Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper pr...Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper presents a novel approach to predict stock prices by integrating Autoregressive Integrated Moving Average (ARIMA) and Exponential smoothing and Machine Learning (ML) techniques. Our study aims to enhance the predictive accuracy of stock price forecasting, which can significantly impact investment strategies and economic growth in this research paper implement the ARIMAML proposed method to predict the stock prices for Investment Bank of Iraq.展开更多
This study examines the relationship between Environmental,Social,and Governance(ESG)factors and stock prices as well as investment performance.ESG factors have become increasingly relevant in investment decisions as ...This study examines the relationship between Environmental,Social,and Governance(ESG)factors and stock prices as well as investment performance.ESG factors have become increasingly relevant in investment decisions as investors prioritize companies with sustainable practices.Using a sample of publicly-traded companies,this research analyzes the impact of ESG factors on stock prices and investment returns.The findings suggest that companies with strong ESG performance tend to have higher stock prices and better investment performance than those with weak ESG performance.The study also highlights the significance of the individual components of ESG,such as environmental policies and corporate governance practices,on stock prices and investment returns.Overall,this research provides valuable insights for investors seeking to incorporate ESG factors into their investment decision-making processes.展开更多
This study investigates the stock price–economic activity nexus in 12 member countries of the Organization for Economic Cooperation and Development(OECD)by employing monthly data over the period 1981:1–2018:3.For th...This study investigates the stock price–economic activity nexus in 12 member countries of the Organization for Economic Cooperation and Development(OECD)by employing monthly data over the period 1981:1–2018:3.For this purpose,the study uses Granger causality in the frequency domain in the panel setting by decomposing the symmetric and asymmetric fluctuations.This methodology determines whether the predictive power of interested variables is concentrated on quickly,moderately,or slowly fluctuating components.Our findings show that the stock prices have predictive power for future long-term economic activity in the panel setting.However,economic activity has more reliable information for stock prices for negative components.Additionally,empirical findings for asymmetric shocks are not fully consistent with those of symmetric ones.Besides,the country-specific results provide different causal linkages across members and frequencies.These findings may provide valuable information for policymakers to design proper and effective policies in OECD countries regarding the stock market and economic activity nexus.展开更多
With the rapid expansion of the RMB exchange rate’s floating range,the effects of the RMB exchange rate and global commodity price changes on China’s stock prices are likely to increase.This study uses both auto reg...With the rapid expansion of the RMB exchange rate’s floating range,the effects of the RMB exchange rate and global commodity price changes on China’s stock prices are likely to increase.This study uses both auto regressive distributed lag(ARDL)and nonlinear ARDL(NARDL)approaches to explore the symmetric and asymmetric effects of the RMB exchange rate and global commodity prices on China’s stock prices.Our findings show that without considering the critical variable of global commodity prices,there is no cointegration relationship between the RMB exchange rate and China’s stock prices,and the coefficient of the RMB exchange rate is not statistically significant.However,when we introduce global commodity prices into the NARDL model,the result shows that the RMB exchange rate has a negative effect on China’s stock prices,that there indeed exists a long-run cointegration relationship among the RMB exchange rate,global commodity prices,and stock prices in the NARDL model,and that global commodity price changes have an asymmetric effect on China’s stock prices in the long run.Specifically,China’s stock prices are more sensitive to increases than decreases in global commodity prices.Thus,increases in global commodity prices cause China’s stock prices to decline sharply.In contrast,the same magnitude of decline in global commodity prices induces a smaller increase in China’s stock prices.展开更多
Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile...Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile often accompa-nied by thin trading-volumes and they are susceptible to more manipulation compared to mature markets. Technical analysis of stocks and commodities has become a science on its own;quantitative methods and techniques have been applied by many practitioners to forecast price movements. Lagging and sometimes leading technical indicators pro-vide rich quantitative tools for traders and investors in their attempt to gain advantage when making investment or trading decisions. Artificial Neural Networks (ANN) have been used widely in predicting stock prices because of their capability in capturing the non-linearity that often exists in price movements. Recently, Polynomial Classifiers (PC) have been applied to various recognition and classification application and showed favorable results in terms of