Actual Price Research Articles

Distinctive Assessment of Neural Network Models in Stock Price Estimation

INTRODUCTION: Due to its potential to produce substantial returns and reduce risks, stock price prediction has garnered a lot of attention in the financial markets. OBJECTIVES: A comparison of neural network models for stock price prediction is presented in this research report. METHODS: Through this study, I aim to compare, on the basis of the precision and accuracy, the performance of different neural network models for stock price prediction. LSTM model along with RNN model accuracy in predicting the next day’s stock price i.e., which model can predict closest to the actual value. RESULTS: It is found that LSTM works better than RNN in predicting a value closer to the actual open price stock value. CONCLUSION: A comparison between the models shows LSTM is the more accurate model.

Pricing and Optimization Model of European Options

Financial market forecasts are gradually gaining the attention of investors. The options market is an integral and important part. Commonly used analysis methods include traditional BS formula and CRR model. This article obtained the transaction data of SP&500 from February 2013 to February 2018, used the data of the first 4 years to train the model, and finally explored the actual price and predicted value of the European call option. To forecast the stock price, a hidden Markov model (HMM) is first applied. And on this basis, improve the parameter estimation in the traditional method BS-CRR, and compare each model with the actual value of the option. Compared with the traditional model, to some extent, the Hidden Markov Model can predict the state of the stock market., get rid of the limitations of the BS model assumptions, predict the direction of the price trend, and verify the feasibility of its application in the financial field.

Value‐at‐Risk under Measurement Error

We propose a method for estimating Value‐at‐Risk that corrects for the effect of measurement errors in stock prices. We show that the presence of measurement errors might pose serious problems for estimating risk measures. In particular, when stock prices are contaminated, existing estimators of Value‐at‐Risk are inconsistent and might lead to an underestimation of risk, which can result in extreme leverage ratios within the held portfolios. Using a Fourier transform and a deconvolution kernel estimator of the probability distribution function of actual latent prices, we derive a robust estimator of Value‐at‐Risk in the presence of measurement errors. Monte Carlo simulations and real data analysis illustrate satisfactory performance of the proposed method.

Уніфікація показників для контролю за різними формами монопольної влади

The paper demonstrates the necessity and possibility to unify the controlling indicators for different types of monopoly (monopolistic competition, oligopoly, natural monopoly) in order to increase efficiency of the monopoly control. Especially this concerns the complex types of monopoly from the point of view of its control, for example, oligopoly. The complexity of this type of monopoly control is associated with the actual inevitability of the appearance of tacit pricing coordination among the oligopoly participants. And the problem is not so much in admitting that the very fact of such a coordination is difficult to prove as in defining the harm it causes to the market and the entire economy. This paper shows the possibility to use the quantitative indexes of monopoly control proposed based on author’s CMI-model of macroeconomic dynamics, to apply these indexes for various types of monopoly. A distinctive feature of this model is the possibility to calculate the vector of “natural” prices for any sector (i.e., the prices that correspond to the state of perfect competition even if it is impossible to reach this state in the existing markets). And the comparison of the actual market price with the “natural” one allows us to control a monopoly using the price indexes alone. In turn, it allows us to perform a monopoly control at various hierarchical levels of the economic system (a firm, an economic sector and economy as a whole). Such a control enables us to calculate the degrees of the monopoly impact both on the entire economy, and on its various sectors. Quantitative values of these degrees can be used as evidence in antitrust litigations and for choice of corresponding instruments to “punish” the monopoly for such abuses. The paper reveals the mechanism of use of the proposed formulas for determination of the monopolistic power and degree of its impact on economy and various economic sectors for the cases of oligopoly and natural monopoly. Also, this paper demonstrates the mechanism of the influence of antimonopoly policy on the configuration of business cycle and on economic growth rate (when other policies are neutral). This opens up the possibility to combine the antitrust policy with the anticyclical and fiscal ones, because, under recession, increased monopolistic power in an individual sector may help the whole economy get out of the crisis.

