Forex(foreign exchange)is a special financial market that entails both high risks and high profit opportunities for traders.It is also a very simple market since traders can profit by just predicting the direction of ...Forex(foreign exchange)is a special financial market that entails both high risks and high profit opportunities for traders.It is also a very simple market since traders can profit by just predicting the direction of the exchange rate between two currencies.However,incorrect predictions in Forex may cause much higher losses than in other typical financial markets.The direction prediction requirement makes the problem quite different from other typical time-series forecasting problems.In this work,we used a popular deep learning tool called“long short-term memory”(LSTM),which has been shown to be very effective in many time-series forecasting problems,to make direction predictions in Forex.We utilized two different data sets—namely,macroeconomic data and technical indicator data—since in the financial world,fundamental and technical analysis are two main techniques,and they use those two data sets,respectively.Our proposed hybrid model,which combines two separate LSTMs corresponding to these two data sets,was found to be quite successful in experiments using real data.展开更多
The article first addresses the following questions:“Why does gross domestic product(GDP)rises,but the stock market value falls?”;“Among the macroeconomic factors,which factor has a greater impact on the promotion ...The article first addresses the following questions:“Why does gross domestic product(GDP)rises,but the stock market value falls?”;“Among the macroeconomic factors,which factor has a greater impact on the promotion of investment value in the securities market?”.With these questions in mind,we put forward a hypothesis emphasizing on the impact of macroeconomic factors on the value of the stock market based on existing research and used the regression method to verify this hypothesis.The following conclusions were drawn:(1)variables that have a positive nonlinear relationship with stock market value include balance of payments surplus,rising GDP level,M1,the whole society’s fixed asset investment,and national per capita disposable income;(2)variables that have a negative nonlinear relationship with stock market value include deposit,loan interest rate,new RMB loan amount,consumer price index(CPI),and producer price index;(3)deposit reserve ratio has an S-shaped curve relationship with stock market value;(4)exchange rate has an inverted U-shaped curve relationship with stock market value.展开更多
Macroeconomic Overview:The U.S.dollar has fallen 4%against a broad collection of widely circulated currencies since January.While the magnitude of the decline may not appear large,it does signal an important reversal ...Macroeconomic Overview:The U.S.dollar has fallen 4%against a broad collection of widely circulated currencies since January.While the magnitude of the decline may not appear large,it does signal an important reversal relative to recent years.Against the same broad collection of currencies,the U.S.dollar had consistently strengthened since 2013。展开更多
The aim of the article is to present non-clasical copyrighted algorithm for prediction of time series, presenting macroeconomic indicators and stock market indices. The algorithm is based on artificial neural networks...The aim of the article is to present non-clasical copyrighted algorithm for prediction of time series, presenting macroeconomic indicators and stock market indices. The algorithm is based on artificial neural networks and multi-resolution analysis （the algorithm is based on Daubechies wavelet）. However, the main feature of the algorithm, which gives a good quality of the forecasts, is all included in the series analysis division into, a few partial under-series and prediction dependence on a number of other economic series. The algorithm used for the prediction, is copyrighted algorithm, labeled M.H-D in this article. Application of the algorithm was performed on a series presenting WIG 20. The forecast of WIG 20 was conditional on trading the Dow Jones, DAX, Nikkei, Hang Seng, taking into account the sliding time window. As an example application of copyrighted model, the forecast of WIG 20 for a period of two years, one year, six month was appointed. An empirical example is described. It shows that the proposed model can predict index with the scale of two years, one year, a half year and other intervals. Precision of prediction is satisfactory. An average absolute percentage error of each forecast was： 0.0099%---for two-year forecasts WIG 20; 0.0552%--for the annual forecast WIG 20; and 0.1788%---for the six-month forecasts WIG 20.展开更多
The aim of this article is to present author＇s application of wavelets to predict short-term macroeconomic indicators Proposed to predict short-term time series （in particular for predicting macroeconomic indicators...The aim of this article is to present author＇s application of wavelets to predict short-term macroeconomic indicators Proposed to predict short-term time series （in particular for predicting macroeconomic indicators）, proprietary model is based on wavelet analysis with Haar wavelets, Daubechies wavelets, and adaptive models; they are the trend crawling model and alignment exponential model. Adaptive models have been modified through the introduction of wavelet function and combined into a single forecast model. Obtained from conducted research results, it shows the model an effective instrument to predict the short-term.展开更多
The aim of this study is to examine the profitabilily of multi-finance companies. This study uses macroeconomic determinants and fundamental variables as factors that affected profitability. The samples of the study w...The aim of this study is to examine the profitabilily of multi-finance companies. This study uses macroeconomic determinants and fundamental variables as factors that affected profitability. The samples of the study was multifinance company in Indonesia over period 2005-2007. The study uses an unbalanced panel data as a methodology. The result suggests that, the ownership of financial assets doesn＇t significantly affect multi-finance performances. This result indicates that multi-finance face difficult situation to generate profit from the credit given. And the result also suggests that all macroeconomic determinants affect multi-finance profitability, with more concern on inflation that have negative significant.展开更多
The real estate valuation activity in the Republic of Moldova dates from 2002, with the adoption of Law No. 989 on Evaluation Activity. Nevertheless, the legislative and methodological framework is extremely limited. ...The real estate valuation activity in the Republic of Moldova dates from 2002, with the adoption of Law No. 989 on Evaluation Activity. Nevertheless, the legislative and methodological framework is extremely limited. There are no national professional standards. Meanwhile, the situations that require real estate valuation are multiplying and diversifying. Therefore, the valuers apply the provisions of the International Valuation Standards (IVS) and of the European Valuation Standards (TEGoVA) adapting them to the national realities by virtue of their knowledge and experience. Information stored in databases plays an important role in the real estate valuation process. In this paper, the authors analyse the content of existing databases in the Republic of Moldova (the databases of normative documents, the information system for the cadastre of real estate, the statistical databases, and specialized databases), the quality of the included information, as well as the applicability of that information in the real estate valuation process. The disadvantages, limitations and shortcomings of databases are highlighted, and measures are proposed to increase their usefulness.展开更多
文摘Forex(foreign exchange)is a special financial market that entails both high risks and high profit opportunities for traders.It is also a very simple market since traders can profit by just predicting the direction of the exchange rate between two currencies.However,incorrect predictions in Forex may cause much higher losses than in other typical financial markets.The direction prediction requirement makes the problem quite different from other typical time-series forecasting problems.In this work,we used a popular deep learning tool called“long short-term memory”(LSTM),which has been shown to be very effective in many time-series forecasting problems,to make direction predictions in Forex.We utilized two different data sets—namely,macroeconomic data and technical indicator data—since in the financial world,fundamental and technical analysis are two main techniques,and they use those two data sets,respectively.Our proposed hybrid model,which combines two separate LSTMs corresponding to these two data sets,was found to be quite successful in experiments using real data.
