Target Price Research Articles

Price Setting and Price Stickiness: A Behavioral Foundation of Inaction Bands

Abstract This paper puts forward a behavioral theory of price setting where managers maximize perceived profits following a process of mental accounting. The theory predicts a pricing rule that is similar to—but crucially different from—that of a standard menu cost theory: there is an inaction band, but there are two rather than just one target prices, depending on whether the firm updates its price upwards or downwards. The calibrated model replicates two patterns of price microdata that standard menu cost models have difficulty accounting for: (i) The distribution of price changes has both small and large price changes, and (ii) the hazard function of price changes is downward sloping initially, that is, firms that have just recently changed their price have a higher probability of changing it again, while this probability becomes constant thereafter.

Price predictability in limit order book with deep learning model

ABSTRACT This study explores the prediction of high-frequency price changes using deep learning models. Although state-of-the-art methods perform well, their complexity impedes the understanding of successful predictions. We found that an inadequately defined target price process may render predictions meaningless by incorporating past information. The commonly used three-class problem in asset price prediction can generally be divided into volatility and directional prediction. When relying solely on the price process, directional prediction performance is not substantial. However, volume imbalance improves directional prediction performance.

Theoretical study on demand-side management to reduce imbalance between electricity supply and demand

The gap between electricity supply and demand, which is the electricity imbalance, can have negative economic impacts on retail electricity suppliers. This paper presents a methodology for designing demand response (DR) programs while mitigating the negative impacts. The DR programs require the retailers to set target electricity consumption and prices of economic incentives for customers who cooperate with the DR programs. First, the target electricity consumption is determined by solving a problem of social welfare maximization in which the objective function is the sum of economic surplus of the retailers and their customers and impacts of electricity imbalance. The optimal electricity prices or the optimal rebate levels are then calculated under the principle that rational customers act to maximize their economic surplus. Through numerical simulations, the authors confirmed that the proposed methodology mitigated the impacts of electricity imbalance. Meanwhile, results of the numerical simulations showed that in practical situations, there were cases where the retailers unable to reduce the electricity imbalance profitably. Therefore, the authors additionally discuss ways to assess the electricity imbalance, which motivates the retailers to reduce the imbalance, in such cases.

Wandering Drunkards Walk after Fibonacci Rabbits: How the Presence of Shared Market Opinions Modifies the Outcome of Uncertainty.

Shared market opinions and beliefs by market participants generate a set of constraints that mediate information through a not-so-unstable system of expected target prices. Price trajectories, within these sets of constraints, confirm or disprove the likelihood of participant expectations and cannot, de facto, be considered permutable, as literature has shown, since their inner structure is dynamically affected by their own progress, suggesting per se the presence of both heat and cycles. This study described and discussed how trajectories are built using different alphabets and suggests that prices follow an ergodic course within structurally similar tessellation classes. It is reported that the courses of price moves are self-similar due to their a priori structure, and they do not need to be complete in order to create the conditions, in resembling conditions, for the appearance of the well-known and commonly used Fibonacci ratios between price trajectories. To date, financial models and engineering are mostly based on the mathematics of randomness. If these theoretical findings need empirical validation, such a potential infrastructure of ratios would suggest the possibility for a superstructure to exist, in other words, the emergence of exploitable patterns.

Hospital–Skilled Nursing Facility Integration and Participation in a Bundled Payment Initiative

