Accounting standards, disclosure requirements and foreign company listings on stock exchanges

Some have claimed that there would be more foreign companies listed on American stock exchanges if foreign companies could use International Financial Reporting Standards （IFRS） instead of American generally accepted accounting principles （GAAP） and could be exempted from some of the disclosure requirements of the Securities and Exchange Commission and of the Sarbanes-Oxley Act. In spite of these requirements, as of December 31, 2007, there are approximately 421 non-U.S, companies valued at $11.4 trillion listed on the New York Stock Exchange （NYSE）. Of these 421 companies, 41 companies are from China. This study examines the reasons for Chinese companies choosing to list on the New York Stock Exchange and their experiences with incremental disclosure and listing requirements on the Shanghai, Hong Kong, London, and New York stock exchanges. The lesson for foreign companies everywhere should be that foreign companies should search for those cross listings adding value and not be searching for countries and stock exchanges with weak disclosure and listing requirements.

Optimization of Portfolio of Stocks at ZSE through the Analysis of Historical Data

Decision-making of investors at the stock exchange can be based on the fundamental indicators of stocks, on the technical indicators, or can exist as a combination of these two methods. The paper gives emphasis to the domain of technical analysis. In the broader sense the technical analysis enables the dynamics of the expected future values of the shares estimation. This can be performed on the basis of the data on historical trends of the revenues, profits and other indicators from the balance sheet, but also on the basis of historical data on changes in the values of the shares. Companies generally belong to the different sectors that have different presumptions of development resulting from the global market trends, technology and other characteristic. Processing of historical data values of the outstanding shares of the Zagreb Stock Exchange （ZSE） is origination of this research. Investors are interested to know the estimation of future returns for the stocks as well as the size of the risk associated with the expected returns. Research task in this paper is finding the optimal portfolio at the ZSE based on the concept of dominant portfolio by Markowitz approach. The portfolio is created by solving non-linear programming problem using the common software tools. The results of obtained optimal portfolios contain relevant conclusions about the specifics of the shares as well as the characteristics of the industrial sectors but also provide a further knowledge about diverse sectors treatment at the stock exchange in a multi-year period.

S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

This study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange.To achieve the objectives,the study uses descriptive statistics;tests including variance ratio,Augmented Dickey-Fuller,Phillips-Perron,and Kwiatkowski Phillips Schmidt and Shin;and Autoregressive Integrated Moving Average(ARIMA).The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series,using the ARIMA model.The results reveal that the mean returns of both indices are positive but near zero.This is indicative of a regressive tendency in the longterm.The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values,with few deviations.Hence,the ARIMA model is capable of predicting medium-or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

Forecasting risk using auto regressive integrated moving average approach: an evidence from S&P BSE Sensex

The primary objective of the paper is to forecast the beta values of companies listed on Sensex,Bombay Stock Exchange(BSE).The BSE Sensex constitutes 30 top most companies listed which are popularly known as blue-chip companies.To reach out the predefined objectives of the research,Auto Regressive Integrated Moving Average method is used to forecast the future risk and returns for 10 years of historical data from April 2007 to March 2017.Validation accomplished by comparison of forecasted and actual beta values for the hold back period of 2 years.Root-Mean-Square-Error and Mean-Absolute-Error both are used for accuracy measurement.The results revealed that out of 30 listed companies in the BSE Sensex,10 companies’exhibits high beta values,12 companies are with moderate and 8 companies are with low beta values.Further,it is to note that Housing Development Finance Corporation(HDFC)exhibits more inconsistency in terms of beta values though the average beta value is lowest among the companies under the study.A mixed trend is found in forecasted beta values of the BSE Sensex.In this analysis,all the p-values are less than the F-stat values except the case of Tata Steel and Wipro.Therefore,the null hypotheses were rejected leaving Tata Steel and Wipro.The values of actual and forecasted values are showing the almost same results with low error percentage.Therefore,it is concluded from the study that the estimation ARIMA could be acceptable,and forecasted beta values are accurate.So far,there are many studies on ARIMA model to forecast the returns of the stocks based on their historical data.But,hardly there are very few studies which attempt to forecast the returns on the basis of their beta values.Certainly,the attempt so made is a novel approach which has linked risk directly with return.On the basis of the present study,authors try to through light on investment decisions by linking it with beta values of respective stocks.Further,the outcomes of the present study undoubtedly useful to academicians,researchers,and policy makers in their respective area of studies.

