Stock Coverage Research Articles

Gasoline Policy Simulation to Increase Responsiveness Using System Dynamics: A Case Study of Indonesia’s Gasoline Downstream Supply Chain

In the supply chain, inventory planning plays a crucial role in achieving a balance between supply and demand. This study aims to investigate a supply chain policy that can achieve an impressive level of responsiveness, reaching close to 100%. Additionally, by focusing on the supply chain of RON 92 gasoline in Indonesia, the research aims to identify the key factors influencing responsiveness instability. To achieve these objectives, a simulation model was used to analyze the inventory policy, considering various factors such as demand, production, safety stock coverage, transportation delays, production capacity, and importation due to insufficient production capacity. The results showed that forecasting accuracy is the main determinant of the responsiveness rate. Moreover, maintaining a minimum inventory level of 28 days yielded an impressive 99% responsiveness rate, provided that the deviation in demand does not exceed 5% of the forecast. The analytical tool used in the system dynamics framework was a simulation, which significantly contributed to the research findings. However, it is important to note that this research has limitations, specifically in its inability to analyze crude oil supply. Therefore, further research is necessary to thoroughly examine this aspect and gain a more comprehensive understanding of the overall supply chain dynamics.

Do analysts become better forecasters over time Evidence from India

There are conflicting studies in literature about how the analyst forecast bias, which can be driven by various factors, have trended over time. In this context, we study analyst forecasts in India over the past two decades (1998-2018), as well as before and after the global financial crisis (GFC) in 2008-2009, to find out whether the forecasts have actually become less biased and more accurate over time as the stock coverage and market liquidity has improved. In our initial tests, we find that analyst forecast accuracy has indeed improved over time/after GFC in India, as expected, as the markets have become more mature and efficient. However, when we control for the market turnover, representing the information environment as an additional factor, we find that the underlying bias has actually increased over time. Our results show that the improvement in forecast accuracy of analysts in India over the past two decades is mainly due to the improved information environment.

A Novel MILP Model for the Production, Lot Sizing, and Scheduling of Automotive Plastic Components on Parallel Flexible Injection Machines with Setup Common Operators

In this article, a mixed integer linear program (MILP) model is proposed for the production, lot sizing, and scheduling of automotive plastic components to minimize the setup, inventory, stockout, and backorder costs, by taking into account injection molds as the main index to schedule on parallel flexible injection machines. The proposed MILP considers the minimum and maximum inventory capacities and penalizes stockout. A relevant characteristic of the modeled problem is the dependence between mold setups to produce plastic components. The lot sizing and scheduling problem solution results in the assignment of molds to machines during a specific time period and in the calculation of the number of components to be produced, which is often called lot size, following a sequence-dependent setup time. Depending on the machine on which the mold is setup, the number of units to be produced will be distinct because machines differ from one another. The stock coverage, defined in demand days, is also included in the MILP to avoid backorders, which is highly penalized in the automotive supply chain. Added to this, the proposed model is extended by considering setup common operators to respond to and fulfill the constraints that appear in automotive plastic enterprises. In this regard, the MILP presented solves a lot-sizing and scheduling problem, emerged in a second-tier supplier of a real automotive supply chain. Finally, this article validates the MILP by performing experiments with different sized instances, including small, medium, and large. The large-sized dataset is characterized by replicating the amount of data used in the real enterprise, which is the object of this study. The goodness of the model is evaluated with the computational time and the deviation of the obtained results as regards to the optimal solution.

Company Visit Disclosure Regulation and Analysts’ Information Acquisition

There is a debate on whether company visits, an important channel of analysts’ information acquisition, should be regulated similar to management disclosure under Regulation Fair Disclosure. Exploiting a Shenzhen Stock Exchange regulation, this study examines the impact of forcing the timely disclosure of analysts’ company visits on analysts’ information acquisition. We find that the regulation creates a chilling effect on analysts’ private information acquisition activities, negatively affecting analysts’ stock coverage, company visits and research report issuance. However, for the analysts who do issue earnings forecasts following the regulation, we find no evidence that the accuracy of such forecasts is lower, consistent with a rational expectation equilibrium.