recog-nition rates and computational complexity as compared to ANN. In this paper, we present two prediction models for predicting securities’ prices. The first model was developed using back propagation feed forward neural networks. The second model was developed using polynomial classifiers (PC), as a first time application for PC to be used in stock prices prediction. The inputs to both models were identical, and both models were trained and tested on the same data. The study was conducted on Dubai Financial Market as an emerging market and applied to two of the market’s leading stocks. In general, both models achieved very good results in terms of mean absolute error percentage. Both models show an average error around 1.5% predicting the next day price, an average error of 2.5% when predicting second day price, and an average error of 4% when predicted the third day price.展开更多
Is it true that there is an implicit understanding that Brownian motion or fractional Brownian motion is the driving force behind stock price fluctuations? An analysis of daily prices and volumes of a particular stock...Is it true that there is an implicit understanding that Brownian motion or fractional Brownian motion is the driving force behind stock price fluctuations? An analysis of daily prices and volumes of a particular stock revealed the following findings: 1) the logarithms of the moving averages of stock prices and volumes have a strong positive correlation, even though price and volume appear to be fluctuating independently of each other, 2) price and volume fluctuations are messy, but these time series are not necessarily Brownian motion by replacing each daily value by 1 or –1 when it rises or falls compared to the previous day’s value, and 3) the difference between the volume on the previous day and that on the current day is periodic by the frequency analysis. Using these findings, we constructed differential equations for stock prices, the number of buy orders, and the number of sell orders. These equations include terms for both randomness and periodicity. It is apparent that both randomness and periodicity are essential for stock price fluctuations to be sustainable, and that stock prices show large hill-like or valley-like fluctuations stochastically without any increasing or decreasing trend, and repeat themselves over a certain range.展开更多
This paper demonstrates a significant,long-running relationship between stock prices and domestic interest rates in Turkey’s financial markets for the period of 2001 M1-2017 M4.Cointegration analysis is investigated ...This paper demonstrates a significant,long-running relationship between stock prices and domestic interest rates in Turkey’s financial markets for the period of 2001 M1-2017 M4.Cointegration analysis is investigated using the autoregressivedistributed lag bounds(ARDL Bounds)test and vector autoregressive cointegration.Additionally,cointegrating equations such as the fully modified ordinary least square,dynamic ordinary least squares,and canonical cointegrating regression are applied to check the long-run elasticities in the concerned relationship.The ARDL Bounds and Johansen Cointegration test results show that,dynamically,both prices are significantly related to each other.The cointegrating equation outcomes demonstrate elasticities whereby both coefficients have negative signs.Additionally,the same results are corroborated by the impulse response where all variables respond negatively to each other.展开更多
Theorems of iteration g-contractive sequential composite mapping and periodic mapping in Banach or probabilistic Bannach space are proved, which allow some contraction ratios of the sequence of mapping might be larger...Theorems of iteration g-contractive sequential composite mapping and periodic mapping in Banach or probabilistic Bannach space are proved, which allow some contraction ratios of the sequence of mapping might be larger than or equal to 1, and are more general than the Banach contraction mapping theorem. Application to the proof of existence of solutions of cycling coupled nonlinear differential equations arising from prey-predator system and A&H stock prices are given.展开更多
Stock market plays a pivotal role in firms’expansion and turns economic growth.In the literature,because of the importance of stock markets to the real economy,the smooth and risk-free operation of the stock market h...Stock market plays a pivotal role in firms’expansion and turns economic growth.In the literature,because of the importance of stock markets to the real economy,the smooth and risk-free operation of the stock market has attracted significant attention.The finance literature contains a large number of studies that examine the stock price behaviour with some emphasis on the determinants of the relationship between the equity prices and the financial market activities.The present study reviews the previous works of the effect of financial market variables and stock price.Five selected financial market variables,market capitalization,earnings per share,price earnings multiples,dividend yield,and trading volume are reviewed in this study.In the past literature,there are the opinions of the positive significant relationship between market capitalization and stock price.To find the relationship between dividend yield and stock price,there are two broad schools of thoughts.Both of the relevance and irrelevance theory of Gordon and Modigliani have the strong evidence in the current literature that keeps on the dilemma and provides the scopes for future research.Price-earnings multiples are analyzed in the past literature by using different variables.Based on that,it is evidenced that price-earnings multiples have a negative significant effect on stock price.The reviewed studies state the cointegrating relationship between the stock price and the trading volume as the trading volume is a source of risk.展开更多
