Individual Price Research Articles

Improving out-of-sample forecasts of stock price indexes with forecast reconciliation and clustering

In this paper, we propose a novel approach to improving forecasts of stock market indexes by considering common stock prices as hierarchical time series, combining clustering with forecast reconciliation. We propose grouping the individual stock price series in various ways including via metadata and using unsupervised learning techniques. The proposed approach is applied to the Dow Jones Industrial Average Index and the Standard & Poor 500 Index and their component stocks, and the results obtained with different grouping approaches are compared. The results empirically demonstrate that the combined use of clustering and reconciliation improves the forecast accuracy of the stock market indexes and their constituents.

Quantifying Medical Waste at a Veterans Affairs Operating Room.

Introduction: Medical waste is an environmental, financial, and administrative burden to the health care system. Attempts to decrease waste should begin by quantifying the amount of waste at an individual facility. This study attempts to quantify the amount of medical waste associated with operative cases at an urban Veterans Affairs Medical Center (VAMC). Methods: The study was a prospective of analysis of surplus equipment and supplies accumulated by a single surgical team over a 6-week period from a VAMC operating room. The equipment and supplies were counted and weighed. The cost of the most common items was calculated using standard procurement values. Results: Overall, there were 81 pieces of surplus equipment and 1122 pieces of surplus medical supplies. The most common piece of equipment was a towel clip, and the most common medical supply was a blue towel. The total weight of the equipment was 72.2 kg. The five most common items were blue towels, suture, gloves, gowns, and gauze pads. Based on standard pricing, the individual price for each of the five above items was $1.32, $1.84, $4.05, $5.74, and $0.13, respectively. Over the 6-week period, the total cost of the five most common items was $1,764.56. Finally, the total weight of the surplus items was 72.2 kg. Conclusions: Operative waste includes equipment and supplies that increase time, effort, and costs. Quantifying the waste allows each facility the opportunity to introduce potential strategies to reduce extraneous medical equipment and supplies.

A Novel NLP-based Stock Market Price Prediction and Risk Analysis Framework

Stock market prediction is an interesting and complex problem that has recently been in the limelight, thanks to the significant accuracy achieved by deep learning models. However, a complete platform with prediction and risk analysis ability is unavailable. In the current work, we present a novel framework for investment analysis designed to create ease for investors and provide a confidence measure along with the stock price to depict the risk involved in investing in stocks of a particular company. The model integrates two different approaches successfully to improve accuracy significantly. The model inputs two sources – a stock price dataset depicting the original scores as numerals and textual data extracted from Reddit news articles. The traditional problem of stock price prediction is dealt with using LSTMs on individual stock prices. At the same time, the confidence is represented by a risk value calculated intelligently using XGBoost and LSTM output. We have deployed natural language processing techniques for performing sentiment and subjectivity analyses, which are then used to extract features for further investigation in the study. The results show that an accuracy of 94% for stock trend prediction can be achieved using PCA as the feature extractor with tuned parameters for XGBoost and around 76% accuracy for stock price prediction with a tuned LSTM. It removes the hassle for investors to research the project or company they want to invest in and provides all relevant analysis and data.

Stock price delay and the cross-section of expected returns: A story of night and day

We examine how individual stock price reacts to intraday and overnight market information. By examining stock betas, we document a stark gap between day beta and night beta and nontrivial asynchronous beta, which captures the slow diffusion of information. We provide evidence that this information delay can predict future stock returns over a one-month period. Long-short portfolios sorted on the gap between day beta and night beta and asynchronous beta generate raw returns of 0.8% and 0.39% and risk-adjusted alphas of 0.77% and 0.28% per month. These results are robust to alternative asset pricing models and when controlling for firm characteristics, such as size, book-to-market ratios, liquidity, investor recognition and limits-to-arbitrage characteristics. We also explore the heterogeneity of the information delay premium and find that the predictive power of information delay for future return is stronger among small, value firms, less visible firms, illiquid firms and firms with higher limit-to-arbitrage. We also provide evidence that our measure of price delay indeed offers incremental information for cross-section return prediction after we explicitly control for the conventional measure of Hou and Moskowitz (2005)’s price delay.

Is Dhaka Stock Exchange An Efficient Capital Market? - Evidence Of Weak-Form Efficient Market Hypothesis

This paper uses Random walk hypothesis to test the efficiency of Dhaka Stock Exchange Ltd (DSE), an emerging capital market of Bangladesh. In this paper, with Runs test, Dickey-Fuller Unit root test have been processed and analyzed to observed the behavior of daily return of Dhaka Stock Exchange indices during the study period. The sample includes the daily price indices of all securities listed on the DSE general, DSI (All Share), DSE-30, and Daily indices listed with DSE. As a proxy of movement of individual stock prices, daily closing prices of 30 companies operating in the Banking sector has been analyzed. The results provide evidence that DSE does not follow the random walk model and so DSE is not efficient even in weak form. To improve this capital market, the timely disclosure and dissemination of information to the shareholders and investors on the performance of the listed companies should be given emphasized.

