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    Financial Forecasting Using Natural Assignment Help

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    Financial Forecasting Using Natural Assignment Help


    FINANCIAL FORECASTING USING NATURAL PROCESSING LANGUAGE

    INTRODUCTION  

    In this study, the main focus is given on the issues that are present regarding the financial forecasting and on the ways the issues can be solved. Natural language processing (NLP) is considered as one of the most useful tools since it is becoming more powerful due to the availability of data and different techniques that have been developed in earlier days. It is important to forecast all the important aspects  finance in order to make the business successful. However, in recent days, it is becoming difficult to manage all the statements of income that are present in the business; therefore, to solve the issues the usage of NLP is becoming more recommendable. The DSGE model is also a very important part in the field of accounting. The literature review part of this report has discussed the importance of the models in brief. The financial forecasting and the auditing part involves a lot of workforces and in that case, the different techniques of data mining help to reduce the workforce which makes the work easier and less time consuming as well. 

    study                             

    Conduction of this research is essential since it aims to analyse all the important activities that are present while using natural processing language to forecast the finances. This research paper can be used to make different companies aware of the importance of NLP. It will help the companies to manage all the tasks efficiently within a very short span of time. Moreover, this paper will also help the researchers to conduct further researchers regarding the uses of NLP in financial forecasting. By doing this, it will help the people to gain more knowledge regarding different aspects of NLP and its

    The history regarding the NLP started in the year 1950, though various articles were also published before this date. An article was created by Alan Turing regarding the topic "Computing machinery and intelligence" which was later recognized as the creation of intelligence. The author of this article said that within three to five years, national processing language would be used by various companies in order to solve their issues regarding the management of financial documents. However, it has been found that originally the progress was much slower than it was expected to be and thus failed to meet their expectations. According to Fisher, Garnsey and Hughes (2016), in the year 1960, some important systems of natural language processor was developed, and they had a high significance. Till the year 1980, the NLP systems were based on some complex rules that were hand-written. However, after that time, there has been a revolution in the usage of NLP's with the introduction of machine learning algorithms for the processing of language.

    LITERATURE REVIEW (RESEARCH OBJECTIVES AND BACKGROUND )

    a To find out issues in various conventional techniques used for financial forecasting 

    b To identify the advantages and disadvantages of Natural language processing in financial forecasting

    c To analyse the impact on financial forecasting after using Natural Language Processing

    d To recommend various strategies for making  use of NLP tools in financial forecasting more efficient

    Background

    The background of the research deals with the importance of financial forecasting within an organisation. The organisations need to determine the growth and development in the financial field for experiencing financial stability within the establishment. The different organisations have to set their daily goals and implement the different strategies so that they can deal with the future risks and dangers related to financial stability. Different people have the authority to examine the different financial dealings within the organisation. But with the emergence of the new organisations, the workload on these people is increasing a lot. That is the reason why the organisations are relying upon the data mining process. The data mining is processed with the different existing data within an organisation. Thus the workload on the people can be reduced. In order to give a clear impression of the data mining and the financial forecasting process, a brief literature review has been done. 

    Critical analysis based on articles 
    1. Usefulness, localizability, humanness, and language-benefit: additional evaluation criteria for natural language dialogue systems 
    Methodologies Results and debates from the literature

    The computational methodologies were used in this article to analyse the importance of natural language processing in the management of issues that are related to the financial forecasting. The ALICE chatbot architecture was also used to manage the efficiency level of forecasting in business. AIML files were used as default categories for making practical use of the NLP tools in forecasting finances.    

    The dialogue system used in this article has helped in understanding the approaches that needs to be used for determining the approaches that can be used to deal with their consumers. It will help in managing all the details of finance that are involved while conducting the business. 

    Natural language tools employ different modern techniques in order to understand, learn and produce contents related to human language. As per the views of AbuShawar and Atwell (2016), financial forecasting is used to predict the revenue rates, costs, variables and capital that can be required to develop the business. However, some other authors believe that it becomes difficult to prepare all the records manually, and thus, it is important for the companies to adopt the automated technology. This will help them to complete all the tasks efficiently in the required time and thus can prevent them from suffering any major loss.

