＂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.

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

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.

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.

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.

Polish Government's Tax Response to COVID-19 Pandemic

1.Introduction The rapid spread of COVID-19 is the challenge for all the countries and their economies.The sugges-t ions presented in media show that the COVID-19 pandemic will result in recession.This seems a fairly obvious observation resulting from the growing number of infections in most countries,closing schools and workplaces and promoting social distancing measures,as well as sharp declines on global stock exchanges.