David Enke

Missouri University of Science and Technology, Engineering Management and Systems Engineering, Faculty Member

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Papers by David Enke

Gas-phase Exposure History Derived from Material-Phase Concentration Profiles

Epidemiology, Nov 1, 2006

Non-reactive gas-phase pollutants such as benzene diffuse into indoor furnishings and leave behin... more Non-reactive gas-phase pollutants such as benzene diffuse into indoor furnishings and leave behind a unique materialphase concentration profile that serves as a record of the past gas-phase indoor concentrations. The inverse problem to be solved is the diffusion equation in a slab such as vinyl flooring. Using knowledge of the present material-phase concentration profile in the slab, we seek to determine the historical material-phase concentration at the surface exposed to indoor air, and hence the historical gas-phase concentration, which can be used directly to determine exposure. The problem as posed has a unique solution that may be solved using a variety of approaches. We use a trained artificial neural network (ANN) to derive solutions for hypothetical exposure scenarios. The ANN results show that it is possible to estimate the intensity and timing of past exposures from the material-phase concentration profile in a building material. The overall method is limited by (1) the resolution of techniques for measuring spatial material-phase concentration profiles, (2) how far back in time we seek to determine exposure and (3) the representational power of the ANN solution. For example, we estimate that this technique can estimate exposure to phenol up to 0.5 y in the past from analyses of vinyl flooring.

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Volatility Forecasting Using a Hybrid GJR-GARCH Neural Network Model

Procedia Computer Science, 2014

Volatility forecasting in the financial markets, along with the development of financial models, ... more Volatility forecasting in the financial markets, along with the development of financial models, is important in the areas of risk management and asset pricing, among others. Previous testing has shown that asymmetric GARCH models outperform other GARCH family models with regard to volatility prediction. Utilizing this information, three popular Neural Network models (Feed-Forward with Back Propagation, Generalized Regression, and Radial Basis Function) are implemented to help improve the performance of the GJR(1,1) method for estimating volatility over the next forty-four trading days. During training and testing, four different economic cycles have been considered between 1997-2011 to represent real and contemporary periods of market calm and crisis. In addition to stress testing for different neural network architectures to assess their performance under various turmoil and normal situations in the U.S. market, their synergy along with another econometric model is also accessed.

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Hedge fund replication using strategy specific factors

Financial Innovation, Mar 3, 2019

Hedge funds have traditionally served wealthy individuals and institutional investors with the pr... more 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.

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

Financial Innovation, Jun 15, 2019

Big data analytic techniques associated with machine learning algorithms are playing an increasin... more 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.

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Cost allocation using intelligent agents for new transmission investment under electricity deregulation

Due to electrical power restructuring a dramatic change has been made to the generation and trans... more Due to electrical power restructuring a dramatic change has been made to the generation and transmission sectors of the power industry. Rules and legislation are continuously changing. The gap between the transmission expansion need and the proposed construction of transmission is widening. The new paradigm for cost allocation suggests that parties who have the need and/or benefit from the new transmission investment should pay the costs. The socialization methods of the past have been shown to be unfair to some market and network participants. The decentralization of the cost allocation must be considered. To promote more competition, transmission has to be expanded or upgraded to remove congestion and market power. The cost allocation of new investment in transmission has to be recalculated. The locational marginal price is used to indicate the benefit for each player. The proposed method is based on agent-based game theory and is compared with traditional cost allocation methods. A real option analysis is applied to deal with future uncertainty factors.

Interest rate prediction: a neuro-hybrid approach with data preprocessing

International Journal of General Systems, Feb 5, 2014

ABSTRACT The following research implements a differential evolution-based fuzzy-type clustering m... more ABSTRACT The following research implements a differential evolution-based fuzzy-type clustering method with a fuzzy inference neural network after input preprocessing with regression analysis in order to predict future interest rates, particularly 3-month T-bill rates. The empirical results of the proposed model is compared against nonparametric models, such as locally weighted regression and least squares support vector machines, along with two linear benchmark models, the autoregressive model and the random walk model. The root mean square error is reported for comparison.

