Different Types Of Stocks Research Articles

An Experimental Study and Analysis of Long-Term Multi-Trending Trajectory Forecasting of Stock Indices Using Time Series Inferential Statistical Projection

The focus of this study is on time series inferential statistical projection and analysis for long-term multi-trending trajectory forecast modelling. Inferential statistics was used for projection and analysis of long-term multi-trending trajectory forecasting. The proposed model is trained, tested and validated on three different types of stocks viz. NVIDIA, UNG and IBM taken from the NASDAQ stock market index. In Phase I, the long-term deterministic multi-trending model is assessed and fitted. Model skewed residual series is assessed using the goodness of fit in Phase II. The future stock close prices and long-term multi-trending trajectory paths are simulated and analyzed in Phase III. Finally, in Phase IV, epsilon-skew-normal results and normal distribution assumptions are compared. The experimental result shows that an uptrend trajectory of historical close price simulates an uptrend trajectory in the forecasted close price, a downtrend trajectory of historical close price simulates a downtrend trajectory in the forecasted close price, a sideways or mixed trend trajectory of historical close price simulates a sideways or mixed trend trajectory in the forecasted close price.

An Experimental Study and Analysis of Long-Term Multi-Trending Trajectory Forecasting of Stock Indices Using Time Series Inferential Statistical Projection

The focus of this study is on time series inferential statistical projection and analysis for long-term multi-trending trajectory forecast modelling. Inferential statistics was used for projection and analysis of long-term multi-trending trajectory forecasting. The proposed model is trained, tested and validated on three different types of stocks viz. NVIDIA, UNG and IBM taken from the NASDAQ stock market index. In Phase I, the long-term deterministic multi-trending model is assessed and fitted. Model skewed residual series is assessed using the goodness of fit in Phase II. The future stock close prices and long-term multi-trending trajectory paths are simulated and analyzed in Phase III. Finally, in Phase IV, epsilon-skew-normal results and normal distribution assumptions are compared. The experimental result shows that an uptrend trajectory of historical close price simulates an uptrend trajectory in the forecasted close price, a downtrend trajectory of historical close price simulates a downtrend trajectory in the forecasted close price, a sideways or mixed trend trajectory of historical close price simulates a sideways or mixed trend trajectory in the forecasted close price.

What Can Cluster Analysis Offer Stock Investors? Evidence from the China’s Energy Industry

China is one of the world’s major producers and consumers of energy. The investment value of China’s energy industry has attracted the attention of investors at home and abroad. Few studies, however, have specifically investigated investment ratings in China’s traditional energy industry. This study, therefore, uses scientific analysis methods to help investors measure the investment value and returns of China’s energy industry. From the perspectives of market performance and earnings management, we select factors that influence stock value evaluation indicators and undertake an empirical analysis using financial statement data for 2020 from the Wind database. Based on a factor analysis of the main financial indicators (e.g. amplitude, turnover rate, gross profit margin of sales, growth rate of operating revenue), we obtain five main factors: stock market performance, trading heat, profit quality, profit scale, and profit potential. The [Formula: see text]-means algorithm in Python is then used to analyse 56 stocks in China’s energy industry, and we divide their investment ratings into six grades: risk stocks, prudent holding, undetermined class, hold rating, ordinary rating, and buy rating. By identifying the group characteristics of different types of stocks, this study can provide a decision-making basis for investors while also having reference value for research institutions, financial departments, and government departments.