Review on Workplace Spirituality

The Corona Pandemic was a time to learn and grow for people and organizations all around the world. The organizations understood the importance of efficient and innovative management techniques. Certain organizations were in fact thriving, with customers and employees valuing the organizations. despite the lower sales and insufficiency of funds. they were ready to forfeit a discount, pay more than the actual price and share their income to pool resources for the organization. Researchers found that organizational commitment was more in such organizations. On further analysing the reviews of researchers, it was observed that organizational spirituality started from an individual, usually the leader, and quickly became the organization’s culture. There was an increase in workplace joy, health, commitment, creativity, security and a feeling of home within the organization. In this paper, we have attempted to summarize the concept of workplace spirituality from various original papers which were published on ‘Web of science’ database. The idea is to have a complete and concise understanding of the term workplace spirituality.

РЕТРОСПЕКТИВНИЙ АНАЛІЗ ВАРТОСТІ ПОХИБКИ ПРОГНОЗУ ДЛЯ ПОБУДОВИ БАЛАНСУЮЧИХ ГРУП ВИРОБНИКІВ З ВІДНОВЛЮВАНИХ ДЖЕРЕЛ ЕНЕРГІЇ

The significant increase in the installed capacity of power plants with renewable energy sources and the imbalance of the financial system of the wholesale electricity market of Ukraine prompted the Ministry of Energy to develop an alter-native support mechanism for RES producers. The introduction of a feed-in tariff (FIP), which compensates for the difference between the actual sale price of electricity and the "green" tariff, can help producers receive more money immediately after the electricity is released. However, studies have shown that exiting a balancing group without form-ing a new one can lead to increased costs associated with forecasting error. It is important for manufacturers to form independent balancing groups to compensate for negative consequences. The findings of the article show that there is no single optimal balancing group for all manufacturers, but some groups are often repeated. Switching to a separate balancing group can have a significant economic effect for the manufacturer, reducing the cost of forecasting error compared to being solely responsible for the imbalance. However, the balancing group determined by the method of retrospective calculation of the cost of the forecast error is not stable in the long term. Ref. 8, fig. 2, tab. 3 Key words: renewable sources, electricity market, short-term forecasting, forecast interval, deep learning neural net-works, cost estimation.

The Impact of the Opening Policy of the COVID-19 on Catering, Tourism and Hotel Industries

On December 7, 2022, China officially released management of the COVID-19, and the economic level of various industries began to gradually rebound, and people's consumption levels also gradually improved. In the consumer industry, the recovery of the catering, tourism, and hotel industries was first perceived by the market. In order to explore the impact of the COVID-19 liberalization policy on these three industries, this paper used the ARIMA model to analyze the stock closing price data of the industries before the COVID-19 liberalization, simulated the stock price trend without the implementation of the COVID-19 liberalization policy, and compared it with the actual stock price. Through the analysis, it was found that the policy of comprehensive COVID-19 control has promoted the economic development of China's catering, tourism, and hotel industry. The economic situation of all three industries has rebounded to a certain extent. Especially for the catering industry, the promotion effect is most obvious, followed by the hotel industry, and finally the tourism industry. The research in this article can help the government better respond to the current economic recovery situation, propose corresponding incentive policies, and promote the healthy development of China's catering, tourism, and hotel industries.

The Impact of China's Pandemic Deregulation Policy on SSEC and SZSE Indexes

In early stages of the COVID-19 outbreak, Chinese stock market experienced a sharp decline as investors became increasingly concerned about the economic consequences of the pandemic. As the pandemic continued to spread globally, the Chinese government implemented strict measures to control its spread, which included lockdowns, travel restrictions, and other forms of social distancing. Although these measures worked, economy was drastically affected with many stores closing down and consumer expenditures significantly declining. This, meanwhile, led to a decrease in corporate profits and a reduction in investor confidence. However, on December 8, 2022, the Chinese government issued a pandemic deregulation policy identifying people would return to their normal life. In this paper, prices of the Shanghai Stock Exchange Composite (SSEC) index and Shenzhen Securities Component (SZSE) index were retrieved and ARIMA method was adopted to predict the stock prices for a period after the pandemic. The author compared forecast prices with the actual stock prices and then analyzed the implications of the deregulation policy on the stock market. These two indexes are only a snapshot of the Chinese economy, and certain informative feedback can be obtained through this study, which is helpful to relevant investors and policy makers.