Reduced institutional postacute care has been associated with savings in alternative payment models. However, organizations may avoid voluntary participation if participation could threaten their own revenues. To characterize the association between hospital-skilled nursing facility (SNF) integration and participation in Medicare's Bundled Payments for Care Improvement Advanced (BPCI-A) program. This is a cross-sectional analysis of hospital participation in BPCI-A beginning with its launch in 2018. Each SNF-integrated hospital was matched with 2 nonintegrated hospitals for each of 4 episode-specific analyses. Fifteen hospital-level variables were used for matching: beds, case mix index, days, area SNF beds, metropolitan location, ownership, region, system membership, and teaching status. Hospitals were also matched on episode-specific volume, target price, and the interaction of target price and case mix. Episode-specific logistic models were estimated regressing hospital participation on integration and the previously listed variables. The marginal effect of integration on participation was then calculated. Analysis took place from August 2022 to May 2024. Hospital-SNF integration, as defined by common ownership and referral patterns and identified using cost reports, Medicare claims, and Provider Enrollment, Chain, and Ownership System records. Additional sources included records of target prices and participation, the Area Health Resources File, and the Compendium of US Health Systems. Participation in BPCI-A. In total, 1524 hospitals met criteria for inclusion in the hip and femur (HFP) analysis, 1825 were included in the major joint replacement of the lower extremity (MJRLE) analysis, 2018 were included in the sepsis analysis, and 1564, were included in the stroke-specific analysis. Across episodes, 191 HFP-eligible hospitals (12.5% of HFP-eligible hospitals), 302 MJRLE-eligible hospitals (16.5%), 327 sepsis-eligible hospitals (16.2%), and 185 sepsis-eligible hospitals (11.8%) were SNF integrated. In total, 79 hospitals (5.2%) participated in the HFP episode, 128 (7.0%) participated in the MJRLE episode, 204 (10.1%) participated in the sepsis episode, and 141 (9.0%) participated in the stroke episode. Integration was associated with a 4.7-percentage point decrease (95% CI, 2.4 to 6.9 percentage points) in participation in the MJRLE episode. There was no association between integration and participation for HFP (0.5-percentage point increase in participation moving from nonintegrated to integrated; 95% CI, -2.9 to 3.8 percentage points), sepsis (1.0-percentage point increase; 95% CI, -2.2 to 4.2 percentage points), and stroke (0.3-percentage point decrease; 95% CI, -3.1 to 3.8 percentage points). In this cross-sectional study, there was an uneven association between hospital-SNF integration and participation in Medicare's BPCI-A program. Other factors may be more consistent determinants of selection into voluntary payment reform.

Loyalty Currency and Mental Accounting: Do Consumers Treat Points Like Money?

Problem definition: Loyalty programs have greatly expanded in scale and scope, and loyalty points issued by firms serve as a new form of currency alongside the traditional currency of money. In this paper, we study how consumers decide to pay with points or money for a purchase and how these decisions are affected by consumers’ points earning characteristics. Methodology/results: We develop a model of consumers’ payment choices and estimate it on proprietary loyalty program data from a major U.S. airline company using a hierarchical Bayesian framework. Our results demonstrate that mental accounting, the subjective perceived value of points, and the reference exchange rate play important roles in consumers’ payment choices. Moreover, the primary points earning source and the total earning level are jointly associated with consumers’ attitudes toward points and money: Consumers who earn many points and mostly with the focal firm tend to value points more than money, while those who earn few points or mostly through a cobranded credit card tend to value money more than points. To better understand heterogeneity in consumers’ attitudes toward points, we propose a probabilistic segmentation of consumers and identify four behavioral segments with distinctive characteristics. Through counterfactual analysis, we demonstrate how a firm can implement money and point pricing policies to optimally target and influence consumers’ payment choices. Managerial implications: It is important for firms to understand how consumers think about points and decide to pay with points or money as it affects firms’ cash flows, profitability, and consumer loyalty and engagement. We demonstrate that firms can design optimal price targeting policies by utilizing the significant level of heterogeneity in consumers’ attitudes toward points. Furthermore, our results indicate that firms that are expanding their partnership networks and offering more points earning sources should consider the impact that this would have on consumers’ mental accounting of money and points, and consequently their payment choices. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0287 .

Stock Price Reactions to the Information and Bias in Analyst-Expected Returns

ABSTRACT I use a novel decomposition to estimate information and bias components from the returns implied by analyst price targets and provide evidence that prices simultaneously under-react to information and over-react to bias. Price reactions to information are permanent, and prices drift in the direction of their initial reaction for up to 12 months. Price reactions to bias are transitory, and prices reverse their initial reaction after about three months. Price reactions are relatively efficient. Approximately 85 percent of the total price reaction to information occurs during price target announcement months. Market participants are able to mostly (but not fully) debias analyst-expected returns before incorporating them into prices, with the announcement-month reaction to bias being relatively weak at about 15 percent of its reaction to information. A trading strategy analysis implies that mispricing induced by bias is only about one-third of that implied by prior research. JEL Classifications: G12; G14; G40.

