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Stereotype of Muslim Women being oppressed do my history assignment: do my history assignment
Stereotype of Muslim Women being oppressed
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The Muslim women veil or hijab has in the recent past become the pinnacle of oppression for Muslim women from the western world. Different nations have even prohibited different clothing’s especially in public places for clothing’s associated with Muslims. These prohibitions are based on the notion that the women need to be saved from the harsh Muslim rules and that these practices conflict with western beliefs. Stereotyping refers to a mental picture adopted by one group in regards to another different group that shares a given characteristic. Stereotypes help manage the complexities of life. An assortment of things comes to mind of numerous western individuals when they think of Muslim women. For example, most people perceive Muslim women as veiled and mysterious victims of male oppression awaiting liberation. Others think of Muslim women as uneducated foreigners with whom westerners have nothing or little in common. Unless someone’s social circle includes Muslim acquaintances and friends, a person might think negatively of Muslim women, and this has been created and motivated by negative stereotypes of the media. Usually, these images have little to do with the real life of these women and may have been formed to suit other people’s interests. There are several cognitive theories that explain how stereotypes like those that are described above are formed. There are three main theories that explain the formation of stereotypes. These include social learning, illusory correlation and ultimate attribution error.
These theories are based on the argument that stereotypes are formed from the information people get from different sources. For example, when people perceive members of a particular group doing something, or are told that the group is a certain way or read stories and articles about that group, people keep account of all this information in their brain whether it is accurate or not. Therefore, every time people learn something about a group, it is kept in the knowledge store. However, the way people’s brain encodes information is subject to several biases. For example, at times people learn the correct information and, therefore, form stereotypes that have some truth. At other times, people perceive associations that may exist or may not exist, therefore, forming false stereotypes. Whichever way these stereotypes are formed they significantly affect the way people perceive and behave towards others.
Muslim women have been subjected to a lot of stereotypes. These stereotypes have mainly been furthered and sustained in society because of lack of information regarding these women. There are several examples of the stereotypes that have been formed against Muslim women. For example, many view Muslim women as oppressed individuals who lack the freedom to lead their lives as they wish. Westerners also think that Muslim women are uneducated, and, therefore, lack opportunities to work professionally. One of the reasons why people think this way is because they think that all Muslim women are bred for marriage and life as caretakers of their families and homes. These stereotypes are critical as they influence how people behave towards Muslims and Muslim women.
The cognitive theory of social theory points out that people can learn by just observing other people’s behaviors in the context in which it exists. For example, children observe and copy the behaviors of their peers and parents; therefore, if their parents show stereotypic attitudes or behave in ways that are stereotypic, children will also learn to behave in that manner. Their perceptions and behaviors are reinforced as they grow when they are punished or reinforced. Many American children have grown up knowing that Muslims are terrorists and that Islam is an oppressive religion especially to the women. Children get these perceptions from watching the news. These perceptions are further reinforced by the fact that their parents hold the same perceptions and behave in ways that reinforce the perception that Islam is oppressive and violent towards its women.
Clearly, the perception of Muslim women as uneducated and oppressed by their religion and men is nothing but a stereotype. The issues facing most of the Muslim women all over the world today are similar to those facing most women population in other parts of the world. These women are not different from the rest of the women population in the world. There is a population of elite Muslim women who have been successful in their lives, and who have exercised autonomy and power within economic and social networks. Some of these women are leaders in their chosen careers and countries. It is impossible to argue that all these women share a status. As it follows, stereotypes of Muslim women as especially oppressed bear little similarity to the real situation. Yet the attempt to describe the status of women in Islam persists.
Illusory correlation explains stereotype formation by indicating that people can form perceptions of an association when one is not present. For example, in many cases minority groups are different, and in many cases, people form false negative associations between minorities and attributes or behaviors. For example, people overestimate the frequency of distinctive pairings. Therefore, because Muslims are the minority in the western world, people are usually quick to form false associations between them and some of their characteristics. For example, people are quick to associate the hijab that most Muslim women wear with oppression. Hamilton and Gifford carried out a study in 1976 to indicate how illusory correlation leads to the formation of stereotypes. Their study had to do with the study of 39 behaviors describing members of two groups. The study wanted to show how certain behaviors were most notable and which were associated with popular, social or irritable behavior.
There are several examples of Muslim women all over the world who have shown that these stereotypes are false. Lack of information and awareness of such achievements is one of the reasons why these stereotypes persist in society. For example, many women who are dedicated Muslims, who even wear the hijab and follow the Islam religion to the letter have successful careers. Despite their religion and being Muslim women, as well as perceived oppression, they are some of the best professionals in the United States. Furthermore, a lot of Muslim women are free to follow all their dreams as they travel all over the world and are even pursuing their education. These are just a few examples that show that the perception that Muslim women are oppressed is just a stereotype.
Ultimate attribution error attributes ones behavior to their personality, ignoring other situational constraints that might be present. This is to mean that society will likely associate the negative actions of a member of an out-group to his or her disposition. For example, the negative actions of some Muslim men like violence and oppression towards their women is likely to be associated with the situation of the rest of the Muslim women. While it is true that some Muslim men oppress their women, society is likely to generalize this because society views Islam negatively. In an in-group, the same behavior would not be generalized as it would be attributed to a situation and not to the person’s disposition.
