Future Price Changes Research Articles

Estimating a Drug's Price After Loss of Exclusivity as a Function of Its Cost of Goods Sold.

The majority of cost-effectiveness analyses of pharmaceuticals do not incorporate future price changes that are expected once generic competition enters the market. A common rationale for not doing so is the uncertainty around what postloss of exclusivity price to model. The objective of this study is to assess if a drug's price postloss of exclusivity can be estimated on the basis of its cost of goods sold (COGS). First, stakeholders were engaged to understand pricing practices for generic drugs. Then peer-reviewed literature, gray literature, and manufacturer financial statements were reviewed to estimate the typical manufacturer profit margin over COGS. Using pricing data from the Mark Cuban CostPlus Drug Company (MCCPDC), estimates of COGS were calculated by drug form. Finally, a COGS-based approach to estimating a drug's price postloss of exclusivity was tested. COGS were estimated for 2168 unique National Drug Codes (NDCs) reported in the MCCPDC price list by removing the typical manufacturer profit margin (50%) from the manufacturer prices (net of any markup or fees from the MCCPDC). A tablet/capsule, the most common form in the price list, had a median COGS of $0.10 per tablet/capsule. Estimating a drug's price postloss of exclusivity as the median COGS for that drug form times two (to reflect a 50% profit margin), produces estimates of postloss of exclusivity prices that account for drug-specific attributes. This study provides an evidence-based approach to estimate a drug's price postloss of exclusivity as a function of its COGS for incorporation into cost-effectiveness analyses.

Real-world cost-effectiveness of panel-based genomic testing to inform therapeutic decisions for metastatic colorectal cancer

BackgroundMutations in KRAS and NRAS are associated with a lack of response to cetuximab and panitumumab, two biologics used for third-line therapy of metastatic colorectal cancer (mCRC). In British Columbia, Canada, eligibility for cetuximab or panitumumab was first based on single-gene KRAS testing. OncoPanel, a multi-gene next-generation sequencing panel with both KRAS and NRAS, was introduced in 2016. Our objective was to estimate the real-world cost-effectiveness of OncoPanel versus to single-gene KRAS testing to inform eligibility for cetuximab or panitumumab in mCRC. MethodsUsing population-based administrative health data, we identified a cohort of mCRC patients who had received a KRAS or OncoPanel test, and completed prior chemotherapy in 2010–2019. We matched KRAS- and OncoPanel-tested patients (1:1) using genetic matching to balance baseline covariates. Mean and incremental 3-year costs, survival, and quality-adjusted survival were estimated using inverse-probability-of-censoring weighting and bootstrapping. We conducted scenario-based sensitivity analysis for key costs and assumptions. FindingsAll OncoPanel-tested cases (n=371) were matched to a KRAS-tested comparator. In the KRAS and OncoPanel groups, respectively, 55·8 % and 41·2 % of patients were potentially eligible for cetuximab or panitumumab based on mutation status. Incremental cost and effectiveness of OncoPanel were $72 (95 % CI: −6387, 6107), −0·004 life-years (95 % CI: −0·119, 0·113), and −0·011 quality-adjusted life-years (95 % CI: −0·094, 0·075). Reductions in systemic therapy costs were offset by increased costs in other resources. Results were moderately sensitive to time horizon and changes in testing or treatment cost. InterpretationThe use of OncoPanel resulted in more precise targeting of cetuximab and panitumumab, but there was no change in incremental cost or quality-adjusted survival. Understanding the balance of costs achieved in practice can provide insight into the effect of future changes in testing policy, test cost, treatment eligibility, or drug prices in this setting.

Long Short Term Memory Method and Social Media Sentiment Analysis for Stock Price Prediction

The stock market is a complex arena of interest yet uncertainty. Trading stocks, binaries, gold, and bitcoin is growing in popularity, but is prone to price fluctuations influenced by economic and political factors. Social media, particularly Twitter, is where views on companies are shared. Social media sentiment analysis can provide additional insights to evaluate potential future stock price movements, preventing unwanted speculation. The purpose of this research is to develop a Tesla stock price prediction model by integrating the Long Short-Term Memory (LSTM) method and social media sentiment analysis from Twitter to improve prediction accuracy. Stock price data is obtained from Kaggle and Twitter sentiment data is processed through pre-processing. Evaluation values such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are lower in the model with sentiment indicating the ability of the model to more accurately model the dynamics of stock price movements. Lower MSE and RMSE indicate that the model's predictions are closer to the true values, and therefore, the model can be considered more reliable in projecting future stock price changes. These results provide support for the use of Twitter sentiment analysis as a useful source of additional information in improving the prediction accuracy of LSTM regression models in the context of stock market analysis

Are Changes in Crude Oil Prices Predictable?