While the literature on inflation and stock prices is plentiful,there is little literature on deflation and stock prices.This paper explores the empirical data and makes a theoretical analysis of the likely impact on ...While the literature on inflation and stock prices is plentiful,there is little literature on deflation and stock prices.This paper explores the empirical data and makes a theoretical analysis of the likely impact on stock prices when expectations change from inflation to deflation.Deflation has a bad name among some economists and most investors.However,from a stock market perspective,deflations’bad name may not be well-deserved.Several observations support this:1)The 1930s was a statistical outlier and not representative for a deflationary period and deflation does not seem to create recessions,causality goes the other way;2)real stock returns are positive and around average in the periods leading up to and following the onset of deflation;3)when moving from low inflation to mild deflation,P/E ratios are virtually unchanged;and 4)peak P/E ratios seem to be reached at inflation rates close to zero.The author proposes three possible explanations for the seemingly disconnect between the empirical data and the“default”ex ante belief of most economists and investors:availability heurist,deflation illusion,and tax related issues in connection with the tax hypothesis.展开更多
We propose and apply a new algorithm of principal component analysis which is suitable for a large sized, highly random time series data, such as a set of stock prices in a stock market. This algorithm utilizes the fa...We propose and apply a new algorithm of principal component analysis which is suitable for a large sized, highly random time series data, such as a set of stock prices in a stock market. This algorithm utilizes the fact that the major part of the time series is random, and compare the eigenvalue spectrum of cross correlation matrix of a large set of random time series, to the spectrum derived by the random matrix theory (RMT) at the limit of large dimension (the number of independent time series) and long enough length of time series. We test this algorithm on the real tick data of American stocks at different years between 1994 and 2002 and show that the extracted principal components indeed reflects the change of leading stock sectors during this period.展开更多
This paper examines presence of some stylized facts of short-term stock prices in the banking sector of the Nigerian Stock Market (NSM). Non-normality, lack of autocorrelation in the returns at first lag and significa...This paper examines presence of some stylized facts of short-term stock prices in the banking sector of the Nigerian Stock Market (NSM). Non-normality, lack of autocorrelation in the returns at first lag and significant positive autocorrelation in higher magnitude returns, widely studied in other markets, are investigated using daily closing stock prices of the four major Nigerian banks (Access, First, Guaranty Trust and United Bank for Africa (UBA)), from 2001 to 2013;encompassing periods of different financial scenarios. Jarque-Bera (JB), Doonik-Hansen, Kolmogrov-Smirnov and Ljung-Box (Q) test statistics are applied. Our findings reveal that the four banks stocks behave slightly different, but generally possess the stylized facts found in other markets. Observed is that, while the distributions of the returns for two of these banks (First and UBA) are approximately symmetric and leptokurtic;those of Access and Guaranty Trust banks are significantly non-symmetric and leptokurtic, thus non-normally distributed. Also established is that, while autocorrelation functions of daily returns are either negative or zero, those of both absolute returns and the squared returns are mostly positive. The autocorrelations of absolute returns are found to be predominantly positive and more persistent than those of the squared returns;indicating volatility clustering. Consequently, we conclude that the short-term stock prices of these banks behave like those of other markets. Some implications of the results for financial investment and stock market behaviour in the banking sector of NSM are discussed.展开更多
In this paper,the models of increment distributions of stock price are constructed with two approaches. The first approach is based on limit theorems of random summation. The second approach is based on the statistica...In this paper,the models of increment distributions of stock price are constructed with two approaches. The first approach is based on limit theorems of random summation. The second approach is based on the statistical analysis of the increment distribution of the logarithms of stock prices.展开更多
