Price Information Research Articles

The effect of geopolitical uncertainty on stock liquidity: evidence from Taiwan misfired missile event

ABSTRACT We investigate the effect of geopolitical uncertainty on stock liquidity by using the Taiwan misfired missile event as a natural experiment. This exogenous event provides us with a unique opportunity to overcome the challenges of time recognition and confounding economic effects in the identification. We find that geopolitical uncertainty has a significantly negative effect on the stock liquidity of Taiwan-related firms. The mechanism test shows that the increased stock price risk and information asymmetry are the two main channels through which geopolitical uncertainty decreases stock liquidity. Moreover, we find that firms’ higher information transparency and regulators’ timely information disclosure on geopolitical events can effectively mitigate the negative impact of geopolitical uncertainty on stock liquidity.

Insider Trading Restrictions and Informed Trading in Peer Stocks

Using a uniquely constructed data set of trades by corporate insiders in all stocks, we find that, after insider trading regulations become stricter, insiders are 20% more likely to trade in peer stocks and that such trades become more profitable. The increase in both the probability and profitability of peer-stock trades is driven by the insider’s information that is fungible to industry peers. Stricter insider trading laws are designed to improve liquidity and price informativeness in capital markets. We show that peer trading dampens these intended benefits of the insider trading regulation. This paper was accepted by Ranjani Krishnan, accounting. Funding: This work was supported by NSE-NYU Stern Initiative on the Study of Indian Financial Markets. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02907 .

ARIMA-GARCH MODEL IN OVERCOMING HETEROSCHEDSDATICITY IN STOCK PRICE PREDICTION (CASE STUDY: PT INDOFOOD, TBK (INDF))

Ensuring access to future stock price information holds significant weight for investors in formulating investment strategies. The Indonesian capital market serves pivotal economic and financial roles within the economy, offering various instruments, such as stocks. Among these, blue-chip stocks are recognized for their minimal risk exposure. The impact of the COVID-19 pandemic is anticipated to influence stock price dynamics, including those of blue-chip stocks. Statistical methodologies, such as Autoregressive Integrated Moving Average (ARIMA), are commonly utilized for stock price prediction. However, the efficacy of ARIMA is contingent upon the fulfillment of homoscedasticity assumptions. Failure to meet this assumption due to fluctuating stock price developments poses a challenge. Consequently, an ARIMA-GARCH hybrid model has been developed to address heteroskedasticity concerns in stock price forecasting. This study focuses on INDF stock data, exemplifying a blue-chip stock with positive performance. Results indicate that combining ARIMA-GARCH models, particularly the ARIMA(0,1,3)-GARCH(1,3) model, yields optimal predictions for subsequent stock prices. The MAPE value of the ARIMA-GARCH model stands at 1.41%, indicating superior performance compared to standalone ARIMA modeling. These findings are expected to serve as a valuable resource for investors navigating investment decisions.

Firm-specific investor sentiment and stock price informativeness

This paper investigates the influence of firm-specific investor sentiment on stock price informativeness in China. Using a composite firm-specific investor sentiment index, we find that sentiment positively influences stock price synchronicity and future price crash risk for opaque stocks. We use firm-specific investor sentiment proxy to develop a market sentiment proxy, which could predict aggregate price synchronicity beyond extant market sentiment indicators. Our results suggest that the composite sentiment index is a reliable proxy and investor sentiment negatively affects price informativeness.