There are reasons why these stereotypes still exist even in times when there is sufficient information to disapprove them. One of the reasons why stereotypes exist is categorization. This is the process through which objects and ideas are differentiated, recognized and understood. Social categorization is especially critical when it comes to stereotyping groups. This is the process of ordering the social environment in terms of groupings or categories or people in a way that is meaningful to the concerned person.
Social categorization is also the assignment of an individual who has met someone in a group because of the traits the new person has in common with other individuals with whom a person has had experience. For example, when a person meets a Muslim woman wearing a hijab, it is comfortable for that person to categorize the woman as oppressed because maybe the person has had experience with other women who thought or felt that hijab is oppressive. People tend to categorize other people into categories, and this is one of the reasons why stereotypes exist. There are several reasons for social categorization. For example, the brain is trained to look for patterns, something that promotes cognitive efficiency and ensures that one’s attention is attracted to the relevant information. Therefore, it is easy for people to categorize Muslim women as oppressed because they are all similar in that they practice Islam and wear hijab, a sign of oppression for many western people. This objectifies the fact that stereotypes represent people’s accumulated knowledge regarding a particular group.
There are two main forms of processes of forming stereotypes. These include the automatic process and the controlled process. The automatic process occurs without intention, is not demanding cognitively, is inescapable and occurs outside conscious awareness. The controlled process requires intention, conscious thought, requires capacity and motivation and takes cognitive resources. The process of forming a stereotype is composed of four elements. These include categorization, activation of the stereotype, motivation or capacity and application of the stereotype.
Usually, when individuals perceive members of a group for which there are clear stereotypes, like Muslim women, stereotypes are likely to be activated without awareness and spontaneously. On the other hand, if the target has not been categorized, then the stereotype will not be activated. Gilbert and Hixon carried out a study in 1991 to show the process of activation. The study indicated that people were more likely to activate their stereotypical perceptions of others in cases where they had categorized the individuals. Spencer, Fein, Wolfe, Fong and Dunn in 1998 carried out another study to show how activation of stereotypes works and concluded that contexts usually influence the relevance of information to a perceiver. They indicated that people were likely to activate stereotypes of others in cases where their self- images were threatened.
Once people have formed stereotypes, they require capacity and motivation not to stereotype. This is to mean that people need cognitive energy and desire to prevent themselves from stereotype application. However, in cases when people are not motivated to individuate other people, they will utilize stereotypes to judge them. In another study, Gilbert and Hixon argued that people will make use of stereotypes to judge others if they do not have the ability to individuate them. Smith, Miller, Maitner, Crump, Garcia-Marques and Mackie in 2006 emphasized this by pointing out that people will use stereotypes to judge others if they are not motivated to individuate them. For example, in the case of Muslim women, most people do not have any desire to see them as individuals and; therefore, they use the stereotype that they are oppressed to judge them.
People are able to sustain stereotypes about Muslim women because stereotypes are self- perpetuating. People, mainly in the western world, direct their attention to information, affecting the way in which they understand that information, therefore, confirming their bias about Muslim women. This also affects their memory processes or the way they recall information making it possible for the stereotypes to be long lasting. This way the stereotype of Muslim women being oppressed will forever stay unless people stop confirming their biases about them.
The Stock Market Price Prediction Concept history assignment help
Stock Market Price Prediction Using Artificial Neural Networks
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Executive Summary
The review has discussed various literatures on artificial neural networks and their use on stock market forecasting. Artificial neural networks (ANNs) are used in the analysis, interpretation and prediction of financial data. ANNs assist in the prediction of financial trends by applying case-based reasoning, learning algorithms and genetic algorithms to data to improve the accuracy and reliability of predicted results. They provide developers and investors with the tools and techniques for prediction using ratios and indices. Investors can then use these ratios to determine the most appropriate time for buying or selling securities in the stock exchange. Research on Python programming for stock market prediction is limited because most of the literature focus on the software packages developed using MatLab. This has prompted my research into modelling neural networks using MatLab language. An analysis of software packages in the market that support stock market prediction shows that MatLab can be interfaced with other programs provides object classes and methods for instantiating and invoking network elements, and prints object results into a text file. This text file can be invoked by any simulator package to convert the character strings in the text file into a model. The MatLab language is a powerful, adaptable and efficient solution that supports the conversion of financial data into models. Investors can then use these models to predict stock trends and determine when to buy or sell securities in the stock exchange.
Table of Contents
Literature Review………………………………………………………………………………………………………. 4
2.1 Introduction……………………………………………………………………………………………………………. 4
2.2 Stock Markets…………………………………………………………………………………………………………. 4
2.3 History and Development of Techniques for Predicting Stock Market Performance………………. 5
2.4 Artificial Neural Networks………………………………………………………………………………………….. 6
2.4.1 Predicting Stock Market Prices using Artificial Neural Networks………………………………………. 8
2.5 Software for Stock Market Price Prediction using ANN…………………………………………………… 12
2.5.1 Types of Software………………………………………………………………………………………………… 12
2.5.2 Using MatLab for ANN Modelling……………………………………………………………………………. 13
2.6 Gaps Identified………………………………………………………………………………………………………. 14
2.7 Conclusion…………………………………………………………………………………………………………….. 14
2. Literature Review
In a study that was carried out to find out whether the ANNN technique is reliable enough for predicting stock prices came up with results that indicated that most stock brokers trusted the technique. Through the use of questionnaires, the model was evaluated by a number of stockbrokers. The questionnaire was designed in a manner that it made it easy for the participants to fill in a couple of minutes. It required the stockbrokers to answer a number of questions that focused on the kind of techniques used by brokers, their satisfaction level with their techniques and their willingness to use the network in the future. Four out of the seven participants indicated that depended on the ANN technique by a percentage of 75, while the rest indicated that they would depend on it by about 25 percent. Five participants in the study indicated that the technique was 100 percent applicable in all stock exchange companies. It was concluded that the technique is extremely essential in predicting or in forecasting stock prices (Zhang, Jiang & Li 2004).