    2. Natural language based financial forecasting: a survey. Artificial Intelligence Review

    Methodologies Results and debates from the literature

    Machine Learning methods as well as knowledge-based techniques were used in sentimental analysis since it is an essential component of this research paper. Various social media websites were also taken into consideration for gathering information regarding the benefits of NLP in financial forecasting. Semantic modelling helped to gather the views of various people who think that using NLP can help them to track all the important records of finances that are involved in the business.  
    The research helped in understanding the mainstream philosophies that are involved in this article. Importance of various frameworks of forecasting were also gathered and thus it motivated in conducting future research about the same topic in order to help various business persons to deal with the financial issues by using NLP. The techniques used for this article helped to analyse a large amount of financial articles.   
     
    As stated by Xing et al. (2018), forecasting of finance is considered as the future estimation of the outcomes related to finance for a company. It is essential in managing the entire budget that can be required for the development of the business. In contrary Capps et al. (2015) said that natural optimism is also an essential technique that can be used the mangers to deal with all the financial records.  
     
    3.Financial Time Series Prediction Using Deep Learning
    Methodologies Results and debates from the literature
    A classical linear statistical method that includes ARIMA and machine learning models was used in this article which helped in analysing the temporary dynamics that are involved with financial assets. The non-analytical structures of financial assets has increased the difficulty level in the market of finance and therefore, this method was used to resolve the issues. Deep learning techniques were also used in this article to find a solution to deal with financial problems.   
    It was found that NLP is one of the important techniques that can be used by the mangers to manage all the financial aspects of their business.
     
    Navon and Keller (2017) stated that at times the values of the product change in the market and thus it becomes important for the management department of the companies to maintain all the records manually. Moreover, maintaining the data manually is also time-consuming. Therefore, it becomes essential for the senior authorities of the companies to use NLP in order to overcome their issues.
    4.Text Mining for Big Data Analysis in Financial Sector
    Methodologies Results and debates from the literature
    Research methodologies such as bibliometric technique and systematic literature reviews have been used for developing this research study. Various stages that were involved in this study were planning stage, conduction stage and reporting stage.  
     
    It has been found that NLP is an important computer program that can be used in analysing and processing large amounts of data. It can be used to deal with the challenges that are involved in detecting fraud related to financial activities. M
     
    According to Pejić Bach et al. (2019), the big data analytics is a very convenient way for managing the financial issues in business. Moreover, the use of textual data for improving the models related to the dynamics of financial marketing has been significantly important in the market. With the growth of financial reports and articles, the usage of automated services are becoming more in demand in order to gain a competitive advantage. 
    5. Risk Analysis and Prediction of the Stock Market using Machine Learning and NLP
    Methodologies Results and debates from the literature
    Sentiment analysis has been used in this article to analyse the risks that are present regarding stock market. The chartist theories were also used to find all the hidden information regarding the usage of NLP in doing the stock market analysis and managing financial attributes.   
    The results helped to prove that the historical data is very strong evidence to find the importance of a tool that can be able to solve the issues of managing finance. The usage of NPL was also found by conducting the semantic analysis.  
     
    As stated by Lokesh et al. (2018) the machine learning has been providing a graph that can be used to manage the values of stock by representing all the values in y-axis and x-axis. Some other authors also said that various techniques such as the use of NPL can be very effective in predicting the exact value of the company in the stock market. However, Frankel et al. (2017) stated that the old methods of managing the finances are also useful and all the business entities must have adequate knowledge regarding the usage of those techniques.          
    6. Financial Stock Market Forecast using Data Mining Techniques
    Methodologies Results and debates from the literature
    The economic condition of a country or a company can be determined based upon the different forecast of the financial stock market. The forecast of the stock market gives an ideal picture of the future financial stability of an organisation. This forecast will help the organisation to make a plan or set a goal according to the financial stock market forecasting. To do so, it is very important to deal with the different techniques to estimate the forecast of the stock market shortly. Amongst the various processes, the data mining is such a process which has a lot of credibility and is very appropriate to determine the future of the stock market. 
     
