Actual Price Research Articles

Incorporating Stock Prices and Social Media Sentiment for Stock Market Prediction: A Case of Indonesian Banking Company

Forecasting the stock market is one of the most popular topics to be discussed in many fields. Many studies, especially in information technology have been conducted machine learning algorithms to achieve a more accurate prediction of the stock market. This research aims to find the effectiveness in predicting stock market performance by utilizing social media sentiment in combination with historical data. In addition, this research uses a machine learning algorithm to train a model to predict the stock price of each bank and training the model on a dataset that included the historical stock prices of the bank, as well as the sentiment scores of the social media posts about the bank and evaluate the performance of the model by comparing the predicted stock prices to the actual stock prices. The research shows that the R2 and RMSE score model that has been built with its historical data has slightly better performance than the model that has been built with the combination of historical data and social media sentiment. The finding indicates that the research method is closely correlated and affected to the performance of the stock market prediction.

중개 실무자 관점에서 본 부동산 실거래 신고 제도의 문제점과 개선방안

The real estate transaction reporting system is designed to prevent real estate speculation and tax evasion due to double contracts, false or untruth reports, etc., and to ensure transparency in real estate transactions The Act on Reporting Real Estate Transactions was fully revised and went into effect on January 1, 2006. Due to the regulations of the real estate transaction reporting system, real estate speculation and tax evasion due to double contract writing and false/false reporting can be seen to have decreased to some extent, but cases of false reporting that differs from the actual transaction price have not disappeared. According to the Act on Real Estate Transaction Reporting, etc., if a real estate transaction is not reported or a false report is made, a fine is imposed. However, I know from experience in brokerage practice that the problem of real estate transaction reporting cannot be solved with the punishment standards alone. This study sought to find ways to improve the effectiveness of the real estate transaction reporting system by examining the problems of the real estate transaction reporting system from the perspective of brokerage practitioners and suggesting any shortcomings or areas for improvement in the system. Specifically, in this study, in relation to the problems and improvement measures of the actual real estate transaction reporting system, report manipulation due to false or false reports, proposals for unification of the reporting system and the establishment of a real estate exchange, controversy over those required to report, and cases of reporting of lease and monthly rent transactions. Major issues included clarification of reporting obligations and contract conclusion date standards. In addition, we hope that the real estate transaction reporting system will not be perceived as a regulation on real estate transactions, but will be reborn as a system that protects people’s property rights, ensures order and transparency in transactions in the real estate market, and provides various benefits related to real estate to the people.

Collaborative Vehicle-to-Grid Operations in Frequency Regulation Markets

Problem definition: We study the operations of electric vehicles (EVs) providing frequency regulation services to the electric grid in vehicle-to-grid (V2G) systems. In particular, individually owned EVs collaboratively bid in the regulation market, coordinated by a platform that operates the network of charging equipment. We study how the platform determines optimal pricing incentives for drivers to plug in their EVs, accounting for heterogeneous driving schedules. Methodology/results: We model the platform’s pricing optimization problem as a bilevel program: At the upper level, the platform determines hourly rebates for EV owners to plug in their EVs and capacity bids in the regulation market; at the lower level, individual travelers optimize their travel and charging schedules in response to pricing incentives. To account for uncertainties and heterogeneity in regulation market prices and travel patterns, we adopt distributionally robust optimization techniques to formulate the problem as a mixed-integer second-order cone program. We conduct a computational study based on the California Household Travel Survey data set and actual frequency regulation prices. Our results show that the ability to offer time-varying rebates and install workplace chargers can significantly improve the V2G platform’s expected profits. Managerial implications: As EV adoption progresses past the nascent stage, V2G business models become more viable. Successful implementation of V2G provides economic incentive for switching to EVs, potentially helps sustain adoption growth, and complements the growth of renewable power by helping stabilize the grid. Our findings shed light on the design of driver incentives for V2G systems. Funding: R. Tang acknowledges support from the National Natural Science Foundation of China [Grant 72201206]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0133 .