Another study argued that nonlinearity characters appear in mist financial data and that ANN can be an extremely useful technique to model effectively, the relations that occur between the data. According to the study, neural network can be used to mine data or information that is valuable from a historical mass of information and can effectively be used in areas and fields of finance. The study implies that because of these applications, the functions of neural networks have been increasingly popular for the last few years. ANN techniques are indicated in the study as natural methods of solving issue or problems that involve recognition of patterns and learning. As a result, it can predict stocks by detecting patterns in the information or data through recognition and learning of patterns (Roy & Roy 2008).
Another study argues that applications of ANN have been attracting attention from different discipline; stock markets and finance have not been left behind. Finance is an area that has become extremely promising for the application of ANN techniques or models to forecasting returns prices and indices. The article points out that this functionality can be attributed to the ability of ANN models to handle data that is complicated with a lot of ease coming up with great outcomes for the study (Adya & Collopy 1998).
Other studies have been carried out to test the ability of the ANN technique to forecast in the Nifty index. The investigated how effective the technique was in predicting the Nifty Index’s stock return. The findings of the study were positive that the technique is extremely effective in predicting stock returns and prices. There are numerous studies that provide support to the claim that ANN techniques can be extremely useful in predicting stocks (Hassoun 1995).
2.1 Introduction
There are various literature on the application of artificial neural networking techniques on financial data. Artificial Neural Networks (ANN) are popularly used for analyzing and interpreting financial data (Kim 2006, p.519). Artificial neural network algorithms involve the application of case-based reasoning, learning algorithms and genetic algorithms to large and noisy data to improve the accuracy and reliability of predicted results. The algorithms aim to increase the closeness between predicted results and network values by reducing collected data into manageable data sets and condensing them prior to training (Kim 2006, p.519). The paper analyzes literature on the application of artificial intelligence techniques to predict prices in the stock market. It shall discuss the meaning of ANN, the history and development of prediction techniques, application of ANN to stock market prediction, types of software used and the findings on the use of MatLab for forecasting stock market prices. The objective of the paper, therefore, is to develop effective ANN software that can be used in prediction of stock prices, and also to look at some of the applications of MatLab in the development of ANN models.
2.2 Stock Markets
Stock market information is fundamental to the development of techniques for predicting trade prices. This information may highlight external factors affecting the performance of stock prices in the stock exchange. Baker & Wurgler (2008, p.6) propose that ANN developers and stock investors should consider the effect of social and economic factors on buy or sell decisions. They should also recognize the impact of the society and economy on stock prices. This will help the developers and investors understand how low capitalization, price unpredictability, strength of corporate growth strategies, recession and company profits affect the performance of stocks. Additionally, the information will facilitate rationalization of the role of environmental factors on the valuation and predictability of future stock performance.
2.3 History and Development of Techniques for Predicting Stock Market Performance
The methods used to predict stock price indices have drastically changed over the years. Fok, Tam and Ng (2008, p.1) suggest that while previously stock market forecasting relied on statistical methods such as moving-average, technical analysis and linear programming techniques, these methods are not suitable for current stock markets. The traditional techniques were effective at the time because stock market data was predictable and did not produce large data sets. According to Tsang et al. 2007 (p.454), the technical analysis method uses past prices to predict future price on the assumption that history will repeat itself. This method has a higher risk of producing subjective predictions. This is because the technical analysis method does not validate data input using statistical methods and lacks tools for rationalizing the procedures used. The moving-averages technique demonstrates similar characteristics. Tsang et al (2007, p.454) argue that although the moving-averages algorithm caters to non-linear data, it does not provide accurate results for long-term forecasting. They hold that algorithm is suitable for large, non-noisy data sets to generate short-term predictions of stock prices.
Modern techniques using artificial neural networks have compensated for the drawbacks experienced in traditional forecasting methods. This is because ANNs have been designed to accommodate the unpredictability of information, noise and large amount of historical data on stock price trends (Tsang et al. 2007, p.453). Studies show that traditional prediction methods are not effective because stock market indicators are not linear, making it difficult to obtain accurate predictions (Fok, Tam & Ng’s 2008, p.1). In addition, traditional methods cannot accommodate assumptions on the distribution of data, relationships between variables and unpredictability of financial indicators. Consequently, the methods produced inaccurate predictions due to the inclusion of errors from noisy and unfiltered data.
