extfieldsofstudy:
Physics
Computer Science

text:
Research on Quality Analysis Model of Additional Comments Based on Text Analysis

Based on the idea of text analysis, this study analyzes the relevance of text topics, the emotional consistency of fist comments and additional comments, and the information contained in the text, then get out of the online quality assessment model. The calculation results can reflect the quality of additional comments, helping consumers and businesses to identify important information and make correct judgments.

Introduction
In recent years, e-commerce has been developing rapidly, and more and more consumers would like to choose online shopping. The 34th "Statistical Report on the Development of China's Internet Network" showed that users' reviews are the main reference factor for online shopping users to make purchasing decisions. As an important form of online word-of-mouth, the comment system has become a main reference source for consumers' online shopping and it affects consumers' purchasing decisions. However, because of the cashback reward to favorable comments provided by the merchants, the trust of online comments is greatly reduced. Therefore, Taobao stipulates that: Within 180 days (inclusive) from the date of successful transaction, the buyer can add comments after making the Taobao credit evaluation, and the content of additional comments can not be modified. So when consumers browse comments and look for commodity information, they will pay special attention to the contents of additional comments. But in order to obtain more profits, the merchants come up with ways to give money to praise and even hire online water army to give praise. This has undoubtedly caused great trouble to consumers. Therefore, how to "get the fake and take the truth, get the rough and take the fine" and identify high-quality additional comments has become the subject of this research.

Related researches
For the question of the quality of comments, the researches of domestic and foreign scholars mainly focus on the initial comments and their main topics are the following two aspects.

Identify factors that affect the quality of online reviews
Kim S et al. [1] use SVM regression method to predict the quality of comments from five text features: structure, morphology, syntax, semantics and metadata. The experimental results show that comment length, unary grammar and product rating are the key factors to judge the usefulness of comment. Hyunmi Baek [2] believes that the inconsistency of comments, the ranking of reviewers, the number of comments, etc. have a certain impact on the credibility of comments. The research by Huang Tingting  [3] shows that whether the content of the review is closely related to the function of the product is an important indicator for judging the quality of the comment.

Study and analyze the model of online comment quality
Tang Xiaobo et al. [4] propose a topic-oriented high-quality comment mining model, which combines existing useful votes with other weighting indicators to automatically extract high-quality comments under each topic. Li Zhiyu [5] starts with the study of influencing factors of online comment effectiveness and establishes a commentary utility index system. Liu Jingjing et al. [6] note the problem of evaluation bias caused by the cumulative vote, so they use the data labeled manually and evaluate the quality of comments from three aspects: information content, subjectivity and readability.
Based on the carding of above theories and models, it provides an idea for constructing an additional comments quality evaluation index system, which is helpful to the construction of this model.

Theory and method
3.1. LDA model theory LDA (Latent Dirichlet Allocation) is a document theme generation model, which can be used to identify hidden subject information in large document sets or corpora. LDA gains the relationship between vocabularies, documents and topics in the corpus by generating a probability model. The LDA generation process of a document is as follows [7]: • Get the total number of all words in the document N~Poission (β); • Get the distribution of the document on the topic ~Dir(α); • Traverse each word from 1 to n. Fist, get the probability distribution of each topic ~Multinomial (θ m ). Then, get the probability distribution of topics and words ~Multinomial( Φ z ).
Among them, α and β are priori parameters, θ and Φ are estimated parameters. The experiment uses Gibbs sampling to estimate θ and Φ, and predicts the subject probability of the current word while keeping the subject distribution of other words unchanged, and indirectly obtains their values by marginalizing θ and Φ [7]: In the formula, C VK and C MK represent quantity matrixs of dimension V×K and M×K respectively, V is the number of words, K is the number of topics and M is the number of documents. C w −n j VK is the frequency that the word w assigned to the subject j; C d −n j MK is the number of words assigned to the subject j in the document d.
Assign random themes to the words in the document to form the initial Markov chain, and then assign them the subject again according to formula (1) in order to obtain the state of the next Markov chain, and iterate continuously until the Markov chain becomes stable. Finally, use the Gibbs sampling algorithm to estimate the θ value and Φ value for each word [7]: In this way, the probability distribution of each topic and the probability distribution of the topic and the word can be obtained, thereby we can obtain a "topic-word" probability distribution matrix and a "document-topic" probability distribution matrix.

Grey correlation analysis
In 1981, Professor Deng Julong, a Chinese cybernetic expert, first proposed the concept of the gray system. Later, he published many papers and monographs on the gray system and established the gray 2019 2nd International Symposium on Big Data and Applied Statistics Journal of Physics: Conference Series 1437 (2020) 012084 IOP Publishing doi:10.1088/1742-6596/1437/1/012084 3 system theory. The grey system theory puts forward the concept of gray correlation analysis for each subsystem, and intends to seek the numerical relationship between subsystems (or factors) in the system with certain methods. Therefore, the grey correlation analysis provides a quantitative measure for the development and change of a system, which is very suitable for dynamic process analysis. The grey correlation method is also an effective way to determine weights. It aims to determine the weight value by making a reasonable processing of the existing actual data in order to achieve a scientific description of the system development.

Design of the indicator system
For the online comments given by online consumers, this study attempts to establish a reasonable text quality evaluation system oriented to comment content. We can use it to evaluate the quality of users' reviews, identify high-quality additional comments quickly and improve the utility value of online comments.

Data collection
Taobao is currently the largest C2C e-commerce website in China, which has rich online comment resources. This study uses web crawler technology to get a large number of initial comments and additional comments from the website and store them in the corresponding documents.

Data preprocessing
The data obtained from the website is mixed. To ensure data quality and reduce the time wasted on invalid data, the original data needs to be preprocessed. Specific operations include the followings: • Delete the duplicate data. Delete the data whose initial comment is "This user did not fill out the comment" and whose comment content only contains special characters, numbers or pictures.
• Use the NLPIR-ICTCLAS Chinese word segmentation system to the text word segmentation and part of speech tagging.
• Delete the stop words in the comments.

•
Save the processed data

Relevance of comment topic
A study by Titov [8] shows that the higher the relevance of a comment to a topic, the greater its contribution to the topic and the higher the quality of the comment. Therefore, the first indicator in the model constructed in this study is the relevance of the text content to the topic. Referring to the research of Tang Xiaobo [9], we can confirm the idea of judging the relevance of commentary topic: Each comment in the comment text set corresponds to a document in the corpus. The topic distribution of each document can be represented as a vector, and the similarity between documents can be obtained by calculating the distance between documents [10]. Then in the "Comment-Subject" matrix of the corresponding LDA model, the topic distribution of each comment can be expressed as a vector =( 1 ( ) , 2 ( ) , …, ( ) ), where represents the subject distribution of the ith comment, ( ) represents the probability of the ith comment on topic k, and k is the number of topics . If the vector space of the two comments is closer, the similarity between the two comments is higher. Then, take each topic's comment set as a document, and combine the content in the next comment of the theme as a sentence. So we can calculate the similarity between each sentence in the document and other sentences by using the cosine angle, therefore to obtain the similarity of the comment set under a certain theme, which is recorded as Relativity.

