Accuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies

Target prices are often provided as a support for stock recommendations by sell-side analysts which represent an explicit estimate of the expected future value of a company’s stock. This research focuses on mean target prices for stocks contained in the Standard and Poor’s Global Clean Energy Index during the time period from 2009 to 2020. The accuracy of mean target prices for these global clean energy stocks at any point during a 12-month period (Year-Highest) is 68.1% and only 46.6% after exactly 12 months (Year-End). A random forest and an SVM classification model were trained for both a Year-End and a Year-Highest target and compared to a random model. The random forest demonstrates the best results with an average accuracy of 73.24% for the Year-End target and 81.15% for the Year-Highest target. The analysis of the variables shows that for all models the mean target price is the most relevant variable, whereas the number of target prices appears to be highly relevant as well. Moreover, the results indicate that following the rare positive predictions of the random forest for the highest target return groups (“30% to 70%” and “Above 70%”) may potentially represent attractive investment opportunities.

The Valuation and Analysis of Apple Inc

In order to define a reasonable value of Apple’s stocks, a number of valuation models can be considered, including FCFF model, DDM, VC method, FCFE model and multiples valuation model. In the research, we use WACC and DCF calculation to evaluate the Apple company’s further value, and judging whether it is undervalued or overvalued based on historical data and market direction. As different models and data provide various results, they have different limitations and uses. For DCF, although some of the data used in its calculations are based on people's subjective analysis of the future, its valuation serves as a baseline for the target price, making its results relatively conservative and reasonable. Based on the results generated by valuation models, we think Apple still has a great potential to develop, and we advise customers to buy or hold the stock.

The Impact of Target Price Policy on Cotton Cultivation: Analysis of County-Level Panel Data from China

China is an important cotton production area in the world. Since 2014, China has implemented a cotton target price subsidy policy in Xinjiang for 7 years. As the policy implementation time has lengthened, some deep-seated problems have started to emerge. Therefore, it is necessary to summarize and evaluate to clarify the future policy direction of the cotton target price subsidy policy. Based on county-level panel data of Xinjiang and Shandong from 2011 to 2018, this paper used the Propensity Score Matching—Difference in Difference method to analyze the impact of the implementation of cotton target price subsidy policy on cotton planting in Xinjiang. The results showed that: (1) after the implementation of the cotton target price subsidy policy, cotton production was stimulated by the transition, cotton producers’ enthusiasm for cotton production was higher, cotton production increased rapidly, and the yield per unit area decreased, indicating that there was a 'bubble' in cotton cultivation. (2) The target price subsidy policy mainly achieves the expansion of the cotton planting scale by reducing the area of competitive crops. In view of the above research conclusions, this paper further explains its policy implications. It is proposed that the future cotton target price level should be formulated to fully consider the comparative benefits between different crops, to restrict the subjects that enjoy subsidies and the upper limit of subsidies, and strictly implement the concept of green development; it is necessary to guide cotton production out of ecologically vulnerable areas.

Breast Cancer Stage Is Associated With Exceeding Target Price in the Oncology Care Model

PURPOSE: To explore mean difference between Oncology Care Model (OCM) total costs and target price among breast cancer episodes by stage under the Centers for Medicare and Medicaid Services OCM payment methodology. METHODS: Breast cancer episodes from OCM performance period 1-4 reconciliation reports (July 1, 2016-July 1, 2018) were linked with health record data from a large, academic medical center. Demographics, total cost of care (TCOC), and target price were measured by stage. Adjusted differences between TCOC and target price were compared across cancer stage using multivariable linear regression. RESULTS: A total of 539 episodes were evaluated from 252 unique patients with breast cancer, of which 235 (44%) were stage I, 124 (23%) stage II, 33 (6%) stage III, and 147 (27%) stage IV. About 37% of episodes exceeded target price. Mean differences from target price were –$1,782, $2,246, –$6,032, and $11,379 all in US dollars (USD) for stages I through IV, respectively. Stage IV episodes had highest mean TCOC ($44,210 USD) and mean target price ($32,831 USD) but also had higher rates of chemotherapy, inpatient admission, and novel therapy use. After adjusting for covariates, stage IV and ≥ 65-year-old patients had the highest mean difference from target price ($17,175 USD; 95% CI, $12,452 to $21,898 USD). CONCLUSION: Breast cancer episodes in older women with distant metastases most frequently exceeded target price, suggesting that target price did not adequately account for complexity of metastatic cancers. A metastatic adjustment introduced in PP7 represents a promising advancement in the target price methodology and an impact evaluation will be needed.

FLEXIBLE MANAGEMENT OF COSTS, PROFITS AND PRICES USING TARGETED PLANNING

Under market conditions, the main feature of enterprises is the focus on consumer demand, the desire for innovation, the implementation of appropriate scientific and technical innovation policy. Flexibility in management, the ability to restructure quickly without losing new opportunities can be more important than just cost savings. The high degree of uncertainty, which is characteristic of modern conditions of economic development, requires the use of flexible planning tools that allow you to quickly take into account changes in supply and demand, requirements for product quality, prices, etc. In the conditions of competition between producers and the need to improve their economic performance and, above all, profit, employees must have economic tools that would allow them to consider different options for prices, costs, scale of production, changes in the structure of its range and to monitor the consequences of these changes in the economy of the enterprise. One such tool is targeted planning of prices, costs and profits. It is believed that the methodology of target pricing and calculation of target costs was developed in Japan and it is based on a given level of price and profit. The difference between the target price and the target profit is the target cost. But this technology does not work in terms of multi-item production. The problem of the need for flexible management of costs, profits and prices in market conditions is outlining. On the basis of theoretical and empirical researches the critical analysis of foreign scientists’ points of view to the problem of “target profit” planning and “target prime cost” is given. As authors believe the latter are possible only for single-product manufacturing companies. It is suggested to form a flexible system of targeted planning suitable for multi-product companies on the basis of the fixed costs’ distribution in proportion to the marginal profit.