2.4 Artificial Neural Networks
Neural networks are fundamental to the generation of accurate and relevant predictions in the financial market. Fok, Tam and Ng (2008, p.1-2) state that neural network algorithms are preferred methods for generating data forecasts and multivariate financial analysis because they are tolerant to noise, unstable data and unpredictable parameter values during instance selection and simulation modelling. Their support for neural network algorithms emanates from a case study of stock exchanges in the United States, Hong Kong, China and Europe. Tsang et al.’s (2007, p.453) case study on the Hong Kong stock exchange supports the use of artificial neural networks for forecasting purposes. Their case study provides several measures for determining the neural network designs. The measures include output goals, types of inputs needed, the networking architecture, training and testing methods, an optimal topology for the network and procedures for evaluating results (p.456).
Kim (2006, p.519) claim that data mining techniques are effective methods for developing neural networks for forecasting purposes. Data mining techniques of neural networking have evolved over the years to eliminate computational inefficiencies of traditional methods. These techniques use learning algorithms to select sample data to represent features of the population, known as instance selection (Kim 2006, p.519). Instance selection is performed on data because it facilitates the reduction of data for effective learning. This helps eliminate noisy and irrelevant data by segmenting the samples into manageable data instances. However, its main drawback is the high cost of computation and storage during prediction. To reduce these costs, Kim (2006, p.519) proposes that effective reduction should be performed on the data sets. He recommends a fused ANN model using a genetic algorithm for forecasting financial systems. The model uses advanced instance selection techniques to reduce factual dimensions, noise and irrelevant data. Unlike other ANN algorithms, this model uses weights to mitigate the drawbacks of gradient algorithms (p.519).
According to Fok, Tam and Ng (2008, p.1), modern prediction techniques using ANN techniques, such as the algorithm and data mining, are recommended for generating accurate predictions of financial market performance. This is because the techniques can accommodate large amounts of financial data and provide non-linear solutions for stock market forecasting (Kanas & Yannopoulos 2001; Kara, Boyacioglu & Baykan 2011). Fok, Tam and Ng (2008, p.1) note that ANN techniques are flexible because they allow users to input unpredictable and noisy data through filtering and reduction. This segments stock price data into manageable data sets. Moreover, learning algorithms are valuable because they analyze and incorporate historical data to identify relationships between variables, analyse current trends in market indicators using historical data and forecast future behaviour of stock markets. Kim (2006, p.519) concurs that neural network algorithms are useful because they eliminate the inefficiencies of traditional forecasting methods by reducing the loads on computation and storage, and increasing the accuracy of predictions.
Case-based reasoning algorithms can be used in artificial neural networking. Kim (2006, p.520) proposes that case-based reasoning structures should be used for selecting instances from data sets. This is because the case-based structures lower the operational and computation costs incurred by neural networks by reducing the number of repetitive and irrelevant cases. Genetic algorithms are also significant to artificial neural networks. They improve ANNs by facilitating the selection of network topologies, optimizing the subsets of data and help determine the quantity of hidden layering. Kim (2006, p.519-520) proposes the use of genetic algorithms for mining financial information. This is because a genetic algorithm can be used to avoid reaching narrow optimums in artificial neural networks. The author presents a generated prototype algorithm that uses genetic algorithms to develop a model for financial forecasting. The generated prototype algorithm can be used to modify a restricted set of instances in artificial neural network predictions to generate feature-map classifications (Kim 2006, p.520).
2.4.1 Predicting Stock Market Prices using Artificial Neural Networks
Research on the prediction of stock market prices using ANN is sufficient (Mostafa 2010). Historical research on stock market forecasting can be traced to the 1990s, as observed in Kimoto et al.’s (1990) study on learning methods and predictions of the Tokyo Stock Exchange prices (cited in Kim 2006, p.521). This study aimed to evaluate the effectiveness of learning algorithms in estimating returns on stock prices. However, it did not provide any statistical evaluations to support empirical analysis on the findings. Other than learning algorithms, historical research relied on logical programming to predict stock prices. The research predicted the use of ANNs in predicting stock market prices using Boolean operations on data sets to produce rule sets (Kim 2006:521).
Modern ANN algorithms incorporate features to reduce or filter the amount of stock prices collected (Kim 2006, p.521-522). This is because financial data is very noisy and variable, increasing the difficulty of training and obtaining accurate predictions. Therefore, the chosen algorithm should allow users to control and reduce data inputs. Kim (2006, p.523) provides a genetic algorithm for evaluating the performance of the Korean stock exchange. His research shows that training on samples in instance selection using genetic algorithms demonstrate lower error rates compared to samples from neural network selection. By using instance selection, the accuracy increased allowing progressive learning. Kim recommends that a genetic algorithm should be applied in neural network prediction to select instances and optimize the learning technique used.
The author presents three techniques used to classify data instances: Two-level classifiers, condensed nearest neighbour rule and the generated prototype technique. The condensed neighbour algorithm requires that each element of a training set (T) is close to an element in a subset (S) of the same class than of a different class. This algorithm has been further modified to form the edited nearest neighbor and selective nearest neighbor algorithms (Kim 2006, p.520).
While Kim’s research uses a two-level classification algorithm to select instances, Fok, Tam and Ng’s (2008, p.2) approach uses a multi-layered classification technique. The multi-layered approach recognizes that neural networks should consist of input, output and hidden layers. To facilitate connection between the layers, they suggest that interconnected weights should be used. The weights are obtained from a set of training algorithms on data sets, which aim to increase the accuracy between predictions and actual network output.