COVID‑19 and tourism sector stock price in Spain:medium‑term relationship through dynamic regression models

The global pandemic,coronavirus disease 2019(COVID-19),has significantly affected tourism,especially in Spain,as it was among the first countries to be affected by the pandemic and is among the world’s biggest tourist destinations.Stock market values are responding to the evolution of the pandemic,especially in the case of tourist companies.Therefore,being able to quantify this relationship allows us to predict the effect of the pandemic on shares in the tourism sector,thereby improving the response to the crisis by policymakers and investors.Accordingly,a dynamic regression model was developed to predict the behavior of shares in the Spanish tourism sector according to the evolution of the COVID-19 pandemic in the medium term.It has been confirmed that both the number of deaths and cases are good predictors of abnormal stock prices in the tourism sector.

AStudy ofthe Balanced Area of Index's Fluctuation in Shanghai Stock Exchange

Chinese stock market is a developing one. In the present stages, to control scientifically the expansion speed and avoid drastic fluctuations is an important problem. Through analysis of plenty of data of SSE(Shanghai Stock Exchange) Index and relevant economic quotas, we find that the problem of predicting SSE Index is a typical multi variable, nonlinear one. On the basis of the analysis, we apply the technology of fuzzy pattern recognition, to the optimum pattern division of SSE Index's time alignments from Jan. of 1993 to Dec. of 1997, and get a balanced pattern of the stock index fluctuation. At the same time, by using database technology, we find the optimum expansion speed of Shanghai stock, which can make SSE Index fluctuate steadily within the balanced area. We verified this model with the latest data and found it coincides with the reality perfectly. So it has the practical value and provides the policy makers with a scientific basis in controlling the expansion pace.

Stock Prediction Based on Technical Indicators Using Deep Learning Model

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.

Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss.The proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different sectors.The dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next quarter.Our model uses 3 main concepts for forecasting results.Thefirst one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning Factor.The value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters Algorithm.The second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback loop.The third concept is Recommendation System whichfilters and predict the rating based on the different factors.

Analysis of the Bovespa Futures and Spot Indexes With High Frequency Data

Data from the World Federation of Exchanges show that Brazil＇s Sao Paulo stock exchange is one of the largest worldwide in terms of market value. Thus, the objective of this study is to obtain univariate and bivariate forecasting models based on intraday data from the futures and spot markets of the BOVESPA index. The interest is to verify if there exist arbitrage opportunities in Brazilian financial market. To this end, three econometric forecasting models were built： ARFIMA, vector autoregressive （VAR）, and vector error correction （VEC）. Furthermore, it presents the results of a Granger causality test for the aforementioned series. This type of study shows that it is important to identify arbitrage opportunities in financial markets and, in particular, in the application of these models on data of this nature. In terms of the forecasts made with these models, VEC showed better results. The causality test shows that futures BOVESPA index Granger causes spot BOVESPA index. This result may indicate arbitrage opportunities in Brazil.

Analysis of Economic Performance in Mergers and Acquisition

Based on the methods of financial analysis, the direct earnings in mergers and acquisition M&A, profit or loss from stock price fluctuation, influence on the earning per stock(EPS) and revenue growth after M&A were analyzed in detail. And several quantitative models were established in relevant part accordingly. It can be useful to improve the present low efficiency in the M&A performance in Chinese capital market.

The New Portfolio Management Process in the Context of the Use of Artificial Neural Networks

The main objective of this article is to draw attention to the subject of portfolio management process, which is often not discussed in the professional literature. It has been shown that globalization affects the portfolio management process, which is presented in the literature in a similar manner. Thus, in this publication, the presentation of the process was made in terms of the classical one, and then the attempt was made to establish its form after the evolution that results from the above mentioned globalization. In addition, this new form is presented from the perspective of the use of artificial neural networks as organizations which invest cash primarily in financial instruments should take into account the mentioned expert tool for the purpose of further development. The publication also shows the key areas which the professional literature focuses on with regards to the subject of portfolio management. The study used the literature from the area of portfolio management, which is the basis for theoretical consideration, but these results have got the cognitive and practical value. They are a basis for separate quantitative research, and the proposed portfolio management process model can be considered cognitively interesting for researchers and investors.

Study on Chinese companies＇ perception on overseas public listing

This paper reports an investigation on Chinese companies＇ perception on overseas public listing. The investigation is mainly based on a questionnaire survey. The result shows that Chinese companies overwhelmingly accept overseas listing as a preferred option, which may shed some light on other Chinese companies in their evaluation on seeking overseas public listing as a strategic option to the overseas exchanges in their efforts of wooing the Chinese market.

