Target Stock Research Articles

Towards sustainable management of the stock and ecosystem services of urban trees. From theory to model and application

Key messageA model for sustainable planning of urban tree stocks is proposed, incorporating growth, mortality, replacement rates and ecosystem service provision, providing a basis for planning of urban tree stocks.Many recent studies have improved the knowledge about urban trees, their structures, functions, and ecosystem services. We introduce a concept and model for the sustainable management of urban trees, analogous to the concept of sustainable forestry developed by Carl von Carlowitz and others. The main drivers of the model are species-specific tree diameter growth functions and mortality rates. Based on the initial tree stock and options for the annual replanting, the shift of the distribution of the number of trees per age class can be predicted with progressing time. Structural characteristics such as biomass and leaf area are derived from tree dimensions that can be related to functions such as carbon sequestration or cooling. To demonstrate the potential of the dynamic model, we first show how different initial stocks of trees can be quantitatively assessed by sustainability indicators compared to a target stock. Second, we derive proxy variables for ecosystem services (e.g. biomass for carbon sequestration, leaf area for deposition and shading) from a given distribution of the number of trees per age class. Third, we show by scenario analyses how selected ecosystem services and functions may be improved by combining complementary tree species. We exercise one aspect (cooling) of one ecosystem service (temperature mitigation) as an example. The approach integrates mosaic pieces of knowledge about urban trees, their structures, functions, and resulting ecosystem services. The presented model makes this knowledge available for a sustainable management of urban tree stocks. We discuss the potential and relevance of the developed concept and model for ecologically and economically sustainable planning and management, in view of progressing urbanization and environmental changes.

Mandatory Bids in China: You Can Lead a Horse to Water, but You Can’t Make It Drink

This paper studies mandatory bids in China against an institutional backdrop of restrictive IPO requisites. We find that virtually no shares held by external shareholders are tendered in mandatory bids for all the remaining shares. Mandatory bidders’ tactics to avoid tendering by public investors include pressing down their bid prices, and the potential manipulation of target stock prices. In relation to the economic impacts of mandatory bids, we document that the market responds favourably to their announcements, and that targets’ operational performance improves in their wake, consistent with the theoretical prediction that mandatory bids induce efficient transfers of corporate control. Our research is among the earliest empirical works on the mandatory bid rule in a particular jurisdiction. It not only yields interesting results pertaining to the unique Chinese regulatory environment, but also generates useful insights into mandatory bids beyond China.

Social Connections and Information Leakage: Evidence from Target Stock Price Run-Ups in Takeovers

Does information leakage in a target’s social networks contribute to increase in its stock price prior to a merger announcement? Evidence reveals that a target with better social connections indeed experiences a higher pre-announcement price run-up. This effect does not exist during or after merger announcement, or in windows ending two months before the announcement. It is more pronounced among targets with severe asymmetric information, and weaker when public information about an upcoming merger is available prior to the announcement. It is also weaker in expedited deals such as in tender offers.

Informed trading or liquidity trading: a theoretical formulation

This paper provides theoretical formulations of informed trading contrasted with liquidity trading. Informed trading occurs when traders flock to purchase or sell securities, based upon private information. In contrast, liquidity traders purchase or sell securities for their own inventory. Informed traders purchase in large volume over very short periods of time, with a definite direction of price movement. Liquidity trading consists of unlimited market entry of traders, reducing profit per trader, along with a long time period of investment in which the direction of price movement is uncertain, diminishing gains. This paper presents optimal trade prices for informed traders prior to stock mergers, whereupon traders have been observed short selling the acquirer stock, using the proceeds to purchase target stock, or to purchase put options. A capital structure arbitrage strategy is used for liquidity traders, with the trader purchasing or selling bonds at trade prices influenced by the market maker's prices. Proceeds from this strategy may be used to purchase equities.

Does the Disclosure of a Hedge Fund Stock Sale Influence Respective Stock Returns?