文摘The article first addresses the following questions:“Why does gross domestic product(GDP)rises,but the stock market value falls?”;“Among the macroeconomic factors,which factor has a greater impact on the promotion of investment value in the securities market?”.With these questions in mind,we put forward a hypothesis emphasizing on the impact of macroeconomic factors on the value of the stock market based on existing research and used the regression method to verify this hypothesis.The following conclusions were drawn:(1)variables that have a positive nonlinear relationship with stock market value include balance of payments surplus,rising GDP level,M1,the whole society’s fixed asset investment,and national per capita disposable income;(2)variables that have a negative nonlinear relationship with stock market value include deposit,loan interest rate,new RMB loan amount,consumer price index(CPI),and producer price index;(3)deposit reserve ratio has an S-shaped curve relationship with stock market value;(4)exchange rate has an inverted U-shaped curve relationship with stock market value.
文摘Macroeconomic Overview:The U.S.dollar has fallen 4%against a broad collection of widely circulated currencies since January.While the magnitude of the decline may not appear large,it does signal an important reversal relative to recent years.Against the same broad collection of currencies,the U.S.dollar had consistently strengthened since 2013。
文摘The aim of the article is to present non-clasical copyrighted algorithm for prediction of time series, presenting macroeconomic indicators and stock market indices. The algorithm is based on artificial neural networks and multi-resolution analysis （the algorithm is based on Daubechies wavelet）. However, the main feature of the algorithm, which gives a good quality of the forecasts, is all included in the series analysis division into, a few partial under-series and prediction dependence on a number of other economic series. The algorithm used for the prediction, is copyrighted algorithm, labeled M.H-D in this article. Application of the algorithm was performed on a series presenting WIG 20. The forecast of WIG 20 was conditional on trading the Dow Jones, DAX, Nikkei, Hang Seng, taking into account the sliding time window. As an example application of copyrighted model, the forecast of WIG 20 for a period of two years, one year, six month was appointed. An empirical example is described. It shows that the proposed model can predict index with the scale of two years, one year, a half year and other intervals. Precision of prediction is satisfactory. An average absolute percentage error of each forecast was： 0.0099%---for two-year forecasts WIG 20; 0.0552%--for the annual forecast WIG 20; and 0.1788%---for the six-month forecasts WIG 20.
文摘The aim of this article is to present author＇s application of wavelets to predict short-term macroeconomic indicators Proposed to predict short-term time series （in particular for predicting macroeconomic indicators）, proprietary model is based on wavelet analysis with Haar wavelets, Daubechies wavelets, and adaptive models; they are the trend crawling model and alignment exponential model. Adaptive models have been modified through the introduction of wavelet function and combined into a single forecast model. Obtained from conducted research results, it shows the model an effective instrument to predict the short-term.
文摘The aim of this study is to examine the profitabilily of multi-finance companies. This study uses macroeconomic determinants and fundamental variables as factors that affected profitability. The samples of the study was multifinance company in Indonesia over period 2005-2007. The study uses an unbalanced panel data as a methodology. The result suggests that, the ownership of financial assets doesn＇t significantly affect multi-finance performances. This result indicates that multi-finance face difficult situation to generate profit from the credit given. And the result also suggests that all macroeconomic determinants affect multi-finance profitability, with more concern on inflation that have negative significant.
文摘The real estate valuation activity in the Republic of Moldova dates from 2002, with the adoption of Law No. 989 on Evaluation Activity. Nevertheless, the legislative and methodological framework is extremely limited. There are no national professional standards. Meanwhile, the situations that require real estate valuation are multiplying and diversifying. Therefore, the valuers apply the provisions of the International Valuation Standards (IVS) and of the European Valuation Standards (TEGoVA) adapting them to the national realities by virtue of their knowledge and experience. Information stored in databases plays an important role in the real estate valuation process. In this paper, the authors analyse the content of existing databases in the Republic of Moldova (the databases of normative documents, the information system for the cadastre of real estate, the statistical databases, and specialized databases), the quality of the included information, as well as the applicability of that information in the real estate valuation process. The disadvantages, limitations and shortcomings of databases are highlighted, and measures are proposed to increase their usefulness.
