Individual Stock Prices 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.

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.

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%.

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.

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

Forecasting and Error Factor Analysis of the CSI 300 Index Based on the ARIMA Model

The CSI 300 stock index futures are futures contracts with the CSI 300 Index as the underlying asset. The CSI 300 Index represents the overall trend of the Chinese stock market, consolidating a representative variety of stocks for analysis. It is generally believed to reflect the highs and lows of the Chinese stock market, with individual stock price anomalies having limited impact on the overall index. Therefore, studying stock price fluctuations through technical means, focusing on the CSI 300 Index as the research subject, is more appropriate. Correspondingly, it also provides guidance for portfolio operations and institutional or fund investments. Specifically, it plays a guiding role in the research of stock index futures based on the CSI 300 Index. Hence, this paper employs the ARIMA model in time series analysis to establish a model for the CSI 300 Index. Utilizing mathematical methods to process the daily CSI 300 data sampled from January 3, 2023, to December 29, 2023, a one-year period, the ARIMA model is developed using the Python programming language based on economic theory and econometric knowledge. By testing, adjusting, and estimating the model, a reasonable economic interpretation of the model is provided to forecast the CSI 300 Index. Through the analysis of the actual results obtained from this model and the reasons for the differences between the actual values of the CSI 300 Index and the model's predicted values, this study aims to offer valuable insights for enterprises and investors in making relevant decisions.

Technical Indicators of Major Healthcare Companies: A Comprehensive Analysis

This research aims to provide a comprehensive analysis of major healthcare stocks along with the nifty health care index within the healthcare sector. Employing technical indicators such as the Moving Average Convergence Divergence (MACD), Rate of Change (ROC) and Relative Strength Index (RSI) we conduct a detailed trend analysis to identify historical patterns and potential turning points in stock prices. Furthermore, advanced time series forecasting models and predictive analytics are utilized to forecast future share prices for the selected three companies, offering valuable insights for investors and market participants.
 In addition, we investigate the relationship between the stock prices of Apollo Hospital, Fortis Healthcare & Narayana Hruda and the Nifty Healthcare Index. This analysis sheds light on how individual stock prices are influenced by broader sectoral performance. We also explore the impact of MACD, ROC, and RSI technical indicators on the prices of all healthcare stocks through rigorous statistical analysis. Our findings provide healthcare sector stakeholders with a deeper understanding of stock behaviour, potential future movements, correlations with market indices and risk assessment.
 The results of this study enhance the decision-making process for investors and market participants in the healthcare sector. By examining historical trends, forecasting price dynamics, assessing sectoral correlations, evaluating technical indicator effects and risk measurement, this research contributes to a more comprehensive view of these stocks and their positioning within the healthcare industry.

Complex network analysis of global stock market co-movement during the COVID-19 pandemic based on intraday open-high-low-close data

This study uses complex network analysis to investigate global stock market co-movement during the black swan event of the Coronavirus Disease 2019 (COVID-19) pandemic. We propose a novel method for calculating stock price index correlations based on open-high-low-close (OHLC) data. More intraday information can be utilized compared with the widely used return-based method. Hypothesis testing was used to select the edges incorporated in the network to avoid a rigid setting of the artificial threshold. The topologies of the global stock market complex network constructed using 70 important global stock price indices before (2017–2019) and after (2020–2022) the COVID-19 outbreak were examined. The evidence shows that the degree centrality of the OHLC data-based global stock price index complex network has better power-law distribution characteristics than a return-based network. The global stock market co-movement characteristics are revealed, and the financial centers of the developed, emerging, and frontier markets are identified. Using centrality indicators, we also illustrate changes in the importance of individual stock price indices during the COVID-19 pandemic. Based on these findings, we provide suggestions for investors and policy regulators to improve their international portfolios and strengthen their national financial risk preparedness.