The author investigates stock returns surrounding activism-related stock sales. Previous studies focus on the last sale of the hedge fund, while this article examines the initial sale of a target’s stock. The author determines abnormal stock returns using factor models and compares the target’s stock return with the performance of the S&P 500 Index. The additional method might be more intuitive for unsophisticated investors in comparison with the complexity of the factor models. He shows that a hedge fund’s initial sale of stock led to stagnating abnormal returns when activists were successful and negative abnormal returns otherwise. Furthermore, he reveals that target stocks experience similar returns as the S&P 500 and the Russell 1000 Value Index subsequent to the hedge fund’s sale disclosure. The results are relevant for existing shareholders of a target company. TOPICS:Real assets/alternative investments/private equity, information providers/credit ratings, security analysis and valuation, performance measurement Key Findings ▪ Previous studies focus on hedge funds’ last disclosure of the sale of stock in an activist position. This article examines the initial sale of target company stocks, which may indicate the inception of a hedge fund’s complete exit. ▪ Hedge funds’ initial stock sales led to stagnating abnormal returns when activists were successful and negative abnormal returns otherwise. This result is consistent with pre-existing evidence on last-sale disclosures of hedge funds. ▪ From the day after the sale-related disclosure, target stocks perform, on average, like the S&P 500 Index. This finding evokes questions about whether the remaining shareholders should transfer their stock investment into index funds.

Determinants of Hedge Fund Investment in Corporate Endgames

Under German law the corporate endgame process of obtaining full control over a company offers multiple investment opportunities for investors with high investment flexibility, and is therefore particularly attractive to hedge funds. This paper investigates the determinants of hedge fund investment in corporate endgame processes based on a sample of 76 endgame situations of publicly listed German companies and investment data of 326 hedge funds. Examining characteristics of investment targets, we find that hedge funds invest in companies with a non-dominant majority owner and a high stake of index funds as latter’s limited ability to react in change of control situations creates a supportive investment environment. Hedge funds are most likely to invest after takeover consummation and before announcement of a new endgame transaction. Investigating the determinants of ongoing engagement after initial investment, we find that the ownership of other institutional investors, especially hedge funds, positively affects engagement likelihood, serving as a validation of the own investment approach. Abnormal performance and trading liquidity of target stock also positively affect hedge funds’ engagement. The results indicate that the endgame process in Germany is an attractive investment opportunity for hedge funds, while their involvement also adds complexity to the corporate control process.

Assessing the vulnerability of bycatch species from Hilsa gillnet fishing using productivity susceptibility analysis: Insights from Bangladesh

Productivity susceptibility analysis (PSA) is being widely used as a semi-quantitative risk assessment tool in data and capacity limited situations. This tool rapidly and cost-effectively assists in identifying the potential risk of a fishing type regarding its bycatch stocks. The Hilsa (Tenualosa ilisha) is an iconic flagship species and geographical indication product of Bangladesh. We performed a PSA to evaluate the relative risk to bycatch stocks in gillnet fishing (gillnet shares > 95 % of Hilsa catch) along with target stock, Hilsa. Of the 130 identified species, Hilsa and 74 bycatch stocks were subjected to a PSA depending on data availability and the magnitude of capture. We validated our vulnerability results by comparing them with two other empirically derived assessment outcomes, the IUCN Red List and the exploitation rate. We also compared PSA scores with the catch trend of stocks from fisher's subjective recognition. Hilsa was found to be moderately vulnerable to gillnet fishing. The majority of the bycatch were found to be highly susceptible to fishing, with 17 bycatch species found to be in the high-risk category. Five species classified as high-risk group were known to be threatened species listed in the national IUCN Red List. Our finding revealed 82 % accordance level between the exploitation rate and PSA-derived vulnerability scores. It implies that the exploitation rate associated with overfishing corresponds to the vulnerability scores. Moreover, with few exceptions, we found that species with vulnerability score over 1.8 was at depleted stock state. Our result also revealed that around 55 % of inland bycatch and 42 % of marine bycatch is associated with overfishing (vulnerability score > 1.8). Data quality analysis indicated that the majority of bycatch species received the low data quality scores. It emphasizes the need for improved data collection on species-specific life-history traits. However, the baseline information of our current study could assist the fishery managers to formulate a better management plan for the sustainability of Hilsa and the bycatch stocks.