Emotional consistence of comments
Zhou Hong et al. [11] point out that the emotional consistency of the initial comments and the additional comments has a great impact on consumers' information adoption. Therefore, the second indicator in the model constructed in this study is the emotional consistency of the initial and additional comments. This study uses polarity scores to measure the emotions of comments. It is stipulated that the score is positive for positive emotions and negative for negative emotions, and the emotional polarity scores are calculated with reference to Wang Qianqian [12]: • Extract the product attribute words and calculate their weights. In this study, the ICTCLAS word segmentation system is used for word segmentation and part-of-speech tagging, and then the tagged phrases are classified into parts of speech to extract product attribute words. Then the weight of an attribute word is determined according to the frequency of its occurrence in the whole comment corpus.
• Calculate the emotional score of the product attribute words. This study calculates the emotional scores of product attribute words with the method proposed by Zhang Jing and Jin Hao [13].
• Calculate the score of comment text. Suppose there are m comments, in which the i (0 < i < m) comments have y (0 < y <= n) emotional words. And the words' weights are: 1 , 2 , 3 ,…, , emotional scores are 1 , 2 , 3 ,…, , then the polarity score E of the ith comment is E= 1 * 1 + 2 * 2 + 3 * 3 +…+ * . It also stipulates that E is a good rating between 1 and 0.667, a middle rating between 0.666 and 0.334, and a negative rating between 0.333 and 0 • Calculate the consistency score. Wang Changzheng [14] believes that in the comments with additional comments, inconsistent comments can give readers a higher sense of usefulness than consistent comments. Therefore, this study stipulates that if the initial comment contradicts the additional comment sentiment tendency, the consistency score Consistence=1, otherwise Consistence=0.5.

Information amount of comments
It is generally believed that the more information a comment contains, the higher the quality of the comment. Therefore, the third indicator in the model constructed in this study is the amount of information contained in the additional comments. it is generally agreed that the amount of information contained in a comment is proportional to the number of comments. So, this study stipulates that the comments which have less than 10 Chinese characters, information amount Information=0.1, 20~30 Chinese characters, Information=0.2, and so on, 90~100 Chinese characters, Information=0.9, 100 Chinese characters or more, Information=1 4.6. The construction of model Based on the above indicators, this study constructed a multiple linear regression model: Q= 1 *Relativity+ 2 *Consistence+ 3 *Information (4) The weight of each indicator w i =(w 1 ，w 2， w 3 )( 0 < w i < 1 且 Σw i = 1)is determined by the questionnaire method and the gray correlation analysis method. Referring to the research of Zhou Linlin et al. [15], the specific steps are as follows: • Get the weight of each indicator by the questionnaire survey method X=(x ij ) nхm ,i=1, 2, …, n,j=1,2,…,m (5) • Determine the reference sequence 0 , then select the largest weight value from it and assign the value to each indicator as the reference weight value.

Data collection and processing
This study selects online comments of AL MI speaker sold by Xiaomi flagship store as the research object, and uses the reptile software to obtain 1880 data from Taobao. After preliminary screening, 900 valid data are left and divided into 600 training set and 300 test set. Use NLPIR-ICTCLAS Chinese Word Segmentation System to segment and part-of-speech tagging all commentary texts, the specific effect is shown in Table 1. Filter the stop words of texts, and the specific effect is shown in Table 2.

The calculation of review topic relevance
Use Java language to generate LDA probability model for comment text after word segmentation. Set the number of topics to 6, and the number of loop iterations to 100. The "topic-word" matrix and the "comment-theme" matrix are obtained through LDA clustering, as shown in Table 3 and Table 4 respectively.  It is stipulated that in the "comment-topic" matrix, the maximum number of similarities between each comment and the topic should be taken as the theme relevance of the comment.

Calculation of indicator weights
In this study, 500 electronic questionnaires are distributed to people of all ages, and they score the importance of additional comments' three indicators: relevance, consistency and information amount. Finally, 385 valid questionnaires are collected. The questionnaires are divided into four categories: under 20 years old, 20-30 years old, 30-40 years old, and over 40 years old. The data of the collected questionnaires are compiled and calculated, and the weights of the indicators are shown in Table 5. It is stipulated that in the "comment-topic" matrix, the maximum number of similarities between each comment and the topic should be taken as the theme relevance of the comment. According to the steps described in Section 4.6, introduce the gray correlation method, and then the weights of the three indicators are finally determined to be (0.38, 0.31, 0.31). So the model of this study is: Q=0.38*Relativity+0.31*Consistence+0.31*Information (9)

Analysis of Model experiment results
After the experiment, the results calculated by the model are obtained, and the Q value is defined as a low-quality comment in the interval [0, 0.5), and a high-quality comment in the interval [0.5, 1]. Applying the model and conclusion to the test set, we can get the accuracy rate is P=80.73%, the recall rate is R=64.90%, and the F value is F=71.95%. Therefore, it concludes that this model is a good evaluation model for additional comment texts.

Conclusion
With the rapid development of e-commerce, consumers and businesses urgently need a method to help them find useful information from a large number of disorderly online comments, so as to make correct judgments and choices in a short time. Based on the concept of text analysis, this paper proposes a quality evaluation model of online review, and chooses the additional comment of the MI Bluetooth speaker in Taobao as the test object. The usefulness of the model is verified by experiments. This model can help consumers judge the quality of online review and provide services for consumers to make purchasing decisions. At the same time, the model can also help merchants analyze and identify reliable review information to accurately grasp the user's consumer demand. In view of the shortcomings of the model, such as too few commodities as the experimental object, I will continue to improve in the future study.


extfieldsofstudy: Business Physics

s2fieldsofstudy: Business Economics

text:
The logistics approach to perspectives for the digital technologies in Russia

Financial technologies (fintech mechanism), along with regulatory technologies (RegTech mechanism), being innovative socio-economic phenomena dynamically developing both abroad and in domestic conditions, can be considered as financial and information categories arising from the formation of needs of the financial market in adaptation mechanisms to the conditions of the new technological order of Industry 4.0. The purpose of the study is to identify the role of digital finance in the domestic financial system in the context of the digitalization of the economy and the directions of development of legal regulation of modern financial technologies. As a result of the study, prospects for the further development of the FinTech and RegTech mechanisms in the Russian economy were identified, taking into account the features and prospects of the development of the FinTech and RegTech mechanisms in Russia. It was shown that the digital financial cube can be expanded by including a new characteristic of digital assets, reflecting the degree of openness of industrial enterprises in the new technological structure of Industry 4.0. As a result, it could be predicted the interpenetration of both technologies of the financial sector of the Finnet direction and BIM technologies of the Technet direction with those technologies being different in their nature. Fintech mechanism could be treated as an effective instrument for managing the financial resources from the point of view of the logistics methodology. With the purpose of the logistics theory being the development of methods and models of effective distributing the financial flows, the logistics approach to managing the financial resources considers the calculation of expenditures caused by financial decisions regarding the investments, debts, deposits and especially operational activities. The main problem of logistics approach relates to the assessment of interconnection between the material resources flow and cashflow. However, the digital financial technologies enable to make various appraisals being immanent part of logistics models.