This paper investigates the role of Fibonacci retracements levels,a popular technical analysis indicator,in predicting stock prices of leading U.S.energy companies and energy cryptocurrencies.The study methodology foc...This paper investigates the role of Fibonacci retracements levels,a popular technical analysis indicator,in predicting stock prices of leading U.S.energy companies and energy cryptocurrencies.The study methodology focuses on applying Fibonacci retracements as a system compared with the buy-and-hold strategy.Daily crypto and stock prices were obtained from the Standard&Poor’s composite 1500 energy index and CoinMarketCap between November 2017 and January 2020.This study also examined if the combined Fibonacci retracements and the price crossover strategy result in a higher return per unit of risk.Our findings revealed that Fibonacci retracement captures energy stock price changes better than cryptos.Furthermore,most price violations were frequent during price falls compared to price increases,supporting that the Fibonacci instrument does not capture price movements during up and downtrends,respectively.Also,fewer consecutive retracement breaks were observed when the price violations were examined 3 days before the current break.Furthermore,the Fibonacci-based strategy resulted in higher returns relative to the naïve buy-and-hold model.Finally,complementing Fibonacci with the price cross strategy did not improve the results and led to fewer or no trades for some constituents.This study’s overall findings elucidate that,despite significant drops in oil prices,speculators(traders)can implement profitable strategies when using technical analysis indicators,like the Fibonacci retracement tool,with or without price crossover rules.展开更多
The stock market is full of events that affect the sensitivity reaction of investors at a large scale. Individual investor sentiment is just like his/her personal feeling depending upon their nature, risk appetite, an...The stock market is full of events that affect the sensitivity reaction of investors at a large scale. Individual investor sentiment is just like his/her personal feeling depending upon their nature, risk appetite, and market scenario. This research study investigates the investors’ reaction in the stock market for the real estate segment during the massive market crisis in developing countries. Demonetisation of 2016 in India has been taken with the purpose of implementing a pilot study to analyse the overreaction and availability bias. The primary focus was on analysing how the investors react on the information of demonetisation and their pattern of investment in the stock market with a special emphasis on real estate sector where the effect of the event had dramatically changed the stock prices. Therefore, a pre- and post- analysis had been conducted to gauge the prices, sensitivity, and reaction of investors in the stock market. The reaction of the citizens after these events was found to be drastically affected. Five real estate companies had been focused upon in this study to examine the impact of investors’ overreaction owing to the demonetisation and their investment pattern for stocks during pre- and post- demonetisation period at that timeframe. The analysis was done on a shorter period of time so that the impact of overreaction and availability bias can be critically analysed. The paper thus exhibits how investor sentiments and reaction for stock preference had changed over time through statistical study.展开更多
This paper proposes the generalized regression neural network(GRNN)model and multi-GRNN model with a gating network by selecting the data of Shanghai index,the stocks of Shanghai Pudong Development Bank(SPDB),Dongfeng...This paper proposes the generalized regression neural network(GRNN)model and multi-GRNN model with a gating network by selecting the data of Shanghai index,the stocks of Shanghai Pudong Development Bank(SPDB),Dongfeng Automobile and Baotou Steel.We analyze the two models using Matlab software to predict the opening price respectively.Through building a softmax excitation function,the multi-GRNN model with a gating network can obtain the best weights.Using the data of the four groups,the average of forecasting errors of 4 groups by GRNN neural model is 0.012 208,while the average of the multi-GRNN models's with a gating network is 0.002 659.Compared with the real data,it is found that the both results predicted by the two models have small mean square prediction errors.So the two models are suitable to be adopted to process a large quantity of data,furthermore the multi-GRNN model with a gating network is better than the GRNN model.展开更多
文摘Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper presents a novel approach to predict stock prices by integrating Autoregressive Integrated Moving Average (ARIMA) and Exponential smoothing and Machine Learning (ML) techniques. Our study aims to enhance the predictive accuracy of stock price forecasting, which can significantly impact investment strategies and economic growth in this research paper implement the ARIMAML proposed method to predict the stock prices for Investment Bank of Iraq.
文摘This study examines the relationship between Environmental,Social,and Governance(ESG)factors and stock prices as well as investment performance.ESG factors have become increasingly relevant in investment decisions as investors prioritize companies with sustainable practices.Using a sample of publicly-traded companies,this research analyzes the impact of ESG factors on stock prices and investment returns.The findings suggest that companies with strong ESG performance tend to have higher stock prices and better investment performance than those with weak ESG performance.The study also highlights the significance of the individual components of ESG,such as environmental policies and corporate governance practices,on stock prices and investment returns.Overall,this research provides valuable insights for investors seeking to incorporate ESG factors into their investment decision-making processes.