The oil prices influence the transport sector, and subsequently the profitability of trade and the condition of countries’ economies. Therefore, the prediction of future price changes is of high importance. Said prices can be forecasted by macroeconomic or technical methods of analysis, yet both of those are often questioned. In the article, we present a method for a complex verification of the hypothesis that current prices are not influenced by historical changes in oil prices. To do so we applied an alternative and more precise representation of prices than the most commonly used candlestick representation, i.e. the binary temporal representation and utilized advanced statistical testing. In the paper, we also present the results of empirical research performed on the oil price quotations, with the use of advanced software in C++. The research confirms the sensibility of using technical and fundamental analysis.

What is behind housing sentiment?

We utilize topic modeling to analyze the relation between housing media sentiment and house price changes in the United States. We uncover seven distinct topics related to housing market in articles published in the Wall Street Journal, New York Times, and USA Today during the 2000–2021 period. Not all topics show significant relation to house price movements. Sentiments of articles related to economic outlook and house construction are most significant in explaining future house price changes suggesting that news media discussions about economic fundamentals stand behind the link between news media sentiment and house price changes.

The Influence of Markov Chain and Properties of Principal Component Solutions in The Analysis of Share Price Movements for Stock Market

The stock market performance and operation has been widely recognized as a significantly viable investment field in financial markets. Therefore, this paper studied stochastic analysis of Markov chain and PCA in the closing share price data of Access and Fidelity banks (2016-2022) through Nigeria Stock Exchange. The share prices were transformed into 3-steps transition probability matrix solution to cover this number of years. The striking focus of this is on two merged banks where their transition probability matrix was considered. The future share prices changes were known. The criteria of obtaining four share prices which formed from the two merged banks 2x2 matrices were given and analytical solution of principal component were considered for future stock price changes. From the solution matrix of two merged banks showed that the has the best probability of price increasing in the near future: 12%, best probability of reducing in future by 22% and best probability of no-change in the near future by 21% which is a tool for proper decision making in the day-to-day management of the bank; which shows it is profit making organization and are hopeful for future investment plans both short or

Analysis of Consumer Decision-making from the Perspective of Anchoring Effect

The anchoring effect is a kind of heuristic deviation. When people make judgments about someone or something, they are easily dominated by the first impression or the first information, which is like an anchor sinking into the sea to fix people's thoughts somewhere. This paper analyses the fluctuation of inflation rate, the reduction of interest rate, and the change of COVID-19 policy in China in recent years. Consumers' price preferences are anchored by past prices, which prevents them from accurately predicting future price changes. If consumers believe that prices will remain the same in the future, they will use the current price as a benchmark for future prices, and they will base their consumption decisions on this price. However, if the price changes in the future, consumers will be priced wrong, so they will make wrong consumption decisions. Because of the anchoring effect, people have impulsive consumption, retaliatory consumption, and conservative consumption. In order to reduce the negative impact of the anchoring effect on the economy, this paper suggests that people should be fully aware of the market price, make reasonable expectations of inflation, avoid impulse consumption and retaliatory consumption.

Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm.

Copper is an important mineral and fluctuations in copper prices can affect the stable functioning of some countries' economies. Policy makers, futures traders and individual investors are very concerned about copper prices. In a recent paper, we use an artificial intelligence model long short-term memory (LSTM) to predict copper prices. To improve the efficiency of long short-term memory (LSTM) model, we introduced a simulated annealing (SA) algorithm to find the best combination of hyperparameters. The feature engineering problem of the AI model is then solved by correlation analysis. Three economic indicators, West Texas Intermediate Oil Price, Gold Price and Silver Price, which are highly correlated with copper prices, were selected as inputs to be used in the training and forecasting model. Three different copper price time periods, namely 485, 363 and 242 days, were chosen for the model forecasts. The forecast errors are 0.00195, 0.0019 and 0.00097, respectively. Compared with the existing literature, the prediction results of this paper are more accurate and less error. The research in this paper provides a reliable reference for analyzing future copper price changes.

