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
Modeling implied volatility(IV)is important for option pricing,hedging,and risk management.Previous studies of deterministic implied volatility functions(DIVFs)propose two parameters,moneyness and time to maturity,to ...Modeling implied volatility(IV)is important for option pricing,hedging,and risk management.Previous studies of deterministic implied volatility functions(DIVFs)propose two parameters,moneyness and time to maturity,to estimate implied volatility.Recent DIVF models have included factors such as a moving average ratio and relative bid-ask spread but fail to enhance modeling accuracy.The current study offers a generalized DIVF model by including a momentum indicator for the underlying asset using a relative strength index(RSI)covering multiple time resolutions as a factor,as momentum is often used by investors and speculators in their trading decisions,and in contrast to volatility,RSI can distinguish between bull and bear markets.To the best of our knowledge,prior studies have not included RSI as a predictive factor in modeling IV.Instead of using a simple linear regression as in previous studies,we use a machine learning regression algorithm,namely random forest,to model a nonlinear IV.Previous studies apply DVIF modeling to options on traditional financial assets,such as stock and foreign exchange markets.Here,we study options on the largest cryptocurrency,Bitcoin,which poses greater modeling challenges due to its extreme volatility and the fact that it is not as well studied as traditional financial assets.Recent Bitcoin option chain data were collected from a leading cryptocurrency option exchange over a four-month period for model development and validation.Our dataset includes short-maturity options with expiry in less than six days,as well as a full range of moneyness,both of which are often excluded in existing studies as prices for options with these characteristics are often highly volatile and pose challenges to model building.Our in-sample and out-sample results indicate that including our proposed momentum indicator significantly enhances the model’s accuracy in pricing options.The nonlinear machine learning random forest algorithm also performed better than a simple linear regression.Compared to prevailing option pricing models that employ stochastic variables,our DIVF model does not include stochastic factors but exhibits reasonably good performance.It is also easy to compute due to the availability of real-time RSIs.Our findings indicate our enhanced DIVF model offers significant improvements and may be an excellent alternative to existing option pricing models that are primarily stochastic in nature.展开更多
This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets.It is also motivated by a lack of empirical studies on wh...This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets.It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns,particularly at the sectoral level of data.We specifically assess Bitcoin prices’ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons,based on daily data from November 22,2017,to December,30,2021.The findings show that Bitcoin prices have significant predictive power for US stock volatility,with an inverse relationship between Bitcoin prices and stock sector volatility.Regardless of the stock sectors or number of forecast horizons,the model that includes Bitcoin prices consistently outperforms the benchmark historical average model.These findings are independent of the volatility measure used.Using Bitcoin prices as a predictor yields higher economic gains.These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors,which is important for practitioners and policymakers.展开更多
This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—cove...This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—covering momentum,volatility,volume,and trend-related technical indicators—are subjected to three distinct feature selection approaches.Specifically,mutual information(MI),recursive feature elimination(RFE),and random forest importance(RFI).By extracting an optimal set of 20 predictors,the proposed framework aims to mitigate redundancy and overfitting while enhancing interpretability.These feature subsets are integrated into support vector regression(SVR),Huber regressors,and k-nearest neighbors(KNN)models to forecast the prices of three leading cryptocurrencies—Bitcoin(BTC/USDT),Ethereum(ETH/USDT),and Binance Coin(BNB/USDT)—across horizons ranging from 1 to 20 days.Model evaluation employs the coefficient of determination(R2)and the root mean squared logarithmic error(RMSLE),alongside a walk-forward validation scheme to approximate real-world trading contexts.Empirical results indicate that incorporating momentum and volatility measures substantially improves predictive accuracy,with particularly pronounced effects observed at longer forecast windows.Moreover,indicators related to volume and trend provide incremental benefits in select market conditions.Notably,an 80%–85% reduction in the original feature set frequently maintains or enhances model performance relative to the complete indicator set.These findings highlight the critical role of targeted feature selection in addressing high-dimensional financial data challenges while preserving model robustness.This research advances the field of cryptocurrency forecasting by offering a rigorous comparison of feature selection methods and their effects on multiple digital assets and prediction horizons.The outcomes highlight the importance of dimension-reduction strategies in developing more efficient and resilient forecasting algorithms.Future efforts should incorporate high-frequency data and explore alternative selection techniques to further refine predictive accuracy in this highly volatile domain.展开更多
The freshness and quality indices of whiting (Merlangius merlangus) influenced by a large number of chemical volatile compounds, are here analyzed in order to select the most relevant compounds as predictors for these...The freshness and quality indices of whiting (Merlangius merlangus) influenced by a large number of chemical volatile compounds, are here analyzed in order to select the most relevant compounds as predictors for these indices. The selection process was performed by means of recent statistical variable selection methods, namely robust model-free feature screening, based on quantile correlation and composite quantile correlation. On the one hand, compounds 2-Methyl-1-butanol, 3-Methyl-1-butanol, Ethanol, Trimethylamine, 3-Methyl butanal, 2-Methyl-1-propanol, Ethylacetate, 1-Butanol and 2,3-Butanedione were identified as major predictors for the freshness index and on the other hand, compounds 3-Methyl-1-butanol, 2-Methyl-1- butanol, Ethanol, 3-Methyl butanal, 3-Hydroxy-2-butanone, 1-Butanol, 2,3-Butane- dione, 3-Pentanol, 3-Pentanone and 2-Methyl-1-propanol were identified as major predictors for the quality index.展开更多
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.