Introduction
The logistics approach consists of the development of both methods and models of effective distributing the financial flows considering the calculation of expenditures caused by financial decisions regarding the investments, debts, deposits and especially operational activities. The main problem of logistics approach relates to the assessment of interconnection between the material resources flow and cashflow. However, the digital financial technologies enable to make various appraisals being immanent part of logistics models. The high importance of digital technologies and the development trends of the financial market caused by their development determines the relevance of the research topic. A wide range of digital technologies implementation differ from energy market [1] to personnel management [2,3]. The processes of changes in the financial industry under the influence of digitalization necessitate the development of regulation of new aspects of the functioning of the financial market. Transformation of the financial market inspires the processes of transformation of state regulatory mechanisms. Financial technologies (fintech mechanism) along with regulatory technologies (regtech mechanism) are new socio-economic phenomena that are developing dynamically and use modern information and communication technologies to provide various financial services. This general characteristic gives rise to a point of view, which boils down to the fact that "RegTech" is a special kind of "FinTech" [4]. In our opinion, both concepts "FinTech" and "RegTech" are close, since they are based on the use of one technological basis -modern information and communication technologies -and are used in the same field -on the modern financial market. Thus, FinTech and RegTech can be considered financial and information categories that arose as a result of the development of information technologies and the formation of the needs of the financial market in adaptation mechanisms to the conditions of the new technological structure of Industry 4.0.
In modern conditions, there is a rapid increase in the number of startups in the fintech industry, so it is difficult to name the exact number of fintech projects currently existing. According to the Moody's agency, back in 2015, the number of fintech startups exceeded 4000, and the volume of investments in fintech startups in 2015 amounted to more than $ 19 billion [5]. A year later, in 2016, the volume of investments in the fintech industry increased five-fold and exceeded $ 100 billion [4]. From 2014 to 2018, the volume of venture investments in fintech companies increased almost 3 times -from $ 7.7 billion to $ 19.4 billion. The number of venture investment deals in fintech companies for the study period increased from 725 to 1083 [6]. In 2016, there was a slight decline in venture capital investments in fintech companies: the number of transactions decreased by 1.5% and amounted to 836, while the total volume of venture investments in fintech companies decreased by 13% and amounted to $ 12.8 billion. According to experts, the decline in investment in fintech companies in 2016 was due to investors' concerns caused by the fast growth of the fintech industry and the likelihood of a bubble in the market [7]. Over the study period, the total value of venture investment deals in US fintech companies increased from $ 5.2 billion to $ 7.1 billion. The peak in investment was reached in 2015, when it amounted to $ 7.7 billion. In 2016, the total volume of venture investment in fintech companies in the United States fell to $ 5.5 billion. Over the study period, the number of transactions is growing at a faster rate than the total investment. This indicates a decrease in the average value of a venture investment transaction in a US fintech company. In the EU, there is also a tendency to increase the number of investment transactions -from 122 transactions in 2014 to 201 transactions in 2018 with a significant reduction in the average value of an investment transaction in 2016 with a further increase in the value of one investment transaction [6]. In the Asia-Pacific region, during the study period, there has been a steady increase in the volume of venture investments in fintech companies and the average cost of an investment transaction in venture financing.
Traditional financial market players have formed effective systems to comply with dynamically changing and ever-increasing demands from financial market regulators. Nevertheless, FinTech startups are striving to occupy the most profitable links in value chains in the financial services sector, attracting millennial consumers through a predominantly online service delivery model.

Materials and methods
The financial industry is actively using new technologies and as quickly as possible adapts the achievements of scientific and technological progress to its own needs, while undergoing transformation in the new technological structure of Industry 4.0. It should be noted the work of foreign researchers on the development of the fintech mechanism, such as Arner D., Barberis J., Buckley R. [8], Kocianski S. [7], Schueffel P. [9]. Modern studies of the "RegTech" mechanism are devoted to the problems of the development of state regulation of the financial sector, directions of  [10] considers the development of the RegTech mechanism abroad in the context of the development of the financial market. Meola A. [11] studies the possible directions of the future development of the RegTech mechanism in Asian financial markets using China as an example. Shaw W. [12] discusses the opportunities and threats posed by the development of regulatory technologies in the new technology industry of Industry 4.0. Eyers J. [13] discusses the possibilities of using modern digital technologies for the development of regulatory mechanisms of the financial market. Povetkina N.A. and Ledneva Yu.V. [14] consider the relationship between the FinTech and RegTech mechanisms and the direction of development of regulatory technologies in Russia.
The FinTech and RegTech mechanisms are rapidly penetrating many sectors of the economy and the areas of its state regulation. In the domestic scientific literature, there is currently no single understanding of the contents of the terms "FinTech" and "RegTech". The etymology of the word "FinTech" in the Russian language implies that it is a complex abbreviation, which is formed from the English phrase Financial technology. The spelling "financial technology" as a rule is replaced by the English abbreviation FinTech, and such spelling is currently quite legitimate. In Russian and international practice, there are three approaches to the definition of fintech. First, fintech is a new sector of the global economy, which is formed by enterprises specializing in improving the technical and economic efficiency of financial products. In the framework of this approach, another definition is used: Fintech is a segment of the financial market that specializes in providing digital financial products for various categories of consumers.
Secondly, the term "FinTech" is used to refer to technology companies that integrate new information technologies into traditional activities within the financial market.
Thirdly, the term "FinTech" is used to refer to practical activities that are aimed at the efficient use of modern software to satisfy consumer demand for various products of the financial market.
Finally, "RegTech" actually represents "a market of information and legal technologies designed to help financial companies comply with the requirements of audit bodies (regulators) or, in other words, provide assistance in the field of regulatory compliance" [14].
One can single out a wide range of scientific papers devoted to the problems of developing the FinTech and RegTech mechanisms. In the works of such domestic researchers as Maslennikov V.V., Fedotova M.A. and Sorokin A.N. [15] highlights the dynamics of the development of the fintech mechanism on a global scale and in Russia. In the work of Nikitina T.V., Nikitina M.A. and Halper M. A. [16] a study is being conducted of the importance of the fintech mechanism for the development of the modern financial market. The interconnection of the fintech mechanism and innovations in the banking sector is considered in the work of Bakhareva A.A. [17]. Prospects for the development of the fintech segment in Russia are considered in the work of Pertseva S.Yu. [18]. Currently, the attention of Russian researchers is paid mainly to the problems of developing the fintech mechanism, in contrast to foreign researchers devoting much less attention to the RegTech mechanism. Fintech startups initially focus on customizing their services, and the active use of digital technologies allows them to reduce the cost of services and, accordingly, use a more flexible pricing policy -from lending to capital management. A special ecosystem of the fintech sector is being formed (Figure 1).  The advent of online platforms in the field of consumer and commercial lending has enabled individuals and legal entities to quickly and conveniently borrow. Thus, the development of financial technologies leads to the transformation of the structure of the financial market, changing the algorithm of its functioning and contributing to the development of the dynamically developing sector of the fintech financial market at the intersection of the digital technology and financial services sectors.

Results. Spreading the concept of digital financial cube on BIM technology
The concept of a financial cube was proposed by Gomber, Koch, and Siring [19] to systematize concepts united by the concept of digital finance as a set of new financial products, financial enterprises, finance-related software, and new forms of communication and customer interaction provided by FinTech companies and innovative financial services providers. The concept of a digital financial cube is considered in three dimensions: digital financial business functions, applied technologies (and technological concepts), as well as relevant financial institutions. In this formulation of the problem of systematizing digital finance, a traditional digital financial cube should be considered.

