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.

＂Bankers Are Stingy？＂ Re-Examining Stock Exchanges and Public Debt in Prewar Shanghai （1920s-1930s）

This review article surveys new studies of China＇s economy in the early twentieth century that have been published in both China and the West. It analyses the nuances that we find in these recently published studies and how those might improve our conventional understanding of the era, with particular emphasis on the link between fiscal revenue and stock-exchanges. First, a detailed introduction treats the evolution, beginning in the nineteenth century, of Shanghai＇s segmented stock exchanges in the context of wider global currents. Section two reprises the still common notion that heavy domestic borrowing by the Nationalist （Kuornintang, or GMD） government in the 1920s-1930s forestalled industrialization. Section three discusses at length the degree to which Chinese banks in that period may be seen as merely a GMD conduit of borrowing. Chinese banks were probably more conducive to Shanghai＇s industrialization than is usually acknowledged, and they also played a key role in stabilizing China＇s monetary environment well beyond their perceived focus on managing public debt. But more evidence needs to come to light, and this article sets out the areas in which future research might advance our knowledge. The conclusion will underscore how the various findings of scholars might, as a whole, remould current conceptions.

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.

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.

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.

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.

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.

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.

Is There an Impact of Stock Exchange Consolidation on Volatility of Market Returns？

The aim of the paper is to provide some evidences on relationships among the degree of financial integration, stock exchange markets, and volatility of national market returns. In this paper, the authors employ correlation and cluster analyses in order to investigate the impact of stock exchange consolidation on volatility of market returns, in terms of a financial integration between involved stock exchanges before and after the merger. By using the Generalized Autoregressive Conditional Heteroskedasticity （GARCH） （1.1） model, the authors test the change in volatilities of national stock exchange markets involved in the following stock exchange integration case studies： Euronext, Bolsasy Mercados Espanoles （BME）, and Swedish-Finnish financial services company （OMX）. These three case studies are considered as completed cases of market consolidation, where the data are available enough to conduct the current research. By using daily data of national returns of engaged European stock markets from 1995 to 2007, the paper investigates the influence of stock exchange consolidation on volatility of national stock market returns. The obtained results confirm the gradual decrease of volatility in each of the integrated stock markets. However, the level of decrease in terms of volatility depends on economic characteristics of each engaged market and its degree of integration with other financial services. The results of correlation and cluster analyses confirm that stock operators have created significantly non-official integration links through cross-memberships and cross-listings even before the consolidations. Thus, the mergers among stock exchanges can be considered as the rational consequences of the high internal co-movements between involved markets. Furthermore, stock exchange markets with strong non-official integration links show an immediate decrease of volatility after the merger, meanwhile for others, it takes several years before the volatility can decrease as markets should reach the full integration.

Stock Indices and New Technologies

Stock indices were created to accurately reflect changes in the prices of stock exchange securities included in them.Indices are divided into price-weighted and value-weighted indices.In price-weighted indices,each security has the same impact on the index,which is the arithmetic mean of the stock values of the companies it comprises.A value-weighted index accounts for changes in the value of shareholdings.By choosing the right composition of share packages,one can change the impact of a given security on the index.Market dynamics also change the composition of the index by removing securities and replacing them with others.The impact on the index grows as the price of a given security increases.Recently,index values have been strongly influenced by new technology companies,which have greatly appreciated during the SARS-CoV-2 pandemic.The aim of this work is to show the changes in two indices in particular:US NASDAQ Composite,and the new competitive SSE STAR index,an index from the Shanghai Stock Exchange.

Shanghai Lan Sheng Corp.—First Chinese Foreign Trade Company Listed on Shanghai Stock Exchange

The Shanghai Lan Sheng Corp., which used to be called the Shanghai Stationery and Sporting Goods Import and Export Company, touched off great repercussions in the international mass media and among its counterparts after it was renamed after its general manager Zhang Lansheng and its stocks were listed for transactions on

Regulation of the Warsaw Stock Exchange： History and Operating Rules

The aim of this article is to present the principles of the operation of the Warsaw Stock Exchange （WSE）, which is the most important institution of the capital market in Poland. The article is an attempt to explore the history and structure of the exchange and its investment possibilities in order to understand the number of existing security features which make cooperation with this institution-with appropriate assumptions and diversification-an activity providing profits to potential investors who are aware of its operating rules. This topic deserves a historical outline because of the socialist past and the current status of the WSE, now called the most dynamically growing capital market in Central and Eastern Europe.

Trend Analysis of Exchange Rate of the Ghana Cedi against the US Dollar Using Time Series

Exchange rate volatility or its stability is a key determinant of the state of a country’s economy. The Ghana cedi’s performance against the US dollar in recent times has been the worse in the past decade. This has resulted in high inflation, high cost of living and high cost of production in Ghana. Despite the recent economic recovery growth, the cedi continues to strife in high rate of exchange against the dollar. This study examines and models a trend, and makes predictions of future rates of the cedis against the US dollar. Methodology: The study used a 13-year data of exchange rates of Ghana cedi and the US dollar spanning from 2010 to mid-2023 from the Bank of Ghana’s economic data on exchange rates, Ghana Stock Exchange and the World Bank. The ARIMA and SARIMA models were used to model the trends and for forecasting, taking into the consideration the asymmetric and seasonal effect of the data. Results: The outputs show that, the Ghana cedi will continue to rise but steadily against the US dollar for the remaining months of 2023 except in December, and continue to decline afterwards through into 2024. Conclusion: The cedi continues to weaken in value and the strength of its purchasing power. A weaker currency depicts a “junk” economy which affects its foreign investment. As the US dollar continues to rise, the Government and policy makers must implement effective policies to stabilize its rise against the cedi. Export of commodities must increase in addition to import restrictions to balance trade deficit and to strengthen the Ghana Cedi.

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.

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.

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.