Are analysts’ Forecasts Reliable? A Machine Learning-Based Analysis of the Target Price Accuracy

This paper examines the accuracy of target price forecasts made by sell-side analysts, focusing on predicting target price accuracy using machine learning approaches. Utilizing a dataset of target price forecasts for U.S. listed companies from 1999 to 2021, we employ ensemble methods and incorporate market-level, firm-level, and analyst-level information to predict target price accuracy in terms of target price errors and target price achievement. The long-short portfolio constructed based on our predictions significantly outperform the benchmark in terms of cumulative return and Sharpe ratio.

Practical Applications of When and How Are Analysts’ Price Targets and Recommendations Useful?

Practical Applications of When and How Are Analysts’ Price Targets and Recommendations Useful?

Do sell-side analysts react too pessimistically to bad news for minority-led firms? Evidence from target price valuations

We find that the adverse impact of bad news on analysts’ valuations is 57% larger when the CEO is Non-White, resulting in more pessimistic valuations for Non-White CEOs relative to their White counterparts. Non-White CEO firms are more likely to surpass analysts’ valuation targets in the subsequent 12 months, suggesting that this racial gap lacks economic justification. To provide further evidence of a racial bias: (1) we triangulate our empirical findings with corroborating evidence from a controlled experiment and (2) we provide evidence that analysts’ valuation disparities towards Non-White CEO firms become larger when race relations are worse. Increases in CEO familiarity attenuate these disparities, suggesting the bias we document appears to be subconscious. Our findings suggest that resources allocated towards educating a firm’s stakeholders about the potential impact of implicit racial biases and increasing self-awareness may be impactful in promoting equality within capital markets.

The Effect of Dispersion on the Informativeness of Consensus Analyst Target Prices

Consensus analyst target prices are widely available online at no cost to investors. In this paper, we examine how the amount of dispersion in the individual target prices comprising the consensus affects the predictive association between the consensus target price and future returns. We find that returns implied by consensus target prices and realized future returns are positively correlated when dispersion is low, but they become highly negatively correlated when dispersion is high. Further analyses suggest that the differing effect of dispersion stems from incentive-driven staleness in price targets by some analysts after bad news. As a stock performs poorly and some analysts are slow to update their target prices, dispersion increases, and the consensus target price becomes too high. This has important implications for how consensus analyst target prices should inform investment decisions. We show that a hedge strategy taking a long (short) position in stocks with the highest predicted returns among stocks with the lowest (highest) dispersion earns more than 11% annually. Finally, we show that the negative correlation between consensus-based predicted returns and future realized returns for high-dispersion stocks exists mainly for stocks with high retail interest, suggesting that unsophisticated investors are misled by inflated target prices that are available freely online. This paper was accepted by Suraj Srinivasan, accounting. Funding: The authors acknowledge financial support from Indiana University and Yale University. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.03549 .

Target price policy and rural household income: Evidence from China

Target price policy and rural household income: Evidence from China

Information Discovery by Analysts

We provide direct evidence that analysts engage in information discovery, and that investors value this discovery. The innovation in our article is to read through the summaries of over 3,500 analyst reports to identify whether analysts engage in discovery and/or interpretation. Analysts discover new information from management sources (example: personal meetings) and non-management sources (example: channel checks). Information discovery is prevalent in 17% of the reports. Investors react more strongly to analyst reports containing changes in price targets or Earnings per Share if those reports are accompanied by information discovery. Economic determinants predict whether a report contains discovery.

Physician Group Practices Accrued Large Bonuses Under Medicare's Bundled Payment Model, 2018-20.