Do Firms Strategically Internalize Disclosure Spillovers? Evidence from Cash‐Financed M&As

ABSTRACTWe investigate whether managers internalize the spillover effects of their disclosure on the stock price of related firms and strategically alter their disclosure decisions when doing so is beneficial. Using data on firm‐initiated disclosures during all‐cash acquisitions, we find evidence consistent with acquirers strategically generating news that they expect will depress the target's stock price. Our results suggest the disclosure strategy leads to lower target returns during the negotiation period when the takeover price is being determined and results in a lower target premium. These findings are robust to a battery of specifications and falsification tests. Our results are consistent with expected spillovers influencing the timing and content of firms’ disclosures in M&A transactions.

Multisite supply planning for drug products under uncertainty

AbstractIn the pharmaceutical industry, the goal of a supply planner is to make efficient capacity allocation decisions that ensure an uninterrupted supply of drug products to patients and to maintain product inventory levels close to the target stock. This task can be challenging due to the limited availability of manufacturing assets, uncertainties in product demand, fluctuations in production yields, and unplanned site downtimes. It is not uncommon to observe uneven distribution of product inventories with some products carrying excess inventories, while other products may be close to a stockout. Maintaining high stock levels can have economic repercussions due to the risk of expiration of unused products (whereas products facing a stockout can adversely affect the treatment regimen of patients). The network complexity of pharmaceutical supply‐chains coupled with regulatory constraints and siloed planning systems force supply planners to rely on manual (error‐prone) decision‐making processes. Such an approach results in suboptimal capacity allocation and inventory management decisions. In this work, we propose a stochastic optimization methodology for the production scheduling of multiple drug products in lyophilization units across multiple sites. The framework leverages information obtained from historical and forecast data to generate scenarios of uncertain parameters (e.g., yield, demand, and downtimes) that can realize in the future. The optimization model determines a product filling schedule that maintains product stock levels close to targets under diverse scenarios. We show that this approach helps in avoiding reactive scheduling and in maintaining a more robust production plan than deterministic procedures (which ignore uncertainty). Specifically, planning under a stochastic optimization approach reduces the number of scenarios under which backlogs are observed and also reduces the magnitude of the backlogs.

Margin trading and stock idiosyncratic volatility: Evidence from the Chinese stock market

In this paper, we find that the idiosyncratic volatility (IV) effect on expected returns exists and cannot be explained by other variables in the Chinese stock market. The Chinese stock market launched margin trading in March 2010. We therefore study the margin trading target and non-margin trading target stocks separately and find that the IV effect exists in both stock groups. The IV effect of the margin trading target stocks can be explained by the turnover ratio, whose mechanism shows that the short sale constraint hinders the expression of the seller’s heterogeneous beliefs. However, the IV effect of the non-margin trading target stocks cannot be interpreted by other variables. In comparison to margin trading target stocks, non-margin trading target stocks are more likely to have the lottery characteristics and their gambling behavior is more pronounced.

Ecosystem-Based Harvest Control Rules for Norwegian and US Ecosystems

Management strategy evaluation (MSE) provides a simulation framework to test the performance of living marine resource management. MSE has now been adopted broadly for use in single-species fishery management, often using a relatively simple ‘operating model’ that projects population dynamics of one species forward in time. However, many challenges in ecosystem-based management involve tradeoffs between multiple species and interactions of multiple stressors. Here we use complex operating models, multi-species ecosystem models of the California Current and Nordic and Barents Seas, to test threshold harvest control rules that explicitly address the linkage between predators and prey, and between the forage needs of predators and fisheries. Our investigation led to three main results. First, consistent with studies based on single-species operating models, we found that compared to constant F=FMSY policies, threshold rules led to higher target stock biomass for Pacific hake (Merluccius productus) in the California Current and mackerel (Scomber scombrus) in the Nordic and Barents Seas. Performance in terms of catch of these species varied depending partly on the biomass and recovery trajectory for the simulated stock. Secondly, the multi-species operating models and the harvest control rules that linked fishing mortality rates to prey biomass (zooplankton) led to increased catch variability; this stemmed directly from the harvest rule that frequently adjusted Pacific hake or mackerel fishing rates in response to zooplankton, which are highly variable in these two ecosystems. Thirdly, tests suggested that threshold rules that increased fishing when productivity (zooplankton) declined had the potential for strong ecosystem effects on other species. These effects were most apparent in the Nordic and Barents Seas simulations. Further developing and testing such ecosystem-level considerations can be achieved with the Atlantis end-to-end ecosystem models applied here, which have the added benefit of tracking the follow-on effects of the harvest control rule on the broader ecosystem.