Traditional digital financial cube
The traditional digital cube is characterized by a systematization of the concepts of digital finance in three dimensions (Figure 2).  Figure 2. Digital Finance Cube [19].
We can agree with the researchers of this extensive topic in a general methodological approach applied to the solution of the problem of systematizing the conceptual apparatus of digital finance in the form of a cube in three dimensions (business functions, technologies and institutions). The idea by itself of such a scientific search was borrowed from a scientific work devoted to research in the field of recognition of financial fraud (Financial fraud detection, Figure 3, [20]).
In general, this logic of scientific research can be taken as the basis for further research in the field of a broader object of research -the transformation of economic relations in the new technological structure of Industry 4.0. However, from the perspective of further improving the tools of digital finance, the approach of the traditional cube may cause difficulties in terms of visualization of 3 dimensions of each of the 5 main elements of the Fintech ecosystem (discussed in detail in [21]): 1. FinTech startups (e.g. payments, asset management, lending, crowdfunding, capital markets and FinTech insurance companies); 2. Technology developers (e.g. big data analytics, cloud computing, cryptocurrency and social media developers); 3. Government (e.g. financial regulators and legislators);  In addition, it is very difficult to use this model to consider the possible consequences of the introduction and development of digital technologies in the industrial production sector. What new financial instruments and technologies will be required to service transforming business processes in industry? For analysis, we consider in more detail the development of the direction of new production technologies of Technet. A roadmap for the development of "end-to-end" digital technology "New Manufacturing Technologies" (SC SCT NPT), which involves achieving industrial leadership in hightech industries based on a list of activities until 2024 and classifies the following subtechnologies as new manufacturing digital technologies: 1) digital design, mathematical modeling and product or product life cycle management (Smart Design); 2) technologies of "smart" production (Smart Manufacturing) [22]. Consider how these technologies will affect the development of Fintech.
A radical change in the paradigm of technology application in the 2010s. due to the shift of the "center of gravity" in global competition to the design stage, the rapid development of computer and supercomputer engineering technologies, computer optimization technologies, new materials development technologies, additive manufacturing technologies, the Internet of things / industrial Internet, machine learning, big data and cloud computing, artificial intelligence for a key role in the new technology industry of Industry 4.0. digital twins began to claim [23, p. 10]. A new type of business process system has emerged with the following characteristics: 1) creation of digital platforms, unique ecosystems of advanced digital technologies; 2) development of a system of digital models of both newly designed products and production processes; 3) digitalization of the entire product life cycle from a concept idea, design, production, operation, maintenance to disposal [24, p. 6].
The concept of digital twins (according to A.I. Borovkov) is defined as a clusters of complex multidisciplinary mathematics models with a high level of relevance to real materials, real objects / structures / machines / devices ... / technical and cyberphysical systems, physical and mechanical processes including technological and production processes. These models are described by 3D nonstationary non-linear partial differential equations [25,.
The widespread introduction of digital counterparts of products and objects leads to the emergence of such a thing as a "digital asset", which in turn requires the development of mechanisms for calculating the value of a digital asset (DA), its accounting, etc. DA involves the involvement of digital models in the financial management process, the formation of a portfolio of debt obligations, investment management, digital finance. At the same time, the concept of DA clearly does not fit into any of the faces of the cube, it is rather a digital financial and production technology. In addition, the question arises of the legal regulation of such an asset, and as a result, a change in the prevailing pricing models in the development of DT, as the basis of DA. These issues need to be addressed both in legal and in the production plane.
It is possible to propose the expansion of the traditional digital financial cube by including digital assets in the research object (for example, in relation to the provision of capital construction objects as a mortgage to a credit institution). Then, the very concept of a cube can be transformed from a trihedron to a tetrahedron, with the addition of a "facet", in terms of the digital finance.
The fundamental basis of a scientific study of the prospects for the development of digital finance may be the new paradigm of digital design and modeling of globally competitive products of a new generation (developed by Borovkov) [26]. Guided by the approach of conducting basic research based on the world scientific and technological frontier, the tetrahedron can be expanded, adding new faces. The resulting digital financial pentahedron is obtained by adding an additional facet to the regulatory face that reflects the previously mentioned new digital manufacturing technologies Smart Design and Smart Manufacturing, which allows us to consider the pentahedron and draw a fundamental conclusion. Introduction to the financial terminology of the concept of a digital asset will expand the conceptual framework of financial technologies FinTech and proceed to the development of new models and methods of financial asset management based on new terminology.
The thesis about the possibility of interpenetration of FinTech technologies and Technet technologies is based on the prevailing distribution of investments in startups and on the forecast of revolutionary changes in the financial market. The maximum share of investments falls on fintech startups in the field of payments and electronic commerce -38%.
In second place is investment in fintech startups in banking and lending -26%. 13% of investments in 2018 came from fintech startups in the field of investment and money management. Investments in fintech startups aimed at creating aggregators of financial solutions amounted to 12%. In fintech startups in the field of healthcare in 2018, 6% of the total amount of funds was invested. 4% of investments come from fintech startups in the insurance industry and only 1% from startups developing software for managing financial business processes ( Figure 5).
Currently, various measures are being implemented at the state level to implement "soft" regulation of the FinTech sector and RegTech sector, and the boundaries of legal regulation of the FinTech and RegTech sectors are gradually being formed. In 2018, the Bank of Russia introduced "The Main Directions for the Development of Financial Technologies for the Period 2018-2020" [27]. Bank of Russia proposals provide for the implementation of a number of infrastructure initiatives aimed at developing the fintech sector. In particular, it is proposed to create a platform for digital registration of transactions, to create a marketplace platform for providing financial services to various consumer groups, to create a system for transmitting interbank financial messages, as well as to create an end-toend identifier of a financial services consumer. In addition, the "Guidelines for the Development of Financial Technologies for the period 2018-2020" focuses on applied developments on the introduction of digital technologies in supervisory and regulatory activities in order to increase their effectiveness. Several fintech accelerators operate in the Russian Federation, functioning as special centers that offer the necessary resources to the founders of fintech startups. The fintech accelerator of the "Startup Academy of Skolkovo" provides training and financing to the founders of fintech startups [28]. Fintech-accelerator GenerationS is aimed at the implementation of technological startups of federal significance and solves the tasks of three interested parties: potentially successful fintech startups get support, corporations get the opportunity to optimize business processes as a result of the introduction of new technologies, investors get the opportunity to invest in potentially successful fintech projects [28]. In addition, it should be noted such Russian fintech accelerators as Future  8 Fintech and QIWI Universe. The Future FinTech program gives the founders of fintech startups the opportunity to test created products and services in the fintech industry in the process of cooperation with real domestic companies and banks. QIWI Universe FinTech Accelerator was created by QIWI Group to support three segments of the fintech market: young FinTech startups, medium-sized businesses in the fintech sector, and large-scale businesses in the FinTech sector [28].
The results of the study prove that the financial market could be identified as sector transforming for revolutionary changes which was mentioned while launching an association for the development of FinTech in Russia [29]. We assume that it could refer to the consumer banking, payments and transfers, investment management, brokerage services, insurance. Among the innovations in the field of lending, the emergence of alternative lending models, the development of credit risk assessment through the use of non-traditional data sources and big data analysis, the acceleration of credit operations with a parallel reduction in operating costs of credit organizations.
Thus, we can consider the development of FinTech technologies taking into account the developing technologies for creating digital doubles, smart and virtual factories, etc.

Conclusion
In the context of the digitalization of the economy, new participants, new products and algorithms appear in the financial market both globally and locally, which forces traditional participants in financial markets to adapt to changing conditions and modernize their own products. Accordingly, in the future, with the further development of information technologies, further changes in the financial industry can be expected both globally and nationally. The Russian fintech sector is behind the world both quantitatively and qualitatively. FinTech and Regtech are special financial and informational categories that describe dynamic mechanisms at the junction of the digital technology and financial services sectors in the modern financial market. The FinTech and RegTech mechanisms are closely interlinked -the development of the fintech sector inspires the need for regulation of new financial technologies and, accordingly, the development of the RegTech sector.
New entrants to the financial market -FinTech startupsare integrating digital technology with traditional financial services. This provides the fintech sector with important competitive advantages over traditional financial market participants. The rapid development of the FinTech sector in quantitative terms leads to qualitative changes in the financial market due to the transformation of the traditional value chain in the financial market.
However, the inclusion of working with digital assets in the financial technology sector, the involvement of BIM-technologies in assessing the value of a business in the process of forming a loan portfolio, and also by increasing the transparency of the borrowing enterprise by analogy with the openness of the economic behavior of physical faces. The question of the mechanisms of technology interpenetration between Technet and FinTech remains open despite a common understanding that innovations in the fintech sector are based on the rapid development of information and communication technologies and include the emergence of new digital applications that significantly facilitate the implementation of various transactions, the emergence of alternative processing networks, and the intensification of the use of mobile devices for financial transactions. It could be proved that Fintech mechanism becomes an effective instrument for managing the financial resources from the point of view of the logistics methodology. The logistics approach considers the calculation of expenditures caused by financial decisions regarding the investments, debts, deposits and especially operational activities. The main problem of logistics approach relates to the assessment of interconnection between the material resources flow and cashflow. As far as the development of the models of the financial flows is concerned, the digital financial technologies enable to make various appraisals being immanent part of logistics models.


