Inventory Signals

Among practitioners, inventory is often thought to be the root of all evil in operations management. The stock market hates it, the media abhors it, and managers have come to fear it. But high inventory levels can also be the result of strategic buying and high-availability strategies. The problem is that when the market sees lots of inventory, it cannot tell whether it is because of poor or smart operations. We hypothesize that inventory has a signaling role. In our model, publicly- traded firms use inventory levels to signal their operational competence to the market. There is a separating equilibrium that leads some firms to maintain inventory levels below what their capability could achieve. We offer this as one explanation why, for example, stock-outs are pervasive even among operationally competent firms. We provide empirical evidence for the assumptions behind this inventory signaling hypothesis: (1) the market cannot tell the difference between “good” and “bad” inventory; and (2) the counterfactual: the market punishes firms when it can tell that their inventory is bad, such as when they write off supplies. Consistent with these assumptions, we find that inventory levels do not explain firm value. And on average, stocks suffer an abnormal negative return of 7% in the month of announcing inventory write-offs.Inventory, signaling, operations management, asymmetric information

Why Funds of Funds?

Private equity funds of funds (FOFs) have become big business. Today, FOFs form 14% of new money raised. I test six explanations for the rise of FOFs. First, I find that FOFs do not generally deliver superior returns. They do, however, do well enough for the limited partners (LPs) that hire them. Second, FOFs allow small LPs to scale upward, to invest in more funds. However, I find that they do not contribute to diversification. What they really do is to provide smaller LPs avenues to lower the cost of fund management. Third, FOFs allow large LPs to scale downward, to invest vast amounts over a short duration. However, the mechanism is imperfect because LPs can either use many FOFs and risk coordination problems among them or few FOFs and risk getting held up. Fourth, FOFs are used by LPs with weaker governance structures. Fifth, there is some evidence that LPs use FOFs to learn to invest in new areas, but the support is weak. Last, the use of FOFs is partly due to cyclical booms.Venture capital, private equity, agency, economies of scale, outsourcing

A Catering Theory of Analyst Bias

We posit a theory that runs counter to how conventional wisdom thinks about analyst bias, that it is the result of distorted incentives by “upstream” factors like the analysts’ employers. We suggest that analysts are also heavily influenced by the beliefs of investors downstream, the purported victims of analyst bias. We adapt Mullainathan-Shleifer’s theory of media bias to build a theory of how analysts cater to what investors believe. The theory also predicts that competition among analysts does not reduce their bias. We provide empirical support for this theory, using an enormous dataset built from over 6.5 million analyst estimates and 42.8 million observations on investor holdings, which we argue is a proxy for what investors’ beliefs. We use a simultaneous-equations model for estimation, with instruments to rule out alternative interpretations of the direction of causality. For additional robustness, we investigate the time series of analyst bias and heterogeneity in investor beliefs from 1987 through 2003. Dickey-Fuller tests show that both have unit roots, but we establish that cointegration holds. Further, we employ a vector- autoregressive model to show Granger-causality between the two.Analyst bias, behavioral finance, media bias

Does Competition Kill Ties?

Venture capital firms (VCs) form syndicates that compete to invest in deals. Does more competition makes it less likely that VCs will choose syndicate partners based on past ties? Using over 200,000 observations on how VCs choose each other in 572 biotech deals in Massachussetts from 1967 through 2004, I find the answer is: yes. The theory of embeddedness argues that past ties can explain the pattern of who works with who. I interpret my finding as a first step in demarcating when embeddedness might apply and when atomistic, calculative, economic forces might be a better explanation of who works with who.economics of sociology, embeddedness, venture capital, ties, competition

Inventory and the Stock Market

About inventory models, a concern is 'often made [that] any resemblances between the models constructed and reality are purely coincidental.' One set of factors not usually considered in textbook models of inventory decisions is suggested by well-documented evidence in macroeconomics, that the stock market affects investment decisions. Does the stock market also affect inventory decisions, and how? I study four hypotheses. The first is that the market could be a side-show, with no impact on firms' decisions. The second is that the market influences inventory decisions via a financing channel. When the market over-values firms, firms can get easier and cheaper financing, and tend to increase their inventory. The third is a dissipation channel. When the market over-values firms, firms are less disciplined and let inventories rise. The last is a catering channel. When the market discounts high-inventory firms, firms decrease their inventory, and vice versa. I report evidence that rejects the first, weakly supports the second and third, and strongly supports the fourth hypotheses. This evidence contributes to an emerging area for empirical research, at the intersection of finance and operations management.inventory, stock market, sentiment, operations management

What\u27s in Design Rationale?

A few representations have been used for capturing design rationale. To understand their scope and adequacy, we need to know how to evaluate them. In this article, we develop a framework for evaluating the expressive adequacy of design rationale representations. This framework is built by progressively differentiating the elements of design rationale that, when made explicit, support an increasing number of the design tasks. Using this framework, we present and assess DRL (Decision Representation Language), a language for representing rationales that we believe is the most expressive of the existing representations. We also use the framework to assess the expressiveness of other design rationale representations and compare them to DRL. We conclude by pointing out the need for articulating other dimensions along which to evaluate design rationale representations

The IDENTIFY study: the investigation and detection of urological neoplasia in patients referred with suspected urinary tract cancer - a multicentre observational study

Objective To evaluate the contemporary prevalence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC] and renal cancer) in patients referred to secondary care with haematuria, adjusted for established patient risk markers and geographical variation. Patients and Methods This was an international multicentre prospective observational study. We included patients aged ≥16 years, referred to secondary care with suspected urinary tract cancer. Patients with a known or previous urological malignancy were excluded. We estimated the prevalence of bladder cancer, UTUC, renal cancer and prostate cancer; stratified by age, type of haematuria, sex, and smoking. We used a multivariable mixed-effects logistic regression to adjust cancer prevalence for age, type of haematuria, sex, smoking, hospitals, and countries. Results Of the 11 059 patients assessed for eligibility, 10 896 were included from 110 hospitals across 26 countries. The overall adjusted cancer prevalence (n = 2257) was 28.2% (95% confidence interval [CI] 22.3–34.1), bladder cancer (n = 1951) 24.7% (95% CI 19.1–30.2), UTUC (n = 128) 1.14% (95% CI 0.77–1.52), renal cancer (n = 107) 1.05% (95% CI 0.80–1.29), and prostate cancer (n = 124) 1.75% (95% CI 1.32–2.18). The odds ratios for patient risk markers in the model for all cancers were: age 1.04 (95% CI 1.03–1.05; P < 0.001), visible haematuria 3.47 (95% CI 2.90–4.15; P < 0.001), male sex 1.30 (95% CI 1.14–1.50; P < 0.001), and smoking 2.70 (95% CI 2.30–3.18; P < 0.001). Conclusions A better understanding of cancer prevalence across an international population is required to inform clinical guidelines. We are the first to report urinary tract cancer prevalence across an international population in patients referred to secondary care, adjusted for patient risk markers and geographical variation. Bladder cancer was the most prevalent disease. Visible haematuria was the strongest predictor for urinary tract cancer

Essays on object lens, a tool for supporting information-sharing

Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1987.Bibliography: leaf 71.by Kum-Yew Lai.M.S