Investor Relations （IR） in Business Communication on the Example of Poland

This paper focuses on investor relations （IR） as an element of companies＇ communication with the external environment. It shows the place and role of IR in communication with the general market, and the stock exchange market in particular. The study is illustrated with an example of IR in Poland, a relatively new European Union （EU） member state and an emerging market. As can be seen, IR goes beyond the traditionally understood maintenance of communication with the capital market. It is also shown that the main role of IR ought to be the reduction of investment risk and enhancement of the companies＇ value, which is of particular significance in light of the current globalization of capital markets. It has been found that Polish companies, especially those listed on the Warsaw Securities Exchange （WSE）, commonly rely on IR as a means of communication with the environment. And yet, as the results of the analysis show, the quality of that communication is still far from satisfactory, despite efforts undertaken by various external entities to encourage more focus on the development of IR, or understanding of the company＇s relevant staff of its important role.

Relation Between Relative Efficiencies and Brand Values of Global Turkish Banks Trading on Istanbul Stock Exchange

The global competition in banking sector, global capital flows, and proliferation of financial markets have been forcing banks to utilize their resources in an efficient way and use various methods to determine and increase their performances against the competitors. Within this context, the relative efficiency measurement and statistical （parametric） efficiency measurements that employ （non-parametric） mathematical programming based on Data Envelopment Analysis （DEA） method are such instruments and they are used to determine brand value and financial performance that are pivotal factors in company mergers, acquisitions, and joint venture activities. This study works with Turkish banks whose brand values have been calculated by Brand Finance and whose brand values have been listed in Global Banking 500 for the years between 2010 and 2012. Firstly, using the banks＇ data published by Public Disclosure Platform （PDP/KAP）, a non-parametric model with three inputs and four outputs has been developed. Relative and super efficiencies of the banks have been measured by mathematical programming based DEA and the efficiency scores that come out of this analysis have been ranked, resulting in an ＂efficiency ranking of the banks＂. Following this, the efficiency ranking of these banks has been compared with brand value ranking of Brand Finance and their similarity/correspondence has been assessed.

The Impact of Macroeconomic Fluctuations on Stock Exchange Markets： A Comparative Analysis on CEECs

Stock exchange market responses to macroeconomic fluctuations show deviations between countries in terms of direction, magnitude and duration due to the idiosyncratic characteristics of the countries. The paper empirically searches for the identification of these variations for CEECs, namely Czech Republic, Hungary, Poland, Slovak Republic and also Turkey for the period of December, 1999 to December, 2009. The empirical analyses demonstrate that for each CEEC, stock exchange market responds positively to industrial production and to appreciation of local currency. Czech Republic and Hungary display negative and the rest display positive response to M1, whereas the response of stock market to CB policy rate shows mixed results for each country. Besides, foreign exchange market returns are found to be the variable with the highest significance in explaining the stock exchange market returns. These findings point out to arbitrage opportunities for investors and give insight to Monetary Policy Authorities about the Monetary Transmission Mechanisms of the countries.

The Impact of Turnover Ratios on Jordanian Services Sectors＇ Performance

Profitability ratios are a group of financial ratios that indicate how much profit a business is earning within a certain context, while asset utilization ratios indicate how efficient a business is in operating its assets to generate cash. The difference between profitability ratios and turnover ratios is the fact that turnovers are more specific. While profitability ratios measure overall performance in terms of profits, asset utilization ratios focus on specific measurements within the business） We conduct this study to verify the impact of turnover ratios on Jordanian services sectors＇ performance during the period from 2009 to 2012. The study showed that there is no significant impact of turnover ratios on Jordanian services sectors＇ profitability, and by testing the main and sub hypotheses, the study revealed that there is no significant impact of turnover ratios on Jordanian services sectors＇ return on assets （ROA）, there is no significant impact of working capital turnover on Jordanian services sectors＇ ROA, there is no significant impact of total asset turnover on Jordanian services sectors＇ ROA, and there is no significant impact of fixed asset turnover on Jordanian services sectors＇ ROA. Also, the study showed that there is no significant impact of turnover ratios on Jordanian services sectors＇ return on equity （ROE）, there is no significant impact of working capital turnover on Jordanian services sectors＇ ROE, there is no significant impact of total asset turnover on Jordanian services sectors＇ ROE, and there is no significant impact of fixed asset turnover on Jordanian services sectors＇ ROE. Moreover, the study concluded that the educational services sector has the lowest working capital turnover and healthcare services sector has the highest. In addition, we find that the hotels and tourism sector has the lowest total asset turnover ratio, while the utilities and energy sector has the highest and that the hotels and tourism sector has the lowest fixed asset turnover, while the utilities and energy sector has the highest. The transportation sector has the lowest ROA and technology and communication sector has the highest. Finally, we find that transportation sector has the lowest ROE and the technology and communication sector has the highest.