Unraveling the Impact of the Federal Reserve Rate Hike Policy on Amazons Stock Price Based on ARIMA Model

At the beginning of 2022, the inflation rate of the US economy was high. Consequently, the Federal Reserve initiated a series of 11 consecutive interest rate hikes on March 16, 2022. In this macroeconomic environment of rising interest rates, this article aims to use the ARIMA model to predict Amazons stock price after the interest rate hike, based on its stock price before the interest rate hike. It also seeks to compare the difference between the predicted stock price and the actual stock price to assess the impact of the successive rate hike policies on Amazons stock price. The study found that the Feds continuous rate hike policy would significantly reduce Amazons stock price, with its long-term relative decline even exceeding 30 percent. In comparison to previous articles that focused on the stock market or the Federal Reserves interest rate hike policy, this article no longer concentrates solely on theoretical analysis or macroeconomic market changes. Instead, it utilizes quantitative methods to place a greater emphasis on Amazon, a representative American internet company. This more specific perspective and more specific data enable investors to gain a clearer understanding of the direction and magnitude of the impact of interest rate hike policies on individual company stock prices. Based on the research results, this paper suggests that policymakers should carefully consider the policy of raising interest rates to avoid causing panic in the stock market. Entrepreneurs should strive to create more positive news, and investors should be cautious about holding stocks in an environment of rising interest rates.

The Energy Commodities Price and The Energy Sector Index: Evidence from Indonesia Stock Exchange

Purpose: This study aims to determine the effect and correlation of daily and weekly returns on commodity prices on the IDX Energi index on the Indonesia Stock Exchange. Theoretical Framework: Commodities are goods in the form of raw materials or raw materials that have a selling value. Energy commodities are mining products in the form of extracted fossils which can be used as energy sources. Stock price indices can be divided into several categories, namely stock market indices, sectoral stock price indices and individual stock prices. The stock market index is a representation of the total listed stock prices based on certain criteria and methods and is evaluated periodically. Method: The research period used was January 25 2021 to July 30 2023. The analysis technique used was multiple regression and Dynamic Conditional Correlation Generalized AutoRegressive Conditional Heteroskedasticity. Results: The results showed that the daily and weekly returns from energy commodities had a significant positive effect on the return on the IDX Energy index. Weekly returns on commodity prices can predict returns on the IDX Energi index better than daily returns. Energy commodities are also correlated with the IDX Energi index, but the dynamic correlation pattern of natural gas price returns to the IDX Energi index is different from the dynamic correlation pattern between world oil and coal prices. In general, returns on oil, natural gas and coal prices have an effect and are correlated with returns on the IDX Energi index. Conclusions: The research results can be used as a decision making in investing, especially investing in companies that are included in the IDX Energi sector. The weekly return of the IDX Energi index can be predicted through returns on energy commodity prices, but the contribution is still small. Subsequent research can add new variables that can affect the movement or return of the IDX Energi index, besides that it can examine with a longer sample period.

Research on the Anchoring Bias in Stock Investment: Evidence from Chinese Stock Market

Under the influence of factors such as information loss, information asymmetry and personal experience, people can be easily affected by anchoring effect and make wrong decisions. The anchoring effect is a cognitive bias whereby an individual's decisions are influenced by a particular reference point or anchor. This influence can be reflected in many aspects, such as the promotion activities in shopping malls, the choice of insurance products, or the decision to sell or buy in the stock market. In recent years, scholars have gradually found that anchoring effect will influence various decisions made by investors in the stock market to a certain extent, and therefore affect the price of individual stocks in the stock market. Based on previous research experience, the paper summarizes the changes in investors' decisions caused by anchoring deviation in China's stock market and its impact on specific stock prices. Therefore, this study on anchoring bias in the stock market has important implications for investors, traders, and financial analysts. By understanding the ways in which cognitive biases can shape decision-making in the stock market, individuals can take steps to mitigate the effects of these biases and make more rational investment decisions.

Comparing the Effects of the Nasdaq and Hang Seng on the Stock Performance of New Oriental

By the end of 2021, New Oriental declared that it would discontinue offering K-9 curricu-lum tutoring available at all its learning centers nationally. It will refocus its efforts and al-locate its resources to academic marketing strategies unrelated to K9 subject education. The goal of this article's research is to evaluate, using regression and CAPM models, the effects of the Nasdaq and Hang Seng on the returns on New Oriental stock. To determine their beta values and intercept terms, regression analysis of New Oriental stock returns, Nasdaq index returns, and Hang Seng index returns were performed. The CAPM model, which mainly investigates the correlation between projected returns and risky assets in the stock market, acts as the asset pricing model. Beta is a crucial variable. The beta coeffi-cient is a risk indicator that measures how volatile an individual stock or stock fund's price is in comparison to the overall stock market. The result is Nasdaq has a higher risk-free rate of interest and lower Beta value. Hang Seng has a lower risk-free rate of interest and higher Beta value.