Optimisation Models for Inventory Management with Limited Number of Stock Items

Background: Stocks of raw materials and finished products are found in all units of logistics systems and require significant financial means of management. For this reason, scientifically justified approaches to stock management and cost minimisation must be explored. Despite the existence of many such approaches in literature and practice, each case has its own specificities and specificities to which stock management models should be adapted. In this article, the aim of the authors is to propose an approach to determine optimal supply sizes from different types of stocks (more than one is known in the literature as multi-nomenclature) that minimises only the cost of inventory management. The cost of inventory is not included. Methods: The article used the methods of mathematical optimisation, the method of least squares, and regression analysis. The scope of the models in the article is inventory management, with a limited number of stock keeping units. Time series data for the delivered quantities and time series data for the costs of stock management are used. Both time series use the same time period. Results: The constructed specific nonlinear mathematical models for optimising the total cost of stock management are approbated based on sample data and the results obtained are analysed. Conclusions: The created mathematical models and methods for optimising the total cost of stock management may be used by logistics managers to minimise the total costs of inventory management.

Mutual fund flows and seasonalities in stock returns

We propose a flow-based explanation for two long-standing anomalies in empirical finance – Sell in May and the January effect. We find that mutual fund flows exhibit similar seasonal patterns as stock returns. After controlling for fund flows both calendar effects become insignificant. We provide new evidence on what drives this correlation. We show that return seasonality is due to unanticipated fund flow driven by uninformed (flow-motivated) retail investor trading. Active funds indicate flow-induced price pressure with a corresponding reversal of the effect, while passive funds suggest feedback trading instead. These seasonalities are remarkably pervasive, exhibiting little variation across different types of stocks, and are equally strong in periods of either high or low sentiment.

Forecasting stock market return with nonlinearity: a genetic programming approach

The issue whether return in the stock market is predictable remains ambiguous. This paper attempts to establish new return forecasting models in order to contribute on addressing this issue. In contrast to existing literatures, we first reveal that the model forecasting accuracy can be improved through better model specification without adding any new variables. Instead of having a unified return forecasting model, we argue that stock markets in different countries shall have different forecasting models. Furthermore, we adopt an evolutionary procedure called Genetic programming (GP), to develop our new models with nonlinearity. Our newly-developed forecasting models are testified to be more accurate than traditional AR-family models. More importantly, the trading strategy we propose based on our forecasting models has been verified to be highly profitable in different types of stock markets in terms of stock index futures trading.

Traders’ Networks of Interactions and Structural Properties of Financial Markets: An Agent-Based Approach

An information-based multiasset artificial stock market characterized by different types of stocks and populated by heterogeneous agents is presented and studied so as to determine the influences of agents’ networks on the market’s structure. Agents are organized in networks that are responsible for the formation of the sentiments of the agents. In the market, agents trade risky assets in exchange for cash and share their sentiments by means of interactions that are determined by sparsely connected graphs. A central market maker (clearing house mechanism) determines the price process for each stock at the intersection of the demand and the supply curves. A set of market’s structure indicators based on the main single-assets and multiassets stylized facts have been defined, in order to study the effects of the agents’ networks. Results point out an intrinsic structural resilience of the stock market. In fact, the network is necessary in order to archive the ability to reproduce the main stylized facts, but also the market has some characteristics that are independent from the network and depend on the finiteness of traders’ wealth.

Static and dynamic factors in an information-based multi-asset artificial stock market

An information-based multi-asset artificial stock market characterized by different types of stocks and populated by heterogeneous agents is presented. In the market, agents trade risky assets in exchange for cash. Beside the amount of cash and of stocks owned, each agent is characterized by sentiments and agents share their sentiments by means of interactions that are determined by sparsely connected networks. A central market maker (clearing house mechanism) determines the price processes for each stock at the intersection of the demand and the supply curves. Single stock price processes exhibit volatility clustering and fat-tailed distribution of returns whereas multivariate price process exhibits both static and dynamic stylized facts, i.e., the presence of static factors and common trends. Static factors are studied making reference to the cross-correlation of returns of different stocks. The common trends are investigated considering the variance–covariance matrix of prices. Results point out that the probability distribution of eigenvalues of the cross-correlation matrix of returns shows the presence of sectors, similar to those observed on real empirical data. As regarding the dynamic factors, the variance–covariance matrix of prices point out a limited number of assets prices series that are independent integrated processes, in close agreement with the empirical evidence of asset price time series of real stock markets. These results remarks the crucial dependence of statistical properties of multi-assets stock market on the agents’ interaction structure.