Environmental Value Assessment of Plastic Pollution Control: A Study Based on Evidence from a Survey in China

A scientific environmental management decision is based on the correct assessment of environmental value. Aiming to objectively and accurately assess the environmental value of plastic pollution treatment, in this paper, we design a choice experiment with four item characteristic attributes consisting of source reduction, recycling, cleanup and remediation and price. With the research data of 450 interviewed residents, a choice experiment method (CEM) and numerical simulation were used to comprehensively assess the environmental value of plastic pollution treatment in China, incorporating discount rates and future price changes of environmental products into the study. The results showed that: (1) residents’ willingness to pay per capita for source reduction, remediation and recycling was CNY 32.79, CNY 25.27 and CNY 15.78, respectively; (2) from the perspective of compensation surplus, the residents were willing to pay CNY 147.68 per capita for environmental improvement; (3) the dynamic curve of the value of plastic pollution control displayed an increasing, then gradually declining trend, and its total economic value of 100a was CNY 21,259.97 per capita; and (4) the model simulated the possible trajectory of future changes in plastic pollution control (three scenarios of constant, decreasing and increasing rates of development of plastic pollution control services) and found that addressing the plastic pollution problem early is more conducive to improving the overall welfare of society. This study can provide a basis for scientific evaluation of the benefits of plastic pollution management and allocation of pollution management resources.

Exploring the relationship between global economic policy and volatility of crude futures: A two-factor GARCH-MIDAS analysis

This study investigates the impact of global economic policy on the volatility of crude oil futures using a two-factor GARCH-MIDAS (Mixed Data Sampling) analysis. The core of the problem statement is the need to understand how global economic policy affects the volatility of crude oil futures, which has significant implications for investors, decision-makers, and market participants. The study aims to determine the correlation between critical global economic policy variables and crude oil futures volatility. This study combines the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework and the Mixed Data Sampling (MIDAS) method to create a Two-Factor GARCH-MIDAS model. This model thoroughly analyzes the long- and short-term dynamics between economic policy and crude oil futures volatility by incorporating macroeconomic and financial market data. The analysis covers a specific period from 2010 to 2021 and considers both prosperous and challenging economic times. The study's narrow geographic focus on a region with significant global economies and oil markets ensures a thorough representation of monetary and fiscal policies and changes in crude oil futures prices. The analysis provided insight into the complex link between volatility in crude oil futures and global economic policy. The results provide insightful information about how changes in interest rates, fiscal policies, and trade regulations affect the volatility of crude oil futures. The study advances knowledge of the connections between economic policy and energy markets, enabling investors and decision-makers in the international oil market to make more informed choices.

Generalized Methods on Access Bank Share Price Changes and Variations in Nigeria Stock Market

Purpose: Stock market performance and operation have been widely recognized as a viable investment field in financial markets. This paper studied the share price movement of Access Bank, PLC.Methodology:The share price data was subjected to a 3-step transition matrix for independent share prices, which enabled us to proffer precise conditions for obtaining future share price changes and placed criteria that enabled us to come up with a 2x2 matrix solution of Principal Component Analysis (PCA) on the share price of Access bank.Findings: The solution matrix of the stochastic analysis showed that Access Bank PLC has the best probability of price increasing shortly by 12%, the best probability of reducing in the future by 21%, and the best probability of no change shortly by 20%.Implication:More so, an increase in the trading days also increases the value of future share price changes, and other statistical variables were also found. Finally, the influences of the relevant parameters of stock market variables were effectively discussed in this paper.

Development of Machine Learning Based Flight Price Prediction System for Indian Market

Abstract: Airlines usually keep their price strategies as commercial secrets and information is always asymmetric, it is difficult for ordinary customers to estimate future flight price changes. However, a reasonable prediction can help customers make decisions when to buy air tickets for a lower price. Flight price prediction can be regarded as a typical time series prediction problem. When you give customers a device that can help them save some money, they will pay you back with loyalty, which is priceless. Considering the features such as departure time, the number of days left for departure and time of the day it will give the best time to buy the ticket. Features are extracted from the collected data to apply Random Forest Machine Learning (ML) model. Then using this information, we are intended to build a system that can help buyers whether to buy a ticket or not. We have used Random Forest Algorithm which is a popular machine learning algorithm that belongs to the supervised learning technique. It can be used for both Classification and Regression problems in ML. It is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and to improve the performance of the model. With that said, random forests are a strong modelling technique and much more robust than a single decision tree. They aggregate many decision trees to limit over fitting as well as error due to bias and therefore yield useful results. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them.

Comparative Analysis of Machine Learning Performance in House Price Prediction for Ames Iowa

The price of housing is closely related to the quality of people's lives, which is reflected in various aspects (e.g., finding a place to live and managing their finances). On this basis, being able to predict house prices would be a great convenience, and this study examines the accuracy of multiple mechanistic learning regression models in predicting future house price changes. To be specific, this paper downloaded the dataset from Kaggle which includes the training and testing csv files. and ran many different regression techniques including lasso and more models on google Colab. Furthermore, a comparison between the results of different methods is carried out by estimating the mean square error. Then, this research combined some of the best yielded results of the model to combine into a final model and got a result with a high score. According to the analysis and evaluations, the study concluded that the hybrid model resulted in a more accurate prediction than the single model. These results shed light on guiding further exploration of real estate prediction.