A Computational Model for Determining Levels of Factors in Inventory Management Using Response Surface Methodology

Inventory management plays a critical role in balancing supply availability with customer requirements and significantly contributes to the performance of the whole supply chain. It involves many different features, such as controlling and managing purchases from suppliers to consumers, keeping safety stock, examining the amount of product for sale, and order fulfillment. This paper involves the development of computational modeling for the inventory control problem in Thailand. The problem focuses on determining levels of factors, which are order quantity, reorder point, target stock, and inventory review policy, using a heuristic approach. The objective is to determine the best levels of factors that are significantly affected by their responses to optimize them using the response surface methodology. Values of the quantity of backlog and the average inventory amount, as well as their corresponding total costs, are simulated using the Arena software to gain statistical power. Then, the Minitab-response surface methodology is used to find the feasible solutions of the responses, which consist of test power and sample size, full factorial design, and Box–Behnken design. For a numerical example, the computational model is tested with real data to show the efficacy of the model. The result suggests that the effects from the reorder point, target stock, and inventory review policy are significant to the minimum total cost if their levels are set appropriately. The managerial implications of this model’s results not only suggest the best levels of factors for a case study of the leading air compressor manufacturers in Thailand, but also provide a guideline for decision-makers to satisfy customer demand at the minimum possible total inventory cost. Therefore, this paper can be a useful reference for warehouse supervisors, managers, and policymakers to determine the best levels of factors to improve warehouse performance.

Margin Trading and Stock Idiosyncratic Volatility: Evidence from the Chinese Stock Market

In this paper, we find that the idiosyncratic volatility (IV) effect on expected returns exists and cannot be explained by other variables in the Chinese stock market. The Chinese stock market launched margin trading in March 2010. We therefore study the margin trading target and non-margin trading target stocks separately and find that the IV effect exists in both stock groups. The IV effect of the margin trading target stocks can be explained by the turnover ratio, whose mechanism shows that the short sale constraint hinders the expression of the seller’s heterogeneous beliefs. However, the IV effect of the non-margin trading target stocks cannot be interpreted by other variables. In comparison to margin trading target stocks, non-margin trading target stocks are more likely to have the lottery characteristics and their gambling behavior is more pronounced.

Dome-shaped selectivity in LB-SPR: Length-Based assessment of data-limited inland fish stocks sampled with gillnets

Inland fisheries can support complex social-ecological systems and contribute to food security. However, many target stocks lack monitoring and quantitative assessment. There may be scope to apply current data-limited assessment models in these fisheries. The Length-Based Spawning Potential Ratio (LB-SPR) model requires only fish length data and input estimates of key life history parameters. LB-SPR has been seldom applied in inland systems, partly due to the current model assumption of asymptotic fishing gear selection, characteristic of trawls. In contrast, most inland fisheries operate gillnets or hooks, having dome-shaped selection curves. This is problematic because the largest size classes of fish are not fully retained by the gear, but their absence in the catch will be assumed by the LB-SPR model to reflect fishing mortality. LB-SPR was extended to include dome-shaped selection. Case study assessments were undertaken using survey gillnet catches of brown trout Salmo trutta from four Irish lakes. Bayesian Monte Carlo methods were used to quantify uncertainty around estimates of life history parameters for each stock. Despite small sample sizes, the model produced estimates of SPR that corresponded to expert knowledge of stock status. The extended LB-SPR can be applied to assess data-limited inland fish stocks, and other stocks for which selection is dome-shaped.

Fisheries for the future: greenhouse gas emission consequences of different fishery reference points

Abstract Global fisheries have for long been scrutinized in terms of ecosystem effects but only more recently for their greenhouse gas emissions. These emissions are dominated by fuel use on fishing vessels and the levels are often neglected side effects of resource overexploitation. Using a simple production model, Pella-Tomlinson, we illustrate how fuel efficiency (fuel use per unit of catch) varies with the level of exploitation and biomass depletion. For this model, fuel use per unit catch rises hyperbolically with fishing effort—it is relatively flat at low levels of effort but rises steeply as effort increases and biomass and catch decline. In light of these findings, the general fuel efficiency relationship with common fishery reference points on stock status is discussed, as well as other means of reducing fuel use and thus greenhouse gas emissions. We conclude that much may be gained by considering fuel efficiency in setting reference points for target stock biomass in fisheries and encourage further investigations.

