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Publisher: Ke Ai   (Total: 15 journals)   [Sort by number of followers]

Showing 1 - 15 of 15 Journals sorted alphabetically
Advances in Climate Change Research     Open Access   (Followers: 12, SJR: 0.321, h-index: 5)
Animal Nutrition     Open Access   (Followers: 17)
Bioactive Materials     Open Access   (Followers: 1)
Chronic Diseases and Translational Medicine     Open Access  
Emerging Contaminants     Open Access  
Geodesy and Geodynamics     Open Access  
Green Energy & Environment     Open Access   (Followers: 2)
Infectious Disease Modelling     Open Access   (Followers: 1)
J. of Finance and Data Science     Open Access   (Followers: 2)
J. of Natural Gas Geoscience     Open Access  
Non-coding RNA Research     Open Access  
Petroleum     Open Access  
Plant Diversity     Open Access  
Synthetic and Systems Biotechnology     Open Access  
World J. of Otorhinolaryngology - Head and Neck Surgery     Open Access  
Journal Cover Journal of Finance and Data Science
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   ISSN (Print) 2405-9188
   Published by Ke Ai Homepage  [15 journals]
  • On the design of financial products along OBOR

    • Abstract: Publication date: June 2018
      Source:The Journal of Finance and Data Science, Volume 4, Issue 2
      Author(s): Weiping Li, Daxiang Jin
      We propose a design of fundamental indexes of equity and bond for the One Belt One Road (OBOR) to increase the market effect, instead of only using the OBOR construction investment funds to initiate the OBOR. Background and data are briefed, the methodology and the value-indifferent weighting are explained. We also illustrate an explicit computation of the fundamental index of the equity for the OBOR by using the available data from 12 countries.

      PubDate: 2018-05-07T13:16:35Z
  • Selecting appropriate methodological framework for time series data

    • Abstract: Publication date: June 2018
      Source:The Journal of Finance and Data Science, Volume 4, Issue 2
      Author(s): Min B. Shrestha, Guna R. Bhatta
      Economists face method selection problem while working with time series data. As time series data may possess specific properties such as trend and structural break, common methods used to analyze other types of data may not be appropriate for the analysis of time series data. This paper discusses the properties of time series data, compares common data analysis methods and presents a methodological framework for time series data analysis. The framework greatly helps in choosing appropriate test methods. To present an example, Nepal's money–price relationship is examined. Test results obtained following this methodological framework are found to be more robust and reliable.

      PubDate: 2018-05-07T13:16:35Z
  • Financial news predicts stock market volatility better than close price

    • Abstract: Publication date: June 2018
      Source:The Journal of Finance and Data Science, Volume 4, Issue 2
      Author(s): Adam Atkins, Mahesan Niranjan, Enrico Gerding
      The behaviour of time series data from financial markets is influenced by a rich mixture of quantitative information from the dynamics of the system, captured in its past behaviour, and qualitative information about the underlying fundamentals arriving via various forms of news feeds. Pattern recognition of financial data using an effective combination of these two types of information is of much interest nowadays, and is addressed in several academic disciplines as well as by practitioners. Recent literature has focused much effort on the use of news-derived information to predict the direction of movement of a stock, i.e. posed as a classification problem, or the precise value of a future asset price, i.e. posed as a regression problem. Here, we show that information extracted from news sources is better at predicting the direction of underlying asset volatility movement, or its second order statistics, rather than its direction of price movement. We show empirical results by constructing machine learning models of Latent Dirichlet Allocation to represent information from news feeds, and simple naïve Bayes classifiers to predict the direction of movements. Empirical results show that the average directional prediction accuracy for volatility, on arrival of new information, is 56%, while that of the asset close price is no better than random at 49%. We evaluate these results using a range of stocks and stock indices in the US market, using a reliable news source as input. We conclude that volatility movements are more predictable than asset price movements when using financial news as machine learning input, and hence could potentially be exploited in pricing derivatives contracts via quantifying volatility.

