Predicting the daily return direction of the stock market using hybrid machine learning algorithms

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.

Eight of ten patients return to daily activities, work, and sports after total knee arthroplasty

BACKGROUND Besides return to work(RTW)and return to sports(RTS),patients also prefer to return to daily activities(RTA)such as walking,sleeping,grocery shopping,and domestic work following total knee arthroplasty(TKA).However,evidence on the timelines and probability of patients’RTA is sparse.AIM To assess the percentage of patients able to RTA,RTW,and RTS after TKA,as well as the timeframe and influencing factors of this return.METHODS A retrospective cohort study with prospectively collected data was conducted at a medium-sized Dutch orthopedic hospital.Assessments of RTA,RTW,and RTS were performed at 3 mo and/or 6 mo following TKA.Investigated factors en-compassed patient characteristics,surgical characteristics,and preoperative patient-reported outcomes.RESULTS TKA patients[n=2063;66 years old(interquartile range[IQR]:7 years);47%male;28 kg/m2(IQR:4 kg/m2)]showed RTA ranging from 28%for kneeling to 94%for grocery shopping,with 20 d(IQR:27 d)spent for putting on shoes to 74 d(IQR:57 d)for kneeling.RTW rates varied from 62%for medium-impact work to 87%for low-impact work,taking 33 d(IQR:29 d)to 78 d(IQR:55 d).RTS ranged from 48%for medium-impact sports to 90%for low-impact sports,occurring within 43 d(IQR:24 d)to 90 d(IQR:60 d).One or more of the investigated factors influenced the return to each of the 14 activities examined,with R²values ranging from 0.013 to 0.127.CONCLUSION Approximately 80%of patients can RTA,RTW,and RTS within 6 mo after TKA.Return is not consistently in-fluenced by predictive factors.Results help set realistic pre-and postoperative expectations.

The volatility of returns from commodity futures:evidence from India

Background:This paper examines the pattern of the volatility of the daily return of select commodity futures in India and explores the extent to which the select commodity futures satisfy the Samuelson hypothesis.Methods:One commodity future from each group of futures is chosen for the analysis.The select commodities are potato,gold,crude oil,and mentha oil.The data are collected from MCX India over the period 2004–2012.This study uses several econometric techniques for the analysis.The GARCH model is introduced for examining the volatility of commodity futures.One of the key contributions of the paper is the use of theβterm of the GARCH model to address the Samuelson hypothesis.Result:The Samuelson hypothesis,when tested by daily returns and using standard deviation as a crude measure of volatility,is supported for gold futures only,as per the value ofβ(the GARCH effect).The values of the rolling standard deviation,used as a measure of the trend in the volatility of daily returns,exhibits a decreasing volatility trend for potato futures and an increasing volatility trend for gold futures in all contract cycles.The result of the GARCH(1,1)model suggests the presence of persistent volatility and the prevalence of long memory for the select commodity futures,except potato futures.Conclusions:The study sheds light on significant characteristics of the daily return volatility of the commodity futures under analysis.The results suggest the existence of a developed market for the gold and crude oil futures(with volatility clustering)and show that the maturity effect is only valid for the gold futures.

Spectral Analysis of Stock Data Series and Evidence of Day-of-the-Week Effects

Daily return series of Dow Jones Industrial Average Index (DJIA) and Shanghai Conposite Index are investigated using spectral analysis methods. The day of the week effect is found in the frequency domain in both stock markets. Time domain performances of the daily returns are also studied. Although both markets have a clear weekly component in the frequency domain, they show some different behaviors with respect to the day of the week effects.