Stock Price Prediction During the COVID-19 Pandemic Based on the GRU Algorithm

Stock price prediction during the Covid-19 pandemic has emerged as a significant research domain within the financial sector, giving rise to a multitude of neural network-based methods aimed at forecasting stock prices. This paper uses multiple machine learning models and analyzes the stock fund flows to attempt to learn and predict price trends from a dynamic perspective. The data used in this study includes the closing price, change rate, and fund flow of an A-share Pharmaceutical stock in the most recent 1000 trading days. The Mean Squared Error (MSE) is used as the model evaluation metric, and the models MSE can reach 0.013 after training. The predictions generated by the model exhibit a high degree of alignment with the actual price trends, indicating its accuracy in short-term price trend prognostication. These findings substantiate the efficacy of the model for stock price prediction during the pandemic period, thereby contributing to the body of knowledge within the field of financial forecasting.

부동산 허위 매물 계약취소의 개선 방안에 관한 연구

The timing of real estate transaction reporting is a critical factor in enhancing the transparency and stability of transactions. Currently, the reporting period for real estate transactions is kept short, within 30 days from the contract date, with the aim of providing quick information on real estate prices and reducing the distortion of prices. However, this policy has side effects due to false transactions and price issues.
 False transactions refer to trading houses or real estate at prices significantly different from their actual value. False transactions can affect phenomena such as real estate speculation and price agitation, making it difficult to grasp the actual market prices, and allowing some speculators to take unfair advantage of the price confusion caused by false transactions.
 Options for adjusting the timing of real estate transaction reports include reporting based on the final payment date or the registration date. Reporting based on the final payment date can prevent false transactions as it is reported after the transaction is completed, and reporting based on the registration date can prevent false transactions as it is reported after the transaction is legally completed.
 Adjusting the period for reporting real estate transactions is an effective solution to resolve issues of false transactions and pricing problems, and to enhance the stability of real estate transactions. Therefore, the government should actively review and implement adjustments to the reporting period for real estate transactions.

Long- and Short-Term Memory Model of Cotton Price Index Volatility Risk Based on Explainable Artificial Intelligence.

Market uncertainty greatly interferes with the decisions and plans of market participants, thus increasing the risk of decision-making, leading to compromised interests of decision-makers. Cotton price index (hereinafter referred to as cotton price) volatility is highly noisy, nonlinear, and stochastic and is susceptible to supply and demand, climate, substitutes, and other policy factors, which are subject to large uncertainties. To reduce decision risk and provide decision support for policymakers, this article integrates 13 factors affecting cotton price index volatility based on existing research and further divides them into transaction data and interaction data. A long- and short-term memory (LSTM) model is constructed, and a comparison experiment is implemented to analyze the cotton price index volatility. To make the constructed model explainable, we use explainable artificial intelligence (XAI) techniques to perform statistical analysis of the input features. The experimental results show that the LSTM model can accurately analyze the cotton price index fluctuation trend but cannot accurately predict the actual price of cotton; the transaction data plus interaction data are more sensitive than the transaction data in analyzing the cotton price fluctuation trend and can have a positive effect on the cotton price fluctuation analysis. This study can accurately reflect the fluctuation trend of the cotton market, provide reference to the state, enterprises, and cotton farmers for decision-making, and reduce the risk caused by frequent fluctuation of cotton prices. The analysis of the model using XAI techniques builds the confidence of decision-makers in the model.