Stock management errors and their impact on the productivity of the organization / field study in a sample of stores in the city of Duhok

The Inventory is an important element for organizations, especially the industrial ones' these organizations are trying to keep different types of stock to prevent stalled or production stopped, and this does not mean that the stock does not have such importance in other especially trade organizations, The role played by the stock in trade organizations no less important in the survival and continuity in the market and to maintain their customers. What is important here how to manage inventory properly balanced, in order to avoid the stalled or stopped production processes, or the failure to meet the needs of customers in the event of shortages in stockpiles as well as the case when the increase in the amount of inventory will lead to the survival of part of the head frozen money in the form of stocks is difficult to convert into cash because that depends on the length of the period of production or sale (Abdul Qadir and Abbas, 1991: 167).To reach balance and proper management of the stock to be working to remove and overcome all the mistakes and obstacles that affect inventory management properly, especially during the storage process and turn it into positive points can be invested in inventory management with the aid including availability of technology and ready-made software used in inventory management field.

To Study the Demographic Profile of Investors, their Attitude and Preferences towards various Investment Avenues in Stock Market

Investors are investing in stock market as number of investment avenues are available for them with different risk and return characterstics. Investors, acquire these securities from the stock market depending upon their requirement and needs in order to create and construct that portfolio where they are able to generate superior risk adjusted returns. Similarly, their preference for different types of stock varies according to their requirements. Moreover, investors with difference in their income, age, occupation, education and across other variables have preference for different avenues. The researcher has made attempt in the present study to define in detail demographic profile of the investors in the study area and then to study investors across different variable their preferences for different avenues. Moreover, different investors have different investment motives for investing in volatile market. Some wish to get higher returns, some invest because of liquidity, some for tax purpose etc. The researcher has attempted to study investment objectives of the investor and in order to achieve the objectives strategy used by the investors to achieve the desired results.

Application of the Improved Differential Evolution Algorithm in Portfolio

Aiming at the NP hard problem of portfolio optimization, an improved differential evolution algorithm is proposed. In this algorithm, the mutation operator and crossover operator are set up adaptively, and then according to the characteristics of the mutation itself, two kinds of mutation operators with global search ability and local search ability are improved .The improved algorithm can improve the convergence speed and ensure the precision of the algorithm. Through five stocks of the same type and 20 different types of stocks for empirical analysis, the results show that the proposed algorithm has a certain guiding role in solving the problem of portfolio optimization.

Intraday patterns in time-varying correlations among Central European stock markets

In this paper we investigate intraday relationships between three Central European stock exchanges: those in Frankfurt, Vienna and Warsaw. They represent different types of stock markets: two of them are developed, while the last is an emerging market. Via DCC-GARCH models we analyze and compare time-varying conditional correlations of intraday returns of the main indices of the stock exchanges. We study the impact of important public information, US macroeconomic news announcements, on the strength of interrelationships between the markets. Additionally, we analyze diurnal patterns in time-varying correlations on different days of the week.

Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction

Recently, the piecewise linear representation (PLR) method has been applied to the stock market for pattern matching. As such, similar patterns can be retrieved from historical data and future prices of the stock can be predicted according to the patterns retrieved. In this paper, a different approach is taken by applying PLR to decompose historical data into different segments. As a result, temporary turning points (trough or peak) of the historical stock data can be detected and inputted to the backpropagation neural network (BPN) for supervised training of the model. After this, a new set of test data can trigger the model when a buy or sell point is detected by BPN. An intelligent PLR (IPLR) model is further developed by integrating the genetic algorithm with the PLR to iteratively improve the threshold value of the PLR. Thus, it further increases the profitability of the model. The proposed system is tested on three different types of stocks, i.e., uptrend, steady, and downtrend. The experimental results show that the IPLR approach can make significant amounts of profit on stocks with different variations. In conclusion, the proposed system is very effective and encouraging in that it predicts the future trading points of a specific stock.