    Data Mining is such a process which deals with various sources of data. The data which are increasing in number day by day can be used in this way to determine the stock market scenario in the future context. This generation of people has to deal with a pile of data which is sometimes unmanageable and that is the reason why data mining comes with the much-anticipated solution. The data mining helps to anticipate the outcome of the future stock market by analysing the existing data related to a certain organisation. There are five methods which can be used for the better outcome of the Data mining process. According to Kannan et al. (2010), the five methods include the Typical Price, Stochastic Momentum Index, Chaikin Money Flow indicator, Bollinger Bands, Relative strength index. There are three kinds of prices related to the stock market forecasting and they are a high priced, low price and the closing price. The typical price is based on these three kinds of prices. The total amount has been calculated and therefore, as a result, the typical price can be earned. This helps a lot in determining the pricing forecast.
     
    There are other methods as well. Chaikin Money Flow indicator indicates the fact that the stock price has two types of activities which is closely related to the future or the flow of money in an organisation. There are two terms specifically and they are accumulation and distribution. According to this theory if the money flow of the stock closes before the midpoint then the accumulation will be carried out whereas if the money flow closes after the midpoint the distribution process will be carried out. Thus through these processes, the data mining can be carried. The data mining can throw lights upon the probability of the changes in the stock market in the following day after assessing the data. This theories and this information are important for any organisation. The Financial stock market is a very important place which determines the success and failure of a specific organisation. That is the reason why the organisations should also put more focus on the data mining process. All of the above-discussed elements are conveyed through the neat observation of the article. The methodology that has been used in this article is the secondary data collection method. Through this method, the article has introduced several figures and numbers associated with the different stock market scenarios. How the data mining process can take place within an organisation has been also described in this article. Besides that, the different sources of the information have given this article a lot of credibility too. This article has been able to present a clear image of the importance of the data mining process in the context of the forecast of the stock market. 
     
    7. Detection of financial statement fraud and feature selection using data mining techniques
    Methodologies Results and debates from the literature
    In an organisation, financial system fraud is a very damaging case. This can damage the financial stability of an organisation. Along with all that the financial system fraud can also be a reason for the different financial losses within an organisation. The different data mining processes can help the organisation to deal with the different financial fraud systems within an organisation. Besides that, the data mining process also helps to identify the financial frauds as well. Various methods associated with data mining have been used for examining the exact financial frauds. 
    A total of 202 Chinese companies have been taken for better observation on the cases of financial fraud statements. The comparison method has been applied between these organisations as well. This article shows the fact that financial fraud is a very crucial and harmful issue in today’s scenario. These cases of frauds can be seen more in Chinese organisations. In the traditional way, one method has been used for detecting financial frauds. That method is known as the auditing system. The auditors are the ones who examine and observes the financial statements of the organisation and after that, they can identify the frauds happened within the organisation. Along with that, there are various elements of fraud as well which needs to be known by the organisation for their benefits. In this twenty-first century, the number of organisations is increasing in a very huge amount and this has huge pressure on the people who are associated with the auditing process. The different data mining process can genuinely help those people by reducing the extra workload. In this way, there is a very rare chance of mistakes in the identification of the different fraud cases within the organisation. 
     
    The financial statements are the documents which reflect the financial status of the organisation. Based on the financial fraud the organisation’s financial states can fall as well which is not that beneficial for the organisation. There are several parts of the financial statements related to the organisation which need to be maintained in order to make out the actual financial status of the organisation. Balance sheets are a very crucial part of the financial statement where different financial dealings and the accounting activities gets placed. The basic income statement, the cash flow statement and the statement of the retail earnings has been accounted here. These elements of the financial statement finally complete the statement. But if there are any discrepancies the balance sheet or any of the elements mentioned above the financial statement fraud can happen. As stated by Ravisankar et al. (2011), the different methods of data mining that can be used in the context of identifying the financial frauds are the Multilayer Feedforward Neural Network, Logistics Regression, Group Method of Data Handling, Support Vector Machines and others. The different elements of the financial statements have to be identified separately to detect the actual fraud or the source of the fraud. Thus the data mining process helps in the identification of the financial frauds and it can also help in reducing the workload on the auditors too. This article has made a thorough discussion of the above-discussed topic. Besides that this article has relied on the primary data collection method and the secondary data collection method as well. That is the reason why this article has got a lot of credibility and a strong platform to showcase the importance of the identification of financial statement fraud.