Current Challenges in Operation, Performance, and Maintenance of Photovoltaic Panels

The installed solar capacity in the European Union has expanded rapidly in recent years. The production of these plants is stochastic and highly dependent on the weather. However, many factors should be considered together to estimate the expected output according to the weather forecast so that these new PV plants can operate at maximum capacity. Plants must be operated in coordination with maintenance operations and considering actual energy market prices. Various methods have recently been developed in the literature, ranging from the most impactful artificial-intelligence-based generation estimation methods to various diagnostic and maintenance methods. Moreover, the optimal operational and maintenance strategy usually depends on market regulation, and there are many concerns related to the distribution system operator. This review article aims to summarize and illustrate the challenges of operating and maintaining solar power plants and the economic and technical importance of these problems.

Real estate price estimation through a fuzzy partition-driven genetic algorithm

Evaluating the actual price of a residential property is a critical issue in the real estate market. Real estate market practitioners gauge a property's price by considering features such as property type and residential area. Subsequently, they evaluate the property's intrinsic features, such as condition, sun exposure, scenic views, and ancillary amenities. Finally, extrinsic features such as the proximity of services and infrastructure are assessed. This paper proposes a new genetic approach for selecting residential properties that meet the purchase offer and the intrinsic and extrinsic characteristics desired by the client. Since the real estate market's changes can influence extrinsic features, the method introduces price fluctuations of properties. Extrinsic features are modelled as fuzzy partitions: each fuzzy set describes a qualitative aspect of the corresponding feature that, expressed in a linguistic term, has a human-like interpretation. Then, a deviation value (fluctuation) from the average price of the property is considered for each fuzzy set in the partition. All the property features, extrinsic and intrinsic, are encoded in the chromosome genes of the genetic algorithm. The fitness function calculates the distance between the unit price of the property and the purchase offer. Some case studies were conducted in various Italian municipalities, using the average price per square meter of residential properties the Osservatorio del Mercato Immobiliare (OMI) assigned. Depending on customer requirements and preferences, different OMI zones were selected using additional characteristics such as type, location, conservation, and proximity to various urban services. The results demonstrated the effectiveness of the proposed approach for all the case studies, showing how the optimal solution represents a good compromise between customer preferences and market offerings.

A Prediction of Option Price via Two Volatility Computations with Application to a 50ETF Option

Financial derivative trading is integral to stock markets, leading to high option price volatility due to increased trading volume. Determining a reasonable option price is complex and requires extensive research in fields like Economics, Applied Mathematics, and Finance Engineering. The Black-Scholes (BS) equation provides a scientific pricing tool for options by considering five parameters: stock price (S), option strike price (K), risk-free interest rate (r), time to expiration (τ), and volatility (σ). Notably, all model parameters except volatility σ can be directly observed from market data, necessitating the determination of this parameter from historical data when applying the BS model in practice.In this report, we examine two widely used computation methods for volatility. The first method involves a simple statistical calculation of historical data, resulting in the historical volatility (HV). The second method utilizes the BS model in a "backward" manner: given any previous option price and four other parameters, we solve the BS equation to obtain the implied volatility (IV). To determine such an IV parameter, we propose a generalized fixed-point iterative solver for solving a complex nonlinear equation. By employing a well-designed initial guess, we demonstrate that this fixed-point solver achieves global and rapid convergence.The two volatilities lead to different predictions of the option price. This report examines if the BS model accurately predicts the option price using these volatilities. It presents an empirical study on a 50ETF option, where we use t-tests to check the hypothesis and determine the relationship between predicted and actual option prices. The study finds that using implied volatility in the BS model provides significantly more accurate predictions compared to historical volatility.

지방 아파트 가격 결정 요인과 특성 분석

1. CONTENTS
 (1) RESEARCH OBJECTIVES
 The purpose of this study is to identify the characteristics that influence the determinants of apartment prices between high and low apartment prices within the same autonomous district in the regional submarket, and to further confirm whether there is a price gap between branded and non-branded apartments.
 (2) RESEARCH METHOD
 For this purpose, a multiple regression analysis was conducted targeting all apartments traded over the past three years in Buk-gu, Busan (Hwamyeong-dong, Mandeok-dong), using the actual apartment transaction price as the dependent variable and independent variables related to apartment structure, complex, location, and brand characteristics.
 (3) RESEARCH FINDINGS
 As a result of the analysis, the variables that commonly had a significant positive (+) influence on the actual transaction price were the year of occupancy and the number of apartments, while the variables that had a negative (-) influence were the exclusive use area. In Hwamyeong-dong, a high-price area, the distance to elementary schools, convenience facilities, and apartment brands had a positive (+) effect, while in Mandeok-dong, a low-price area, they had a negative (-) effect.
 2. RESULTS
 This means that there may be a gap in apartment prices even within the same area, and even for non-branded apartments, the price may be higher than branded apartments if the area is close to a station or close to convenience facilities. In this study, these results were presented as marketing Unique Selling Point related to the supply of regional and non-branded apartments.