Integrating Economic Theory, Domain Knowledge, and Social Knowledge into Hybrid Sentiment Models for Predicting Crude Oil Markets

For several decades, sentiment analysis has been considered a key indicator for assessing market mood and predicting future price changes. Accurately predicting commodity markets requires an understanding of fundamental market dynamics such as the interplay between supply and demand, which are not considered in standard affective models. This paper introduces two domain-specific affective models, CrudeBERT and CrudeBERT+, that adapt sentiment analysis to the crude oil market by incorporating economic theory with common knowledge of the mentioned entities and social knowledge extracted from Google Trends. To evaluate the predictive capabilities of these models, comprehensive experiments were conducted using dynamic time warping to identify the model that best approximates WTI crude oil futures price movements. The evaluation included news headlines and crude oil prices between January 2012 and April 2021. The results show that CrudeBERT+ outperformed RavenPack, BERT, FinBERT, and early CrudeBERT models during the 9-year evaluation period and within most of the individual years that were analyzed. The success of the introduced domain-specific affective models demonstrates the potential of integrating economic theory with sentiment analysis and external knowledge sources to improve the predictive power of financial sentiment analysis models. The experiments also confirm that CrudeBERT+ has the potential to provide valuable insights for decision-making in the crude oil market.

Comparison of Future Price Prediction in Context of Machine Learning Approach

Contemporarily, futures markets have become prosper on account of its intrinsic risk hedging function as well as double-side trading advantages compared to Chinese stock market. In this case, finding the suitable and appropriate way to predict future price changes will be a crucial thing for investors and traders of commodity. In context of theoretical analysis in terms of summary of previous models, this article firstly selects features and market information of all trading days from 2016 to 2021 as input. The research sample is the CSI300 stock index futures, and 1-daylow-frequency data is selected. Three price prediction models were built in terms of the data, i.e., linear regression, random forest, and support vector machine algorithms. Accordingly, the random forest algorithm has the lowest error, high accuracy and stability in the prediction of stock index futures price. These results shed light on guiding further exploration of future price prediction based on specific machine learning approach.

Evaluating the economic feasibility of replacing propane with solid biofuels for grain drying – a scenario analysis

AbstractThis study investigates the economic feasibility of replacing propane with biomass (solid biofuels) for grain drying under four scenarios. Two scenarios are based on the recently increased price of propane and the possible future changes in prices for propane and biomass. The other scenarios assess the impact of two policies on the cost of energy when carbon pricing is included and a financial incentive program is designed to share the purchase and installation cost of clean technologies such as biomass burners. A farm business in Canada is used as the case study to compare and contrast the energy cost of drying wheat grain over the range of 160 000–400 000 bushels (4355–10 890 t) using wood chip or wood pellet burners instead of propane. The results indicate that, under all scenarios, biomass burners are a more economic option than propane burners for drying grain. In the baseline scenario (no carbon pricing/financial support), cost reductions of 34–62% can be achieved by using a wood chip burner instead of a propane burner. This reduction is estimated to be 52–58% for wood pellet burners versus the propane burner. Every $10tCO2e‐1 increase in the carbon pricing adds about 1.7% to the total drying cost for the propane burner. This further increases the cost saving of switching from propane to biomass, as no carbon pricing is applied to biomass as a renewable energy source. A 50% cost sharing arrangement as a financial support to purchase and install biomass burners can provide a further 2–7% cost saving.