Seeking Analyst Coverage: Steering User-Generated Content Using Monetary Incentives

Against the backdrop of the recent decline of professional analyst (PA) stock coverage, we study whether research support by non-professional analysts (NPAs) can increase stock market liquidity. In particular, we employ an instrumental variables approach to estimate the causal impact of NPA coverage on the market liquidity of covered stocks. We find that firms that are covered by NPAs in a given quarter display substantially higher market liquidity (as measured by trading volume). To identify the mechanism underlying this relationship, we investigate which types of firms exhibit the highest liquidity increase from NPA coverage. We find that the impact of NPA coverage is stronger when there is no simultaneous PA coverage, as well as for smaller and growth stocks. These findings suggest that NPA coverage stimulates market liquidity by increasing investor recognition and reducing information asymmetry, where investor recognition seems to be the dominant channel.

Do Corporate Disclosures Constrain Strategic Analyst Behavior?

We show that U.S. analysts alter their behavior in response to a randomly assigned shock that exogenously varies the timeliness and cost of accessing companies’ mandatory disclosures in the cross-section of investors: analysts drop coverage of stocks with high disclosure quality, issue less optimistic and more accurate forecasts that are less bold and less informative, and collectively reduce forecast dispersion. Our investigation of possible channels favors the explanation that analysts reduce a strategic component of their behavior: the changes are more pronounced among analysts with stronger incentives to strategically skew their forecasts, such as affiliated analysts and those catering to retail investors. We conclude that mandatory disclosure can be a substitute for information production by analysts, whose behavior is constrained by investors’ ability to verify their forecasts using corporate filings.

Underlying risk preferences and analyst risk‐taking behavior

AbstractWe investigate the relationship between underlying risk preferences on analysts’ work‐related decisions. Specifically, we examine whether facial width‐to‐height ratio (fWHR), an innate personal characteristic that has been linked to financial risk tolerance, is associated with analysts’ stock coverage decisions and the boldness of their earnings forecasts and stock recommendations. We find that high‐fWHR analysts cover firms with lower earnings predictability, and issue bolder forecasts and recommendations. Our findings shed new light on the black box of analyst decision making, assisting investment practitioners in evaluating the information content produced by different types of analysts and understanding the observed dispersion in analyst forecasts.

Social-Media Sentiment, Limited Attention, and Stock Returns

Using tweets from StockTwits and machine-learning classification techniques, we find that social-media sentiment predicts positively and significantly future stock returns, and, importantly, such positive predictability decreases when the number of stocks users follow increases. The return predictability appears to stem from users’ ability to forecast future earnings. Further tests reveal that reduced predictability due to stock coverage is significant only for firms that are complex, opaque, and thus hard to analyze. The evidence suggests that stock analysis by users with a more complex portfolio is inferior due to attention and time constraints.

Threshold behavior of optimal safety stock coverage in the presence of extended production disruptions

PurposeThe purpose of this paper is to investigate the effects of extended production disruptions because of process quality breakdowns on operational and financial performance. The investigation is conducted over the market cycle of a highly profitable product, e.g. a patented pharmaceutical. In particular, the study evaluates performance effects of different inventory level policies. The paper considers different degrees of availability of a substitute product.Design/methodology/approachThe study uses simulation experiments based on system dynamics methodology to derive inventory policies on handling infrequently happening but severe production process breakdowns. The simulation model is inspired by empirical case study research and is based on standard modeling formulations from the literature.FindingsThe scenario analyses show that the optimal level of safety stock coverage time depends in a highly non-linear manner on various economic parameters and shows threshold behavior.Research limitations/implicationsIn subsequent studies, the effect of the degree of backlogging and the effect of the repeated occurrence of quality breakdowns on the results can be investigated.Practical implicationsThe critical importance of safety stock and its non-linear relationship to economic product characteristics is emphasized.Originality/valueMotivated by a real-world case study, the paper uses standard model formulations to derive insights for a specific business situation after considering uncertainty in the environment.