The type of ethnomathematics in fish trading in Sapuran Market, Wonosobo

Mathematics and culture are two inseparable entities. Mathematics grows and develops based on the problems in the lives of people with different cultural backgrounds. They can develop mathematics in their own way. Many people view that mathematics and culture are two different entities. This makes it difficult for them to learn mathematics. Sujoko (2011, p. 13) reveals that the method of the implementation of environmental education is a very important method and plays a very important role in quality learning process. Yusuf in Astuti and Purwoko (2017, p. 3) states that ethnomatmatics is mathematics that grows and develops in a particular culture according to a certain culture. D’Ambrosio (1985, p. 44) states: “Building a bridge between culture and mathematics is an important step to understand different ways of thinking which can bring about several types of mathematics; this is the so called ethnomathematics. This may mean that a varieties of mathematical concepts can be discovered in culture, and therefore it can be concluded that mathematics and culture are interrelated. Mathematics can be born from a culture, so that a pattern is found and then utilized as one of mathematics learning resources, which is concrete and found around the students”.

INTRODUCTION
Mathematics and culture are two inseparable entities. Mathematics grows and develops based on the problems in the lives of people with different cultural backgrounds. They can develop mathematics in their own way. Many people view that mathematics and culture are two different entities. This makes it difficult for them to learn mathematics. Sujoko (2011, p. 13) reveals that the method of the implementation of environmental education is a very important method and plays a very important role in quality learning process. Yusuf in Astuti and Purwoko (2017, p. 3) states that ethnomatmatics is mathematics that grows and develops in a particular culture according to a certain culture. D' Ambrosio (1985, p. 44) states: "Building a bridge between culture and mathematics is an important step to understand different ways of thinking which can bring about several types of mathematics; this is the so called ethnomathematics. This may mean that a varieties of mathematical concepts can be discovered in culture, and therefore it can be concluded that mathematics and culture are interrelated. Mathematics can be born from a culture, so that a pattern is found and then utilized as one of mathematics learning resources, which is concrete and found around the students".
The teaching of mathematics in schools is too formal and different from what the students experience in their daily lives, so that mathematics is viewed as a difficult subject. Hatimah (2009, p. 318) writes that school is a place for teaching formal culture, and therefore it is very important to discover the concepts of mathematics in culture in order to make it easier for the students to learn mathematics because they participate directly and experience the culture of their everyday lives, not Counting technique; Ethnomathematics; Fish trading process This study aims to reveal the type of ethnomathematics used in the process of counting fish in Sapuran Market, Wonosobo, which can be applied in teaching processes. The method used in this study is the observation of fish trading and interviews with fish traders in Sapuran Market, Wonosobo. The research findings show that the process of counting fish in the market applies ethnomathematics, including the addition of integer and reduction of the integer. The mathematical concept used in the addition operation is the storing technique, while the reduction operation applies the borrowing technique. The implementation of mathematics teaching using the ethnomathematics approach adopted from the activities of fish counting at Sapuran Market, Wonosobo can help improve students' understanding of the addition and reduction materials for elementary school.
This is an open access article under the CC-BY-SA license.

-Ethnomathematics Journal
Volume 1, No. 1, March 2020 just listening to their teacher's explanation, which they get in the teaching and learning process in school.

METHOD
This research is a quantitative study. It was conducted in Surodento Village, Sub-district of Sapuran, Wonosobo Regency. The data were collected through observation and interviews with fish traders at Sapuran Market, Wonosobo. The subjects of the observation are the fish traders at Sapuran Market, Wonosobo and the subjects of the application are grade I students of elementary schools. The subjects of the observation are established by considering that they are the most knowledgeable people about the information we have expected, so that we get accurate data, regardless of their social status and level of education. The interviews were conducted by using the semi-structured interview technique in order to get comprehensive information.
The source of information is some people chosen based on some consideration. The consideration for the data source people is that they are the most knowledgeable people about the information we have expected. In this study, the data source is the organizer of the association of fish sellers at Sapuran Fish Market, whose name is Wintoro. The data analysis was done by reducing the data that had been collected and developed them to be a certain pattern so as to become a hypothesis. The data collected are generally qualitative in nature, although quantitative data were also accepted. The data analysis was conducted in order to construct the results of the observation and interviews systematically, so that we could draw a conclusion which is easy to understand by writers and readers.

FINDINGS AND DISCUSSION
The method used in counting fish in Wonosobo is the Wilangan Method. The word wilangan is derived from Javanese which means number. Based on the result of the observation and interview with the head of the association of fish sellers at Sapuran Market, whose name is Wintoro, the Wilangan Method began to be used by his father, whose name is Cipto Pawiro, more commonly called Mbah Cip by the surrounding community. Mbah Cip worked as a fish seller. This method was taught to Mbah Cip by a friend of his who came from Muntilan, Central Java. The method has been used by two generations with the same preserved pattern. In the process of selling and buying things, parents often ask their children to accompany them to the market. This is very good for the parents to introduce their children the mathematical concepts at their early age. The children are enthusiastic to see the process of selling and buying fish. Unfortunately, however, many parents are not aware that this can become a mathematics learning activity for their children.

Figure 1. Fish Selling and Buying Activity
First, the seller divides the fish counting in order to make it easy to count the fish, for example in counting 50 fish, he will divide figure 50 into multiple 5, thus resulting in 50 : 5 = 10 counts, with each counting containing 5 then the calculation needs to be done only until it comes to 10 counts. This counting stage is in line with the theory revealed by Paige (1978: 147), which says division is the opposite of multiplication. If a × b = c then c : a = b and c : b = a. Division can be defined as repeated reduction. If a : b = c, then c is obtained from a -b -b -b -b -b ... = 0 and b is as many as c. This is supported by the research by Izzatul Munawwaroh (2016)  The second stage is starting the counting with addition operation using the technique of saving. In the Wilangan Method, saving is executed for each sum of 5. This is in line with the theory by Darhim (1997:466) which says that the addition operation in integer is often called addition only. The notation or symbol used is (+) for addition operation and (-) for reduction. Symbol (+) is commonly for addition operation or plus. While symbol (-) is reduction operation or minus. Therefore, the operation of (2 -5) is adding a positive number of two to a negative number of five, and can be written in the form of k 2 + (-5) which means 2 -5. The addition operation of integer is close in nature. The close characteristics of addition operation is the operation of an integer and another integer which results in another integer.

Figure 2. Couting Process Using Wilangan Method
In the counting process, the seller uses an aid in the form of a plastic plate for taking fish. As an example, a buyer wants to buy 20 fish and then the seller will take randomly 3, 4, 2, 5, 3, 5 and the process of counting can be illustrated as follows:  3 + 4 = 7, saving is done for each sum of 5, and thus in the seller's view point, 7 has a value of 1 and 2 remnants.  1 and 2 + 2 remnants = 1 and 4 remnants  1 and 4 + 5 remnants = 1 and 9 remnants, is done the saving again and added to the first saving to become 2 and 4 remnants  2 and 4 + 3 remnants = 2 and 7 remnants, is done the saving again and added to the second saving to become 3 and 2 remnants  3 and 2 + 5 remnants = 3 and 7 remnants is done the saving again and added to the third saving to become 4 and 2 remnants Because the buyer wants 20 fish and the counting of the seller has arrived to value 4, then the counting is over and 2 fish is taken because it is the remnants in the counting. This technique of counting can be modified into an innovation in the teaching of addition operation with a varied sum of numbers, like in the following illustration.

and 4 remnants
Verbalizing the counting is different from the conventional one because the saving is done when the number reaches the value of 5. With the small number of the counting and with the traders being used to addition operation, they become really good at the addition operation, not just memorizing the result of each pair of numbers. Mastering the addition operation process, the students will find it easy to solve problems related to addition operation. They will know the steps to solve the addition operation problems. Addition is the basic materials that have to be mastered in order to learn mathematics. By thorough mastery of the addition concept, the students will find it easy to understand the next materials taught to them.