Corporate resilience during crises and the role of ESG factors: Evidence from the COVID-19 pandemic

This study explores the relationship between environmental, social, and governance (ESG) factors and investor behavior during the COVID-19 crisis, utilizing a sample of S&P 500 companies. Findings suggest that these factors may not have had a significant influence on investment decisions during this period. Despite a notable overall market response to major COVID-19 related events, our findings reveal that ESG factors provide minimal explanatory power for individual stock price returns. This observation invites further investigation into the conditions under which ESG considerations are prioritized by investors.

Analyzing individual rent price ratios in eastern German agricultural land markets: A GAMLSS approach

AbstractThis study explores the rent price ratio in agricultural land markets, crucial for evaluating market efficiency, policy needs, and farmer decision‐making. Traditionally, the analyses faced challenges due to the absence of concurrent sale and rent data for the same land, potentially leading to biased results. This research avoids this problem by utilizing a unique dataset from Eastern Germany, encompassing auction data from 2016 to 2019. This dataset is distinctive, containing both the purchase and lease price offers by individual farmers for the same agricultural land at a single point. Thus, this study overcomes the conventional limitations of using aggregated or non‐contemporaneous data. Hence, the focal point is to unravel the intricate relationship between individual rent price ratios and a myriad of influencing factors, including land characteristics and local farming structures. The use of Generalized Additive Models for Location, Shape, and Scale provides a robust framework for this analysis, offering a nuanced understanding of the distributional properties of individual rent price ratios. The study provides robust empirical evidence that highlights the need for adaptive and region‐specific policies if policy makers insist on intervening in the agricultural land market. Furthermore, promising points of departure for future research are identified. [EconLit Citation: Q15].

Forward Guidance under Imperfect Information

AbstractI study the effect of forward guidance in a flexible‐price economy in which both the private sector and the central bank are subject to imperfect information about the aggregate state of the economy. When forward guidance is provided, the central bank reveals its current imperfect information and commits to a policy rule that makes future policy conditional on perfect information that is only available in the future. The information provided by forward guidance makes individual prices more responsive to firm‐specific technology shocks, which increases production efficiency at the cost of higher cross‐sectional price variation. The net effect improves social welfare.

Sectoral recommendation system for medium term investment using technical indicators

Stocks of different companies are generally categorized into various industrial sectors based on the domain they operate. Individual stock prices are much more difficult to predict, and an investment bears a significant risk of unanticipated failure while thematic investment allows diversification within a sector and hence avoids the risk of failure of a single stock. In thematic investment, a user identifies one or more industrial sectors based on thematic preference and then distributes the corpus among various stocks from those sectors. If sectors are chosen wisely then it is possible to have a positive return even with the sudden downfall of some stock. To identify the sectors that will yield a positive return in the medium term, we have selected ten technical indicators (TI), and designed backpropagation neural networks (BPNN) with scaled conjugate gradient learning. We have evaluated the BPNN models with 12 sectoral index price history from NSE and achieved a comparable accuracy of 81.05% in prediction. In the next step, we have predicted sectoral index movement for one month to six months in the future, and based on the predicted gains we ranked the sectors. Based on the given investment horizon, top-k(=4) sectors are recommended to an investor. We validated our strategy on NSE sectoral data and achieved a recommendation accuracy of 87.5%.

ANALISIS PENGARUH HARGA, PELAYANAN, DAN PROMOSI TERHADAP KEPUTUSAN PENGGUNAAN JASA OJEK ONLINE “GOJEK” PADA MAHASISWA STIE JAYAKARTA TAHUN 2024

This article was conducted with the aim of evaluating the impact of prices, services and promotions offered by the Indonesian Gojek company on the decision to use Gojek online motorcycle taxi services among STIE Jayakarta 2024 students. A numbers-based approach was used in the descriptive method. The required sample of 63 individuals was obtained using the Slovin method. This study revealed that although individual price is not a significant factor in consumer decisions, usage decisions are strongly influenced by service and promotions. The level of certainty of the coefficient of determination is 52%. This proves that price, service and promotion variables influence 52% of usage decisions, while the remaining 48% can be analyzed by factors not investigated by this article.