文摘This study investigates the stock price–economic activity nexus in 12 member countries of the Organization for Economic Cooperation and Development(OECD)by employing monthly data over the period 1981:1–2018:3.For this purpose,the study uses Granger causality in the frequency domain in the panel setting by decomposing the symmetric and asymmetric fluctuations.This methodology determines whether the predictive power of interested variables is concentrated on quickly,moderately,or slowly fluctuating components.Our findings show that the stock prices have predictive power for future long-term economic activity in the panel setting.However,economic activity has more reliable information for stock prices for negative components.Additionally,empirical findings for asymmetric shocks are not fully consistent with those of symmetric ones.Besides,the country-specific results provide different causal linkages across members and frequencies.These findings may provide valuable information for policymakers to design proper and effective policies in OECD countries regarding the stock market and economic activity nexus.
基金supported by the Fundamental Research Funds for the Central Universities(2019CDSKXYGG0042,2018CDXYGG0054,2020CDJSK01HQ01)National Social Science Funds(16CJL007).
文摘With the rapid expansion of the RMB exchange rate’s floating range,the effects of the RMB exchange rate and global commodity price changes on China’s stock prices are likely to increase.This study uses both auto regressive distributed lag(ARDL)and nonlinear ARDL(NARDL)approaches to explore the symmetric and asymmetric effects of the RMB exchange rate and global commodity prices on China’s stock prices.Our findings show that without considering the critical variable of global commodity prices,there is no cointegration relationship between the RMB exchange rate and China’s stock prices,and the coefficient of the RMB exchange rate is not statistically significant.However,when we introduce global commodity prices into the NARDL model,the result shows that the RMB exchange rate has a negative effect on China’s stock prices,that there indeed exists a long-run cointegration relationship among the RMB exchange rate,global commodity prices,and stock prices in the NARDL model,and that global commodity price changes have an asymmetric effect on China’s stock prices in the long run.Specifically,China’s stock prices are more sensitive to increases than decreases in global commodity prices.Thus,increases in global commodity prices cause China’s stock prices to decline sharply.In contrast,the same magnitude of decline in global commodity prices induces a smaller increase in China’s stock prices.
文摘Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile often accompa-nied by thin trading-volumes and they are susceptible to more manipulation compared to mature markets. Technical analysis of stocks and commodities has become a science on its own;quantitative methods and techniques have been applied by many practitioners to forecast price movements. Lagging and sometimes leading technical indicators pro-vide rich quantitative tools for traders and investors in their attempt to gain advantage when making investment or trading decisions. Artificial Neural Networks (ANN) have been used widely in predicting stock prices because of their capability in capturing the non-linearity that often exists in price movements. Recently, Polynomial Classifiers (PC) have been applied to various recognition and classification application and showed favorable results in terms of recog-nition rates and computational complexity as compared to ANN. In this paper, we present two prediction models for predicting securities’ prices. The first model was developed using back propagation feed forward neural networks. The second model was developed using polynomial classifiers (PC), as a first time application for PC to be used in stock prices prediction. The inputs to both models were identical, and both models were trained and tested on the same data. The study was conducted on Dubai Financial Market as an emerging market and applied to two of the market’s leading stocks. In general, both models achieved very good results in terms of mean absolute error percentage. Both models show an average error around 1.5% predicting the next day price, an average error of 2.5% when predicting second day price, and an average error of 4% when predicted the third day price.
文摘Is it true that there is an implicit understanding that Brownian motion or fractional Brownian motion is the driving force behind stock price fluctuations? An analysis of daily prices and volumes of a particular stock revealed the following findings: 1) the logarithms of the moving averages of stock prices and volumes have a strong positive correlation, even though price and volume appear to be fluctuating independently of each other, 2) price and volume fluctuations are messy, but these time series are not necessarily Brownian motion by replacing each daily value by 1 or –1 when it rises or falls compared to the previous day’s value, and 3) the difference between the volume on the previous day and that on the current day is periodic by the frequency analysis. Using these findings, we constructed differential equations for stock prices, the number of buy orders, and the number of sell orders. These equations include terms for both randomness and periodicity. It is apparent that both randomness and periodicity are essential for stock price fluctuations to be sustainable, and that stock prices show large hill-like or valley-like fluctuations stochastically without any increasing or decreasing trend, and repeat themselves over a certain range.
