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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: 14, SJR: 0.485, CiteScore: 1)
Animal Nutrition     Open Access   (Followers: 18, SJR: 0.442, CiteScore: 1)
Bioactive Materials     Open Access   (Followers: 1)
Chronic Diseases and Translational Medicine     Open Access  
Emerging Contaminants     Open Access   (SJR: 1.233, CiteScore: 3)
Geodesy and Geodynamics     Open Access   (SJR: 0.469, CiteScore: 1)
Green Energy & Environment     Open Access   (Followers: 2)
Infectious Disease Modelling     Open Access   (Followers: 2)
J. of Finance and Data Science     Open Access   (Followers: 3)
J. of Natural Gas Geoscience     Open Access   (SJR: 0.783, CiteScore: 1)
Non-coding RNA Research     Open Access  
Petroleum     Open Access  
Plant Diversity     Open Access  
Synthetic and Systems Biotechnology     Open Access   (SJR: 0.841, CiteScore: 0)
World J. of Otorhinolaryngology - Head and Neck Surgery     Open Access  
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Journal of Finance and Data Science
Number of Followers: 3  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2405-9188
Published by Ke Ai Homepage  [15 journals]
  • Effect of daily dividend on arithmetic and logarithmic return

    • Abstract: Publication date: Available online 27 June 2018Source: The Journal of Finance and Data ScienceAuthor(s): Md. Noman SiddikeeAbstractI have extended the arithmetic and logarithmic equations of the daily return by including daily dividend. To do this, firstly, I have mathematically broadened the scope of the two mostly used formulas of daily return by including daily dividend. Next, I have developed a couple of daily dividend estimation models from both pre and post stockholders' perspective. While developing those models, I have functionally used the compounding factors of time value theory. Finally, I have empirically examined the statistical robustness of Model-1. The findings of the study revealed that inclusion of daily dividend significantly increased the daily and monthly arithmetic and logarithmic returns of the securities. However, after inclusion of daily dividend, the long run variances of the both arithmetic return series remains same whereas the long run variances of both logarithmic return series significantly turns down to around zero percent direct a sharp decline of the risk of logarithmic return. Moreover, after inclusion of daily dividend the Value at Risk (VaR) of the daily logarithmic return declines sharply validates Model 1 for computing the daily logarithmic return.
  • Financial news predicts stock market volatility better than close price

    • Abstract: Publication date: June 2018Source: The Journal of Finance and Data Science, Volume 4, Issue 2Author(s): Adam Atkins, Mahesan Niranjan, Enrico GerdingAbstractThe 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.
  • Selecting appropriate methodological framework for time series data

    • Abstract: Publication date: June 2018Source: The Journal of Finance and Data Science, Volume 4, Issue 2Author(s): Min B. Shrestha, Guna R. BhattaAbstractEconomists 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.
  • On the design of financial products along OBOR

    • Abstract: Publication date: June 2018Source: The Journal of Finance and Data Science, Volume 4, Issue 2Author(s): Weiping Li, Daxiang JinAbstractWe 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.
  • Index Option Returns and Systemic Equity Risk

    • Abstract: Publication date: Available online 26 May 2018Source: The Journal of Finance and Data ScienceAuthor(s): Weiping Li, Tim KrehbielAbstractIn an environment characterized by stochastic variances and correlations, we demonstrate through construction of the equilibrium index option value from constituent components, that the generalized PDE identifies the stochastic elements differentially affecting index option prices relative to prices of aggregated constituent stock options. A unified treatment of the generalized partial differential system for index and constituent stock options in Theorem 1 illustrates that nonlinear interactive terms emanating from stochastic correlation affect index option price and return essentially different from contributions to the aggregated risks of the constituent stock options. Our study contributes to the growing evidence of priced correlation risk in markets for index and constituent stock options.Theorem 1 illustrates the pricing differential, while Proposition 1 illustrates that the pricing differential produces a quantifiable metric of the measure of the nonlinear interactive terms. The quantifiable metric is constructed from the difference between the model free implied variance of the index and a weighted aggregate of the model free implied variances of the constituent stocks. Proposition 2 identifies that index variance risk premium includes additional significant contributions from the nonlinear interactive risks not present in the aggregated returns of the constituent stocks. The nonlinear interactive risks produce a wedge between the instantaneous expected excess index and aggregated stock option returns.
  • Stock Price Prediction Using Support Vector Regression on Daily and Up to
           the Minute Prices

    • Abstract: Publication date: Available online 27 April 2018Source: The Journal of Finance and Data ScienceAuthor(s): Bruno Miranda Henrique, Vinicius Amorim Sobreiro, Herbert KimuraAbstractThe 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.
  • Regulatory learning: How to supervise machine learning models' An
           application to credit scoring

    • Abstract: Publication date: Available online 18 April 2018Source: The Journal of Finance and Data ScienceAuthor(s): Dominique Guégan, Bertrand HassaniAbstractThe 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.
  • Improved parameter estimation of Time Dependent Kernel Density by using
           Artificial Neural Networks

    • Abstract: Publication date: Available online 17 April 2018Source: The Journal of Finance and Data ScienceAuthor(s): Xing Wang, Chris P. Tsokos, Abolfazl SaghafiAbstractTime 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.
  • Estimation of market immediacy by Coefficient of Elasticity of Trading
           three approach

    • Abstract: Publication date: Available online 6 March 2018Source: The Journal of Finance and Data ScienceAuthor(s): Richard Wamalwa WanzalaAbstractThis 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 R2 and standard errors demonstrate that CET3 is a better measure of market immediacy than CET1 and CET2.
  • Market resiliency conundrum: is it a predicator of economic growth'

    • Abstract: Publication date: March 2018Source: The Journal of Finance and Data Science, Volume 4, Issue 1Author(s): Richard Wamalwa Wanzala, Willy Muturi, Tobias OlwenyAbstractResiliency 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.
  • Participation against competition in banking markets based on cooperative
           game theory

    • Abstract: Publication date: March 2018Source: The Journal of Finance and Data Science, Volume 4, Issue 1Author(s): Rahim Khanizad, Gholamali MontazerAbstractThe 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.
  • The effects of mergers and acquisitions on stock price behavior in banking
           sector of Pakistan

    • Abstract: Publication date: March 2018Source: The Journal of Finance and Data Science, Volume 4, Issue 1Author(s): Zahoor Rahman, Arshad Ali, Khalil JebranAbstractMergers 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.
  • Stock repurchase and Arab Spring empirical evidence from the MENA region

    • Abstract: Publication date: March 2018Source: The Journal of Finance and Data Science, Volume 4, Issue 1Author(s): Foued HamoudaAbstractThis 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.
  • An equity fund recommendation system by combing transfer learning and the
           utility function of the prospect theory

    • Abstract: Publication date: Available online 14 February 2018Source: The Journal of Finance and Data ScienceAuthor(s): Li Zhang, Han Zhang, SuMin HaoAbstractInvestors 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.
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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