Collecting silver artefacts has traditionally been a very popular hobby.Silver is addictive,therefore the number of potential collectors and investors appears to grow each year.Unfortunately,increases in the interest ...Collecting silver artefacts has traditionally been a very popular hobby.Silver is addictive,therefore the number of potential collectors and investors appears to grow each year.Unfortunately,increases in the interest and buying potentials resulted in a number of forgeries manufactured and introduced to the open antique market.The items such as early silver candlesticks dictate a very high price,for many high quality fakes show very good appearances and matching similarities with originals.Such copies are traditionally manufactured by casting using the original items as patterns.Small details and variances in design features,position and shape of hallmarks,including the final surface quality are usual features to distinguish the fakes from the originals.This paper presents results of a study conducted on several silver candlesticks,including two artefacts bearing features of those produced in the mid 18th century,one original Italian candelabrum from Fascist era,and small candlesticks made in the early 20th century.Also,the paper presents some interesting contemporary coins-replicas of many those produced in different countries.The coins were offered for sale by unscrupulous dealers via auctions and e-bays.Finally the main results and findings from this study are discussed from a manufacturing point of view,such as fabrication technology,surface quality and hallmarks,which will help the collectors,dealers and investors to detect and avoid forgeries.展开更多
Candlestick charts display the high,low,opening,and closing prices in a specific period.Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate.These patterns capture i...Candlestick charts display the high,low,opening,and closing prices in a specific period.Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate.These patterns capture information on the candles.According to Thomas Bulkowski’s Encyclopedia of Candlestick Charts,there are 103 candlestick patterns.Traders use these patterns to determine when to enter and exit.Candlestick pattern classification approaches take the hard work out of visually identifying these patterns.To highlight its capabilities,we propose a two-steps approach to recognize candlestick patterns automatically.The first step uses the Gramian Angular Field(GAF)to encode the time series as different types of images.The second step uses the Convolutional Neural Network(CNN)with the GAF images to learn eight critical kinds of candlestick patterns.In this paper,we call the approach GAF-CNN.In the experiments,our approach can identify the eight types of candlestick patterns with 90.7%average accuracy automatically in real-world data,outperforming the LSTM model.展开更多
Continuous glucose monitoring(CGM)is a popular technology among the diabetic population,especially in patients with type 1 diabetes and those with type 2 diabetes treated with insulin.The American Diabetes Association...Continuous glucose monitoring(CGM)is a popular technology among the diabetic population,especially in patients with type 1 diabetes and those with type 2 diabetes treated with insulin.The American Diabetes Association recommends standardization of CGM reports with visual cues,such as the ambulatory glucose profile.Nevertheless,interpreting this report requires training and time for CGM to be cost-efficient.In this work it has been proposed to incorporate Japanese candlestick charts in glucose monitoring.These graphs are used in price analysis in financial markets and are easier to view.Each candle provides extra information to make prudent decisions since it reports the opening,maximum,minimum and closing glucose levels of the chosen time frame,usually the daily one.The Japanese candlestick chart is an interesting tool to be considered in glucose control.This graphic representation allows identification of glucose trends easily through the colors of the candles and maximum and minimum glucose values.展开更多
The structural modeling of open-high-low-close(OHLC)data contained within the candlestick chart is crucial to financial practice.However,the inherent constraints in OHLC data pose immense challenges to its structural ...The structural modeling of open-high-low-close(OHLC)data contained within the candlestick chart is crucial to financial practice.However,the inherent constraints in OHLC data pose immense challenges to its structural modeling.Models that fail to process these constraints may yield results deviating from those of the original OHLC data structure.To address this issue,a novel unconstrained transformation method,along with its explicit inverse transformation,is proposed to properly handle the inherent constraints of OHLC data.A flexible and effective framework for structurally modeling OHLC data is designed,and the detailed procedure for modeling OHLC data through the vector autoregression and vector error correction model are provided as an example of multivariate time-series analysis.Extensive simulations and three authentic financial datasets from the Kweichow Moutai,CSI 100 index,and 50 ETF of the Chinese stock market demonstrate the effectiveness and stability of the proposed modeling approach.The modeling results of support vector regression provide further evidence that the proposed unconstrained transformation not only ensures structural forecasting of OHLC data but also is an effective feature-extraction method that can effectively improve the forecasting accuracy of machine-learning models for close prices.展开更多