Long-term management of building stocks

The built environment is composed of buildings, infrastructures and cultural landscapes (stocks). These man-made artefacts constitute a complex capital (natural, man-made, human, social and cultural) and its evolution is a crucial parameter in sustainable development. Their composition and dynamics can be modelled as flows or as capitals or combinations of the two. Examples of the modelling of the long-term behaviour of different types of stock are given and their behaviour is analysed and discussed. A resource economic framework is presented and strategies for two different types of stocks, old/stationary and new/expanding, are compared. The importance of institutional regimes for the long-term management of stocks and the relation to demographic and retirement finance are discussed. The long-term institutional aspects have to be taken into account to avoid dramatic losses of economic, social and cultural capitals in the coming decades. L'environnement construit est composé de bâtiments, d'infrastructures et de paysages aménagés (parcs et jardins). Ces artéfacts fabriqués par l'homme constituent un capital complexe (naturel, façonné par l'homme, humain, social et culturel) et son évolution est un paramètre crucial du développement durable. Leur composition et leur dynamique peuvent être modélisées sous la forme de flux ou de capitaux ou d'une combinaison des deux. L'article donne des exemples de la modélisation du comportement à long terme de différents types de stocks de bâtiments puis analyse et examine leur comportement. Il présente un cadre d'analyse d'économie de ressource et compare des stratégies pour de deux types différents de stocks de bâtiments, à savoir un stock ancien/statique et un stock nouveau/en extension. L'article examine ensuite l'importance des régimes institutionnels pour la gestion à long terme de stocks de bâtiments ainsi que leurs rapports avec l'évolution démographique et les plans de financement de la retraite. Les aspects institutionnels dans le long terme doivent être pris en compte pour éviter les pertes dramatiques de capitaux économiques, sociaux et culturels dans les décennies à venir. Mots clés: gestion des actifs, stocks de bâtiments, remise en état, rénovation, économie de ressources, développement durable

Testing for Expected Return and Market Price of Risk in Chinese A-B Share Markets: A Geometric Brownian Motion and Multivariate Garch Model Approach

There exists the phenomenon of so-called dual listed stocks in some security markets: companies are allowed to issue different types of stocks facing segmented investors. Although these stocks share the same firm-specific risk, and enjoy identical dividend and voting policy, the price of these different types of stocks is not the same and the so-called pricing puzzle arises. Some previous studies show this seemingly deviation from the law of one price can be solved due to different expected return and market price of risk for investors holding heterogeneous beliefs. This paper provides empirical evidence for that argument by testing the expected return and market price of risk between Chinese A- and B-share stocks. Models with dynamic of Geometric Brownian Motions are adopted, multivariate GARCH models are also introduced to capture the feature of time-varying volatility in stock returns. The results suggest that the different pricing can be explained by the difference in the expected return and market price of risk between A and B shares in Chinese stock markets. However, the significance of the difference between market prices of risk becomes disappearing for both markets if GARCH models are used.

Monetary Integration and Real Estate Markets: The Impact of Euro on European Real Estate Equities

This paper assesses the impact of the monetary integration project on different types of stock returns in Europe. In order to isolate European monetary factors, the impact of global equity integration is investigated. European countries are sub‐divided according to the differences in the timing and degree of monetary integration. Analysis shows that national equity indices are strongly influenced by global market movements, with a European stock factor providing additional explanatory power. The global and European factors explain small cap and real estate stocks much less well – suggesting an increased importance of local and national drivers. However, there is little evidence to suggest that monetary integration is causing the phenomenon. For all sectors investigated, the European factor affects non‐Eurozone members and non‐EU members in the same way as Eurozone members. Further, the importance of global and European factors has almost invariably increased over time. For real estate equities, the largest increase in correlation was for non‐Eurozone and non‐European Union markets. One possible interpretation is that broader regional economic integration rather than more narrow monetary integration is driving the European factor.