ENHANCING TECHNICIAN & PLUMBER HIRING STRATEGIES WITH PYTHON: A TRENDSETTING COMPUTER SCIENCE ANALYSIS

Abstract: The Online Platform for Technician and Plumber Hiring is a digital marketplace designed to streamline the process of connecting individuals and businesses with skilled technicians and plumbers for maintenance and repair services. The platform offers an intuitive interface accessible via web browsers and mobile applications, allowing users to search, compare, and hire technicians and plumbers based on their specific needs and preferences. Service providers create detailed profiles showcasing their qualifications, experience, areas of expertise, and customer reviews, allowing service seekers to evaluate the suitability of technicians and plumbers for their specific requirements. The platform also offers advanced search and filter functionality, allowing users to narrow down their options based on location, services offered, availability, pricing, and ratings. It facilitates seamless booking and scheduling of service appointments, allowing users to specify the date, time, and nature of the required services. Clear pricing and payment information are provided for each service, ensuring transparency and eliminating ambiguity for both service seekers and providers. After service completion, users can rate and provide feedback on the quality of work and customer service received from technicians and plumbers, maintaining accountability and encouraging continuous improvement within the service provider community. The platform also offers customer support services to address inquiries, resolve issues, and facilitate communication between parties if disputes arise during or after service delivery. Mediation mechanisms are in place to ensure fair resolution of conflicts. IndexTerms – HTML, CSS, AngularJs , NodeJs , Bootstrap, MySQL

The Effect of Algorithmic Trading on Management Guidance

ABSTRACT I investigate whether algorithmic trading (AT) affects the provision of management guidance. Existing research finds that AT decreases fundamental information acquisition before earnings announcements and consequently reduces the informativeness of prices. To compensate for reduced information acquisition, I predict and find that managers at firms with more AT activity increase the quantity and quality of guidance issued at earnings announcements. Evidence is consistent with managers responding to reduced information acquisition, as opposed to changes in liquidity, and results suggest guidance in response to AT is effective at reducing information asymmetry. These findings identify a new channel through which AT affects stock price informativeness by documenting a link to managers’ disclosure decisions. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G14; G19; G10.

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

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

Stock Trend Prediction with Machine Learning: Incorporating Inter-Stock Correlation Information through Laplacian Matrix

Predicting stock trends in financial markets is of significant importance to investors and portfolio managers. In addition to a stock’s historical price information, the correlation between that stock and others can also provide valuable information for forecasting future returns. Existing methods often fall short of straightforward and effective capture of the intricate interdependencies between stocks. In this research, we introduce the concept of a Laplacian correlation graph (LOG), designed to explicitly model the correlations in stock price changes as the edges of a graph. After constructing the LOG, we will build a machine learning model, such as a graph attention network (GAT), and incorporate the LOG into the loss term. This innovative loss term is designed to empower the neural network to learn and leverage price correlations among different stocks in a straightforward but effective manner. The advantage of a Laplacian matrix is that matrix operation form is more suitable for current machine learning frameworks, thus achieving high computational efficiency and simpler model representation. Experimental results demonstrate improvements across multiple evaluation metrics using our LOG. Incorporating our LOG into five base machine learning models consistently enhances their predictive performance. Furthermore, backtesting results reveal superior returns and information ratios, underscoring the practical implications of our approach for real-world investment decisions. Our study addresses the limitations of existing methods that miss the correlation between stocks or fail to model correlation in a simple and effective way, and the proposed LOG emerges as a promising tool for stock returns prediction, offering enhanced predictive accuracy and improved investment outcomes.

How consumers with hedonic (vs utilitarian) purchase motive use item-price (vs price-item) presentation order as a mechanism to justify their hedonic purchase

PurposeThis study aims to investigate the moderating role of hedonic and utilitarian purchase motives for the presentation order effect. Although past research finds that presenting item first and price later (e.g. 70 items for $29) increases consumers’ purchase intention more than presenting the information in the opposite order (e.g. $29 for 70 items), the effect was mostly examined in a hedonic consumption context. This study examines whether the effect is applicable for hedonic purchases but is less applicable for utilitarian purchases, and why.Design/methodology/approachSeven experiments tested the moderating effect of purchase motives for the presentation order effect. Two serial mediation analyses were conducted to examine the underlying mechanism.FindingsThe “item-price” (vs “price-item”) order increases hedonic purchases, but not utilitarian purchases. Because consumers feel guilty about hedonic purchases, they engage in motivated information processing to perceive greater value from their hedonic purchase when item (benefit) information is presented first and price (cost) information is presented later. Perceiving greater value reduces guilt, which consequently increases hedonic purchases. In contrast, the order effect is not observed for utilitarian purchases that do not elicit guilt. When a price discount is offered, the order effect is reversed because actual savings justify hedonic purchases better than perceived savings resulting from motivated information processing.Practical implicationsWhen promoting hedonic products, marketers are recommended to present item information before price information, unless a price discount is offered, in which case the price should be presented first.Originality/valueThis research introduces a novel moderator for the presentation order effect and a novel underlying mechanism, driven by the motivation to alleviate guilt associated with hedonic purchases.