개발 호재가 아파트 분양 프리미엄에 미치는 영향

1. CONTENTS
 (1) RESEARCH OBJECTIVES
 The purpose of this study is to examine how significant the construction of Samsung Electronics' Pyeongtaek Campus and the SRT Pyeongtaek-Jije Station, which are favorable developments in the Pyeongtaek area, affect the premium of apartment sales rights.
 (2) RESEARCH METHOD
 Hedonic price model was used for the factors affecting the actual transaction price, and multiple regression analysis was applied to find out the factors affecting the premium of the actual transaction right.
 (3) RESEARCH FINDINGS
 Model 1 (related to SRT Pyeongtaek Jije Station) empirical analysis showed that variables such as the number of floors, 2021 contract rankings, household parking, interest rates, exchange rates, and Pyeongtaek apartment sales price index had a positive (+) effect. The distance from the SRT Pyeongtaekji Station, a characteristic of favorable development, was found to have the second-largest influence on the dependent variable among the variables.
 Model 2 (related to Samsung Electronics' Pyeongtaek Campus) empirical analysis showed that the number of floors, 2021 contract ranking, exclusive area, number of parking lots by generation, interest rate, exchange rate, Pyeongtaek apartment sales price index had a positive (+) effect. Whether it was completed in 2018, the distance to Samsung Electronics' Pyeongtaek campus, the distance to the school, distance to commercial facilities, and the overall stock index were significant and had a negative (-) effect. The distance from Samsung Electronics' Pyeongtaek campus, a characteristic of favorable development, was found to have the greatest influence on the dependent variable among the variables.
 2. RESULTS
 The results of the study provide basic data that can be used to predict the real estate market when developing station areas around high-speed railway stations by grasping the impact of the opening of SRT Pyeongtaek Jije Station on the premium of the apartments around the area. This is expected to be useful data in grasping the degree of change in the value of local real estate assets and analyzing the development effect when constructing additional transportation facilities in the future

사회기반시설이 아파트 가격에 미치는 영향 연구 - 서울 서리풀 터널 사례를 중심으로

This study aimed to understand the impact of social infrastructure investment on apartment prices. Using a difference-in-differences(DID) regression analysis, we empirically analyzed the impact of the new establishment of infrastructure on apartment prices in Bangbae-dong, Seoul by the commencement and completion of the Seoripul Tunnel, examining the effects step-by-step and by distance from the street using buffer analysis. Our findings revealed that the influence on apartment prices varied depending on the commencement and completion stages. For the commencement phase, a significant increase in actual transaction prices was observed within 500m when comparing 6 to 9 months before and after the construction started. On the other hand, during the completion phase, no significant influence was observed at the exact time of completion. However, when considering three months before completion, statistically significant positive changes were observed in both the subsequent 9 months and within distances of both 500m and 1km. This research provides an empirical evidence of the actual fluctuations in apartment prices due to large-scale social infrastructure investments in the area. The effects of social infrastructure on housing prices can manifest differently across time and distance from it. Such complexities suggest that related policy responses require much more careful research.