Analysis and Prediction of Bollinger Bands to Predict Stock Prices in Determining Investment Strategies

It is critical for stock investors to be able to forecast future stock values in order to determine potential gains or losses. A technical analysis approach can be used to anticipate stock prices in the realm of stock investment. A technical analysis approach was used to construct the Bollinger bands detecting application system. Technical analysis is a means of observing price variations over a specific time period by employing historical data and indicators. The goal of this research is to predict Bollinger bands and define the circumstances for buying and selling stocks in technical analysis. The object of this study is the closing price of BBRI.JK, BBCA.JK and BMRI.JK during 2022. Data is analyzed using Bollinger Bands and decision to buy the right stock is when the actual price of the stock intersects with the lower band because at that time the stock price is increasing, while the decision to sell the right is when the actual price of the stock intersects with the upper band because at that time the stock price will decrease. In addition, the results of the Bollinger bands prediction for n + 1 from the last day of the sample, December 30, are respectively the upper and lower Bollinger bands on January 2, 2023, which are 5.012,948 and 4.775,052 for BBRI.JK shares, 8.755,919 and 8.446,581 for BBCA.JK shares, and 5.321,991 and 4.764,259 for BMRI.JK shares.

Forecasting the price index on the energy market of Ukraine considering the effect of the “green” tariffs laws

The "green" tariff laws in effect in Ukraine were adopted without the necessary technical and economic justification. These laws provide RES owners a wide range of preferences, which in total lead to hypertrophied losses of the Ukrainian energy market. In the process of the state developing a set of measures to radically improve the existing situation, it is necessary to have a wide range of methods of system analysis, including the determination of price indices on the energy market. In the paper a new method and a corresponding mathematical model for determining price indices on energy markets are proposed and tested on real data in conditions of solar and wind power plants functioning in the integrated energy system structure and when "green" tariff laws actions. The model determines the size of the electricity production sector, where competitive relations are maintained. Then the total costs of the energy market are calculated for the summary production of electricity in the power system at both market and fixed prices. The new price is defined as the ratio of total costs to the capacity of electricity production in the sector with competitive relations. The price index is calculated as the ratio of the new price to the market price recorded on the energy market in the base year. Calculations based on real data of the Ukraine’s IES for 2021 showed that if there were no RES in the energy system structure, the market price for electricity would be 2.17 times lower than the actual price. Research of the price index on the energy market of Ukraine at the development of its IES in accordance with the approved government documents showed that at the level of 2030 the average price for electricity will increase by 3.74 times. The country's economy will not be able to withstand such an electricity prices increasing. To solve this problem of national importance, a fundamentally new approach must be implemented. One of the effective solutions is the organization of RES functioning based on the principle of self-sufficiency, when RES ensures frequency and power stability with its own energy. Keywords: market price, price index, energy system, energy market, preference, payback.

Iron Ore Price Prediction Based on Multiple Linear Regression Model

The fluctuation of iron ore prices is one of the most important factors affecting policy. Therefore, the accurate prediction of iron ore prices has significant value in analysis and judgment regarding future changes in policies. In this study, we propose a correlation analysis to extract eight influencing factors of iron ore prices and introduce multiple linear regression analysis to the prediction. With historical data, we establish a model to forecast iron ore prices from 2020 to 2024. Taking prices in 2018 and 2019 as samples to test the applicability of the model, we obtain an acceptable level of error between the predicted iron ore prices and the actual prices. The prediction model based on multiple linear regression has high prediction accuracy. Iron ore prices will show a relatively stable upward trend over the next five years without the effects of COVID-19.

The Analysis of the Stock Price of AAPL Based on the Comparison Between the Intrinsic Stock Price and Actual Stock Price

As Apple becomes a world-leading technology company, it is no doubt that its stock has brought a huge amount of profit to its investors. Many investors rely on financial modules to estimate the change in stock values and make the final decision. They contribute significantly to the decision-making of investors. This paper intends to use the composition of the models to evaluate the monthly stock price of AAPL in the past five years with the composition of the models and compare it with the real open stock price. It turns out that the real value was overvalued in the first four years yet undervalued in the fifth year.