The Bundled Payments for Care Improvement Advanced Model (BPCI-A), a voluntary Alternative Payment Model for Medicare, incentivizes hospitals and physician group practices to reduce spending for patient care episodes below preset target prices. The experience of physician groups in BPCI-A is not well understood. We found that physician groups earned $421million in incentive payments during BPCI-A's first four performance periods (2018-20). Target prices were positively associated with bonuses, with a mean reconciliation payment of $139 per episode in the lowest decile of target prices and $2,775 in the highest decile. In the first year of the COVID-19 pandemic, mean bonuses increased from $815 per episode to $2,736 per episode. These findings suggest that further policy changes, such as improving target price accuracy and refining participation rules, will be important as the Centers for Medicare and Medicaid Services continues to expand BPCI-A and develop other bundled payment models.

Consensus Analysts Target Price and Stock Price Returns in Nigeria

This study focuses on the effect of an Investment Valuation technique (Consensus Analysts Target Price) on Stock Price Return of listed companies in the Nigerian Exchange between the period of 2011 and 2023. The ex-post facto research design was employed in this research due to the use of historic (secondary) data from the selected fifteen (15) companies across 3 major sectors on the Nigeria Exchange (NGX). Panel autoregressive distribution lag model was the estimation techniques and inference made on 5% significance level. Prior to the data analysis, a number or pre-estimation test were carried out such as co-integration, homogeneity, and cross-sectional dependence in addition to basis correlation and descriptive analysis. Findings from the short run reveals a significant negative coefficient (-0.273) for lagged Stock Price Return (L.STR), indicating a substantial short-term correction effect. Conversely, the lagged Consensus Analyst Target Price (L.CATP) shows a positive coefficient (0.141) without statistical significance, suggesting a modest positive relationship without an immediate impact on Stock Price Return. Transitioning to the long run, the highly significant negative coefficient (-1.273) for long-run Stock Price Return (lr_STR) emphasizes a robust and persistent negative adjustment effect. Over an extended period, Consensus Analyst Target Price (lr_CATP) introduces a marginally significant positive coefficient (0.077), implying a modest positive impact on Stock Price Return. Based on the results, one recommendation concerning Consensus Analyst Target Price (CATP) and Stock Price Return is to exercise caution in relying solely on CATP for short-term decision-making. Investors should be aware that, in the short term, deviations from the lagged return may not be significantly influenced by CATP. Therefore, considering additional factors or indicators alongside CATP could enhance the accuracy of short-term decision-making in understanding stock price movements.

Does the Textual Tone of Analyst Reports Have Valuable Information? Korean Evidence*

AbstractWe investigate whether the text of analyst reports can provide additional information beyond the recommendation and target price. Positive and negative word lexicons are generated through an automated Bayesian learning method applied to Korean analyst reports spanning from 2016 to 2018. Then, the textual tone of an analyst report is quantified as the difference between the frequencies of positive and negative words in the text. The announcement returns of portfolios sorted by textual tone exhibit significant differences ranging from 1.14% to 2.82% within the same recommendation or target price revision group. Regression analysis also reveals significant association between the textual tone of analyst reports and stock announcement returns, even when controlling for the recommendation and target price. Notably, the text proves to be more informative in negative tones and within firms with limited analyst coverage. Our results indicate that textual analysis can unveil nuanced analyst opinions not captured by numerical information.

СЦЕНАРІЇ ФОРМУВАННЯ РЕЗЕРВІВ ПІДВИЩЕННЯ ЕКОНОМІЧНОЇ ЕФЕКТИВНОСТІ ПІДПРИЄМСТВ СФЕРИ ПОСЛУГ З ОРІЄНТАЦІЄЮ НА ІННОВАЦІЙНИЙ РОЗВИТОК