Analisis Penetapan Harga Jual Produk Dalam Meningkatkan Laba Rumah Makan

The purpose of this research is to determine and analyze selling price determination in increasing profits at Hokky Palopo noodle restaurants. The data analysis method used in this research is the cost plus pricing method, namely determining the selling price by adding a margin (profit) that is used on the costs that have been charged to the goods. The results of the hypothesis testing research, namely the results of the product cost analysis, show a low value plus the profit expected by the owner of RM Mie Hokky Palopo every month is 25% per portion. The selling price of the food per unit (per portion) is IDR 22,570. while the actual price of food per portion at a hokky noodle restaurant is IDR 28,000. Thus, the selling price is determined to be more than the cost price which only reaches 18,058. per portion. The results of the first profitability analysis based on detailed income data, obtained a profitability ratio of 19.34% per month, meaning that every sale of IDR 100 was able to provide an operating profit of 80.66 per portion. Meanwhile, the results of the second profitability analysis using calculated product cost data. Based on the product cost data used as the basis for calculating operating profit, it was obtained that it was 10.18%, meaning that every sale of IDR 100 was able to provide an operating profit of IDR 89. 82 per portion.

Using Artificial Intelligence for Stock Price Prediction and Profitable Trading

Investing in the stock market is generally considered a safe and reliable way to protect and grow your savings over the long term. However, stock investments are risky and investing in the wrong companies can lead to financial loss. This research paper aims to see how the emerging field of Artificial Intelligence (AI) can be used to predict a stock’s future movement and capitalize on it by anticipating the movement and automatically buying or selling the stock, in an attempt to safely generate profit while minimizing risk. In this paper, a Multi-Layer Perceptron regressor model was trained to predict a stock’s future price using the stock’s previous prices, two industry competitors’ previous stock prices, and previous daily crude oil prices. The model was evaluated based on its average accuracy to the actual price in percent, and it was found that the model had an average 1.7% error on AAPL (Apple Inc) stock. An automated trading bot was then created and used the model’s daily output to perform a simulated backtest, where it was given a simulated $100,000 portfolio. Its performance was measured in multiple different trading scenarios. It was found that the model can reliably outperform stocks in fairly flat or downward market conditions and still profit on upward-moving stocks, but can still end up failing to profit if the underlying stock it was given drops in price significantly over the testing period.

A multi-dimensional assessment of the accuracy of analyst target prices

This paper provides the first assessment for the quality of analyst target forecasts within an emerging market. We find that analysts' target forecasts tend to commit systematically upward bias (9.4%), large absolute pricing error (24.8%), over-prediction of the actual price changes (21%), and a low proportion (54%) of correct directional forecasts. The forecasting quality decays over time as the information in the target prices becomes obsolete, long before the one-year expiry period indicated in the analyst reports. Nevertheless, analysts’ target forecasts consistently outperformed alternative forecasts generated solely based on historical data, which confirms the price discovery function of analyst forecasting. A number of factors are found to be influential to the target performance. Target quality is adversely associated with stock idiosyncratic risk, prior index volatility, the aggressiveness of the target forecasts, and the optimism of consensus targets. In addition, more accurate target prices are delivered by brokerages that have prior experience with the industry and the companies under review, but not by brokerages that possess monopoly power on the stock-specific information.

Comparative analysis of machine learning techniques for cryptocurrency price prediction

The emergence of cryptocurrencies has revolutionized the concept of digital currencies and attracted significant attention from financial markets. Predicting the price dynamics of cryptocurrencies is crucial but challenging due to their highly volatile and non-linear nature. This study compares the performance of various models in predicting cryptocurrency prices using three datasets: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models analyzed include Moving Average (MA), Logistic Regression (LR), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). The objective is to uncover underlying patterns in cryptocurrency price movements and identify the most accurate and reliable approach for predicting future prices. Through the analysis, it could be observed that MA, LR, and ARIMA models struggle to capture the actual trend accurately. In contrast, LSTM and CNN-LSTM models demonstrate strong fit to the actual price trend, with CNN-LSTM exhibiting a higher level of granularity in its predictions. Results suggest that deep learning architectures, particularly CNN-LSTM, show promise in capturing the complex dynamics of cryptocurrency prices. These findings contribute to the development of improved methodologies for cryptocurrency price prediction.