      PubDate: 2018-05-07T13:16:35Z
  • Stock Price Prediction Using Support Vector Regression on Daily and Up to
           the Minute Prices

    • Abstract: Publication date: Available online 27 April 2018
      Source:The Journal of Finance and Data Science
      Author(s): Bruno Miranda Henrique, Vinicius Amorim Sobreiro, Herbert Kimura
      The purpose of predictive stock price systems is to provide abnormal returns for financial market operators and serve as a basis for risk management tools. Although the Efficient Market Hypothesis (EMH) states that it is not possible to anticipate market movements consistently, the use of computationally intensive systems that employ machine learning algorithms is increasingly common in the development of stock trading mechanisms. Several studies, using daily stock prices, have presented predictive system applications trained on fixed periods without considering new model updates. In this context, this study uses a machine learning technique called Support Vector Regression (SVR) to predict stock prices for large and small capitalisations and in three different markets, employing prices with both daily and up-to-the-minute frequencies. Prediction errors are measured, and the model is compared to the random walk model proposed by the EMH. The results suggest that the SVR has predictive power, especially when using a strategy of updating the model periodically. There are also indicative results of increased predictions precision during lower volatility periods.

      PubDate: 2018-05-07T13:16:35Z
  • Regulatory learning: How to supervise machine learning models' An
           application to credit scoring

    • Abstract: Publication date: Available online 18 April 2018
      Source:The Journal of Finance and Data Science
      Author(s): Dominique Guégan, Bertrand Hassani
      The arrival of Big Data strategies is threatening the latest trends in financial regulation related to the simplification of models and the enhancement of the comparability of approaches chosen by financial institutions. Indeed, the intrinsic dynamic philosophy of Big Data strategies is almost incompatible with the current legal and regulatory framework as illustrated in this paper. Besides, as presented in our application to credit scoring, the model selection may also evolve dynamically forcing both practitioners and regulators to develop libraries of models, strategies allowing to switch from one to the other as well as supervising approaches allowing financial institutions to innovate in a risk mitigated environment. The purpose of this paper is therefore to analyse the issues related to the Big Data environment and in particular to machine learning models highlighting the issues present in the current framework confronting the data flows, the model selection process and the necessity to generate appropriate outcomes.

      PubDate: 2018-05-07T13:16:35Z
  • Improved parameter estimation of Time Dependent Kernel Density by using
           Artificial Neural Networks

    • Abstract: Publication date: Available online 17 April 2018
      Source:The Journal of Finance and Data Science
      Author(s): Xing Wang, Chris P. Tsokos, Abolfazl Saghafi
      Time Dependent Kernel Density Estimation (TDKDE) used in modelling time-varying phenomenon requires two input parameters known as bandwidth and discount to perform. A Maximum Likelihood Estimation (MLE) procedure is commonly used to estimate these parameters in a set of data but this method has a weakness; it may not produce stable kernel estimates. In this article, a novel estimation procedure is developed using Artificial Neural Networks which eliminates this inherent issue. Moreover, evaluating the performance of the kernel estimation in terms of the uniformity of Probability Integral Transform (PIT) shows a significant improvement using the proposed method. A real-life application of TDKDE parameter estimation on NASDQ stock returns validates the flawless performance of the new technique.

      PubDate: 2018-05-07T13:16:35Z
  • Estimation of market immediacy by Coefficient of Elasticity of Trading
           three approach

    • Abstract: Publication date: Available online 6 March 2018
      Source:The Journal of Finance and Data Science
      Author(s): Richard Wamalwa Wanzala
      This paper promulgates an innovative measure of market immediacy; that is, Coefficient of Elasticity Trading Three (CET3). The data from Nairobi Securities Exchange has been used to estimate market immediacy (proxied by three versions of CET; that is, CET1, CET2 and CET3). On the other hand, macroeconomic data on economic growth, general government final consumption expenditure, foreign direct investment (FDI) and inflation for the same period were obtained from Kenya National Bureau of Statistics. An Ordinary Least Square (OLS) regression with economic growth as a regressand and market immediacy and macroeconomic array of conditional information set as regressors have been used to determine which version of CET is more robust than the rest. The diagnostic tests consisted among others Granger causality, Augmented Dicker Fuller test (ADF) and Autoregressive Distributed Lag (ARDL) model analysis. The OLS regression p-values, Adjusted R 2 and standard errors demonstrate that CET3 is a better measure of market immediacy than CET1 and CET2.

      PubDate: 2018-05-07T13:16:35Z
  • Market resiliency conundrum: is it a predicator of economic growth'

    • Abstract: Publication date: March 2018
      Source:The Journal of Finance and Data Science, Volume 4, Issue 1
      Author(s): Richard Wamalwa Wanzala, Willy Muturi, Tobias Olweny
      Resiliency provides fundamental insights on the speed at which the marginal price impact increases as transaction volume increases in the stock market yet very few empirical research has been dedicated to its study. Consequently, this study was directed towards determining whether market resiliency is a predicator of economic growth. Secondly, the study also sought to examine whether real interest rate and risk premium moderate the relationship between stock market resiliency and the economic growth in Kenya. To solve the conundrum on the relationship between market resiliency and economic resiliency growth, a sagacious moderating regression analysis (MRA) was used. The liquidity and variance ratios were used as measures of resiliency while real interest rate and risk premium were taken as moderating variables. The CUSUM plots were used to determine the stability of the model. The results of this study shows that market resiliency is a predicator of economic growth and both real interest rates and risk premium moderates the relationship between stock market resilience and the economic growth in Kenya.