The article notes the priority tasks and goals of enterprises in the service sector, the solution of which requires the use of progressive approaches, and the formation of reserves is attributed to them. It is emphasized that the formation of reserves is the main element of ensuring the economic efficiency of entrepreneurial activity, and it is especially important when the strengthening of innovative development is expected. Depending on the industry, the scenarios for the formation of enterprise reserves may differ and, accordingly, require a thorough theoretical-methodical and scientific-applied justification. The absence of a universal approach to the formation of reserves for increasing the economic efficiency of enterprises in the service sector, which would simultaneously focus on the development of innovative activities, was revealed. It is important for enterprises in the service sector to master universal approaches to the formation of reserves, which allow both to ensure economic efficiency, innovative development, and to strengthen the readiness to overcome existential challenges and threats. It is appropriate to follow a structural approach, according to which the allocation of reserves will take place according to clear criteria, according to the principles of balance and rationality. It is suggested that the formation of reserves for increasing the economic efficiency of enterprises in the service sector with an orientation towards innovative development should be carried out according to price and marketing policy scenarios. The tools of price policy include sales, lending, discounting, break-even, high and target prices, etc. Instead, in the marketing policy, priority is given to tools such as diversification of services, brand management, benchmarking, digitalization, technological cooperation, etc.

Do financial analysts care about ESG ?

This article studies whether financial analysts value Environmental, Social and Governance (ESG) criteria when issuing target prices. Raw results show that analysts issue lower target prices to firms with high ESG scores. We show that this relationship is, in fact, driven both by the existing size bias in ESG data and by the industry-level structure of ESG scores. When controlling for these elements, we actually find that financial analysts are more optimistic about firms that have high E, S, and G scores. Notably, the effect is more pronounced for the environmental (E) score, with a one standard deviation change associated with a 2.09 percentage point increase in the analysts’ target prices implied returns.

Early Experiences With Bundled Payments for Care Improvement for Major Bowel Surgery.

Bundled Payment (BP) models are becoming more common in surgery. We share our early experiences with Bundled Payments for Care Improvement for major bowel surgery. Patients undergoing major bowel surgery between January and October 2021 were identified using Medicare Severity-Diagnosis Related Group (MS-DRG) codes. Major drivers of cost in a BP model are reported and compared to the Fee-For-Service (FFS) payment model. A total of 202 cases (173 FFS vs 29 BP) were analyzed. The mean BP cost per Clinical Episode was $28,340. Eleven patients (38%) in the BP model had costs greater than the Target Price. The drivers of cost in the BP model were 59% acute care facility, 17% physician services, 9% post-acute care facilities, 8% other, and 7% readmissions. Clinical Episode of care costs varied considerably by MS-DRG case complexity. Robotic surgery increased costs by 35% (mean increase $3724, P < .01). The 90-day readmission rate was 17% for a mean cost of $11,332 per readmission. Three patients (10%) were discharged to a skilled nursing facility at an average cost of $11,009, while fifteen patients (52%) received home health services at a mean cost of $2947. Acute care facility costs were similar in the BP vs FFS groups (mean difference $1333, P = .22). Patients undergoing major bowel surgery are a heterogeneous population. Physicians are ideally positioned to deliver high-value, patient-centered care and are crucial to the success of a BP model. The post-acute care setting is a key component of improving efficiency and quality of care.

Price distortion forced by recycling competition under trade-in collaboration

Renowned brands and recycling agencies tacitly integrate their business flows together in the current trade-in industry. The collaborative trade-in programme breaks the hierarchy of closed-loop supply chains and provides seamless services for consumers. In this study, we develop a stylised model to capture the change in supply chain structure and scrutinise trade-in collaboration and recycling competition between sellers and recyclers. We find that, under intensive recycling competition, price targeting new consumers is downward distorted by the seller to occupy more trade-in demands in the old consumer segment. Meanwhile, the rebate of the recycler is suppressed by the collaboration constraint. By distorting the price, the seller obtains the predominance in recycling competition while sacrificing part of profits from new consumers. The price distortion indicates the competition spill-over from the old consumer segment to the new consumer segment. Furthermore, the establishment of autonomous recycling reaches a Pareto improvement for the whole system when the competition is mild; otherwise, these two entities fall into a zero-sum scenario. Our study offers insights into recycling value allocation and trade-in partner choice and, theoretically, contributes to the economic literature that price distortion can even be forced by competition with the absence of information asymmetry.