CONCLUSION
Based on the result of the discussion, it can be concluded that in the tradition of counting fish at Sapuran Market there is a mathematical model in the form of the concept of addition using the technique of saving. This concept can be applied in teaching year 1 students of elementary school under the theme Myself, with the aim of improving the students' understanding of addition operation using the technique of saving. Saving can be modified when the amount is 10. The amount of saving is in tens and the rest is one-digit number. By applying ethnomathematics, the tradition of buying and selling fish at Sapuran Market, Wonosobo can improve the students' understanding of addition operation; the students not only memorize the result of each pair of numbers but also understand how to solve the addition operation of each pair. To add using the technique of saving, the students can make the first number even and become 10. The even number is taken from the second number, and then the rest of the even number 10 is in the form of one-digit number. The moral teaching is the character of accuracy, carefulness, honesty, law-obedience, care, discipline, mutual help, respecting others, responsibility, and persistence. Mathematics will remain difficult, but how can we make the difficult thing become enjoyable to learn? Therefore, by making the process interesting to learn, it is expected that the students' fear in learning mathematics will be eliminated.


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Behavioral Bias in Individual Investment Decisions: Is It a Common Phenomenon in Stock Markets?

This paper aims to investigate the behavioral factors that influence individual investment decision making at a developing country stock market; the Sudanese Stock Exchange Market. The Study employs a cross-sectional survey design as well as analytical methods to collect the necessary data and establish the relationship between the study variables. Data is collected through a structured questionnaire from a sample of 203 individual investors and Correlation and Regression methods are used to conduct the analysis. The findings of the paper provide evidence that behavioral biases play a noticeable role in individual investment decision making process regardless of the degree of development of the stock market. The paper demonstrates that heuristic and market factors play a dominant role in the process of individual decision making in the Khartoum Stock Exchange. The factors that have a significant impact on individual investment decision making process include Representativeness, Overconfidence, Anchoring, Historical cost of stock, Customer preferences, Loss aversion, Mental accounting, Other investors‟ trading volume, and Quick reaction to changes in other investors „decisions. Factors that have an insignificant impact include Availability bias, Change in stock prices, Regret aversion, and Other investors‟ decisions and choices.

Introduction
Since the mid-1950s, the field of finance has been dominated by the traditional Finance model. Traditional finance, also referred to as standard finance is based on theories and principles founded on the premise that investors behave rationally and, thus, have focused on models that assume rationality. According to these theories, specifically, the arbitrage principles of Modigliani and Miller; the portfolio principles of Markowitz; the capital asset pricing theory of Sharpe, Lintner, and Black; and the efficient market hypothesis of Fama, markets are efficient and investors behave rationally when making their investment decisions. The essence of traditional finance is then the availability of information which is incorporated in prices of financial assets and risk-averse investors who base their financial decisions on risk-return tradeoff. However, the repetitive financial crises and the various stock market anomalies, which remained unanswered render the market efficiency hypothesis and rationality of investors questionable. In 1979 Daniel Kahneman and Amos Tversky published their most important work titled "Prospect Theory: An analysis of decision under risk", in which they criticize expected utility theory as a descriptive model of decision making under risk and develop an alternative model called Prospect Theory. Their theory explains the apparent irregularity in human behavior when assessing risk under uncertainty. They negate the notions of efficient market and investors who are rational and consistently risk-averse, paving the way to a new academic discipline; Behavioral Finance.
The pioneers of behavior finance are the psychologist Daniel Kahneman and economist Vernon Smith, who are awarded Nobel Prize in Economics in 2002. Kahneman studied human judgment and decision making under uncertainty, while Smith studied alternative market mechanisms through experimental research. Behavioral finance combines the disciplines of psychology and economics to explain why and how people make irrational, suboptimal financial decisions and elucidate ramifications of such decisions on the efficiency of capital markets, personal wealth, and the performance of corporations. It asserts that the investment decision-making process is influenced by various, unavoidable behavioral biases and that human brains process information using shortcuts and emotional filters even in investment decisions. Thus according to behavioral finance psychological dimensions influence the investment decision making process. As stated by Pompian (2006), instead of a universal theory of investment behavior, behavioral finance research relies on a broad collection of evidence pointing to the ineffectiveness of human decision making in various economic decision-making circumstances. Though the traditional finance field is reluctant to accept the view of psychologists, the later has succeeded in providing answers and explanations to some of the reasons that induce investors to deviate from rational financial decision-making and the sudden and untimely changes in the stock market pricing of securities. The studies reveal that some of the irrational behaviors in the market place include excessive trading, buying and selling stocks without considering their fundamental values, basing decisions on stocks past performance and what others buy and sell and retain loss-making stocks, while selling winning stocks. The behavioral biases researched incorporate Overconfidence, Disposition Effect, Representativeness, Anchoring, Availability, Herd Behavior, and Home Bias.
Most of those researches are carried out in developed American, European, and Asian markets. There is a dearth of research in this area in developing countries, specifically in Middle Eastern and North African markets. Further, the majority of empirical studies are based on secondary data that have been generally collected from discount brokerage house firms or from different databases and are longitudinal in nature. The studies that are based on primary data or that are qualitative in nature are few in number. According to Kumar and Goyal 2015, 75 out of 94 studies are conducted in developed countries and only 21 out of the total of 117 studies are based on primary data. In addition, the majority of past research investigates the impact of either a single bias or a limited number of biases on individuals" behavior. Thus since behavioral biases stem from the fact that a considerably huge number of financial decisions are a result of the interaction of social and psychological factors, studies conducted in Western context cannot be generalized to developing countries. This paper attempts to fill the gap by investigating the impact of a set of behavioral factors on individual investment decision making in an African developing country; Sudan, using primary data-based empirical research. The rest of the paper is organized as follows: Section two provides a review of the empirical studies concerning the impact of behavioral biases on investment decision making in securities markets. Section three describes the methodology followed and the methods of analysis employed. Section four presents the empirical results and their discussion. Section five concludes the paper.