Riemann Hypothesis, Catholic Information and Potential of Events with New Techniques for Financial and Other Applications

In this research we are going to define two new concepts: a) “The Potential of Events” (EP) and b) “The Catholic Information” (CI). The term CI derives from the ancient Greek language and declares all the Catholic (general) Logical Propositions () which will true for every element of a set A. We will study the Riemann Hypothesis in two stages: a) By using the EP we will prove that the distribution of events e (even) and o (odd) of Square Free Numbers (SFN) on the axis Ax(N) of naturals is Heads-Tails (H-T) type. b) By using the CI we will explain the way that the distribution of prime numbers can be correlated with the non-trivial zeros of the function ζ(s) of Riemann. The Introduction and the Chapter 2 are necessary for understanding the solution. In the Chapter 3 we will present a simple method of forecasting in many very useful applications (e.g. financial, technological, medical, social, etc) developing a generalization of this new, proven here, theory which we finally apply to the solution of RH. The following Introduction as well the Results with the Discussion at the end shed light about the possibility of the proof of all the above. The article consists of 9 chapters that are numbered by 1, 2, …, 9.

An Extreme Value Approach to Test the Effect of Price Limits on Volatility

Many stock exchanges around the world enforcing daily price limits on the amount asset prices can change to prevent the market from overreacting and to reduce volatility. Price limits are artificial boundaries set by market regulators who restrict price changes of a stock to a pre-specified range during a trading day or a single trading session. The primary aim of price limit rules is to stabilize the markets during panic trading, to moderate vitality by repressing excessive speculation, and to allow stocks to be traded at prices close to their fair value. However, their impact on the market is a somewhat unresolved issue （Harris, 1998）. Using a methodology of comparing volatility based on the extreme value technique, the authors empirically investigate the impact of price limits on the volatility of the Stock Exchange of Thailand. The empirical results support price limits advocates, suggesting that price limits rules moderate stock price volatility.

Comprehensive Income （CI） Statement＇s Compliance With International Accounting Standard （IAS） 1 （Revised 2007 and 2011）： Evidence From Italian Listed Corporate Groups

The move towards international harmonization of accounting standards has dominated the work program of International Accounting Standards Board （IASB） in the past years. This paper aims to verify the compliance of the comprehensive income （CI） statement format with International Accounting Standard （IAS） 1-presentation of financial statements, which was revised in 2007. The changes introduced by the 2011 revision are also taken into account. For this purpose, this study analyzes the final annual financial statements approved in 2011 by the Italian companies whose shares belonged to the Italia Star segment of Financial Times and Stock Exchange （FTSE）. Given that IAS 1 provides little specific guidance about the presentation of line items and permits many alternative types of format, this paper focuses on information organization in the statement of CI in order to analyze the degree of heterogeneity of financial information. For achieving this goal, this study considers the following issues： （1） presentation of all items of income and expense in an overall statement or in two separate statements; （2） a detailed level of the content in terms of number of items between revenue and net income （NI）; （3） classification of expenses either by nature or by function; （4） number and type of intermediate margins; and （5） presentation of items of other comprehensive income （OCI） either before tax or net of tax. The results show some clear evidences. On the one hand, there is a high diversity in accounting practices, which makes it difficult for users to compare financial information across entities, highlighting the need to complete the joint project of the standards setters （IASB and Financial Accounting Standards Board （FASB）） on financial statement presentation. On the other hand, some alternative types of presentation （e.g., the tendency to split the CI statement into two statements rather than using an integrated solution, the prevalence to disaggregate the expenses by nature, etc.） are used by most of the entities of the sample possibly because of the influence of Italian accounting culture.

Test for Investor Rationality for Companies Listed at the Nairobi Stock Exchange

Investors have traditionally been viewed as economically rational individuals who make decisions based on all available information. They have been assumed to use probability functions to arrive at the most optimal decision. More recent studies propose that investors are irrational and systematically overreact to good and bad information events. The concept of the rational investor has been supported by among others Efficient Market Hypothesis and Modem Portfolio Theory. Other studies opposed to the notion of rational investors have identified psychological biases that influence decision making process of an investor, and leading them to make irrational decisions. Several anomalies have been identified that deviate from rational behavior. The objective of this paper was to test for investor rationality for companies listed at the Nairobi Stock Exchange. This paper tested overreaction by investors to news and performance of companies listed at the Nairobi Stock Market as an anomaly that has been proven in other markets. The test involved forming companies into two portfolios, one of extreme good performers and the other of extreme poor performers during the base year. Performance of these portfolios was analyzed for a nine year period from the year of portfolio formation. The results are consistent with the notion of overreaction, showing that investors overreact to both good and bad news. Over the study period the loser portfolio outperformed the winner portfolio by about 35.92%. This confrere that investors are irrational and make decisions based on some biases.