The decomposition-based vector autoregressive model （DVAR） provides a new framework for scrutinizing the efficiency of technical analysis in forecasting stock returns. However, its relation- ships with other technic...The decomposition-based vector autoregressive model （DVAR） provides a new framework for scrutinizing the efficiency of technical analysis in forecasting stock returns. However, its relation- ships with other technical indicators still remain unknown. This paper investigates the relationships of DVAR model with the Japanese Candlestick indicators using simulations, theoretical explanations and empirical studies. The main finding of this paper is that both lower and upper shadows in Japanese Candlestick Granger contribute to the DVAR model explanation power, and thus, providing useful information for improving the DVAR forecasts. This finding makes sense as it means that the infor- mation contained in the lower and upper shadows should be used when modeling the stock returns with DVAR. Empirical studies performed on China SSEC stock index demonstrate that DVAR model with upper and lower shadows as exogenous variables does have informative and valuable out-of-sample forecasts.展开更多
Since the candlestick patterns were mined,there is a contentious dispute on whether the candlestick patterns have predictive power in academia.To help resolve the debate,this paper uses the data mining methods of patt...Since the candlestick patterns were mined,there is a contentious dispute on whether the candlestick patterns have predictive power in academia.To help resolve the debate,this paper uses the data mining methods of pattern recognition,pattern clustering and pattern knowledge mining to research the predictive power of candlestick patterns.In addition,we propose the similarity match model and nearest neighbor-clustering algorithm to solve the problem of similarity match and clustering of candlestick series,respectively.The experiment includes testing the predictive power of the Morning Star pattern and Evening Star pattern with the testing dataset of the candlestick series data of Shanghai 180 index component stocks over the latest 10 years.Experimental results show that(1)There have some spurious patterns in the existing candlestick patterns.However,after further classification of a spurious pattern based on its shape feature,those patterns with special shapes still have predictive power.(2)Some patterns do have the predictive power.(3)As there is no precise mathematical definition to describe the existing patterns’predictive power,it is essential to give the mathematical formula for improving the candlestick patterns’prediction performance.展开更多
A Japanese candlestick chart consists of not only the closing price but also the high,low and opening price information.Using the Japanese candlestick,this paper investigates the forecasting power of the shadow in Jap...A Japanese candlestick chart consists of not only the closing price but also the high,low and opening price information.Using the Japanese candlestick,this paper investigates the forecasting power of the shadow in Japanese candlestick chart.Empirical studies performed with the US stock market show that 1)there is a significant Halloween effect in the shadow;2)shadow is valuable for predicting the stock market returns in both statistical and economic sense;3)the predictability reported by the shadow can not be explained by either the CAPM model or the Fama-French three-factor model.This paper confirms that predictability of the stock market can be improved if more price information is used.展开更多
文摘Collecting silver artefacts has traditionally been a very popular hobby.Silver is addictive,therefore the number of potential collectors and investors appears to grow each year.Unfortunately,increases in the interest and buying potentials resulted in a number of forgeries manufactured and introduced to the open antique market.The items such as early silver candlesticks dictate a very high price,for many high quality fakes show very good appearances and matching similarities with originals.Such copies are traditionally manufactured by casting using the original items as patterns.Small details and variances in design features,position and shape of hallmarks,including the final surface quality are usual features to distinguish the fakes from the originals.This paper presents results of a study conducted on several silver candlesticks,including two artefacts bearing features of those produced in the mid 18th century,one original Italian candelabrum from Fascist era,and small candlesticks made in the early 20th century.Also,the paper presents some interesting contemporary coins-replicas of many those produced in different countries.The coins were offered for sale by unscrupulous dealers via auctions and e-bays.Finally the main results and findings from this study are discussed from a manufacturing point of view,such as fabrication technology,surface quality and hallmarks,which will help the collectors,dealers and investors to detect and avoid forgeries.
基金Jun-Hao Chen and Yun-Cheng Tsai are supported in part by the Ministry of Science and Technology of Taiwan under grant 108-2218-E-002-050-.
文摘Candlestick charts display the high,low,opening,and closing prices in a specific period.Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate.These patterns capture information on the candles.According to Thomas Bulkowski’s Encyclopedia of Candlestick Charts,there are 103 candlestick patterns.Traders use these patterns to determine when to enter and exit.Candlestick pattern classification approaches take the hard work out of visually identifying these patterns.To highlight its capabilities,we propose a two-steps approach to recognize candlestick patterns automatically.The first step uses the Gramian Angular Field(GAF)to encode the time series as different types of images.The second step uses the Convolutional Neural Network(CNN)with the GAF images to learn eight critical kinds of candlestick patterns.In this paper,we call the approach GAF-CNN.In the experiments,our approach can identify the eight types of candlestick patterns with 90.7%average accuracy automatically in real-world data,outperforming the LSTM model.