Literature Review
The concern about establishing an organized stock exchange in Sudan dates back to the early nineteen sixties. The Stock Exchange Act of 1982 establishes the stock exchange as a department in the Bank of Sudan, which is expected to function under the control of the Ministry of Finance and Economic Planning. However, many years elapsed before the sincere efforts for establishing an organized stock exchange in Sudan turned to be a reality. The Khartoum Stock Exchange (KSE) Act 1994, abolishing that of 1982, was passed in 1994, and finally, after a search for a stock exchange that goes back over thirty years, Sudan has its first ever stock exchange, which has been in operation for more than 26 years by the end of 2019.
According to behavioral finance scholars, individuals have unavoidable psychological biases that preclude them from making rational investment decisions and negatively affect the market efficiency. Several studies have demonstrated that, in reality, markets are inefficient which is evidenced by the persistence of anomalies. Bounded rationality, cognitive and emotional biases, fundamental heuristics, intuitive reasoning and limited information and reliance on historical performance, past experiences and expectations are cited as causes for irrational behavior and inefficiency (Baker and Nofsinger 2002;Shefrin 2006;Ajmal et al 2011;Bondt et al 2013;Sahietal 2013;Bakar et al 2016).
Research on the impact of behavioral biases on individuals" decisions regarding security trading has generally concentrated on six common biases that affect the investment decision-making process, namely, overconfidence, disposition effect, herding bias, representativeness, anchoring, and availability bias. Additionally, most of the studies examine the impact of a limited number of biases on the individual decision making process. For instance research on overconfidence reveals that this bias negatively affects investment decisions (Gervais et al 2001;Statman et al 2006;Chen 2007;Grinblatt and Keloharju 2009;Shah et al 2012;Bashir et al 2013;Oprean (2014); Kafayat 2014); Bakar and Yi (2015) and Geetika and Singh (2019).  Dhar and Zhu (2006), Frazzini (2006), Chen (2008), Barberis andXiong 2009, Toma (2015), and Geetika and Singh (2019). The evidence arrived at is mixed, some studies report no effect on trading decisions, while others document insignificant impact. Similarly studies on the impact of herding behavior (Demirer and Kutan (2006); Andrikopoulos et al (2014); Balcilar and Demirer (2015); Huang et al. (2015); Galariotis et al (2016); Metawa et al (2018) and Geetika and Singh, (2019), show no evidence and partial evidence of the influence of herding bias on the process of decision making by individual investors. Anchoring bias as proclaimed by Shah et al (2012) has a negative impact on the decision making process, whereas Geetika and Singh, (2019) suggest no effect of this bias. The evidence reported on availability bias varies; Shah et al (2012) document a negative effect, whereas Bakar and Yi (2015) find a significant impact. Studies that have incorporated an array of behavioral factors are few in number (Chandra and Kumar 2012;Kengatharan and Kengatharan 2014;Bakar and Yi, 2016). According to Chandra and Kumar behavioral biases do influence individual investment decision making process. Their findings affirm that heuristic factors such as anchoring, availability bias, and regret aversion are dominant compared to factors suggested under prospect theory such as loss aversion. Kengatharan and Kengatharan results show that there are four behavioral factors affecting the investment decisions of individual investors; herding, heuristics, prospect, and market and that all factors have a moderate impact, whereas anchoring has a high influence and choice of stock has a low influence on investment decision. The findings of Bakar and Yi show that overconfidence, conservatism, and availability bias have a significant impact on the investors" decision making process, while herding behavior has no significant impact.
The most popular and widely used statistical technique in these studies is the regression analysis. Various types of regressions such ordinary least squares, linear regression, logistic regression, and polynomial regression are utilized. Some studies employ a combination of techniques such as analysis of variance, chi-square, t-statistics and structural equation modelling.
Based on the literature review and the characteristics of KSE, the following hypotheses have been set for variables selected as proxies for each of the four groups of the behavioral factors.
Herding Factors: H1: There is no impact of other investors" choice on the process of individual investment decision making.
H2: There is a significant impact of other investors" trading volume on the process of individual investment decision making.
H3: There is no impact of other investors" buying and selling decisions on the process of individual investment decision making.
H4: There is significant impact of quick reaction to changes of other investors "decisions on the process of individual investment decision making.

Heuristic Factors:
H5: There is an insignificant impact of availability bias on the process of individual investment decision making.
H6: There is a significant impact of representativeness on the process of individual investment decision making.
H7: There is a significant impact of overconfidence on the process of individual investment decision making.
H8: There is a significant impact of anchoring on the process of individual investment decision making.

Prospect Factors:
H9: There is a significant impact of loss aversion on the process of individual investment decision making.
H10: There is no impact of regret aversion on the process of individual investment decision making.
H11: There is an impact of mental accounting on the process of individual investment decision making Market factors: H12: There is no impact of changes in stock prices on the process of individual investment decision making.
H13: There is a significant impact of customer preferences on the process of individual investment decision making.
H14: There is a significant impact of historical stock prices on the process of individual investment decision making.

Material and Methods
The paper employs a cross-sectional survey research design and depends mainly on primary data, which is collected through a structured questionnaire. The targeted population is the individual investors trading in Khartoum Stock Exchange. A sample of 204 active investors has been chosen.
The questionnaire consists of five sections. In the first section the respondents are requested to provide personal background information. The second section contains statements that are used as measures of heuristic biases, which include representativeness, overconfidence, anchoring, locality bias, and availability. In the third section statements relating to stock choice, buying and selling of stocks and overreaction to stock price changes, which are used as proxies of herding bias. The third section contains the statements that refer to prospect biases, namely, Loss aversion, regret aversion, and mental accounting. Items that relate to market factors such as historical cost of stocks, changes in stock prices, and customer preferences are the subject of the last section of the questionnaire. All items in the questionnaire are responded to using a five-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). First, the validity and reliability of the instrument are checked through conducting a pilot test and testing the correlation by using the Cronbanch Alpha test. The impact levels of behavioral finance factors on the individual investment decisions making are identified by calculating the values of sample mean of each variable. Because 5-point lickertscales are used to measure the impact levels of these variables, the impact of the mean values of these variables are decided based on the following rules:1-Mean values less than 3 show that the variables have low impact, 2-Mean values from 3 to 4 show that the variables have a moderate impact, 3-Mean values from 4 to 5 indicate that the variables have high impact and 4-Mean values that are more than 5 demonstrate that the variables have very high impact. Further the paper uses factor analysis to simplify the data and reduce the number of variables in the regression model. The factor analysis is carried out following the assumptions recommended by Hair et al. (2010); there must be sufficient number of statistically significant correlations in the matrix., Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy should be at least 0.6, Bartlett"s test of sphericity should be significant at 0.05, communalities of items should be greater than 0.50., the minimum requirement of factor loading 0.50, based on 0.05 significant level, with value of cross loading 0.50 and above, and eigen values should be more than 1.
The data collected is processed and analyzed using SPSS software. To test the relationship between behavioral biases and individual investor decision making, the multiple regression analysis is adopted. Initially the data has been examined for normality, linearity, multicollinearity, and homoscedasticity. Correlation analysis is employed to establish the degree of correlation between the variables of the study.

Results and Discussion
A total of 203 out of 204 questionnaires are received from the respondents, with an overall response rate of 99.5%. The results of Cronbach"s Alpha value as shown in Table 1 indicate that all factors have values greater than 0.6, which implies that the measures have an acceptable level of reliability. The sample for the research is composed of 65% male and 35 % female investors. This composition of sample symbolizes the cultural and social norms of Sudan, where there is a deep rooted notion that the workplace is still a male playground and women participation in the work force is still lagging behind. In terms of age groups 83% of the respondents are within the range of 20 to 35 years. Only 17% are above 40 years. This may reflect a better awareness of youth population about the benefits of investing in stock exchanges and their increased savings compared to aging population. In terms of qualification, the majority of the sample; 85%, hold either a bachelor"s degree, 45% or a master degree 40%. As regarding years of trading experience, 46% of the sample has less than 5 years of experience, 34 percent 5 to 10 years of experience and 20% more than 10 years of experience. The descriptive statistics of qualification and experience appear to be in line with the age characteristics of the respondents, which reflect the dominance of youth population.
Using factor analysis the twenty one items suggested to measure the construct are reduced to eighteen items. All the remaining items have factor loading values above the minimum values of 0.50, with value of cross loading less than 0.50. The first factor of the construct captures all the items of herding factors (Other investors" choice, Other investors" trading volume, Other investors" buying and selling decisions, and Quick reaction to changes of other investors "decisions). The second factor captures six items of heuristic factors (Representativeness, Overconfidence, Anchoring, and Availability bias). The third factor captures three items of market factors (changes in stock prices, customers" preference and historical cost of stock). The fourth factor of the construct captures all the items of prospect factors (Loss aversion, Regret aversion, Mental accounting).  Investors prefer local markets to international markets because they believe them to be safer. You believe that your skills and knowledge of stock market can help you to outperform the market. Investors prefer to invest in local companies because of the easy access to information. You forecast the changes in stock prices in the future based on the recent stock prices. Investors are carefully looking at changes in stock prices they want to invest in.
You analyze the companies" customer preference before you invest in their stocks. Investors depend on the original purchase cost when making a sale decision or when analyzing the trends of listed securities. Investors prefer active securities. Investors are familiar with the fundamentals of dealing with the market when making investment decisions on securities. Investors feels regret when holding long term losing securities. Investors deal with investment decisions separately and ignore the interdependence of those decisions. Investors deal with investment decisions separately and ignore the interdependence of those decisions. You avoid selling shares that have decreased in value and readily sell shares that have increased in value. Table 4 presents the descriptive statistics of the selected behavioral factors. The statistics indicate that the impact of the selected behavioral variables on the individual investment decision making ranges between a moderate and high impact; the mean takes values between 3.8 and 4.3. The Correlation analysis has been conducted after testing the data for normality, linearity, multicollinearity, and homoscedasticity. The normality has been established through the relevant histograms, which shows that most values fall in the center and the curves take the bell-shape. The normal probability plots also show that the residual points are close to the diagonal line. Therefore, the variables are normally distributed. No curvilinear pattern of relationship is apparent from the scatter plots, hence there is no violation of the assumption of linearity. Heteroscedasticidity is checked through the scatter plots of standardized residual. The residual plots take roughly the rectangular shape, which indicates that there is no problem of heteroscedasticidity in the data. The result of Multicollinearity test indicates that all values of the Variance Inflation Factors (VIF) are less than the threshold of 10, all Tolerance values are more than 0.1. This indicates that, there is no multicollinearity in the data.