      PubDate: 2018-05-07T13:16:35Z
  • Stock repurchase and Arab Spring empirical evidence from the MENA region

    • Abstract: Publication date: March 2018
      Source:The Journal of Finance and Data Science, Volume 4, Issue 1
      Author(s): Foued Hamouda
      This paper examines how repurchase programs are used in the MENA region in the context of the political instability associated with the Arab Spring. We extend the knowledge regarding the relationship between stock repurchases and firm performance. We find that repurchase programs are used differently across countries. In fact, repurchases are negatively related to prior stock price performance. However, the market reacts more favorably to repurchases made by low market capitalization firms and by firms with high book-to-market ratio.

      PubDate: 2018-05-07T13:16:35Z
  • The effects of mergers and acquisitions on stock price behavior in banking
           sector of Pakistan

    • Abstract: Publication date: March 2018
      Source:The Journal of Finance and Data Science, Volume 4, Issue 1
      Author(s): Zahoor Rahman, Arshad Ali, Khalil Jebran
      Mergers and Acquisitions are considered as one of the useful strategies for growth and expansion of businesses. These strategies have widely been adopted in developed economies while are quite often practiced in developing countries like Pakistan. This study aims to explore the effect of Mergers and Acquisitions on stock price behavior of banking sector in Pakistan by using event study analysis for the period of 2002–2012. Market Study Method was used to compute the abnormal and cumulative abnormal returns for analyzing pre and post events effect of the phenomenon on share prices. The results reveal mixed observations of the activity of mergers and acquisitions on stock price performance. Our findings indicate that most of the firms experienced negative while some firms have shown positive abnormal and cumulative abnormal returns following the activity. Overall, the results indicate that the market responded negatively towards the phenomenon of mergers and acquisition in Banking sector of Pakistan. The results would be useful in providing new insights to the investors and management in making their investment related decisions.

      PubDate: 2018-05-07T13:16:35Z
  • An equity fund recommendation system by combing transfer learning and the
           utility function of the prospect theory

    • Abstract: Publication date: Available online 14 February 2018
      Source:The Journal of Finance and Data Science
      Author(s): Li Zhang, Han Zhang, SuMin Hao
      Investors in financial markets are often at a loss when facing a huge range of products. For financial institutions also, how to recommend products to the right investors, especially those without previous investment records is problematic. In this paper, we develop and apply a personalized recommendation system for the equity funds market, based on the idea of transfer learning. First, using modern portfolio theory, a profile of equity funds and investors is created. Then, the profile of investors in the stock market is applied to the fund market by the idea of transfer learning. Finally, a utility-based recommendation algorithm based on prospect theory is proposed and the performance of the method is verified by testing it on actual transaction data. This study provides a reference for financial institutions to recommend products and services to the long tail customers.

      PubDate: 2018-05-07T13:16:35Z
  • Research on Impact Factors for Online Donation Behavior of Bank Customer

    • Abstract: Publication date: Available online 13 October 2017
      Source:The Journal of Finance and Data Science
      Author(s): Qing Li
      Donation in various service channels of financial institutions helps people in need and makes great impact on social charity. The purpose of this paper is to analyze impact factors of donation behavior by customers of financial businesses. Before, the questionnaires of this research were designed, interviews with customers were conducted first for the purpose of constructing effective questionnaires. Then, questionnaires were answered by 205 bank customers. Correlation analysis, chi-square and Bayesian Network were used to test consumers’ online donation. Correlation is used to described the degree of income and online donation. Meanwhile, chi-square tests and conditional probability are used to test whether there is a significant association between trust, online donation and donation fields. The findings are that the correlation between the average income and online donation as well as an underlying relationship between trust and online donation. Besides that, trust, brand image as well as average income of customer provide an explanation for their charity behavior and explores the role of key factors that influence on donation fields. There is a clear theoretical as well as practical understanding of online donation of bank customers and the effect of donation fields.