文摘Modeling implied volatility(IV)is important for option pricing,hedging,and risk management.Previous studies of deterministic implied volatility functions(DIVFs)propose two parameters,moneyness and time to maturity,to estimate implied volatility.Recent DIVF models have included factors such as a moving average ratio and relative bid-ask spread but fail to enhance modeling accuracy.The current study offers a generalized DIVF model by including a momentum indicator for the underlying asset using a relative strength index(RSI)covering multiple time resolutions as a factor,as momentum is often used by investors and speculators in their trading decisions,and in contrast to volatility,RSI can distinguish between bull and bear markets.To the best of our knowledge,prior studies have not included RSI as a predictive factor in modeling IV.Instead of using a simple linear regression as in previous studies,we use a machine learning regression algorithm,namely random forest,to model a nonlinear IV.Previous studies apply DVIF modeling to options on traditional financial assets,such as stock and foreign exchange markets.Here,we study options on the largest cryptocurrency,Bitcoin,which poses greater modeling challenges due to its extreme volatility and the fact that it is not as well studied as traditional financial assets.Recent Bitcoin option chain data were collected from a leading cryptocurrency option exchange over a four-month period for model development and validation.Our dataset includes short-maturity options with expiry in less than six days,as well as a full range of moneyness,both of which are often excluded in existing studies as prices for options with these characteristics are often highly volatile and pose challenges to model building.Our in-sample and out-sample results indicate that including our proposed momentum indicator significantly enhances the model’s accuracy in pricing options.The nonlinear machine learning random forest algorithm also performed better than a simple linear regression.Compared to prevailing option pricing models that employ stochastic variables,our DIVF model does not include stochastic factors but exhibits reasonably good performance.It is also easy to compute due to the availability of real-time RSIs.Our findings indicate our enhanced DIVF model offers significant improvements and may be an excellent alternative to existing option pricing models that are primarily stochastic in nature.
文摘This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets.It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns,particularly at the sectoral level of data.We specifically assess Bitcoin prices’ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons,based on daily data from November 22,2017,to December,30,2021.The findings show that Bitcoin prices have significant predictive power for US stock volatility,with an inverse relationship between Bitcoin prices and stock sector volatility.Regardless of the stock sectors or number of forecast horizons,the model that includes Bitcoin prices consistently outperforms the benchmark historical average model.These findings are independent of the volatility measure used.Using Bitcoin prices as a predictor yields higher economic gains.These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors,which is important for practitioners and policymakers.
文摘This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—covering momentum,volatility,volume,and trend-related technical indicators—are subjected to three distinct feature selection approaches.Specifically,mutual information(MI),recursive feature elimination(RFE),and random forest importance(RFI).By extracting an optimal set of 20 predictors,the proposed framework aims to mitigate redundancy and overfitting while enhancing interpretability.These feature subsets are integrated into support vector regression(SVR),Huber regressors,and k-nearest neighbors(KNN)models to forecast the prices of three leading cryptocurrencies—Bitcoin(BTC/USDT),Ethereum(ETH/USDT),and Binance Coin(BNB/USDT)—across horizons ranging from 1 to 20 days.Model evaluation employs the coefficient of determination(R2)and the root mean squared logarithmic error(RMSLE),alongside a walk-forward validation scheme to approximate real-world trading contexts.Empirical results indicate that incorporating momentum and volatility measures substantially improves predictive accuracy,with particularly pronounced effects observed at longer forecast windows.Moreover,indicators related to volume and trend provide incremental benefits in select market conditions.Notably,an 80%–85% reduction in the original feature set frequently maintains or enhances model performance relative to the complete indicator set.These findings highlight the critical role of targeted feature selection in addressing high-dimensional financial data challenges while preserving model robustness.This research advances the field of cryptocurrency forecasting by offering a rigorous comparison of feature selection methods and their effects on multiple digital assets and prediction horizons.The outcomes highlight the importance of dimension-reduction strategies in developing more efficient and resilient forecasting algorithms.Future efforts should incorporate high-frequency data and explore alternative selection techniques to further refine predictive accuracy in this highly volatile domain.
文摘The freshness and quality indices of whiting (Merlangius merlangus) influenced by a large number of chemical volatile compounds, are here analyzed in order to select the most relevant compounds as predictors for these indices. The selection process was performed by means of recent statistical variable selection methods, namely robust model-free feature screening, based on quantile correlation and composite quantile correlation. On the one hand, compounds 2-Methyl-1-butanol, 3-Methyl-1-butanol, Ethanol, Trimethylamine, 3-Methyl butanal, 2-Methyl-1-propanol, Ethylacetate, 1-Butanol and 2,3-Butanedione were identified as major predictors for the freshness index and on the other hand, compounds 3-Methyl-1-butanol, 2-Methyl-1- butanol, Ethanol, 3-Methyl butanal, 3-Hydroxy-2-butanone, 1-Butanol, 2,3-Butane- dione, 3-Pentanol, 3-Pentanone and 2-Methyl-1-propanol were identified as major predictors for the quality index.