Hospitals Automatic Replenishment System Under Surge Demand: a Proactive Priority Management Policy of Replenishment Orders

Recently, hospitals utilize RFID (Radio-frequency identification) enabled Two-Bin system with conveyors for an effective and automatic inventory and replenishment management. In this framework, replenishment orders are typically managed according to continuous review. In case of surge in demand due to special-events and disasters, the resulted big number of replenishment orders is managed in real time according to First-In First-Out (the first order is replenished first) with Priority of Out-of-Stock Medicines (FIFO-POSM) policy. However, this policy lacks flexibility with random increase in medicines consumption and lacks anticipation, leading to stock-outs occurrence. So far, there is not a flexible and anticipative policy for orders priority management in this case. This paper proposes a flexible and anticipative policy based on stock coverage indicator and anticipative system of random variations in medicines demand, to considerably reduce or eliminate stock-outs. RFID enabled Two-Bin system with conveyors is modeled using Colored Timed Petri Nets, first, considering FIFO-POSM policy then considering the proposed policy. Simulation results prove the efficiency of the proposed policy; medicines stock-outs and out-of-stock medicines unavailability durations are respectively reduced by 54.24% and 52.06% during ten days of a public protest.

Crowded Stock Coverage

How does retail trading impact information supply in financial markets? We build a model where sell-side analysts compete for scarce institutional investor attention to maximize volume for brokerages. Analysts cluster in retail-heavy stocks, where investors can trade more aggressively without revealing information and incurring price impact. Empirically, retail trading and coverage are strongly correlated after controlling for factors associated with analyst following. Finally, a market-wide increase in retail demand allows analysts to diversify coverage without sacrificing institutional volume. Throughout 2020, as brokerage fee cuts and COVID-19 triggered a retail trading surge, coverage becomes significantly less driven by retail volumes.

Keeping an Eye on the Competition Observational Learning in Analysts’ Forecasts

Substantial research shows the importance of different network structures in the process of learning from others. Learning in most of these networks takes place when knowledge is transferred via social or business ties. I argue that network structure matters differently for observational learning, defined as an arm’s length process of observing and incorporating information from others who are unable or unwilling to transfer knowledge. I argue that, even in the absence of sociality and collaboration, closed networks improve decision makers’ performance by facilitating observational learning. Closed networks improve performance by focusing decision makers’ attention on relevant sources of information and providing context that is useful for interpreting what is observed. I find that security analysts’ earnings forecasts are more accurate when their stock coverage network is closed. This effect is robust only for analysts who reacted to the opinions their competitors published, which supports observational learning as the underlying mechanism. Learning from competitors’ forecasts is further supported using a natural experiment. I find that the sudden absence of analysts who perished in the September 11, 2001 attacks on the World Trade Center reduced survivors’ forecasting accuracy as a function of the damages to their coverage network.

Does Source of Fundamental Data Matter?

We study the role of the choice of a fundamental database on the portfolio returns of a set of 74 fundamental anomalies. We benchmark Compustat by comparing it to Datastream in the US and find systematic differences in the raw financial statements across the databases. These differences only have a small effect on the returns of anomalies when they are constructed on stock-months existing in both databases. Different stock coverage across the databases, however, leads to large statistically and economically significant disparities in the returns. Profitability anomalies yield negative returns on the Datastream universe.

Supply Strategy: A Quasi-Experiment on the Number And Location of Suppliers

ABSTRACT Objective: Evaluate the impact of a second supply source on price, quality and stock levels at the purchasing company. Relevance: The supply strategy defines how many suppliers an item should have and where it should be located. If working with a single supplier may bring relational benefits, working with multiple sources may reduce opportunism risks. If a local supplier reaps benefits on account of its proximity, a global supplier may result in lower costs. Methodology: The quasi-experimental method was used to follow-up a purchasing policy change at an auto parts company, which took on a second supply source for low complexity items. The research collected data on costs, quality and stock coverage for 1,480 items between 2007 and 2012, analysing new supplier's entrance and location. Results: Introducing a second supplier reduced the price, especially when a new international supplier was introduced. Decreases in quality or increases in stock coverage were not observed.

