To examine the interdependency and evolution of Pakistan’s stock market,we consider the cross-correlation coefficients of daily stock returns belonging to the blue chip Karachi stock exchange(KSE-100)index.Using the ...To examine the interdependency and evolution of Pakistan’s stock market,we consider the cross-correlation coefficients of daily stock returns belonging to the blue chip Karachi stock exchange(KSE-100)index.Using the minimum spanning tree network-based method,we extend the financial network literature by examining the topological properties of the network and generating six minimum spanning tree networks around three general elections in Pakistan.Our results reveal a star-like structure after the general elections of 2018 and before those in 2008,and a tree-like structure otherwise.We also highlight key nodes,the presence of different clusters,and compare the differences between the three elections.Additionally,the sectorial centrality measures reveal economic expansion in three industrial sectors—cement,oil and gas,and fertilizers.Moreover,a strong overall intermediary role of the fertilizer sector is observed.The results indicate a structural change in the stock market network due to general elections.Consequently,through this analysis,policy makers can focus on monitoring key nodes around general elections to estimate stock market stability,while local and international investors can form optimal diversification strategies.展开更多
Stock Market is the market for security where organized issuance and trading of Stocks take place either through exchange or over the counter in electronic or physical form. It plays an important role in canalizing ca...Stock Market is the market for security where organized issuance and trading of Stocks take place either through exchange or over the counter in electronic or physical form. It plays an important role in canalizing capital from the investors to the business houses, which consequently leads to the availability of funds for business expansion. In this paper, we investigate to predict the daily excess returns of Bombay Stock Exchange (BSE) indices over the respective Treasury bill rate returns. Initially, we prove that the excess return time series do not fluctuate randomly. We are applying the prediction models of Autoregressive feed forward Artificial Neural Networks (ANN) to predict the excess return time series using lagged value. For the Artificial Neural Networks model using a Genetic Algorithm is constructed to choose the optimal topology. This paper examines the feasibility of the prediction task and provides evidence that the markets are not fluctuating randomly and finally, to apply the most suitable prediction model and measure their efficiency.展开更多
With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and en...With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and enables investors to quickly identify relevant financial events that may lead to stock market volatility. However, in the research of event detection in the financial field, many studies are focused on micro-blog, news and other network text information. Few scholars have studied the characteristics of financial time series data. Considering that in the financial field, the occurrence of an event often affects both the online public opinion space and the real transaction space, so this paper proposes a multi-source heterogeneous information detection method based on stock transaction time series data and online public opinion text data to detect hot events in the stock market. This method uses outlier detection algorithm to extract the time of hot events in stock market based on multi-member fusion. And according to the weight calculation formula of the feature item proposed in this paper, this method calculates the keyword weight of network public opinion information to obtain the core content of hot events in the stock market. Finally, accurate detection of stock market hot events is achieved.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
A weighted stock network model of stock market is presented based on the complex network theory. The model is a weighted random network, in which each vertex denotes a stock, and the weight assigned to each edge is th...A weighted stock network model of stock market is presented based on the complex network theory. The model is a weighted random network, in which each vertex denotes a stock, and the weight assigned to each edge is the cross-correlation coefficient of returns. Analysis of A shares listed at Shanghai Stock Exchange finds that the influence-strength （IS） follows a power-law distribution with the exponent of 2.58. The empirical analysis results show that there are a few stocks whose price fluctuations can powerfully influence the price dynamics of other stocks in the same market. Further econometric analysis reveals that there are significant differences between the positive IS and the negative IS.展开更多
In this paper, three basic principles for computational stock market are proposed namely,“the Nearest_Time Principle” (NTP),“the Following Tendency Principle” (FTP),and “the Variational Principle on Difference of...In this paper, three basic principles for computational stock market are proposed namely,“the Nearest_Time Principle” (NTP),“the Following Tendency Principle” (FTP),and “the Variational Principle on Difference of Supply and Demand” (VPDSD). The issue, expression, mathematical description and applications of these principles are stated. These applications involve the use in neural networks, basic equations of computational stock market, and the prediction of equilibrium price of stocks etc.展开更多
The SVMs for regression is used to forecast Shanghai stock composite index (SSCI). Implementing structural risk minimization principle, SVMs can overcome the over-fitting problem. The regression uses ε-insensitive lo...The SVMs for regression is used to forecast Shanghai stock composite index (SSCI). Implementing structural risk minimization principle, SVMs can overcome the over-fitting problem. The regression uses ε-insensitive loss function. The training of SVMs leads to a quadratic programming problem and it has a global unique solution. The experiment uses BP neural networks as benchmark for comparison. The results demonstrate that the prediction figure of SSCI can help to find timing for buy or sell, the forecasting variation of SVMs is smaller than that of BP, and the direction forecasting of SVMs is more accurate than that of BP.展开更多
While a large number of studies have been reported in the literature with reference to the use of Regression model and Artificial Neural Network (ANN) models in predicting stock prices in western countries, the Chines...While a large number of studies have been reported in the literature with reference to the use of Regression model and Artificial Neural Network (ANN) models in predicting stock prices in western countries, the Chinese stock market is much less studied. Note that the latter is growing rapidly, will overtake USA one in 20 - 30 years time and thus be-comes a very important place for investors worldwide. In this paper, an attempt is made at predicting the Shanghai Composite Index returns and price volatility, on a daily and weekly basis. In the paper, two different types of prediction models, namely the Regression and Neural Network models are used for the prediction task and multiple technical indicators are included in the models as inputs. The performances of the two models are compared and evaluated in terms of di- rectional accuracy. Their performances are also rigorously compared in terms of economic criteria like annualized return rate (ARR) from simulated trading. In this paper, both trading with and without short selling has been consid- ered, and the results show in most cases, trading with short selling leads to higher profits. Also, both the cases with and without commission costs are discussed to show the effects of commission costs when the trading systems are in actual use.展开更多
文摘To examine the interdependency and evolution of Pakistan’s stock market,we consider the cross-correlation coefficients of daily stock returns belonging to the blue chip Karachi stock exchange(KSE-100)index.Using the minimum spanning tree network-based method,we extend the financial network literature by examining the topological properties of the network and generating six minimum spanning tree networks around three general elections in Pakistan.Our results reveal a star-like structure after the general elections of 2018 and before those in 2008,and a tree-like structure otherwise.We also highlight key nodes,the presence of different clusters,and compare the differences between the three elections.Additionally,the sectorial centrality measures reveal economic expansion in three industrial sectors—cement,oil and gas,and fertilizers.Moreover,a strong overall intermediary role of the fertilizer sector is observed.The results indicate a structural change in the stock market network due to general elections.Consequently,through this analysis,policy makers can focus on monitoring key nodes around general elections to estimate stock market stability,while local and international investors can form optimal diversification strategies.