Forecasting carbon price trends based on an interpretable light gradient boosting machine and Bayesian optimization

The future carbon price is crucial to relevant companies, investors, and carbon policymakers, and the significance of carbon price prediction research is self-evident. However, existing study usually predicts actual carbon prices, rarely considering price trends and lacking reasonable interpretations for the prediction model. Thus, in this study, an interpretable machine learning model is proposed to predict carbon price trends. It integrates five methods, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), two-stage feature selection (TFS), light gradient boosting machine (LightGBM) optimized by Bayesian optimization algorithm (BOA), and SHapley Additive exPlanations (SHAP). The effectiveness of the proposed model is validated with the carbon prices of the Hubei carbon trading market, which has the largest volume among Chinese markets. The experimental results showed that the proposed model outperforms other benchmark models under five evaluation criteria, including AUC, Accuracy, Precision, Recall, and F1 score, on multiple-step predictions. For one-step-ahead prediction, the average hit ratio results are 0.8342, 77.32 %, 77.87 %, 76.83 %, and 76.88 % respectively; for five-step-ahead prediction, the average hit ratio results are 0.7641, 69.25 %, 71.17 %, 71.97 %, and 71.00 % respectively; and for ten-step-ahead prediction, the average hit ratio results are 0.7519, 69.11 %, 73.80 %, 69.61 %, and 71.16 % respectively. The SHAP model interpretation results indicated that the high-frequency intrinsic mode function (IMF) components of the historical carbon price are the most important features for predicting carbon price trends. This study contributes by forecasting both the upward and downward trends of carbon prices through multi-step-ahead forecasting with the LightGBM model and further interpreting the model’s predictions with the SHAP approach. Therefore, the proposed model has excellent forecasting performance with interpretability, which is an effective tool for forecasting carbon price trends.

An empirically sufficient form for Sraffa prices

This paper examines three propositions: Sraffa's comment that Marx was “of course” right in thinking that labor value and “Sraffa price” (prices expressed relative to Sraffa's standard commodity) aggregates are essentially the same: empirical deviations of various aggregate components of total output are around five percent. This should be reflected in small representative models. Second, Pasinetti is correct that actual Sraffa price paths are generally simpler than those in the theoretical literature – as confirmed by the 403 industry price curves of the US 2002 input-output matrix. Third, as has been often demonstrated, Marx's transformation procedure is an iterative procedure for deriving Sraffa prices. Bienenfeld derives powerful first and second iterates, each beginning and ending at the corresponding individual Sraffa price curves. The simple nonlinear form of the second iterate provides excellent fits for the 403 Sraffa price curves. It is therefore an empirically sufficient general form of such price curves.

Assessing out-of-pocket cost differences in treatment for Medicare-eligible patients with favorable prognosis early-stage Hodgkin lymphoma.