文摘This paper demonstrates a significant,long-running relationship between stock prices and domestic interest rates in Turkey’s financial markets for the period of 2001 M1-2017 M4.Cointegration analysis is investigated using the autoregressivedistributed lag bounds(ARDL Bounds)test and vector autoregressive cointegration.Additionally,cointegrating equations such as the fully modified ordinary least square,dynamic ordinary least squares,and canonical cointegrating regression are applied to check the long-run elasticities in the concerned relationship.The ARDL Bounds and Johansen Cointegration test results show that,dynamically,both prices are significantly related to each other.The cointegrating equation outcomes demonstrate elasticities whereby both coefficients have negative signs.Additionally,the same results are corroborated by the impulse response where all variables respond negatively to each other.
文摘Theorems of iteration g-contractive sequential composite mapping and periodic mapping in Banach or probabilistic Bannach space are proved, which allow some contraction ratios of the sequence of mapping might be larger than or equal to 1, and are more general than the Banach contraction mapping theorem. Application to the proof of existence of solutions of cycling coupled nonlinear differential equations arising from prey-predator system and A&H stock prices are given.
文摘Stock market plays a pivotal role in firms’expansion and turns economic growth.In the literature,because of the importance of stock markets to the real economy,the smooth and risk-free operation of the stock market has attracted significant attention.The finance literature contains a large number of studies that examine the stock price behaviour with some emphasis on the determinants of the relationship between the equity prices and the financial market activities.The present study reviews the previous works of the effect of financial market variables and stock price.Five selected financial market variables,market capitalization,earnings per share,price earnings multiples,dividend yield,and trading volume are reviewed in this study.In the past literature,there are the opinions of the positive significant relationship between market capitalization and stock price.To find the relationship between dividend yield and stock price,there are two broad schools of thoughts.Both of the relevance and irrelevance theory of Gordon and Modigliani have the strong evidence in the current literature that keeps on the dilemma and provides the scopes for future research.Price-earnings multiples are analyzed in the past literature by using different variables.Based on that,it is evidenced that price-earnings multiples have a negative significant effect on stock price.The reviewed studies state the cointegrating relationship between the stock price and the trading volume as the trading volume is a source of risk.
文摘While the literature on inflation and stock prices is plentiful,there is little literature on deflation and stock prices.This paper explores the empirical data and makes a theoretical analysis of the likely impact on stock prices when expectations change from inflation to deflation.Deflation has a bad name among some economists and most investors.However,from a stock market perspective,deflations’bad name may not be well-deserved.Several observations support this:1)The 1930s was a statistical outlier and not representative for a deflationary period and deflation does not seem to create recessions,causality goes the other way;2)real stock returns are positive and around average in the periods leading up to and following the onset of deflation;3)when moving from low inflation to mild deflation,P/E ratios are virtually unchanged;and 4)peak P/E ratios seem to be reached at inflation rates close to zero.The author proposes three possible explanations for the seemingly disconnect between the empirical data and the“default”ex ante belief of most economists and investors:availability heurist,deflation illusion,and tax related issues in connection with the tax hypothesis.
文摘We propose and apply a new algorithm of principal component analysis which is suitable for a large sized, highly random time series data, such as a set of stock prices in a stock market. This algorithm utilizes the fact that the major part of the time series is random, and compare the eigenvalue spectrum of cross correlation matrix of a large set of random time series, to the spectrum derived by the random matrix theory (RMT) at the limit of large dimension (the number of independent time series) and long enough length of time series. We test this algorithm on the real tick data of American stocks at different years between 1994 and 2002 and show that the extracted principal components indeed reflects the change of leading stock sectors during this period.