The multi-layered approach uses the standard back propagation algorithm to map input parameters to output values. The algorithm uses two formulas for mapping:
Yp = f (Wohp + θo) and hp = f (Wh xp + θh),
Where Wo and Wh represent the weights of the input and hidden layers, hp represents the vector of the hidden layer, and o and h represent the output and hidden layers respectively (Fok, Tam & Ng’s 2008, p.2). To minimize the computational costs, they recommend that simulators should apply the following cost function: E = ½ Σ ( tp − yp )T (tp − yp ), where tp represents the targeted output parameter for the pattern (represented by p). The authors recommend that further evaluation should be performed using the graduate descent method to modify the weight of connections between neural network nodes. This will ensure that forecast results have an 80 percent accuracy rate in predicted values compared to networked values (Fok, Tam & Ng’s 2008, p.4).
Fok, Tam and Ng’s (2008, p.1) comparison of learning algorithms to predict stock price behaviour shows that the neural network back propagation algorithm provides higher prediction accuracies than linear regression techniques. Tsang et al. (2007, p.453) concur that the back propagation is a useful technique for simulating neural network models in financial forecasting. Their evaluation of the technique’s use in the Hong Kong stock exchange show a high success rate (above 70 percent), which demonstrates its reliability in providing accurate predictions on stock prices
Vanstone & Finnie (2009, p.1) provide a methodology for designing ANNs for stock markets. The methodology separates the process for generating training samples into distinct steps. This allows developers to test each step for correctness and accuracy before proceeding to the next. This testing may be carried out in the context of the stock trading system or out of its context. The methodology aims to address the three core roles of the stock market training system, namely: entry and exit rules, risk control and financial management (p.7). It uses ratios and indexes, instead of actual prices and volumes, to predict the trend of stocks. To test the architecture of the neural network in the context of the stock system, the methodology requires input of ratios as the filter ratio, timeframe (in years), number of securities or stocks to be screened or deduced, probability of a win, probability of a trade loss, average amount that can be won or lost and expectancy ratio.
The following formula can be used to determine the expectancy ratio of a security or investment: Expectancy ratio = ((AW× PW) + (AL ×PL))
|AL|
Where AW is the average amount gained, PW is the likelihood of a win, AL is average amount lost and PL is likelihood of a loss (Vanstone & Finnie 2009, p.11). The expectancy ratio can then be used to determine when to buy or sell stocks in the exchange market. Since the primary aim of stock market trading is to generate profit, the neural network should be subjected to external benchmarking using ratios such as number of trades, payoff index, net profit, annual profit, portfolio stability, Ulcer index, Luck coefficient and Sharpe ratio (Vanstone & Finnie 2009, p.12).
While the stock forecasting methods discussed previously focus on the use of artificial neural networks for predicting prices, few address the influence of environmental factors on stock patterns. Assaleh, El-Baz and Al-Salkhadi’s (2011, p.82) study evaluates the role of political, social and psychological factors in forecasting stock prices. They propose an advanced ANN approach using the Polynomial Classifiers theory to forecast stock prices in the Dubai foreign exchange. In comparison to the ANN approach, the polynomial classifier approach produces better results than neural networks. In the context of economic factors, Weckman et al. (2008, p.36) propose that ANN developers take into consideration the different types of investors in the securities industry including consumers, private businesses, healthcare, financial, energy, manufacturing and telecommunications industries. Baker & Wurgler (2008, p.10) recommend that ANN techniques should also incorporate social factors (such as investor reaction) in predicting stock trends.
2.5 Software for Stock Market Price Prediction using ANN
2.5.1 Types of Software
Various types of software can be used to assist researchers to predicting stock market trends using artificial neural networks. Drewes, Zhou and Goodman (2009, p.1) suggest that ANN developers should use off-the-shelf packages such as MatLab, Brainlab, NeoCortical, PyNET, PyGENESIS, PyNN, and PCSIM (Brüderle et al. 2009). The packages help researchers and professionals evaluate complex neural networks using large data sets. Drewes, Zhou and Goodman (2009, p.1) also recommend that developers should use the Brainlab software, built on the MatLab, for the design, simulation and analysis of neural network models. In addition to providing an abstraction for creating three-dimensional models, Brainlab supports testing using regression techniques, generates algorithms and reports of models, interfaces for robotics and programming using the C or C++ programming language (Drewes, Zhou & Goodman 2009, p.1-2).
The NeoCortical Simulator is also used for modelling complex neural networks. Drewes, Zhou & Goodman (2009, p.1) recommend its use because of its performance and speed efficiency in simulating spiking neural networks. The software also has the capability of simulating large and diverse models. The NeoCortical Simulator also has its drawbacks. The software cannot be adapted, has inadequate tools for experimentation and uses restrictive interfaces. It uses a low-level programming language, which makes it difficult for non-experts to understand and modify. Moreover, the NeoCortical requires external tools to manage the scope of experiments and it provides a restrictive interface for modelling neural networks.