Factors associated with target price status within the oncology care model population.

72 Background: The Oncology Care Model (OCM) is an alternative payment model put forth by the Centers for Medicare and Medicaid Services (CMS), which aims to improve quality of care for cancer beneficiaries, while reducing cost. One of the strategies implemented by CMS to achieve this goal was the development of an episode target price (TP), which uses historical data and episode specific adjusters to calculate a total cost of care goal for each episode. The goal of this analysis was to better understand how CMS risk adjustments could account for episode characteristics, as well as how these characteristics affect likelihood of meeting target price. Methods: OCM performance claims data were abstracted retrospectively from performance periods (PP) 1-6 (episodes initiated from 7/1/2016 – 12/31/2019), in which each episode captured 6 months of care. EHR data was linked to identify cancer staging for OCM Beneficiaries. Any OCM beneficiary with at least one episode in PP 1-6 was included. To assess odds of meeting TP, a logistic regression model with a generalized estimating equation was used to account for patients who contributed multiple episodes. Specific factors evaluated for their association with meeting TP included patient’s age and sex, cancer stage, cancer type, cancer related surgery, clinical trial participation, hospice status, inpatient admissions, observational stays, Medicare part B drug use and radiation therapy. Results: 4,612 episodes were included in analysis, which translated to 2,459 patients, who had an average age of 72 years old and were majority female (50.5%). 2,790 (60.5%) of the episodes met the TP set by OCM. When controlling for covariates, radiation treatment (OR = 1.75, 95%CI: 1.39-2.23), stage 2 compared to stage 4 cancers (OR = 1.86, 95%CI: 1.23-2.80), colorectal cancers (OR = 1.75, 95%CI: 1.11-2.77) and melanomas (OR = 4.35, 95%CI: 2.18-8.67) were significantly associated with increased odds of meeting TP. Novel therapies (OR = 0.19, 95%CI: 0.14-0.26), inpatient admissions (OR = 0.27, 95%CI: 0.22-0.33), observational stays (OR = 0.66, 95%CI: 0.51-0.87) and part B drug use (OR = 0.75, 95%CI: 0.60-0.93) were associated with significantly reduced odds of meeting TP. Conclusions: Our analysis suggests that radiation treatment, as well as selected cancer stage and types may contribute to increased likelihood of meeting TP, which may point to potential areas of cost savings. Conversely, specific resource utilizations such as novel therapy use, inpatient admissions, observational stays, and Medicare part B drug use may decrease the odds of meeting TP, despite being adjusted for in the OCM model. While CMS has recognized that late stage cancers require a more sensitive TP with the metastatic adjustment, other adjustments should also be considered to adequately account for episode characteristics. External validation at other OCM-participating practices is needed to corroborate these results.

The Oncology Care Model and lung cancer: Retrospective review of claims and clinical data at a large academic medical center.

62 Background: The Oncology Care Model (OCM) is a Center for Medicare and Medicaid Innovation (CMMI) alternative payment model designed to enhance value in cancer care. Based on a practice’s historical performance, the model predicts a target price for a 6-month episode of care and adjusts for factors such as age, modality received, geographic location, trend factor, and receipt of a novel therapy. Practices are incentivized to reduce costs of care, allowing for a performance based payment if the total cost of care is below the predicted OCM target price. At our OCM practice, when compared to other malignancy types, lung cancer has disproportionately failed to meet the OCM target. The purpose of our review was to explore the contribution of systemic therapy to total cost within the OCM model for lung cancer episodes. Methods: We reviewed claims and clinical data for the OCM Performance Period 6 (PP6), which corresponds to episodes beginning between 1/2/19-7/1/19 and ending between 7/2/19-12/31/19, for all OCM lung cancer episodes at Northwestern Medical Group. Results: 142 patients were identified with non-small cell (n = 128, 91%) and small cell lung cancer (n = 14, 9%). Patients received a PD1 inhibitor either alone or in combination with chemotherapy (n = 87), tyrosine kinase inhibitors (n = 18), both a PD1 inhibitor and a TKI (n = 2), or chemotherapy alone (n = 35). All systemic therapy use was deemed guideline compliant. 46 patients (33%) had at least 1 cancer-related hospital admission during the episode. 19 patients (13.4%) died during the OCM performance period. 39/142 (27.5%) of patients’ episode costs achieved the OCM target. Among the 103 patients whose total costs exceeded the target, drug costs alone exceeded the target in 67 (65%). Drug costs alone exceeded the total target in 59% (n = 63/107) of those who received PD1 inhibitor or TKI therapy and 11.4% (4/35) of those who received chemotherapy alone. 94% (n = 63/67) of patients for whom drug costs alone exceeded the OCM target received anti-PD1 therapy or a TKI. Conclusions: Drug cost alone exceeded the total target in the majority of OCM lung cancer episodes that did not achieve savings, highlighting the dominant role drugs play in the OCM model. With targeted therapy and immunotherapy already the standard of care in metastatic non-small cell lung cancer, and gaining a foothold in earlier stages of disease, accounting for these therapies in the OCM target price methodology will be critical for oncology practices to be successful within such value-based payment models.