Impacts of analyst coverage initiation on market quality

ABSTRACT We examine whether the initiation of financial analysts’ coverage leads to improved stock-price informativeness and increased market liquidity using the difference-in-differences methodology. The average value of daily total trading volume is significantly higher after than before initiation. The increase in trading volume is higher in the KOSDAQ market than in the KOSPI market, irrespective of investor type. Nonetheless, this increased rate is observed to decline more rapidly over time in the KOSDAQ market. When an analyst initiates coverage on a stock, both price informativeness and market liquidity improve, with different markets driving each effect. The enhancement of price informativeness is significant in the KOSPI, whereas the increase in market liquidity is primarily driven by firms listed on the KOSDAQ.

The influence of online information on consumers’ channel migration behavior of fresh agricultural products

IntroductionCompliance with the latest patterns in online consumption of fresh agricultural products should prioritize the shifts in consumer behavior. This study aimed to clarify the influencing factors of consumers’ channel migration behavior of fresh agricultural products. While the migration of consumers’ consumption of fresh agricultural products to online channels is an undeniable fact, and this trend continues, literature on this topic remains limited.MethodsBased on SOR theory, and from the dual perspectives of information transmission and information reception, this study exploratively introduced the network affinity of consumers, and constructed the concept model of the influencing factors of consumers’ channel migration behavior of fresh agricultural products including information acquisition and risk perception. 416 valid questionnaires were used to conduct structural equation model analysis.ResultsThe results confirm that product information and platform information significantly affect consumers’ channel migration behavior of fresh agricultural products. Product information including feature information and price information has a positive influence on consumers’ channel migration behavior of fresh agricultural products. The same is true for such behavior and the platform information including service information and logistics information. Risk perception plays a partial mediating role in the influence of product information variables and platform information variables on consumers’ online purchasing and migration behavior of fresh agricultural products. Network affinity negatively moderates the causal relationship between product information and risk perception as well as that between platform information and risk perception. The effect is more pronounced for consumers with high network affinity than those with low network affinity.DiscussionThe study presented in this paper offers a replicable theoretical framework for future discussions on consumer channel migration behavior, and enriches the literature on consumer online consumption behavior. It is highly meaningful for further improving the online consumption stickiness, tapping the potential of online consumption and improving the circulation efficiency of fresh agricultural products in the post-pandemic era.

Life cycle sustainability assessment method for concrete

AbstractThis study aimed to develop a life cycle sustainability assessment method for concrete. An environmental impact assessment method was developed using the ELCA technique announced through the ISO 14,040 Series and KOLID, one of the life cycle impact assessment indicators. A life cycle cost assessment method was developed based on the unit price of materials and energy consumption according to the Korea Price Information. A Social Impact Assessment Method was developed by identifying appropriate major social topics and analyzing those suggested by the international organizations (UNEP). In accordance with the ‘Towards a Life Cycle Sustainability Assessment’ proposed by UNEP/SETAC, a Sustainable Concrete Index (SCI) was developed that integrates environmental, economic, and social aspects. In addition, the environmental impact of Korea’s 544 concrete types was classified by compressive strength, and the mode value was proposed as a standard concrete (environmental impact 6,662–10,785 KRW(5.02–8.12 USD), production cost 66,549–79,030 KRW(50.13–59.53 USD) so that it could be compared with the assessment concrete. As a result of selecting one ready-mix concrete business place in Korea and conducting a case study by comparing it with standard concrete, EI, CI, SI, and SCI, were analyzed to be 1.135, 1.134, 0.830, and 1.033, respectively, making it possible to quantitatively evaluate concrete sustainability.