Stock Valuation and Risk Analysis Report: Real Estate Industry

The real estate industry plays a fundamental role in laying the foundation for China's national economic development. Its contribution to driving economic growth, promoting social employment, and optimizing residents' living environments cannot be underestimated. Therefore, objective evaluation of real estate company value is of utmost importance. Investors typically utilize stock valuation models to assess the value of a company's stock. However, the theoretical valuations derived from these models often differ from the market value. This research focuses on an empirical analysis of stock valuation and actual pricing in the real estate sector, using the stock (code: 02007) issued by Country Garden Holdings as the case study. The average price-to-book ratio method is applied to theoretically value the stock, and the resulting valuation is then compared with the actual pricing. The analysis aims to uncover the reasons behind the differences between the two and identify the risk factors involved. The risk factors contributing to a lower stock valuation than the actual pricing is primarily associated with the overall impact of the real estate industry and Country Garden Group's operational risks. This study provides valuable insights for real estate companies seeking to improve their business operations and enhance their value creation capabilities. It also serves as a significant reference for investors when conducting stock valuations.

External Event Crises on The Stock Market: Taking Japan’s Sea Discharge Incident as An Example

On August 22, 2023, the Japanese government announced that it would start discharging nuclear wastewater into the sea from August 24. This decision was widely questioned and opposed by the international community and caused strong concerns in Japan. This article aims to take the Japanese nuclear sewage discharge incident into the sea as an example to explore the impact of social crisis on Japan's domestic stock market. The main research method of this article is based on stock return analysis, using the ARIMA model to predict the Japanese stock price trend after excluding the impact of this event, and then comparing it with real data. The results of this study provide evidence that the predicted stock prices are close to the actual stock prices, indicating that as of mid-September, there was no short-term negative impact on the Japanese stock market due to the event, but from a long-term perspective, the predictions remain It is uncertain whether stock prices in Japan's financial markets will fall because of this incident. The main conclusion drawn from this study is that external events may not have a large impact on stock prices in the short term. Different from previous literature, this study uses ARIMA analysis on daily data and weekly data to obtain the relationship between them. Recommendations include organizing a team of experts; hiring a legal team; establishing a complete procedure to handle social media information, and having a dedicated public relations team may be helpful.

Stock Value Analysis Based on the DDM Model: An Example of Company COSCO

The stock market is unpredictable and there are many ways and means to analyze stocks, DDM is still worth to be analyzed and discussed as one of the less common but very basic and important analytical means. This paper, will focus on GGM in DDM and apply it to COSCO Shipping Holding to find out whether DDM is applicable to this stock and whether this stock is worth investing in. In this paper, it obtains the relevant data from the company's annual report as well as the financial data website, calculate and derive the final estimate by using the DDM formula. The estimated value will be compared with the actual value to determine whether the stock is worth investing in or not. In the end, the estimated value calculated by DDM is much larger than the actual stock price, which means that the stock is seriously undervalued and may bring great returns according to the principle of DDM. Combining the actual situation and objective factors, this paper concludes that the principle and formula of DDM are very important, but it is not applicable to all situations. The necessary conditions are more demanding, and they need to be combined with many other theories to analyze many aspects.

From Indication-Based Pricing to Blended Approach: Evidence on the Price and Reimbursement Negotiation in Italy.

New indications for existing medicines are increasing over time. In most countries, drug pricing and reimbursement conditions are renegotiated every time a new indication is approved. There is a growing interest in the price negotiation model for new indications, specifically comparing an indication-based versus blended approach. However, little evidence currently exists regarding the complexity of these negotiations and their impact on actual prices. Italy has recently transitioned from an indication-based approach to a blended price model. This study aims to measure the impact of price and reimbursement negotiation of new indications on discounts (i.e. actual prices) and on the negotiation duration, used as a proxy of its complexity. We considered new indications approved through a European centralized procedure from January 2013 to March 2022 for which the price and reimbursement status was approved in Italy between January 2015 and March 2022, amounting to 52 new indications. Data on the timeframe of the Italian price and reimbursement process and its phases were obtained from publicly available sources. Discounts for the first indication and their subsequent increases for new indications were estimated by comparing ex-factory prices and tendered prices. To calculate p-values, we employed the Mann-Whitney test, and multiple regression models were utilized to examine correlations between negotiation time and the characteristics of the medicines. The mean time to reimbursement was 603 days, in contrast to 583 days for the first launch. Price negotiation took longer for rare diseases, cancer drugs, and in case of therapies with minor added therapeutic value. On average, the additional discount (on top of discounts for prior indications) was 13%, significantly lower than the mean discount for the first indications approved (24.9%). The discounts increment was lower, but negotiation took longer if a Managed Entry Agreement accompanied the final agreement. Additionally, discounts have increased over the years. The negotiation for new indications takes longer than the first one, and provides, on average, an additional discount of 13%. While our findings bear the potential for significant policy implications, they necessitate prudent interpretation due to a limited number of observations. The increasing trend in additional discounts over time applied to all indications in recent negotiations, may suggest a descending trend of value for new indications and a shift from an indication-based pricing approach to a blended model. Otherwise, budget impact considerations might have outweighed a value-based approach in the recent negotiations. If so, two potential options for restoring a value-based approach are returning to an indication-based pricing or giving explicit and higher weight to value within a blended model.