Monetary non-neutrality in a multisector economy: The role of risk-sharing

We show that the degree of risk-sharing among heterogeneous workers is a key determinant of monetary non-neutrality in a multisector sticky-price model. In this framework, workers are employed across different sectors, earning distinct wages. The limited ability of workers to fully insure against labor income risks results in strategic complementarity in firms' price-setting decisions with respect to aggregate shocks and strategic substitutability with respect to idiosyncratic shocks. These pricing interactions lead to sluggish adjustments of the price level in response to monetary and other aggregate shocks, causing significant fluctuations in the output gap while maintaining large responses of individual prices to idiosyncratic shocks. We illustrate these results across three stylized asset market scenarios: complete markets, non-contingent bond-only markets, and financial autarky.

Property tax‐induced mobility and redistribution: Evidence from mass reappraisals

AbstractI investigate the effect of property tax changes on homeowner mobility and voted tax rates using a panel of assessment and sales records in Ohio. I use redistributive mass reappraisals that cause changes in an individual's tax price of local services to examine how changes in homeowner's tax bills influence mobility. I find that a $0.10 increase in the price per dollar of services leads to a 5% increase in the likelihood of sale with no change in foreclosure probability. I also find suggestive evidence of increased voted tax rates when the ratio of median to mean taxable value decreases.

Shift in house price estimates during COVID-19 reveals effect of crisis on collective speculation

We exploit a city-level panel comprised of individual house price estimates to estimate the impact of COVID-19 on both small and big real-estate markets in California USA. Descriptive analysis of spot house price estimates, including contemporaneous price uncertainty and 30-day price change for individual properties listed on the online real-estate platform Zillow.com, together facilitate quantifying both the excess valuation and valuation confidence attributable to this global socio-economic shock. Our quasi-experimental pre-/post-COVID-19 design spans several years around 2020 and leverages contemporaneous price estimates of rental properties – i.e., off-market real estate entering the habitation market, just not for purchase and hence free of speculation – as an appropriate counterfactual to properties listed for sale, which are subject to on-market speculation. Combining unit-level matching and multivariate difference-in-difference regression approaches, we obtain consistent estimates regarding the sign and magnitude of excess price growth observed after the pandemic onset. Specifically, our results indicate that properties listed for sale appreciated an additional 1% per month above what would be expected in the absence of the pandemic. This corresponds to an excess annual price growth of roughly 12.7 percentage points, which accounts for more than half of the actual annual price growth in 2021 observed across the studied regions. Simultaneously, uncertainty in price estimates decreased, signaling the irrational confidence characteristic of prior asset bubbles. We explore how these two trends are related to market size, local market supply and borrowing costs, which altogether lend support for the counterintuitive roles of uncertainty and interruptions in decision-making.

Patterns and correlations in European electricity prices.

Electricity markets are central to the coordination of power generation and demand. The European power system is divided into several bidding zones, each having an individual electricity market price. While individual price time series have been intensively studied in recent years, spatiotemporal aspects have received little attention. This article provides a comprehensive data-centric analysis of the patterns and correlations of the European day-ahead electricity prices between 2019 and 2023, characteristically abnormal due to the energy crisis in Europe. We identify the dominant communities of bidding zones and show that spatial differences can be described with very few principal components. Most bidding zones in Continental Europe were brought together during the energy crisis: Correlations increased, and the number of relevant principal components decreased. Opposite effects occur in the Nordic countries and the Iberian Peninsula where correlations decrease and communities fragment.

Macroeconomic shocks, market uncertainty and speculative bubbles: a decomposition-based predictive model of Indian stock markets

PurposeStock markets are essential for households for wealth creation and for firms for raising financial resources for capacity expansion and growth. Market participants, therefore, need an understanding of stock price movements. Stock market indices and individual stock prices reflect the macroeconomic environment and are subject to external and internal shocks. It is important to disentangle the impact of macroeconomic shocks, market uncertainty and speculative elements and examine them separately for prediction. To aid households, firms and policymakers, the paper proposes a granular decomposition-based prediction framework for different time periods in India, characterized by different market states with varying degrees of uncertainty.Design/methodology/approachEnsemble empirical mode decomposition (EEMD) and fuzzy-C-means (FCM) clustering algorithms are used to decompose stock prices into short, medium and long-run components. Multiverse optimization (MVO) is used to combine extreme gradient boosting regression (XGBR), Facebook Prophet and support vector regression (SVR) for forecasting. Application of explainable artificial intelligence (XAI) helps identify feature contributions.FindingsWe find that historic volatility, expected market uncertainty, oscillators and macroeconomic variables explain different components of stock prices and their impact varies with the industry and the market state. The proposed framework yields efficient predictions even during the COVID-19 pandemic and the Russia–Ukraine war period. Efficiency measures indicate the robustness of the approach. Findings suggest that large-cap stocks are relatively more predictable.Research limitations/implicationsThe paper is on Indian stock markets. Future work will extend it to other stock markets and other financial products.Practical implicationsThe proposed methodology will be of practical use for traders, fund managers and financial advisors. Policymakers may find it useful for assessing the impact of macroeconomic shocks and reducing market volatility.Originality/valueDevelopment of a granular decomposition-based forecasting framework and separating the effects of explanatory variables in different time scales and macroeconomic periods.