2.5.2 Using MATLAB for ANN Modelling
A study was carried out to determine the structure of two different structures of ANN for two stock price forecasting models. The best architecture or design of the neural network models was established through a number of steps of testing and training of the models. In this particular study, a feed- forward which was three- layered was utilized and trained though the use of error backpropagation. The training using backpropagation training with delta learning rules that are generalised is an algorithm with an iterative gradient that is designed to reduce the mean square error that occurs between desired outputs and the actual output of the utilised feed- forward neural network which is multilayered. In this case, each layer is completely connected to the prior layer, but there are no other connections made by this layer. After the completion of the process of training of the neural network, the MLP weights are ready to use and frozen in the mode of testing (Hassoun 1995).
For the purposes of achieving the appropriate configuration of the model all MatLab activation functions that is hardlim. Compet, tansi, poslin, logsig, radbbas, satlin, purelin, tribas, and satlins were compared with the similar test and training data in the study. The differences that occurred in the test and training errors in all the activation functions of MatLab were not of significant means. Functions with mostly of low level error, logarithmic sigmoid, hyperbolic tangent sigmoid, and purelin were taken and compared for the final models. It was found that these functions of MatLab could be used in coming up with different kinds of ANN models, and that these models could be useful in a number of functions. The study concluded that MatLab was a significant modelling method for subsystems of ANN that could be significant in various applications including stock prices predictions (Mills 1990).
2.6 Gaps Identified
The literature on MatLab toolboxes for stock market prediction is limited because most of the literature focuses on the software packages developed using Python. This has prompted my research into modelling artificial neural networks using the toolbox. Consequently, the focus of my dissertation is to develop a stock market prediction program using MatLab toolbox.
2.7 Conclusion
This paper has analyzed various literatures on ANNs and their use on stock market forecasting. Artificial neural networks are used in the analysis, interpretation and prediction of financial data. ANNs assist in the prediction of financial trends by applying case-based reasoning, learning algorithms and genetic algorithms to data to improve the accuracy and reliability of predicted results. They provide developers and investors with the tools and techniques for prediction using ratios and indices. Investors can then use these ratios to determine the most appropriate time for buying or selling securities in the stock exchange. The literature shows that a few of the ANN software in the market is developed using MatLab tools. MatLab tools can be useful in determining and creating different sets of functions that can in turn be used to come up with different models of ANN. These ANN models can be used for different functions and in different applications. The MatLab tools are powerful, adaptable and efficient applications that support the conversion of financial data into models. Investors use these models to predict stock trends and determine when to buy or sell securities in the stock exchange.
References
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Assaleh, K, El-Baz, H & Al-Salkhadi 2011, ‘Predicting stock prices using polynomial classifiers: The case of Dubai financial market’, Journal of Intelligent Learning Systems and Applications, vol.3, pp.82-89.
Baker, M & Wurgler, J 2006, ‘Investment sentiment in the stock market’, Journal of Economic Perspectives, pp.1-37. Accessed from http://www.people.hbs.edu/mbaker/cv/papers/
Fok, W, Tam, V & Ng, H 2008, ‘Computational neural network for global stock indexes prediction’, Proceedings of the World Congress on Engineering London, UK, 2-4 July.
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Roy, P & Roy, A 2008, ‘Forecasting Daily Returns of Nifty Index – Using the Method of Artificial Neural Network,’ FFMI 2008 Conference Proceedings, pp. 787-800.
Tsang, P, Kwok, P, Choy, S, Kwan, R, Ny, S, Mak, J, Tsang, J, Koong, K & Wong, T 2007, ‘Design and implementation of NN5 for Hong Kong stock price forecasting’, Engineering Applications of Artificial Intelligence, vol.20, pp.453-461.
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Operations and Production Functional Area history homework help: history homework help
Strategic Alignment Worksheet: Operations and Production Functional Area
Your Name:
Overview
Use this strategic alignment worksheet (SAW) to complete your management activities in planning, organizing, leading, and controlling for the Atha Corporation scenario. Refer to each course assessment for further instructions.
Checklist for the Operations and Production Area SAW
Mary Atha, CEO, has provided this checklist of overall organization goals for the operations and production area. Read the checklist and use it as a point of reference for your development of this SAW.
Double current production rate.
Add one production line.
Reduce scrap and waste.
Organize the newly increased staff to efficiently use resources and create a new organizational chart.
Lead the employees by providing a clear mission and goals through carefully crafted communications.
Create adequate controls and communicate performance to the company to ensure organizational focus.
Section 1: Goals and Activities
Develop goals for this functional area and create supporting activities for achieving each goal. (Add more rows if needed.)
Goals:
Supporting activities:
1. Retain existing employees.
What are the supporting activities?
How will this activity achieve the goal?
What is the achievement deadline?
2. Hire eighteen new employees.
What are the supporting activities?
How will this activity achieve the goal?
What is the achievement deadline?
3. Create new organizational chart for the operations and production area.
What are the supporting activities?
How will this activity achieve the goal?
What is the achievement deadline?
4. Improve employee performance.
What are the supporting activities?
How will this activity achieve the goal?
What is the achievement deadline?
5. Social responsibility
What are the supporting activities?
How will this activity achieve the goal?
What is the achievement deadline?
Provision of matching funds
Increase support
3 months
Paying employees for extra hours
Improve employee determination
3 months
Providing training to offer additional knowledge
Provide necessary knowledge
3 months
6. Managing employee performance
What are the supporting activities?
How will this activity achieve the goal?
What is the achievement deadline?
Providing necessary trainings
Improve knowledge
3 months
Acknowledging efforts employed
Increase employee output
3 months
Coordinating employee output
Improve teamwork
3 months
7. Financial and physical resources
What are the supporting activities?