Different portfolio performance based on momentum strategy, market neutral strategy and PEG ratio in volatile market

This paper aims to analyze the return of the investment portfolio based on the judgment of the current market, combining the characteristics of the strategy and the situation of the applicable industry. At present, many research papers have an aperture in understanding and practical application of the above topics, because they have not been comprehensively presented from multiple perspectives such as market and industry conditions and strategic characteristics. Momentum strategy, market neutral strategy and PEG ratio are selected here, combined with representative stocks in the consumer industry, energy industry, Internet and real estate for portfolio construction. By selecting the S&P 500 as a benchmark, it is compared with the historical price trend of individual stocks and the performance of the issuing company. While individual stock prices fluctuate significantly more than the benchmark, it is still possible to judge from the price action which strategy is more suitable. Based on the above analysis, it can be intuitively seen that momentum strategies and market neutral strategies are more suitable for the moment. It has reference value for strategy selection and construction in volatile markets. The analysis of the PEG ratio is relatively simple, and the ratio is only used as the adjustment and correction of the price-earnings ratio, and there are still many defects.

A Study on Performance Evaluation of Balanced Investment Schemes Offered by Selected Asset Management Companies

Investing in mutual funds and diversifying the investment portfolio are two critical strategies for achieving long-term financial success. Mutual funds pool money from multiple investors to invest in a diversified portfolio of stocks, bonds, or other assets. This diversification helps reduce the impact of individual stock or bond price fluctuations, thus mitigating market risk. Mutual funds are managed by experienced fund managers who conduct extensive research and analysis. These professionals make informed investment decisions, saving investors from the need to constantly monitor the market and individual securities. Diversification allows investors to balance risk and return according to their risk tolerance. By spreading investments across different asset classes and risk levels, individuals can achieve their desired risk-return profile. By harnessing the expertise of professional fund managers and diversifying across various assets, investors can navigate market fluctuations with confidence while working toward a more secure financial future. It also contributes to the field of finance by offering a comprehensive analysis of the performance of balanced mutual fund schemes, employing a range of evaluation tools, and emphasizing the critical interplay between risk and return in the investment landscape. Keywords: Balanced mutual fund scheme, Performance evaluation, Asset Management Companies (AMCs), evaluation metrics.

Stock price valuation analysis of the digital technology industry based on the HP-ARIMA method - with 18 listed companies of different volume sizes in its 3 segments as an example

The world securities market has formed a more complete system since its origin in the 17th century, with a considerable number of small and medium-sized investors and institutional investors, and proposing innovative new methods for securities valuation models can assist investors in making more accurate investment decisions. This paper proposes for the first time to use an interdisciplinary analysis method, ARIMA + HP filter analysis, to analyze the valuation of the securities market, by taking the opening prices of individual stocks in digital technology industry segments: blockchain, artificial intelligence, and Beidou navigation as examples for valuation analysis, firstly, the initial data of the opening prices of individual stocks from 2019-2021 are analyzed by HP filter analysis, and secondly, the data are analyzed while doing Tobit model test. By analyzing the long-term trend of the digital technology industry after noise reduction, its corresponding valuation forecast and value judgment results show that the head effect of the digital technology industry is more significant, and the opening price volatility shows a U-shaped relationship with company size and profitability, and the opening price volatility of individual stocks is less, while the shorter the company's establishment time, the greater the stock price volatility. While the past literature focused on the traditional valuation of financial conditions and historical data analysis of a single variable, this paper analyzes the data of individual stocks in the digital technology industry through an interdisciplinary analysis method, which allows for a more comprehensive use of historical data and prediction of possible stochastic fluctuations, providing a more reliable valuation method for securities investors and opening up new ideas for research related to the field of valuation analysis.

Groundfish quota prices

In 2010, the Northeast United States groundfish fishery adopted a catch share system in which Annual Catch Entitlements (quota) are allocated to groups of firms, known as sectors. The quota market has many features that differ from an ideal market: trades are facilitated by sector managers, completed trades are not easily seen by all, and both package and barter trades are common. This paper examines quota prices using a two stage econometric model. In the first stage, transactions data are used to estimate the prices of individual stocks of quota. In the second stage, a hurdle model is used to understand the determinants of quota prices. Despite the many quirks inherent in groundfish management, quota prices are affected by fundamentals in reasonable ways: increases in output prices and decreases in quota available both increase quota prices. Increases in monitoring rates also increase quota prices, evidence that at least some of the groundfish fleet is not always compliant with fishing regulations when not observed.