Coverage of high biomass forests by the ESA BIOMASS mission under defense restrictions

The magnitude of the global terrestrial carbon pool and related fluxes to and from the atmosphere are still poorly known. The European Space Agency P-band radar BIOMASS mission will help to reduce this uncertainty by providing unprecedented information on the distribution of forest above-ground biomass (AGB), particularly in the tropics where the gaps are greatest and knowledge is most needed. Mission selection was made in full knowledge of coverage restrictions over Europe, North and Central America imposed by the US Department of Defense Space Objects Tracking Radar (SOTR) stations. Under these restrictions, only 3% of AGB carbon stock coverage is lost in the tropical forest biome, with this biome representing 66% of global AGB carbon stocks in 2005. The loss is more significant in the temperate (72%), boreal (37%) and subtropical (29%) biomes, with these accounting for approximately 12%, 15% and 7%, respectively, of the global forest AGB carbon stocks. In terms of global carbon cycle modelling, there is minimal impact in areas of high AGB density, since mainly lower biomass forests in cooler climates are affected. In addition, most areas affected by the SOTR stations are located in industrialized countries with well-developed national forest inventories, so that extensive information on AGB is already available. Hence the main scientific objectives of the BIOMASS mission are not seriously compromised. Furthermore, several space sensors that can estimate AGB in lower biomass forests are in orbit or planned for launch between now and the launch of BIOMASS in 2021, which will help to fill the gaps in mission coverage.

Does Media Coverage of Stocks Affect Mutual Funds’ Trading and Performance?

Does Media Coverage of Stocks Affect Mutual Funds’ Trading and Performance?

Does Media Coverage of Stocks Affect Mutual Funds' Trading and Performance?

We study the relation between mutual fund trades and mass media coverage of stocks. We find that funds exhibit persistent differences in their propensity to buy media-covered stocks. Moreover, this propensity is negatively related to their future performance. Funds in the highest propensity decile underperform funds in the lowest propensity decile by 1.1% to 2.8% per year. These results do not extend to fund sells, likely because of funds' inability to sell short. Overall, the findings suggest that professional investors are subject to limited attention.

Indirect structural health monitoring of a simplified laboratory-scale bridge model

An indirect approach is explored for structural health bridge monitoring allowing for wide, yet cost-effective, bridge stock coverage. The detection capability of the approach is tested in a laboratory setting for three different reversible proxy types of damage scenarios: changes in the support conditions (rotational restraint), additional damping, and an added mass at the midspan. A set of frequency features is used in conjunction with a support vector machine classifier on data measured from a passing vehicle at the wheel and suspension levels, and directly from the bridge structure for comparison. For each type of damage, four levels of severity were explored. The results show that for each damage type, the classification accuracy based on data measured from the passing vehicle is, on average, as good as or better than the classification accuracy based on data measured from the bridge. Classification accuracy showed a steady trend for low (1-1.75 m/s) and high vehicle speeds (2-2.75 m/s), with a decrease of about 7% for the latter. These results show promise towards a highly mobile structural health bridge monitoring system for wide and cost-effective bridge stock coverage.

Distribution of stock ratings and analyst recommendation revision

With the implementation of NASD Rule 2711 in 2002, each brokerage firm is now required to publicly disseminate the distribution of stock ratings in each research report. Hence, this paper investigates the relationship between the percentage of buy recommendations and probability of deciding to downgrade or drop coverage of stocks. Our findings suggest that the percentage of buy recommendations can enhance the probability of deciding to downgrade stock ratings and drop coverage of stocks. Furthermore, we find that analysts tend to revise their recommendations downward to hold instead of an unfavorable level. In addition, we find the market tends to heed downgrade decisions of analysts with a higher percentage of buy ratings. This finding is consistent with Barber, Lehavy, McNichols, and Trueman (2006) by applying data at the broker level.This study further investigates the changing dynamic in buy ratio of analysts by partitioning pre and post regulatory reform period. We show that analysts are more likely to revise their ratings to hold and less likely to revise their ratings to sell; in particular after post regulatory reform. Moreover, we observe one key coefficient in the post-Rule period with superior performance in contrast to the pre-Rule period. Compared to the results in the pre-Rule period, the results show that analysts are more likely to revise their ratings to hold and less likely to revise ratings to sell, and more likely to drop coverage of stocks in the post-Rule period. Overall, our findings suggest that the implementation of Rule 2711 will contribute to reduce analysts’ optimistic stock ratings.

GM-021 Changes caused by implementation of the ABC (Activity Based Costing) method for drug supply in a French university hospital

BackgroundDrug supply and stock management are part of the hospital pharmacist’s job. Financial restraints imposed by our institution have led us to consider reducing the value of drugs stocked within...