporary empirical papers on firm-level inventories either examine Just-In-Time (JIT) implementations or inventory levels and their linkages to financial performance (Gaur et al., 2005 and Chen et al., 2007). However, relatively little effort has been spent on understanding inventory dynamics during the calendar year. The few papers that investigate inventory dynamics find that investors seem to fail to fully incorporate the information content that these inventory changes have for sales (Kesavan et al., 2010) or earnings (Kesavan and Mani, 2012). Moreover, most of the previously published empirical research papers that investigate firm-level inventories have either focused on the retail sector alone (Gaur et al., 2005, Chen et al., 2007, Kesavan et al., 2010 and Kesavan and Mani, 2012) or used aggregate samples that did not differentiate between retail and manufacturing firms (Abarbanell and Bushee, 1997, Thomas and Zhang, 2002, Rumyantsev and Netessine, 2007 and Hendricks and Singhal, 2009). Thus, there are fewer clear-cut empirical results available for manufacturers. We conjecture that insights and findings from the retail sector cannot be readily translated to manufacturing companies because the value creation process involved in manufacturing might entail different implications for inventory management. The objective of this paper is to overcome this gap in the operations management literature by studying the implications of abnormal inventory oscillations in the manufacturing sector. We propose two measures with which to study the dynamics of inventories and their linkages to the financial performance of a large panel of U.S. manufacturing companies for the time period from 1991 to 2010. More specifically, in this paper, we analyze the within-year volatility of quarterly inventories (IV) and the year-over-year growth of quarterly inventories relative to sales growth (ABI). Since abnormally high fluctuations in quarterly inventories and abnormal inventory growth indicate temporary mismatches between demand and supply, we expect those measures to convey information regarding the operational risks and opportunities faced by companies. Using a sort-portfolio approach rooted in financial research (e.g., Fama and French, 1992) we find that IV and ABI are both significantly associated with long-term stock returns. While low (high) ABI is significantly correlated with superior (poor) stock returns, we document the opposite for IV. Unlike prior studies (Eroglu and Hofer, 2011, Rumyantsev and Netessine, 2007 and Kesavan and Mani, 2012) our portfolio analysis does not provide any evidence of an inverted U-shaped relationship between inventory and financial performance. However, when we control for common risk factors of stock returns we find that both measures seem to be assymetrically associated with stock returns. Both IV and ABI seem to provide information beyond that provided by common risk factors. Generally, our results for the ABI metric are more robust than those for the IV metric. Even after controlling for various variables, we show that portfolio returns for ABI are highly significant and in many cases monotonically decrease with ABI. Our paper contributes to the recent empirical operations management literature in two ways. First, to the best of our knowledge our paper is the first that explicitly analyzes the volatility of quarterly inventories within the calendar year. We also show that both of our measures for inventory dynamics reveal information that is not already contained in a static view of quarterly- or year-end inventory levels. Second, we provide evidence that the relationship between abnormal inventory growth and stock returns not only holds for retail companies, as shown by Kesavan and Mani (2012), but is also valid for manufacturing companies. These results are robust for various controls. High-level managers could also use our abnormal inventory growth metric as a benchmark to gauge the inventory and stock performance of their company. The implications for the investment community are immediately obvious, as we show that inventory dynamics provide clues about the risks and opportunities faced by a firm. There is anecdotal evidence from the investment community that an investment strategy that considers the mutual growth rates of sales and inventory generates abnormal returns. However, at least for the manufacturing sector, this relationship has not been tested previously with rigorous statistical methods. The paper is structured as follows. In Section 2 we give an overview of relevant empirical papers on firm level inventories. In Section 3, we derive our measures for inventory volatility and abnormal inventory growth. We provide theoretical arguments why and how both measures are related to financial performance. In Section 4, we introduce our research setup, data and methodology. In Section 5, empirical results are presented. In Section 6, we conclude the paper.