Revisiting international house price convergence using house price level data

The literature on house price convergence largely relies on house price indices, based in an arbitrary year, rather than on actual price levels. This is essentially due to the scarcity of comparable house price level data. However, this severely constrains the analysis. In particular, it often forces to discard a large part of the data sample, which leads to a great loss of information and hampers analysis on sub-periods. In this paper, we combine the price level estimates produced by Bricongne et al. (2019) with long OECD house price series to analyse long-run house price convergence across 18 OECD countries. Applying the Phillips and Sul (2007) procedure, we find convergence in OECD nominal house prices, but two separate convergence clubs in real house prices. We find some convergence in euro area real house prices, but no evidence of strengthening after the introduction of the euro. β-convergence regressions (linking changes in house prices to their initial levels) point to conditional convergence, related to the evolution of gaps in real GDP per capita, long-term interest rates and population growth across countries.

Decentralized asynchronous adaptive federated learning algorithm for securely prediction of distributed power data

Introduction: Improving the precision and real-time speed of electricity data prediction while safeguarding data privacy and security holds immense significance for all power system participants’ decision-making. To surmount the issues of exorbitant computational expenses and privacy breaches of traditional centralized prediction methods, this paper proposes a decentralized asynchronous adaptive federated learning algorithm for securely prediction of distributed power data, which makes predictions from distributed data more flexible and secure.Methods: First, each regional node trains its own deep neural network model locally. After that, the node model parameters are uploaded to the decentralized federated learning chain for ensuring local data protection. Asynchronous aggregated update of the global prediction model is then achieved via block mining and shared maintenance. The algorithm has been enhanced based on the traditional federated learning algorithm, which introduces an asynchronous mechanism while adaptively adjusting the regional node model weights and local update step size to overcomes the inefficiency of traditional methods.Results and Discussion: The experimental analysis of actual electricity price data is conducted to compare and analyze with the centralized prediction model, study the impact of model adoption and parameter settings on the results, and compare with the prediction performance of other federated learning algorithms. The experimental results show that the method proposed in this paper is highly accurate, efficient, and safe.

A new customer selection framework for time-based pricing program

Time-based pricing for residents is regarded as a critical component to facilitate integration of renewable energy, but it remains one of the significant barriers to target high potential customers. Randomly recruiting customers into time-based pricing may lead to customers' bad experiences and utility companies' economic losses. Focusing on this issue, this paper proposes a new customer selection framework for time-based pricing using the smart meter data. To utilize smart meter data effectively, a novel feature engineering procedure is designed, covering both of statistical characteristics and load patterns. Then, a Bayesian neural network (BNN) based prediction model is developed by integrating the Bayesian theory into neural networks for identifying high-potential customers. Finally, the prediction uncertainty is quantified to help utility companies to identify when to trust the prediction results. Case studies on an actual time-based pricing pilot demonstrate the effectiveness of the proposed framework. As for the comprehensive metric F1-score, BNN is in the lead, which is 9.0 % higher than neural network, 6.0 % higher than convolutional neural network (CNN), and 3.3 % higher than long short-term memory (LSTM). In addition, by leveraging the quantified uncertainties, the percentage of low-potential households among selected customers drops by 8.92 %, compared to blindly trusting the prediction results. Furthermore, it's found that load pattern features contribute more to model performance than statistical features, revealing that it’s the shape, rather than the amplitude, of daily load curves that determines residential price responsiveness.© 2017 Elsevier Inc. All rights reserved.