文摘Stock Market is the market for security where organized issuance and trading of Stocks take place either through exchange or over the counter in electronic or physical form. It plays an important role in canalizing capital from the investors to the business houses, which consequently leads to the availability of funds for business expansion. In this paper, we investigate to predict the daily excess returns of Bombay Stock Exchange (BSE) indices over the respective Treasury bill rate returns. Initially, we prove that the excess return time series do not fluctuate randomly. We are applying the prediction models of Autoregressive feed forward Artificial Neural Networks (ANN) to predict the excess return time series using lagged value. For the Artificial Neural Networks model using a Genetic Algorithm is constructed to choose the optimal topology. This paper examines the feasibility of the prediction task and provides evidence that the markets are not fluctuating randomly and finally, to apply the most suitable prediction model and measure their efficiency.
文摘With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and enables investors to quickly identify relevant financial events that may lead to stock market volatility. However, in the research of event detection in the financial field, many studies are focused on micro-blog, news and other network text information. Few scholars have studied the characteristics of financial time series data. Considering that in the financial field, the occurrence of an event often affects both the online public opinion space and the real transaction space, so this paper proposes a multi-source heterogeneous information detection method based on stock transaction time series data and online public opinion text data to detect hot events in the stock market. This method uses outlier detection algorithm to extract the time of hot events in stock market based on multi-member fusion. And according to the weight calculation formula of the feature item proposed in this paper, this method calculates the keyword weight of network public opinion information to obtain the core content of hot events in the stock market. Finally, accurate detection of stock market hot events is achieved.
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.
基金The National Natural Science Foundationof China (No70671070 & No70401019)
文摘A weighted stock network model of stock market is presented based on the complex network theory. The model is a weighted random network, in which each vertex denotes a stock, and the weight assigned to each edge is the cross-correlation coefficient of returns. Analysis of A shares listed at Shanghai Stock Exchange finds that the influence-strength （IS） follows a power-law distribution with the exponent of 2.58. The empirical analysis results show that there are a few stocks whose price fluctuations can powerfully influence the price dynamics of other stocks in the same market. Further econometric analysis reveals that there are significant differences between the positive IS and the negative IS.
文摘In this paper, three basic principles for computational stock market are proposed namely,“the Nearest_Time Principle” (NTP),“the Following Tendency Principle” (FTP),and “the Variational Principle on Difference of Supply and Demand” (VPDSD). The issue, expression, mathematical description and applications of these principles are stated. These applications involve the use in neural networks, basic equations of computational stock market, and the prediction of equilibrium price of stocks etc.
文摘The SVMs for regression is used to forecast Shanghai stock composite index (SSCI). Implementing structural risk minimization principle, SVMs can overcome the over-fitting problem. The regression uses ε-insensitive loss function. The training of SVMs leads to a quadratic programming problem and it has a global unique solution. The experiment uses BP neural networks as benchmark for comparison. The results demonstrate that the prediction figure of SSCI can help to find timing for buy or sell, the forecasting variation of SVMs is smaller than that of BP, and the direction forecasting of SVMs is more accurate than that of BP.
文摘While a large number of studies have been reported in the literature with reference to the use of Regression model and Artificial Neural Network (ANN) models in predicting stock prices in western countries, the Chinese stock market is much less studied. Note that the latter is growing rapidly, will overtake USA one in 20 - 30 years time and thus be-comes a very important place for investors worldwide. In this paper, an attempt is made at predicting the Shanghai Composite Index returns and price volatility, on a daily and weekly basis. In the paper, two different types of prediction models, namely the Regression and Neural Network models are used for the prediction task and multiple technical indicators are included in the models as inputs. The performances of the two models are compared and evaluated in terms of di- rectional accuracy. Their performances are also rigorously compared in terms of economic criteria like annualized return rate (ARR) from simulated trading. In this paper, both trading with and without short selling has been consid- ered, and the results show in most cases, trading with short selling leads to higher profits. Also, both the cases with and without commission costs are discussed to show the effects of commission costs when the trading systems are in actual use.