نتیجه گیری انگلیسی
Despite the increasing interest in analyzing firm-level inventories using large panel datasets, there are only a few papers that analyze inventory dynamics in the field of operations management. Almost all of these papers focus on the retail sector, which is fundamentally different from the manufacturing sector. This study intends to overcome this gap in OM research and investigates the link between inventory dynamics and financial performance for U.S. public manufacturing companies. Two metrics of inventory dynamics are proposed. Inventory volatility (IV) measures how quarterly inventories fluctuate throughout the year, while abnormal inventory growth (ABI) quantifies how inventories have grown relative to sales year-over-year. We find that both metrics are significantly associated with stock returns. High IV and low ABI portfolios generate extremely abnormal returns. The results for IV are particularly intriguing, as high volatility is generally considered to be detrimental to operational (and financial) performance. We provide two possible explanations for our findings: (i) business risks and (ii) sales chasing. To confirm that IV does not merely reflect the same risks as the common risk factors already identified by Fama and French (1993) we conduct several robustness checks and estimate a three-factor model to account for the presence of risk factors. We find evidence that IV is related to risk but seems to capture operational and business risk not already incorporated in other risk factors. We also believe that the strong relationship observed between IV and financial performance deserves further research. Our paper is the first study that investigates the financial implications of quarterly inventory volatility. The relationship between ABI and financial performance is oppositional to the one encountered between IV and financial performance. Portfolios with the lowest ABI stocks outperform the highest ABI portfolios. Our results are robust to various controls and adjustments for risk. We conjecture that low ABI firms have achieved operational improvements and efficiency gains that are subsequently reflected in higher stock returns. Sales and earnings surprises could be another source of low ABIs. Our results show that the relationship between abnormal inventory growth and future profitability documented for the retail sector (Kesavan and Mani, 2012) also holds for manufacturing companies. Interestingly, values of ABI close to or equal to one separate poorly performing companies form well performing companies. Investors could use our simple metric as a benchmark to gauge the inventory performances of companies. The efficiency gains reflected by low values of ABI seem to extend into the future. Given the high abnormal returns observed and the fact that we re-sort portfolios only once per year, it might well be possible to derive a profitable trading strategy from our findings. We leave this open to future research. Our findings provide strong support for the anecdotal evidence that short-term inventory movements and stock returns are related (see O’Glove, 1987). As with any study, our research has certain limitations. Although we provide reasonable and plausible explanations for our findings, we do not test those explanations explicitly. Future research could address this issue by analyzing fundamental firm characteristics and their links to inventory volatility and abnormal inventory growth in more detail. It would be informative to know whether IV is a long-lasting firm characteristic and what features distinguish firms with high volatility from firms with low volatility. While we include a control for industry effects into our analysis, it would be helpful to know to what degree our results are industry specific. Eroglu and Hofer (2011) have shown that the implications for inventory leanness are quite different for different industries. As such, a more elaborate approach to control for industry-specific effects could yield additional insights. Due to space constraints, we did not investigate the three inventory subtypes of raw materials (RM), work-in-process (WIP) or finished goods (FG) in isolation. Doing so could be a particularly promising research avenue, as the implications for the financial performance of these sub-inventory types might be very different. Our study inlcuded all the public manufacturing companies listed in the Standard & Poor's COMPUSTAT®-North America database. As such, the sample contains many small firms. Future research could analyze whether the strong relationship we found between inventory dynamics and stock returns also holds for larger companies that are listed in a larger stock index (e.g., the S&P 500) and are thus more closely monitored by equity analysts and fund managers. Finally, as our research uses U.S. data, it is also unclear how macroeconomic conditions influence our results. During our sample period, the U.S. manufacturing sector experienced dramatic changes and a general decline due to an increased tendency to outsource manufacturing activities. Future research could investigate to what degree outsourcing activities interfere with the strong relationships between inventory dynamics and stock performance documented in our study.