Marketing Efficiency in Cut Roses: A Comparative Study of Organised Vis-A-Vis Unorganised Markets

The present study is mainly about the comparative analysis of marketing efficiency of cut roses under organised and unorganised markets. For the present study, the primary data was collected from the Krishna Raja Market, Bengaluru an unorganised market, whereas the International Flower Auction Centre, Bengaluru (IFAB) is an organised market. Data collected from 40 market intermediaries in each market structure and total intermediaries were to be 80. Forty rose vendors are randomly selected to gather marketing constraints in organised market. In the study area, three major marketing channels were observed for cut rose marketing in unorganised market structure: Channel I: Producer – Wholesaler – Retailer – Consumer; Channel II: Producer – Retailer – Consumer; and Channel III: Producer– Commission agent– Retailer – Consumer, whereas in the organised market, two marketing channels were prominent, i.e., Channel I: Producer – IFAB – Wholesaler – Retailer – Consumer and Channel II: Producer – IFAB – Retailer – Consumer. Marketing efficiency was estimated using the Acharya and Agarwal methodology. In this analysis, direct marketing channel was found to be the most efficient for unorganised and organised markets. Marketing constraints were analysed using Rank Based Quotient (RBQ) technique. Quality standards and the lack of price information were the most important problems for the farmers while marketing the flowers in organised markets rather than unorganised markets. Hence, policymakers should provide producers with the required facilities and impart proper knowledge about quality standards to farmers, leading to more income for producers.

How nonlinear benchmark in delegation contract can affect asset price and price informativeness

How nonlinear benchmark in delegation contract can affect asset price and price informativeness

The influence of energy-saving information in online reviews on green home appliance purchase behavior based on machine learning

The renewal of green home appliances is a crucial measure for households to save energy and reduce emissions. However, how online reviews, especially those relate to energy-saving, affect green home appliance purchase behavior (GHAPB) lacks exploration. In this paper, we investigate over 1 million online reviews on about 3,116 types of air conditioner from JD. By applying word2vec, we divide energy-saving related information in the following three types: norm information, environmental health information and price information, and construct dictionaries for each. Then, the effect value of energy-saving information is quantified from perspectives of breadth, depth and intensity through sentiment analysis. The influence of energy-saving information in online reviews on GHAPB is finally analyzed by linear regression and machine learning models. The results show that all energy-saving information has positive impact on GHAPB, and environmental health information is the most important one. In addition, the attributes of online reviews impose a greater influence on GHAPB, comparing with those of products. The in-depth exploration of energy-saving information in online reviews provides targeted recommendations for the manufacturer and the retailer to promote the adoption of green home appliances.

The Level of Importance of the Four P’s by the Customers of Shell, Petron, and Chevron Stations in General Santos City

Consumer-centric companies focus their resources on developing products and establishing strategies that satisfy customers’ needs and wants. The efforts involved in determining customers’ desires require rigorous analysis because of customers’ heterogeneity. Cost leadership, one of the three generic competitive strategies, aims to increase profit by reducing the cost of production and offering a competitively priced product. This strategy is effective in maximizing the limited resources of the company. In relation to the three generic competitive strategies, there are factors that affect customers’ decisions. The factors that affect customers’ preferences are location, brand, behavior, values, product attributes, quality, price, promotion, and perception. This study focused on the four significant factors, which are the Four Ps: product, price, place, and promotion. Respondents, who are customers of Shell, Petron, and Chevron fuel stations in General Santos City, evaluated the level of importance of the Four Ps. A sample size of 120 respondents was chosen to answer the validated questionnaires. A descriptive-quantitative research design and stratified sampling methods were applied in the study. Microsoft Excel and IBM SPSS were the software used to analyze and interpret the data. The findings indicate that the place factor and the promotion factor, among the four factors, have the highest and lowest means, respectively. According to the respondents, price information and brand are very important, since they have the highest mean in price and product factors. The analysis of the Kruskal-Wallis H Test indicates that there are no significant differences in the level of importance of the four Ps among Shell, Petron, and Chevron stations in General Santos City as evaluated by the respondents.

