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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   (Followers: 1)
Synthetic and Systems Biotechnology     Open Access   (Followers: 1, SJR: 0.841, CiteScore: 0)
World J. of Otorhinolaryngology - Head and Neck Surgery     Open Access  
Journal Cover
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]
  • Testing Market Response to Auditor Change Filings: A Comparison of Machine
           Learning Classifiers

    • Abstract: Publication date: Available online 23 August 2018Source: The Journal of Finance and Data ScienceAuthor(s): Richard Holowczak, David Louton, Hakan Saraoglu The use of textual information contained in company filings with the Securities Exchange Commission (SEC), including annual reports on Form 10-K, quarterly reports on Form 10-Q, and current reports on Form 8-K, has gained the increased attention of finance and accounting researchers. In this paper we use a set of machine learning methods to predict the market response to changes in a firm’s auditor as reported in public filings. We vectorize the text of 8-K filings to test whether the resulting feature matrix can explain the sign of the market response to the filing. Specifically, using classification algorithms and a sample consisting of the Item 4.01 text of 8-K documents, which provides information on changes in auditors of companies that are registered with the SEC, we predict the sign of the cumulative abnormal return (CAR) around 8-K filing dates. We report the correct classification performance and time efficiency of the classification algorithms. Our results show some improvement over the naïve classification method.
       
  • Return Smoothing and its Implications for Performance Analysis of Hedge
           Funds

    • Abstract: Publication date: Available online 20 August 2018Source: The Journal of Finance and Data ScienceAuthor(s): Jing-zhi Huang, John Liechty, Marco Rossi Return smoothing and performance persistence are both sources of autocorrelation in hedge fund returns. The practice of pre-processing the data in order to remove smoothing before conducting performance analysis also affects the predictability of hedge fund returns. This paper develops a Bayesian framework for the performance evaluation of hedge funds that simultaneously accounts for smoothing, time-varying performance and factor loadings, and the short-lived nature of reported returns. Simulation evidence reveals that “unsmoothing” predictable, persistent hedge fund returns reduces the ability to detect performance persistence in the second step of the analysis. Empirically, smoothing generates severe biases in standard estimates of abnormal performance, factor loadings, and idiosyncratic volatility. In particular, for funds with high systematic risk, a standard deviation increase in smoothing implies an upward bias in α in excess of 2% annually and a downward bias in equity market beta of more than 20%. For funds with low systematic risk exposure, the smoothing bias is most apparent in estimates of idiosyncratic volatility.
       
  • 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 Siddikee I 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 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.
       
  • Selecting appropriate methodological framework for time series data
           analysis

    • Abstract: Publication date: June 2018Source: The Journal of Finance and Data Science, Volume 4, Issue 2Author(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.
       
  • 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 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.
       
  • 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 Krehbiel In 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 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.
       
  • 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 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.
       
  • 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 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.
       
  • 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 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 R2 and standard errors demonstrate that CET3 is a better measure of market immediacy than CET1 and CET2.
       
  • 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 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.
       
 
 
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