      PubDate: 2017-10-13T23:25:00Z
  • The extent of voluntary disclosure and its determinants in emerging
           markets: Evidence from Egypt

    • Abstract: Publication date: Available online 12 October 2017
      Source:The Journal of Finance and Data Science
      Author(s): Mostafa I. Elfeky
      The primary objective of this study is to test a theoretical framework relating eight major corporate governance determinants with the extent of voluntary disclosure provided by listed firms listed on Egyptian Stock Exchange (EGX). These corporate governance determinants are firm size, firm profitability, firm leverage, board size, independent directors, duality in position, block-holder ownership and Auditor Type. Using a weighted relative disclosure index for measuring voluntary disclosure, the results indicate that there is a positive significant correlation between firm size, firm profitability, firm leverage, independent directors in board, and auditor type, and the overall corporate governance voluntary disclosure extent. This result implies that these variables are the main voluntary disclosure drivers in Egypt. However, a negative significant correlation was found between Block-holder ownership and voluntary disclosure, while no significant correlation was found between board size, and Duality in position, and the overall corporate governance voluntary disclosure extent. The empirical proof from this study promotes the perception of the voluntary corporate disclosure environment in Egypt as one of the emerging markets in the Middle East.

      PubDate: 2017-10-13T23:25:00Z
  • Cooperation on Finance between China and Nepal: Belt and Road Initiatives
           and Investment Opportunities in Nepal

    • Abstract: Publication date: Available online 7 October 2017
      Source:The Journal of Finance and Data Science
      Author(s): Min B. Shrestha

      PubDate: 2017-10-08T14:19:50Z
  • Participation against Competition in Banking Markets based on Cooperative
           Game Theory

    • Abstract: Publication date: Available online 23 September 2017
      Source:The Journal of Finance and Data Science
      Author(s): Rahim Khanizad, Gholamali Montazer
      The issue of increasing profit and reducing operational costs is the most important subject in banking management. One of the ways to solve this problem, is the cooperation (coalition) of banks together in order to reduce costs and simultaneously increase the operating profit. To solve this problem, in the present research, a model is presented for the participation of banks using game theory with which the banks can cooperate to achieve higher profits while providing their services. The model obtained from game theory is used in four private banks. The results indicate that the profit of banks is higher with coalition than acting alone in the market and it would continue with the increasing demand and the presence of more banks. Pearson correlation coefficient indicates that the results of the model match the views of banking experts. This may strengthen the principle of "participation" against "competition" in the banking industry.

      PubDate: 2017-09-29T04:09:05Z
  • High-frequency Volatility Combine Forecast Evaluations: An Empirical study
           for DAX

    • Abstract: Publication date: Available online 23 September 2017
      Source:The Journal of Finance and Data Science
      Author(s): Wen Cheong Chin, Min Cherng Lee
      This study aims to examine the benefits of combining realized volatility, higher power variation volatility and nearest neighbor truncation volatility in the forecasts of financial stock market of DAX. A structural break heavy-tailed heterogeneous autoregressive model under the heterogeneous market hypothesis specification is employed to capture the stylized facts of high-frequency empirical data. Using selected averaging forecast methods, the forecast weights are assigned based on the simple average, simple median, least square and mean square error. The empirical results indicated that the combination forecasts in general shown superiority under four evaluation criteria regardless which proxy is set as the actual volatility. As a conclusion, we summarized that the forecast performance are influenced by three factors namely the types of volatility proxy, forecast methods (individual or averaging forecast) and lastly the type of actual forecast value used in the evaluation criteria.

      PubDate: 2017-09-29T04:09:05Z
  • Does volatility spillover among stock markets varies from normal to
           turbulent periods' Evidence from emerging markets of Asia

    • Abstract: Publication date: Available online 23 June 2017
      Source:The Journal of Finance and Data Science
      Author(s): Khalil Jebran, Shihua Chen, Irfan Ullah, Sultan Sikandar Mirza
      This study investigates the volatility spillover effect among Asian emerging markets in pre and post 2007 financial crisis period. The sample includes five emerging markets of Asia named; China, Pakistan, Hong Kong, Sri Lanka, and India. The asymmetric volatility spillover among the stock markets is examined using an extended EGARCH model. The results highlight certain interesting key findings. We find bidirectional volatility spillover between stock markets of India and Sri Lanka in both sub-periods. However the volatility spillover is bidirectional between stock markets of Hong Kong and India; Pakistan and India in pre-crisis period, while in stock markets of Sri Lanka and Pakistan in post-crisis period. The integration of emerging markets of Asia has important implications for investors and policy makers.

      PubDate: 2017-07-07T02:43:11Z
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