Journal of Finance and Data Science最新文献

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.

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.

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.

In order to explore the relationship between politics-related sentiment and FTSE 100 movements, we conducted a short-window event study of a UK based political event. We collected a sample of over 60,000 tweets using 3 key hashtags during the period of 6 days including before, during and after the 2016 local elections. The study involved performing a collection of correlation and regression analyses to compare daily mood with daily changes in the price of the FTSE 100 at the market level. The findings suggest that there is evidence of correlation between the general mood of the public and investment behavior in the short term; however, the relationship is not yet determined as statistically significant. There is also evidence of causation between public sentiment and the stock market movements, in terms of the relationship between MOOD and the daily closing price, and the time lag findings of MOOD and PRICE. Overall, these results show promise for using sentiment analytics on Twitter data for forecasting market movements.

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.

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.

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.

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.

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.

This study aims to examine the benefits of combining realized volatility, higher power variation volatility and nearest neighbour 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 squares and mean square error. The empirical results indicated that the combination of 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 is 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.