Estimating the productivity of US agriculture: The Fisher total factor productivity index for time series data with unknown prices

AbstractIn this paper, we propose a straightforward way to estimate the Fisher ideal total factor productivity (TFP) index (FI) in cases where price information is unavailable, using ‘shadow prices’ derived from data envelopment analysis (DEA). A Monte Carlo experiment shows that the shadow price Fisher ideal TFP index (SPFI) can effectively estimate the ‘true’ FI with relatively small (and stable) errors. The empirical application to the US agriculture sector (1948–2017) further suggests that the SPFI is a (superior) alternative to the traditional Malmquist DEA, especially in dealing with unbalanced panel or time series data when price data are unknown.

Asset prices’ responses to public information manipulation: The role of market feedback

How does information transmission from financial markets to the real economy affect asset prices’ reaction to manipulated news? We examine this question in a model in which a firm manipulates public information about its productivity, investors trade a financial asset whose payoff is contingent on the firm’s value, and a capital provider decides the capital input to the firm. We analytically demonstrate that the capital provider’s learning from the asset price (i.e., the feedback effect) lowers informed investors’ perceived asset payoff risk, facilitates informed trading, increases price informativeness, and mitigates the asset price’s response to the firm’s manipulation. Consequently, the feedback effect reduces the intensity of financial reporting fraud.

Examining Accessibility of Freestanding Abortion Clinics for People With Disabilities [ID 2683401

INTRODUCTION: People with disabilities face numerous barriers to equitable health care but have a fundamental right to safe abortion care. This study reviews the accessibility of freestanding abortion clinics in the United States for those with disabilities. METHODS: A quantitative data analysis was performed. All freestanding abortion clinics registered with the Abortion Care Network were included except those directly affiliated with a university or general gynecology clinic and those exclusively operating online. Two independent reviewers analyzed clinic websites after an interrater reliability test. Independent variables analyzed included Flesch Reading Ease score, presence of a statement affirming dignity for all persons, online appointment scheduling, transparent pricing, and financial assistance information. RESULTS: Seventy-two clinics were included. No clinic explicitly stated that their facilities were accessible to individuals with disabilities. Fifty-four percent had a statement affirming dignity for all persons, most of which were in the Northeast or West Coast. 6% had an accessibility widget, and 63% had online appointment scheduling. Mean Flesch Reading Ease score was 55.37 correlating to a 10th- to 12th-grade reading level. 33% had transparent pricing information, and 71% offered information on financial assistance. CONCLUSION: Several accessibility barriers were identified on these websites. Freestanding abortion clinics should focus on enhancing accessibility by explicitly affirming dignity for all patients, providing clear information on facility accessibility, and implementing an accessibility widget, online scheduling, and transparent pricing. Furthermore, these websites should be written at an easily comprehensible reading level. These simple changes would make these clinics more accessible for those with disabilities.

Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution

During the last years, European intraday power markets have gained importance for balancing forecast errors due to the rising volumes of intermittent renewable generation. However, compared to day-ahead markets, the drivers for the intraday price process are still sparsely researched. In this paper, we propose a modelling strategy for the location, shape and scale parameters of the return distribution in intraday markets, based on fundamental variables. We consider wind and solar forecasts and their intraday updates, outages, price information and a novel measure for the shape of the merit-order, derived from spot auction curves as explanatory variables. We validate our modelling by simulating price paths and compare the probabilistic forecasting performance of our model to benchmark models in a forecasting study for the German market. The approach yields significant improvements in the forecasting performance, especially in the tails of the distribution. At the same time, we are able to derive the contribution of the driving variables. We find that, apart from the first lag of the price changes, none of our fundamental variables have explanatory power for the expected value of the intraday returns. This implies weak-form market efficiency as renewable forecast changes and outage information seems to be priced in by the market. We find that the volatility is driven by the merit-order regime, the time to delivery and the closure of cross-border order books. The tail of the distribution is mainly influenced by past price differences and trading activity. Our approach is directly transferable to other continuous intraday markets in Europe.