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 .

Technical and Enviro-economic Analysis of a 0.78 kWp PV System

Aim: To determine the technical, economic and environmental performance analysis of an installed PV backup system. Study Design: To attain this goal, analysis were performed using a freely available PVSyst 6.7.0 software tool. Economic evaluation was performed using respective present worth of individual component prices. Environmental performance comparison is made between the PV system and diesel generator when each is used as an independent backup system. Input in-situ measured data were determined, measured and keyed into the tool. Place and Duration of Study: The system is installed in an institutional building in a tropical climatic zone with coordinates 0.42 N and 35.03 E. The system was studied for a period of one year where data was collected and recorded for techno-enviroeconomic analysis. Methodology: The system consists of four PV modules rated at 0.78 KWp, charge controller and an inverter unit and battery bank, and is utilized as a power backup system to supply electricity whenever power failure occurs, which is frequent and real in Kenya. The techno-enviroeconomic performances analysis of the system were evaluated using the PVSyst software, a freely available design and analytical tool, where input data were measured and recorded at the site. Results: The performance analysis of the system showed that PV array efficiency of 13.24%, FF of 0.68, CF of 21.23%, and PR of 73.63% and generated electrical energy of 1092 kWh/year. The LCOE of PV electricity was 0.059 $/kWh, while total saving of CO2 emission in tons of CO2 was 9.0 tCO2 with LCE value of 331 gCO2/ kWh. Conclusion: Techno- enviroeconomic performance results of the studied PV system show that the system can contribute significantly to the mitigation of CO2 emission. This work also could be helpful for consumers and policy makers in the choice of renewable based or fossil based backup generation systems.

A Study on Replenishment Decision of Vegetable Commodities Based on Time Series Forecasting and Linear Programming Models

The automatic pricing and replenishment decisions for vegetable products are based on the fact that vegetable products have relatively short shelf lives, and there is a wide variety of vegetable types for sale. Vegetable purchases typically occur in the early morning. Therefore, businesses often rely on historical sales and demand data for each product to make pricing and restocking decisions, even when they do not have exact information about individual item and purchase prices. This article is based on the data provided by the CUMCM to conduct relevant research.

Does the Performance of Global Commodities Exhibit Co-movement and Long-run Relationship with the Palm Oil Companies?

While numerous papers investigate on the co-movement of global commodities and stock indices, this paper extends to previous papers by focusing on the individual stock price of palm oil companies namely Sime Darby Plantation, Wilmar International, and Astra Agri Lestari. Weekly data spanning from January 2018 to September 2023 was selected and Autoregressive Distributed Lags (ARDL) model employed to demonstrate the possible co-movement and long-run relationship. Empirical results provide sufficient evidence of co-movement between global commodities with Sime Darby Plantation and Astra Agri Lestari in the long-run. This will allow fund managers, individual investors, and stock traders to make investment decisions, trading strategies, and portfolio management based on the global commodity markets.

T49 Style Stock Prediction

Stock market prediction has long been a pursuit of investors seeking to gain insights into the future trajectory of stock prices. Traditionally, analysts relied on fundamental analysis, technical indicators, and market sentiment to forecast stock movements. However, with the advent of machine learning (ML) and artificial intelligence (AI), predictive analytics in the stock market has seen a significant shift. ML algorithms offer the advantage of processing vast amounts of data and identifying complex patterns that might elude human analysts. In the context of stock market prediction, these algorithms can analyze historical stock prices, trading volumes, market indices, news sentiment, and various other factors to generate forecasts. One notable trend in this domain is the utilization of current stock market indices as input features for ML models. By training on historical data of these indices and their corresponding effects on individual stock prices, algorithms can learn to make predictions based on the current state of the market. One common approach is to use techniques like regression, time series analysis, or deep learning models such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). These models can capture temporal dependencies and nonlinear relationships in the data, allowing for more accurate predictions. Additionally, ensemble methods like random forests or gradient boosting can be employed to combine the strengths of multiple models for enhanced forecasting performance. Keywords: Stock market prediction,, financial stocks, stock market indices