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| 1 | Predicting the daily return direction of the stock market using hybrid machine learning algorithms显示文摘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. | Xiao Zhong David Enke | 2019 | Financial Innovation2019，5，1： | 9 |
| 2 | Valuation for the strategic managment of research and development projects 显示文摘 | Neal Lewis David Enke David Spurlock | 2004 | Faculty Research & Creative Works2004，12，： | 1 |
| 3 | The Adaptive Selection of Financial and Economic Variables for Use with Artificial Neural Networks显示文摘 | Suraphan Thawornwong David Enke | 2004 | Neurocomputing2004，56，： | 1 |
| 4 | The Adaptive Selection of Financial and Economic Variables for Use with Artificial Neural Networks 显示文摘 | Suraphan Thawomwong David Enke | 2004 | Neurocomputing2004，56，5： | 1 |
| 5 | The Adaptive Selection of Financial and Economic Variables for Use with Artificial Neural Networks 显示文摘 | Suraphan Thawomwong David Enke | 2004 | Neurocomputing2004，56，5： | 1 |
| 6 | Hedge fund replication using strategy specific factors显示文摘Hedge funds have traditionally served wealthy individuals and institutional investors with the promise of delivering protection of capital and uncorrelated positive returns irrespective of market direction,allowing them to better manage portfolio risk.However,the financial crisis of 2008 has heightened investor sensitivity to the high fees,illiquidity,lack of transparency,and lockup periods typically associated with hedge funds.Hedge fund replication products,or clones,seek to answer these challenges by providing daily liquidity,transparency,and immediate exposure to a desired hedge fund strategy.Nonetheless,although lowering cost and adding simplicity by using a common set of factors,traditional replication products might offer lower risk-reward performance compared to hedge funds.This research explores hedge fund replication further by examining the importance of constructing clones with specific factors relevant to each hedge fund strategy,and then compares the strategy specific clone risk and reward performance against both actual hedge fund performance and hedge fund clones constructed using a more general set of common factors.Testing shows that using strategy specific factors to replicate common hedge fund strategies can offer superior risk-reward performance compared to previous general model clones. | Sujit Subhash David Enke | 2019 | Financial Innovation2019，5，1： | 0 |
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