How will this activity achieve the goal?
What is the achievement deadline?
Renovation and maintenance of equipments
Maintain support of the project
3 months
Increasing flow of cash
Provide support to the project
3 months
Providing assistance
Improve output
3 months
Section 2: Purpose Statement
Write a purpose statement for this functional area of the organization.
Most of us are aware of the term purpose statement, but we may be unsure of what its real meaning is within an organization. It is a statement describing the organization or functional team’s purpose, or the reason for its existence. The purpose of an organization reflects a desired position in the marketplace. It should be a written summary that accurately answers the four questions outlined in the table below. The most effective purpose statements are short, concise, and direct. A good purpose statement should be between 2–3 sentences in length.
Purpose Statement
Who are we?
What do we do?
For whom do we do this?
How do we know when we are getting it done?
Compile your answers to the four questions above into a concise (2–3 sentence) summary statement. This is your purpose statement.
[Insert your purpose statement here.]
Section 3: Performance Standards
Identify performance standards for measuring this functional area team’s performance. (Add more rows if needed.)
Performance standard:
Rationale for including this performance standard:
1.
2.
3.
4.
5.
STRATEGIC DEVELOPMENT AND MARKET ENTRY history assignment help
STRATEGIC DEVELOPMENT AND MARKET ENTRY STRATEGIES
By (Name)
Course/Class
Instructor
University
City/State
Date of Submission
Strategic Development and Market Entry Strategies
Strategy Development
A market entry strategy and strategic development is a method designed by a company to deliver or distribute goods or services into a new target market. In the case of exporting or importing goods and services, it refers to managing and establishing contracts in a foreign nation. Many organizations operate successfully in a niche market environment without the need to expand to new markets. Some organizations achieve increased brand awareness, sales, and business stability by making new entries into new markets. In order to develop a market entry strategy, there is need to carry out a thorough analysis of possible customers and potential competitors (Lymbersky, 2008, 90-1). Some of the major factors to put into consideration when deciding on the viability of the entry strategy include price localization, competition, localized knowledge, trade barriers, and export subsidies. According to Kusuoka and Maruyama (2010, 112-3), the decision of whether to enter and when to enter a particular market will majorly depend on the company’s financial resources, the nature of the product, and the product life cycle. There are several strategies adopted by different companies depending on the favorable strategy and financial ability. However, the most common entry strategies adopted by most companies include: Directly exporting products, sales outsourcing, indirectly exporting products using middlemen, and producing goods in the target market. Other entry strategies include licensing, franchising, exporting, joint ventures, Greenfield project, alliances, and wholly owned subsidiaries (Lymbersky, 2008, 98).
Among the entry strategies listed above, the simplest and most commonly used is the aspect of exporting, which involves the use of either indirect method such as countertrade, or direct method such as the use of an agent. Other complex forms, which involve global operations may include: Export processing zones or joint ventures. Kusuoka and Maruyama (2010, 78-9) assert that when a company has settled on a decision to enter into a new market, there are so many options open to it. The options will in most cases vary in the risks involved, costs likely to be incurred, and to what level the company can exercise control over such options. After the company has decided on the entry strategy to adopt, there is need to decide on specific channels to adopt. Most agricultural products of a commodity nature or raw materials normally make use of distributors, agents, or involve the government, while processed materials majorly rely on sophisticated forms of accessibility. A company that is wishing to enter into a new market is faced with three major issues. The first issue is marketing, which describes how countries and segments can manage, co-ordinate, and implements marketing effort. It also entails how to enter the market; directly or with intermediaries, and with what kind of information. The second issue is sourcing, which describes whether or not to obtain products, to buy, or make the goods. The last issue in this aspect is control and investment, which entails joint venture, acquisition, and global partner. It involves the company deciding on how far it wishes to control and direct its own fate. The degree of attitude, risk involved, and the capability to achieve goals and objectives in the targeted markets are some vital facets on deciding whether to offer a joint venture, to license, or to carry out a direct investment (Kusuoka & Maruyama, 2011, 88-9).
The decisions made on the issue of marketing majorly focus on value chain. The entry alternatives or the strategies adopted by the organization must make sure that the required value chain processes are integrated and performed. When making decisions or forming strategies to enter an international market environment, there is need to pay a detailed attention than in the case of domestic marketing. In the case of entering into new foreign markets, there are a number of strategies that can be adopted to ensure a successful penetration (Keillor & Wilkinson, 2011). The first strategy is to adopt a technical innovation, which involves the production of goods that can be demonstrated and are superior to other products in the target market. The next aspect is the product adaptation strategy, which involves modification of the existing products to fit into the requirements of the target market. This would ensure that the products of the company do not appear irrelevant in the target market.
The other strategy is the aspect of security and availability strategy, which involves putting in measures that would ensure that the company overcomes transport risks by addressing potential risks. The next strategy is the low price strategy, which is the price set by the company to enable it to penetrate into the new market. The company needs to set low prices compared to other companies in order to appeal to and attract its target customers. The last strategy in this aspect is the conformity and total adaptation strategy, which involves the company adapting to the requirements and the conditions set by foreign producers and distribution. There are normally major problems involved when less developed countries strive to market their products in developed nations. Customers in the foreign countries are normally careful and conscious of factors such as currency, quantity, transport costs, and quality of products. Some commodities such as agricultural commodities involve enormous use of resources such as infrastructure since their marketing and production are interlinked (Keillor & Wilkinson, 2011, 78). This may at times surpass the scope of private companies thus calling for the involvement of the government.