Percentage of Variance Explained Percentage of Variance Explained Total Variance Explained (%) Kaiser-Meyer-Olkin (KMO) Bartlett's Test of Sphericity
As shown in Table 5 below the correlation analysis reveals strong association between all the variables of the study. The four behavioral factors are positively and significantly correlated with each other and with the dependent variable. Herding factors are positively and significantly correlated with heuristic factors (r =.418, p < 0.01), market factors (r =.428, p < 0.01), prospect factors (r =.313, p < 0.01), and investment decision (r =.443, p < 0.01). Heuristic factors are positively and significantly correlated with market factors (r =.494, p < 0.01), prospect factors (r =.889, p < 0.01) and investment decision (r =.441, p < 0.01). As regarding the market factors the results show positive and significant association between them and the prospect factors (r =.457, p < 0.01) and investment decision (r =.635, p < 0.01), while prospect factors is positively and significantly correlated with investment decision (r =.333, p < 0.01). The hypotheses are tested using the multiple regression analysis after data has been tested for normality, linearity, multicollinearity and homoscedasticity. The impact of the selected behavioral variables of herding, heuristic, market and prospect factors are demonstrated in Table 6. According    Table 7 shows the R-Squared of the herding factor (0.17), implies that 17% percent of the total variance in individual investment decision making is explained by the herding biases. The F-statistics, which is equal to10.622, and p, which is equal to 0.0000, indicate that the model is significant. The value of the coefficient of determination of dimensions of heuristics, which is R-Squared is equal to 0.302. This result implies that 30.2% of the total variance in individual investment decision making can be explained by the dimensions of heuristics. The F-statistics and p values are 14.830 and 0.0000 respectively, which shows the significance of the model. The R squared of the prospect factors is 0.196, implying that 19.6% of the total variance in individual investment decision making can be explained by the prospect factors. The F-statistics, which is equal to10.622 and p, which is equal to 0.0000 indicate that the model is significant.
The R-Squared for market factors is 0.383, suggesting that 38.3% of the total variance in individual investment decision making can be explained by the market factors. The F-statistics and p values (21.100 and 0.000 respectively) confirm the model"s significance.
Thus the hypothesized impacts of behavioral biases on individual investment decision making, some of which are not in line with past studies results, are supported. The significant impact of overconfidence, representativeness, and quick reaction to changes of other investors "decisions on the process of individual investment decision making reflect the informational and operational inefficiencies of KSE, which has been proven by a number of studies (Onour 2004;Onour 2010;Abbas 2010). Lack of adequate and timely information about quoted companies has led investors to depend on their own abilities in forecasting and predicting the trend of market prices and to base their decisions on their recent experience. Further, investors tend to deal with investment decisions separately and ignore the interdependence of those decisions due to insufficient or absolute information. The significant impact of anchoring and historical cost of stock on the process of individual investment decision making suggests that due to dearth of current, reliable data information that is visibly irrelevant still anchors judgments and affects guesses and predictions. The insignificant impact of herding factors is in line with past studies, which justify this finding on the basis that there are limited studies on herding bias among individual investors (Lee et al., 2004;Fernandez et al., 2011;Choi, 2013). However, the limited influence of herding bias within traders in KSE may reflect the narrow trading activity at the exchange, since individual investors tend to demonstrate herd behavior because they follow the decisions of a large group of traders. The influence of loss aversion and mental accounting on the process of individual decision making in KSE is consistent with the reasoning of the prospect theory. Investors tend to separate money into different categories and assign different risk to each category, which affects their investment decisions. As well investors have a tendency to sell well performing stocks too soon and hold on poorly performing stocks for too long.

Conclusions
The paper investigates the behavioral factors that influence individual investment decision making at the Sudanese Stock Exchange Market. The Study adopts a cross-sectional survey design and employs a number of analytical methods to validate the collected data and establish the relationship between the study variables. Data is collected through a structured questionnaire from a sample of 204 individual investors and Correlation and Regression methods are used to conduct the analysis. The paper evidence that the heuristic and market factors play a dominant role in the process of individual decision making (68.5% of the variability in individual investment decision making can be explained by these factors), whereas the prospect and herding factors are responsible for the remaining inconsistency. The variables that have a significant impact on individual investment decision making in KSE include Representativeness, Overconfidence, Anchoring, Historical cost of stock, Customer preferences, Loss aversion, Mental accounting, Other investors" trading volume and Quick reaction to changes of other investors "decisions. On the other hand Availability bias, Change in stock prices, Regret aversion, Other investors" buying and selling decisions, and Other investors" choice have an insignificant influence on the decision making process.
The findings of the paper provide evidence that behavioral biases play a noticeable role in individual investment decision making process regardless of the degree of development of the stock market. Whether in developed, emerging, or developing countries studies reveal that investors are susceptible to psychological biases when making a buy, sell, or hold decisions. Individual stock preference seems to be most affected by the amount of information available. Thus the impact of behavioral biases is expected to be more apparent in underdeveloped markets, which suffer a lack of reliable, timely information. Reliance on familiarity with a stock and past experience represents a viable way of acquiring information is such markets. Another visible difference between developed and underdeveloped markets relates to the cultural dissimilarities; for instance in developing countries, individual investors generally belong to the same geographical area and thus are more inclined to adopt herding behavior than are diversified investors in more developed markets. Further, in developed markets individual investors tend to invest in professionally managed funds, which are more prone to base decisions on pertinent information and as thus less influenced by herd behavior and the disposition effect. Though the influence of loss aversion and mental accounting on the process of individual decision making is consistent with the reasoning of the prospect theory in different markets, in less developed markets their impact is more observable. In developed markets, investors are more inclined to consider wealth in its entirety and take risks accordingly.
The outcomes of this paper would help market participants, including investors, regulators, and policymakers comprehend and consider the influence of common behavioral patterns that affect the decision making of investors in the Khartoum Stock Exchange. This will enhance the rationality of investment decisions and improve market efficiency. The research has important implications for regulators, management of the exchange, and investors. To mitigate the influence of behavioral biases information that is of better quality, timeliness, and easily accessible to investors should be of high concern. Further, there should be an enforceable requirement for total disclosure of quantitative as well as qualitative information about quoted companies. A conducive investment environment requires knowledgeable investors, which entails enhancing business education through integrating investor psychology within topics of finance. This is necessary because investors may be unfamiliar with the behavioral biases in their personalities and that such biases negatively affect their investment decisions. This recommendation would be feasible, recalling that the majority of the investors at KSE hold either a bachelor"s degree; 45%, or a master's degree; 40%. Further, individual investors should be encouraged to employ the services of professionals and brokers to manage their investment; this will help to reduce the impact of personal biases. The management of the exchange should strive to improve the quality of financial services provided by brokerage firms through workshops and seminars that incorporate the behavioral biases of investors and how could professionals de-bias themselves and make rational investment decisions for their clients.