Essays in Corporate Finance and Institutional Trading

My Ph.D. dissertation, defended at Boston College in June 2005 and became public thereafter, consists of three chapters. All three chapters use Abel Noser (ANcerno) institutional trading data. For details of the dataset, visit the Abel Noser (ANcerno) Data Page: http://ganghu.org/an In the first chapter, I provide a simple yet previously unexplored explanation for the buy-sell asymmetry phenomenon: it is because previous studies use pre-trade benchmark prices to measure implicit trading costs. Buy-sell asymmetry is mainly driven by mechanical characteristics of measures of implicit trading costs. I further argue that different measures of implicit trading costs serve very different purposes: pretrade measures are suitable for measuring trading costs of investment strategies, and during-trade measures are suitable for measuring execution quality. I show that a pre-trade measure can be decomposed into a market movement component and a during-trade measure, and empirically the market movement component is the dominant component. In the second chapter, I analyze the profitability and informativeness of institutional trading in IPOs, using a large sample of proprietary transaction-level trading data. My results can be summarized as follows. First, institutions sell most of their IPO allocations within the first year after the IPO. Second, institutions realize most of the “money left on the table” for IPO allocations sold within the first year. Third, post-IPO institutional trading outperforms a buy-and-hold investment strategy in IPOs. Fourth, institutional trading has predictive power for subsequent long-run IPO performance, even after controlling for publicly available information. Overall, my results indicate that institutions do possess private information about IPOs and receive considerable compensation for participating in these IPOs. In the third chapter, we analyze the daily trades of a large sample of investment managers. We find that opinion divergence among managers arises in different types of stocks than it does with analysts. We test whether various trading patterns can predict stock returns. When managers trade together, future returns are similar regardless if they are buying or selling, however when managers trade against each other, subsequent returns are low, especially for stocks that are difficult to short. A plot of this disagreement-return relationship resembles a smile. This result is consistent with the hypothesis that opinion divergence can cause an upward bias in prices.

Attitudes and Trading Behavior of Stock Market Investors: A Segmentation Approach

This study identifies and characterizes segments of individual investors based on their shared investing attitudes and behavior. A behavioral finance literature review reveals five main constructs that drive investor behavior: investment horizon, confidence, control, risk attitude, and personalization of loss. Ninety individual investors were surveyed via questionnaire on these constructs. A cluster segmentation analysis identified four main segments of individual investors: 1) risk-intolerant traders; 2) confident traders; 3) loss-averse young traders; and 4) conservative long-term investors. Each segment purchased different types of stocks, used different information sources, and had different levels of trading behavior.

Macroeconomic News and Stock Returns in the United States and Germany

Using daily data for the January 1997 to June 2002 period, we analyze the impact of a broad set of macroeconomic news on stock prices in the United States and Germany. With GARCH specifications we test five hypotheses and find that news on real economic activity has a significant impact on stock prices. The effects vary between different types of stocks and depend on the state of the economy. In a boom period, bad economic news may be good news for stock prices. For German stock prices, international news is at least as important as domestic news. The analysis of bihourly data suggests that the main effect occurs within a short period of time.

Macroeconomic News and Stock Returns in the United States and Germany

Using daily data for the January 1997 to June 2002 period, we analyze the impact of a broad set of macroeconomic news on stock prices in the United States and Germany. With GARCH specifications we test five hypotheses and find that news on real economic activity has a significant impact on stock prices. The effects vary between different types of stocks and depend on the state of the economy. In a boom period, bad economic news may be good news for stock prices, For German stock prices, international news is at least as important as domestic news. The analysis of bihouriy data suggests that the main effect occurs within a short period of time.