15 Background: In the treatment of favorable Hodgkin Lymphoma (HL), Level 1 evidence comparing combined modality therapy (CMT) with chemotherapy alone has persistently demonstrated that obviating radiation therapy (RT) worsens recurrence rates despite no significant overall survival difference. As there remains limited evidence on the granularity of the impact of insurance on HL treatment, this project disaggregates costs by insurance plans to enhance the transparency of out-of-pocket (OOP) cost estimations in Medicaid and/or Medicare-eligible (age 65+) patients. Methods: National Comprehensive Cancer Network guidelines were queried to assess the appropriate therapy for HL. Following initial adriamycin/bleomycin/vinblastine/dacarbazine (ABVD) chemotherapy x 2 cycles with Deauville 1-2 response on PET scan, CMT was defined as involved site RT (20 Gy / 10 fractions), while chemotherapy alone was defined as an additional ABVD x 2. Current Procedural Terminology and Healthcare Common Procedure Coding System codes identified treatments. OOP expenditures, deductibles, and copay/coinsurance were computed for Medicaid, Original Medicare, and Medigap Plan G plans via medicare.gov, medicaid.oh.gov, and cms.gov. Price estimates (from actual prices per insurance plan rather than claims data predictions) assume an Ohio hospital location (zip code = 44106) for all treatments. Total costs were calculated based on all treatments plus a 5-year follow-up (not adjusted for inflation). Treatment charges for chemotherapy alone include initial medical oncology office visit, cost of ABVD x 4, and infusion costs for 4 cycles. Additional treatment charges for CMT include initial radiation oncology consultation, treatment planning, dosimetry calculations, treatment device, simulation and verification, RT delivery, on-treatment visits, medical physics consultation, and annual TSH labs. Results: With Original Medicare plans, patients are faced with an OOP cost of 20% for Medicare Part B claims with no cost cap for approved procedures after the deductible. As such, patients are faced with a total 6-year charge of $1,928.95 for chemotherapy alone, and $2,385.28 for CMT. Under Medigap Plan G, patients encounter an OOP charge of $1,151.14 for chemotherapy alone and $1,176.34 for CMT. Medicaid beneficiaries (assuming all treatments are approved by Medicaid) face no OOP expenses for both treatment plans, as all expenses are covered without limit. Conclusions: By comparing OOP costs between Medicare and Medicaid plans, this model improves cost transparency in the treatment of favorable early-stage HL. Our findings indicate that omitting RT in favor of chemotherapy alone yields a cost savings of 2-24% at a 5-year follow-up. Further studies are required to assess whether these savings compensate for the inferior outcomes achieved by omitting RT in this patient population.

STOCK PRICE PREDICTION USING ARTIFICIAL NEURAL NETWORKS WITH THE ADALINE METHOD

Prediction of stock prices has an important role in making investment decisions in financial markets. In this study, researchers propose the use of artificial neural networks with the Adaline method to predict stock prices. The Adaline method is a learning algorithm that iteratively adapts network weights to minimize prediction errors. In the early stages, researchers collect relevant stock price historical data and process it into a training dataset. Next, build an artificial neural network with one input layer, one output layer, and use a linear activation function. The network weights are initialized randomly, and the training process begins. In network training, the researcher applies the Adaline algorithm with the descending gradient method to optimize the objective function. The researcher updates the network weights based on the difference between the actual stock price and the stock price predicted by the network. This process is repeated iteratively until it converges or reaches the specified stopping criteria. After the training is complete, the researcher evaluates the network performance using validation data that was not used in the training. Researchers used evaluation metrics, such as the mean absolute error (MAE) and root mean square error (RMSE), to measure the accuracy of network predictions. In addition, researchers also compared the performance of the Adaline network with other prediction methods, such as linear regression. The experimental results show that the artificial neural network with the Adaline method is able to provide accurate stock price predictions. Researchers observed a significant reduction in prediction error compared to the linear regression method. In addition, the Adaline network also shows good adaptability to changes in market trends. This research makes an important contribution to the development of stock price prediction methods using artificial neural networks. The results can be used as a guide for investors and stock traders to make more informed and effective investment decisions. Keywords : stock price prediction, artificial neural network, Adaline method, learning algorithm, performance evaluation.

An integrated residence smart grid system with enhanced energy management for batteries

Residential consumers can efficiently engage in demand response programs with the help of home energy management systems (HEMSs). The best scheduling of gas, oil, coal, and uranium appliances has yet to be considered by traditional HEMSs, which exclusively control domestic electric devices to lower the cost of electricity usage. The associated demand response (DR) for smart homes is then adjusted to lessen the difference between predicted and actual demand after comparing the actual price to this forecast. Data acquired by Nordpool and publicly available energy information have been used to test the proposed strategy about price demand forecast and DR management. The function is to reduce energy costs for the home and the dissatisfaction brought on by moving, cutting, and replacing loads by changing prices over time. Case studies with accurate data show that the suggested strategy can return on investment bills by up to 85% while maintaining their satisfaction.