Effectively training neural networks for stock index prediction: Predicting the S&P 500 index without using its index data.

We propose a novel method for training neural networks to predict the future prices of stock indexes. Unlike previous works, we do not use target stock index data for training neural networks for index prediction. Instead, we use only the data of individual companies to obtain sufficient amount of data for training neural networks for stock index prediction. As a result, our method can avoid various problems due to training complex machine learning models on a small amount of data. We performed numerous types of experiments to test methods designed for predicting the future price of the S&P 500 which is one of the most commonly traded stock indexes. Our experiments show that neural networks trained using our method outperform neural networks trained on stock index data. Compared with other state-of-the-art methods, our method is conceptually simpler and easier to apply, and achieves better results. We obtained approximately a 5-16% annual return before transaction costs during the test period (2006-2018).

Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning

Standard methods and theories in finance can be ill-equipped to capture highly non-linear interactions in financial prediction problems based on large-scale datasets, with deep learning offering a way to gain insights into correlations in markets as complex systems. In this paper, we apply deep learning to econometrically constructed gradients to learn and exploit lagged correlations among S&P 500 stocks to compare model behaviour in stable and volatile market environments, and under the exclusion of target stock information for predictions. In order to measure the effect of time horizons, we predict intraday and daily stock price movements in varying interval lengths and gauge the complexity of the problem at hand with a modification of our model architecture. Our findings show that accuracies, while remaining significant and demonstrating the exploitability of lagged correlations in stock markets, decrease with shorter prediction horizons. We discuss implications for modern finance theory and our work’s applicability as an investigative tool for portfolio managers. Lastly, we show that our model’s performance is consistent in volatile markets by exposing it to the environment of the recent financial crisis of 2007/2008.

Disclosure Regulation and Corporate Acquisitions

ABSTRACTThis paper examines the effect of disclosure regulation on the takeover market. We study the implementation of a recent European regulation that imposes tighter disclosure requirements regarding the financial and ownership information on public firms. We find a substantial drop in the number of control acquisitions after the implementation of the regulation, a decrease that is concentrated in countries with more dynamic takeover markets. Consistent with the idea that the disclosure requirements increased acquisition costs, we also observe that, under the new disclosure regime, target (acquirer) stock returns around the acquisition announcement are higher (lower), and toeholds are substantially smaller. Overall, our evidence suggests that tighter disclosure requirements can impose significant acquisition costs on bidders and thus slow down takeover activity.

Disclosure Regulation and Corporate Acquisitions

This paper examines the effect of disclosure regulation on the takeover market. We study the implementation of a recent European regulation that imposes tighter disclosure requirements regarding the financial and ownership information on public firms. We find a substantial drop in the number of control acquisitions after the implementation of the regulation, a decrease that is concentrated in countries with more dynamic takeover markets. Consistent with the idea that the disclosure requirements increased acquisition costs, we also observe that, under the new disclosure regime, target (acquirer) stock returns around the acquisition announcement are higher (lower), and toeholds are substantially smaller. Overall, our evidence suggests that tighter disclosure requirements can impose significant acquisition costs on bidders and thus slow down takeover activity.

PRICE PRESSURE OF TRADING AND EQUITY PRICES IN HEDGE FUND ACTIVISM

AbstractIn this article, I offer and examine a price pressure hypothesis, which states that stock prices of activism targets temporarily deviate from fundamentals. Increased demand for target stocks upon the formation of activist positions exerts upward pressure on targets’ stock prices in the short term. Such effects are driven by illiquid stocks whose prices are sensitive to order‐flow imbalances. When activists use private transactions, price pressure effects are muted. As buying pressure subsides and reverses over the long run, targets’ stock prices decline proportionately to predisclosure accumulations, driven again by illiquid stocks. These price dynamics have important implications for activists’ block‐formation strategies and, more generally, shareholder activism.