Implementation
Extended marketing operations involve a marketing mix or business tools used by marketers in marketing. The marketing mix is very significant in determining the brand or the product’s offer and associated with four P’s that include product, price, place, and promotion. This concept majorly focuses on placing the right good in the right location, at the correct price, and at the right time. It involves creating a product required by a particular group of individuals and placing the product at a place that is regularly visited by those same people (Grünig & Morschett, 2012, 97). The product should also display prices that match the value attached to the product by such individuals; this should be done at a time when the individuals are willing to buy the good. However, a lot of work is required in finding out what the target customers really want and locating the places they do their shopping. One needs to figure out the methods involved in producing the product at a price representing the value of the product as perceived by the customer, and at a critical and relevant time. The elements go hand in hand in their operation and getting one element wrong can spell disaster (Grünig & Morschett, 2012, 102-3).
Looking at the first element (product), it should be noted that a company can only sell what is specifically required by the consumer. This implies that marketers should be able to study and understand the needs and wants of the customers and be able to attract them with a product that they are willing to purchase. The marketer needs to tell what the customer needs from the product or service and how the product satisfies the target customer. The marketer should also be able to determine where and how the customer will use the product and how the product should be differentiated to counter the products of its competitors. The second element in this aspect is “price”, which reflects the product’s total cost of ownership. Many factors affect price such as the consumer’s cost to change or the cost incurred in implementing the new product. It also involves the consumer’s cost of not choosing the product produced by the competitor. The marketer should be able to determine whether the customer is price sensitive and how the company’s price will compare with that of its competitor (Capass & Bakstein, 2012, 89).
The next element is “promotion”, which involves manipulatively talking to the target customers with the aim of making them purchase the company’s product. Promotion can take the form of advertising, viral advertising, personal selling, public relations, and any other forms of communication taking place between the consumer and the company. The marketer should be able to know how the company’s competitors carry out their promotions and how they are likely to influence the company’s promotional activities. The next element is “place”, which describes where the product can be gotten by the customer. With the rising use of internet, credit cards, catalogs, and mobile phones, people see no need of moving to any place in order to satisfy their needs or wants. The customers also have several places where they can satisfy their wants and needs. The marketer should be able to determine how the target customers prefer to purchase and how to access them in order to provide convenience to buy. With the rise of hybrid models of purchasing and the internet, “place” is slowly becoming irrelevant. According to Capasso & Bakstein (2012, 96-7), the concept of convenience and place strives to focus on the ease of finding the product, buying the product, and accessing relevant information concerning the product.
The marketing mix model can be used by the company on deciding how to take a new product into a new a market. It can also be used to test the company’s existing marketing strategies and determine their relevance to the company’s goals and objectives. According to Barnhart & Smith, 2012, 112-3, regardless of whether a company is considering an existing offer or a new one, there are definite steps that need to be followed in order to define and promote the company’s marketing mix. The first step is to identify the product or service under consideration. The second step is to analyze and answer the questions on the 4 P’s as discussed above, and the third step is to try asking “what if” and “why” kind of questions to try challenging the offer. For example, asking a question such as what would happen if the price was dropped by 5%? The last step in this aspect is to try “testing” the whole offer once a well-defined marketing mix has been put in place. This can be based on asking consumer based questions, such as if the mix meets customer needs and whether the price charged for the product is favorable for them (Barnhart & Smith, 2012, 88-9).
Critical Reflection
In order to achieve the objectives of designing new market strategies discussed above, there is need for the company to employ superior marketing strategy. By positioning the brand or product correctly, the product will most likely become successful in the market better than the competitor’s products. Even with the best form of strategies, it is necessary for marketers to properly execute their programs in order to achieve extraordinary results. The marketing strategy adopted should also be creative in order to improve the marketing results. The marketing plan may not likely succeed if there is no marketing execution on the models adopted. Improving how marketers enter into new markets can significantly enable them achieve great results without having to change their strategies altogether. At marketing mix level, the marketers can make improvements on their execution by making little changes on the 4 P’s (price, product, place, and promotion) model. Such small changes can be made without having to make changes on the strategic position (Backman, 2004, 56-7). At program level, the marketers can improve their performances by executing and managing their marketing campaigns in a better way.
It is normally believed that adopting a consistent marketing creative strategy across different media (such as Radio, TV, Print and Online), can enhance and amplify the marketing campaign effort. The marketers can also improve their effectiveness in the marketing programs by improving direct mail or editing the contents of the website in order to improve on organic search results. There is also the aspect of marketing infrastructure or the marketing management, which involves improving the marketing business. It should be noted that management of budgeting, motivation, agencies, and co-ordination of marketing practices can result in improved results and improved competitiveness. In the view of Backman (2004, 102), the business results and accountability for brand leadership is normally determined by the effectiveness of brand management. Some factors that should be taken into consideration are the exogenous factors, which often determine how marketers are able to improve their results. Taking advantage of interests, seasonality, or regulatory environment will help the marketers design methods of improving their marketing effectiveness.
References
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