文摘Continuous glucose monitoring(CGM)is a popular technology among the diabetic population,especially in patients with type 1 diabetes and those with type 2 diabetes treated with insulin.The American Diabetes Association recommends standardization of CGM reports with visual cues,such as the ambulatory glucose profile.Nevertheless,interpreting this report requires training and time for CGM to be cost-efficient.In this work it has been proposed to incorporate Japanese candlestick charts in glucose monitoring.These graphs are used in price analysis in financial markets and are easier to view.Each candle provides extra information to make prudent decisions since it reports the opening,maximum,minimum and closing glucose levels of the chosen time frame,usually the daily one.The Japanese candlestick chart is an interesting tool to be considered in glucose control.This graphic representation allows identification of glucose trends easily through the colors of the candles and maximum and minimum glucose values.
基金the financial support from the Beijing Natural Science Foundation(Grant No.9244030)the National Natural Science Foundation of China(Grant Nos.72021001,11701023).
文摘The structural modeling of open-high-low-close(OHLC)data contained within the candlestick chart is crucial to financial practice.However,the inherent constraints in OHLC data pose immense challenges to its structural modeling.Models that fail to process these constraints may yield results deviating from those of the original OHLC data structure.To address this issue,a novel unconstrained transformation method,along with its explicit inverse transformation,is proposed to properly handle the inherent constraints of OHLC data.A flexible and effective framework for structurally modeling OHLC data is designed,and the detailed procedure for modeling OHLC data through the vector autoregression and vector error correction model are provided as an example of multivariate time-series analysis.Extensive simulations and three authentic financial datasets from the Kweichow Moutai,CSI 100 index,and 50 ETF of the Chinese stock market demonstrate the effectiveness and stability of the proposed modeling approach.The modeling results of support vector regression provide further evidence that the proposed unconstrained transformation not only ensures structural forecasting of OHLC data but also is an effective feature-extraction method that can effectively improve the forecasting accuracy of machine-learning models for close prices.
基金supported by the National Natural Science Foundation of China under Grant No.71401033
文摘The decomposition-based vector autoregressive model （DVAR） provides a new framework for scrutinizing the efficiency of technical analysis in forecasting stock returns. However, its relation- ships with other technical indicators still remain unknown. This paper investigates the relationships of DVAR model with the Japanese Candlestick indicators using simulations, theoretical explanations and empirical studies. The main finding of this paper is that both lower and upper shadows in Japanese Candlestick Granger contribute to the DVAR model explanation power, and thus, providing useful information for improving the DVAR forecasts. This finding makes sense as it means that the infor- mation contained in the lower and upper shadows should be used when modeling the stock returns with DVAR. Empirical studies performed on China SSEC stock index demonstrate that DVAR model with upper and lower shadows as exogenous variables does have informative and valuable out-of-sample forecasts.
文摘Since the candlestick patterns were mined,there is a contentious dispute on whether the candlestick patterns have predictive power in academia.To help resolve the debate,this paper uses the data mining methods of pattern recognition,pattern clustering and pattern knowledge mining to research the predictive power of candlestick patterns.In addition,we propose the similarity match model and nearest neighbor-clustering algorithm to solve the problem of similarity match and clustering of candlestick series,respectively.The experiment includes testing the predictive power of the Morning Star pattern and Evening Star pattern with the testing dataset of the candlestick series data of Shanghai 180 index component stocks over the latest 10 years.Experimental results show that(1)There have some spurious patterns in the existing candlestick patterns.However,after further classification of a spurious pattern based on its shape feature,those patterns with special shapes still have predictive power.(2)Some patterns do have the predictive power.(3)As there is no precise mathematical definition to describe the existing patterns’predictive power,it is essential to give the mathematical formula for improving the candlestick patterns’prediction performance.
基金part by the Fundamental Research Funds for the Central Universities under Grant No.20180233the National Natural Science Foundation of China under Grant No.71901066the Fundamental Research Funds for the Central Universities in UIBE under Grant No.19YB03。
文摘A Japanese candlestick chart consists of not only the closing price but also the high,low and opening price information.Using the Japanese candlestick,this paper investigates the forecasting power of the shadow in Japanese candlestick chart.Empirical studies performed with the US stock market show that 1)there is a significant Halloween effect in the shadow;2)shadow is valuable for predicting the stock market returns in both statistical and economic sense;3)the predictability reported by the shadow can not be explained by either the CAPM model or the Fama-French three-factor model.This paper confirms that predictability of the stock market can be improved if more price information is used.