On-demand ride-sourcing markets with cryptocurrency-based fare-reward scheme

Cryptocurrency-based fare-reward scheme has been emerging from on-demand ride-sourcing platforms. The reward scheme provides each passenger with some units of cryptocurrency as rewards for use of ride service. The passengers can redeem the rewards for various redemption options. The redemption options offer the passengers economic benefits to offset a portion of costs of using ride services. In this way, the economic benefits serve as incentives for the passengers to repeatedly use ride services, which leads to increase in passenger demand for the platforms. From a passenger perspective, the reward scheme has a distinctive feature in that economic benefits can fluctuate according to changes in price of cryptocurrency in the future. Therefore, the passengers perceive the benefits differently based on their own risk preference and expectation of future price changes. It creates a challenge for the platforms because the optimal decisions for ride services vary by the value that the passengers place for their cryptocurrency rewards. To tackle this challenge, this paper analyzes the impacts of the reward scheme on on-demand ride-sourcing markets. The analysis is done with a mathematical model where a ride-sourcing platform makes decisions for trip fare, vehicle fleet size and cryptocurrency reward size. The reward decision is available under the assumption that the platform secures as many units of cryptocurrency as it rewards to passengers at their equilibrium price level. The model defines passengers’ perceived value of cryptocurrency rewards in terms of risk aversion level and expectation of future price growth of cryptocurrency. Afterwards, we investigate equilibrium and optimal properties of monopoly, social-optimum and second-best optimum ride-sourcing markets. Our analysis gives several key insights on the markets. First, the markets always locate at normal regime where interactions between passenger demand and vehicle supply for ride services converges to a stable equilibrium state. Second, this study shows that a ride-sourcing platform would make a decision to reward if passengers expect larger economic benefits from future redemption of cryptocurrency rewards than their current value. Third, it is shown that the quality of ride services would get worse because average pick-up time for passengers and drivers gets longer with more passenger retention. Fourth, this paper finds that the markets have more room to improve profit or social welfare as passengers expect larger speculative gains from reward redemption.

Dynamic relationship between equity, bond, commodity, forex and foreign institutional investments: Evidence from India

The interrelationship between equity, bond, commodity and forex movements can provide investors with abundant trading opportunities regardless of whether one market is trending upward or downward. Hence, to understand the interlinkage between markets, this study examines the long-run and causal linkage between forex, G-sec bonds, oil prices, gold rates, foreign institutional investment (FII) flows, and equity market and sectoral index returns. Daily time-series data from August 2012 to August 2021 were considered for empirical analysis. Johansen’s cointegration test revealed that foreign exchanges like USD, Euro, GBP and Yen, oil and gold rates, G-bond returns and FII flows were significantly cointegrated with the stock market and sectoral indices in the long run. Further, Granger causality found a uni-directional relationship between forex rates (i.e., USD, Euro, Yen) and the market, as well as sectoral indices, except Nifty 50 and Nifty IT indices. Oil price movements were found to effectively predict future price changes of Nifty consumer durables, auto, IT indices. Gold prices are useful to predict Nifty-Auto, Bank, Financial Services, Oil & Gas and PSU. The study also found a bi-directional relationship from FII inflows to the stock market and sectoral indices. The findings suggest that forex rates, oil prices and FII flows significantly affect India’s stock market and sectoral performance. The study contributes to the existing literature by comprehensively examining the interlinkage between commodities such as oil and gold, foreign exchanges like USD, Euro, GBP and Yen, G-bond, FII flows and the stock market, and fourteen sectoral indices in the Indian context.

Optimal Signal-Adaptive Trading with Temporary and Transient Price Impact

We study optimal liquidation in the presence of linear temporary and transient price impact along with taking into account a general price predicting finite-variation signal. We formulate this problem as minimization of a cost-risk functional over a class of absolutely continuous and signal-adaptive strategies. The stochastic control problem is solved by following a probabilistic and convex analytic approach. We show that the optimal trading strategy is given by a system of four coupled forward-backward SDEs, which can be solved explicitly. Our results reveal how the induced transient price distortion provides together with the predictive signal an additional predictor about future price changes. As a consequence, the optimal signal-adaptive trading rate trades off exploiting the predictive signal against incurring the transient displacement of the execution price from its unaffected level. This answers an open question from [C. A. Lehalle and E. Neuman, Finance Stoch., 23 (2019), pp. 275--311] as we show how to derive the unique optimal signal-adaptive liquidation strategy when price impact is not only temporary but also transient.

Study on the correlation between Crude oil price and new energy stock price in China and America

In today's new world situation, the consumption structure of energy is constantly changing. All countries attach importance to the use of new energy, vigorously promoting the development of new energy-related industries.Traditional energy and new energy are interchangeable, so there is a complex relationship between crude oil futures market and new energy stock market. China, as an economy with strong energy demand and high dependence on oil, will be affected by changes in oil futures prices. America's new energy policy has two striking sides. On the one hand, due to the lack of consensus, the US has so far failed to come up with new energy development plans and targets at the national level. On the other hand, a series of supportive policies launched by the federal and local governments have enabled the U.S. wind and solar industries to maintain a high growth rate in recent years. In view of this, the research takes WTI crude oil price, Zhongzheng New energy Index and China crude oil price as the research object, analyzes the interaction among them by using VAR model and GARCH model, and predicts the volatility of crude oil price and new energy stock price.