文摘This paper examines presence of some stylized facts of short-term stock prices in the banking sector of the Nigerian Stock Market (NSM). Non-normality, lack of autocorrelation in the returns at first lag and significant positive autocorrelation in higher magnitude returns, widely studied in other markets, are investigated using daily closing stock prices of the four major Nigerian banks (Access, First, Guaranty Trust and United Bank for Africa (UBA)), from 2001 to 2013;encompassing periods of different financial scenarios. Jarque-Bera (JB), Doonik-Hansen, Kolmogrov-Smirnov and Ljung-Box (Q) test statistics are applied. Our findings reveal that the four banks stocks behave slightly different, but generally possess the stylized facts found in other markets. Observed is that, while the distributions of the returns for two of these banks (First and UBA) are approximately symmetric and leptokurtic;those of Access and Guaranty Trust banks are significantly non-symmetric and leptokurtic, thus non-normally distributed. Also established is that, while autocorrelation functions of daily returns are either negative or zero, those of both absolute returns and the squared returns are mostly positive. The autocorrelations of absolute returns are found to be predominantly positive and more persistent than those of the squared returns;indicating volatility clustering. Consequently, we conclude that the short-term stock prices of these banks behave like those of other markets. Some implications of the results for financial investment and stock market behaviour in the banking sector of NSM are discussed.
文摘In this paper,the models of increment distributions of stock price are constructed with two approaches. The first approach is based on limit theorems of random summation. The second approach is based on the statistical analysis of the increment distribution of the logarithms of stock prices.
文摘This paper investigates the role of Fibonacci retracements levels,a popular technical analysis indicator,in predicting stock prices of leading U.S.energy companies and energy cryptocurrencies.The study methodology focuses on applying Fibonacci retracements as a system compared with the buy-and-hold strategy.Daily crypto and stock prices were obtained from the Standard&Poor’s composite 1500 energy index and CoinMarketCap between November 2017 and January 2020.This study also examined if the combined Fibonacci retracements and the price crossover strategy result in a higher return per unit of risk.Our findings revealed that Fibonacci retracement captures energy stock price changes better than cryptos.Furthermore,most price violations were frequent during price falls compared to price increases,supporting that the Fibonacci instrument does not capture price movements during up and downtrends,respectively.Also,fewer consecutive retracement breaks were observed when the price violations were examined 3 days before the current break.Furthermore,the Fibonacci-based strategy resulted in higher returns relative to the naïve buy-and-hold model.Finally,complementing Fibonacci with the price cross strategy did not improve the results and led to fewer or no trades for some constituents.This study’s overall findings elucidate that,despite significant drops in oil prices,speculators(traders)can implement profitable strategies when using technical analysis indicators,like the Fibonacci retracement tool,with or without price crossover rules.
文摘The stock market is full of events that affect the sensitivity reaction of investors at a large scale. Individual investor sentiment is just like his/her personal feeling depending upon their nature, risk appetite, and market scenario. This research study investigates the investors’ reaction in the stock market for the real estate segment during the massive market crisis in developing countries. Demonetisation of 2016 in India has been taken with the purpose of implementing a pilot study to analyse the overreaction and availability bias. The primary focus was on analysing how the investors react on the information of demonetisation and their pattern of investment in the stock market with a special emphasis on real estate sector where the effect of the event had dramatically changed the stock prices. Therefore, a pre- and post- analysis had been conducted to gauge the prices, sensitivity, and reaction of investors in the stock market. The reaction of the citizens after these events was found to be drastically affected. Five real estate companies had been focused upon in this study to examine the impact of investors’ overreaction owing to the demonetisation and their investment pattern for stocks during pre- and post- demonetisation period at that timeframe. The analysis was done on a shorter period of time so that the impact of overreaction and availability bias can be critically analysed. The paper thus exhibits how investor sentiments and reaction for stock preference had changed over time through statistical study.
基金Postdoctoral Granted Financial Support from China Postdoctoral Science Foundation(20100481307)Natural Science Foundation of Shanxi Province,China(No.2009011018-3)
文摘This paper proposes the generalized regression neural network(GRNN)model and multi-GRNN model with a gating network by selecting the data of Shanghai index,the stocks of Shanghai Pudong Development Bank(SPDB),Dongfeng Automobile and Baotou Steel.We analyze the two models using Matlab software to predict the opening price respectively.Through building a softmax excitation function,the multi-GRNN model with a gating network can obtain the best weights.Using the data of the four groups,the average of forecasting errors of 4 groups by GRNN neural model is 0.012 208,while the average of the multi-GRNN models's with a gating network is 0.002 659.Compared with the real data,it is found that the both results predicted by the two models have small mean square prediction errors.So the two models are suitable to be adopted to process a large quantity of data,furthermore the multi-GRNN model with a gating network is better than the GRNN model.