Nonlinear Modeling Using Neural Networks for Trading the Soybean Complex

Procedia Computer Science, 2014

Recently, there has been a spike in the prices and popularity of commodities. On a macroeconomic ... more Recently, there has been a spike in the prices and popularity of commodities. On a macroeconomic level, developing countries are increasing production; while on a microeconomic level, speculative traders are becoming more involved in the market. Agricultural products have a diverse array of factors that can affect the price (i.e. political, government, population, weather, supply and demand). Commodity prices can suffer from extreme volatility in the short term, changing as much as 50% in one year. This research uses the soybean crush spread as a model. The soybean complex adds an interesting component as the underlying soybean product can be crushed into soymeal and soy oil. All three products (soybeans, soymeal, and soy oil) currently have contracts on the Chicago Mercantile Exchange. The crush margin represents the profit margin a processor will receive from crushing the soybeans into the underlying products (soymeal and soy oil). This research adds to the literature of agricultural price forecasting models, using artificial intelligence and nonlinear modeling. The performance of different neural network architectures and inputs to discover desirable returns for both speculative trading and hedging are investigated.

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An expert advisory system for the ISO 9001 quality system

Expert Systems With Applications, Aug 1, 2004

The ISO 9000 quality management system has been widely accepted and adapted as a national standar... more The ISO 9000 quality management system has been widely accepted and adapted as a national standard by most industrial countries. Despite its high popularity and the urgent demand from customers to implement ISO 9000, some major concerns for those organizations that are seeking registration to ISO 9000 include the expensive cost and the lengthy time to implement. The purpose of this paper is to describe an expert advisory system for ISO 9001 implementation by using an expert system shell called Visual Rules Studio. This expert advisory system integrated the ISO 9001 quality system guidelines and an evaluation approach based on the Malcolm Baldrige National Quality Award (MBNQA) criteria into a knowledge-based expert system. By identifying the critical ISO elements and comparing the company's current quality performance with ISO standards, this advisory system provides assessment results and implementation suggestions to the organization. The advisory system has been validated by a group of quality professionals. The following contains a description of the system and a discussion of the validation results. Limitations of the system and recommendations for future research are also discussed.

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A comprehensive cluster and classification mining procedure for daily stock market return forecasting

Neurocomputing, Dec 1, 2017

Data mining and big data analytic techniques are playing an important role in many application fi... more Data mining and big data analytic techniques are playing an important role in many application fields, including the financial markets. However, only few studies have focused on predicting daily stock market returns, and among these studies, the data mining procedures utilized are either incomplete or inefficient. This paper presents a comprehensive data mining process to forecast the daily direction of the S&P 500 Index ETF (SPY) return based on 60 financial and economical features. The fuzzy-means method (FCM) is initially used to cluster the preprocessed data. A principal component analysis (PCA) is applied next to the entire data set and each of seven clusters. The dimension of the entire cleaned data set is then reduced according to the combining results from the entire data set and each cluster. Corresponding to different levels of the dimensionality reduction, twelve new data sets are generated from the entire cleaned data. Artificial neural networks (ANNs) and logistic regression models are then used with the twelve transformed data sets for classification in order to forecast the daily direction of future market returns and indicate the efficiency of dimensionality reduction with PCA. A group of hypothesis tests are performed over the classification and simulation results to show that the ANNs give significantly higher classification accuracy than logistic regression, and that the trading strategies guided by the comprehensive cluster and classification mining procedure based on PCA and ANNs gain higher risk-adjusted profits than the comparison benchmarks, as well as those strategies guided by the forecasts based on PCA and logistic regression models.

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An adaptive stock index trading decision support system

Expert Systems With Applications, Oct 1, 2016

Predicting the direction and movement of stock index prices is difficult, often leading to excess... more Predicting the direction and movement of stock index prices is difficult, often leading to excessive trading, transaction costs, and missed opportunities. Often traders need a systematic method to not only spot trading opportunities, but to also provide a consistent approach, thereby minimizing trading errors and costs. While mechanical trading systems exist, they are usually designed for a specific stock, stock index, or other financial asset, and are often highly dependent on preselected inputs and model parameters that are expected to continue providing trading information well after the initial training or back-tested model development period. The following research leads to a detailed trading model that provides a more effective and intelligent way for recognizing trading signals and assisting investors with trading decisions by utilizing a system that adapts both the inputs and the prediction model based on the desired output. To illustrate the adaptive approach, multiple inputs and modeling techniques are utilized, including neural networks, particle swarm optimization, and denoising. Simulations with stock indexes illustrate how traders can generate higher returns using the developed adaptive decision support system model. The benefits of adding adaptive and intelligent decision making to forecasts are also discussed.

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Intelligent technical analysis based equivolume charting for stock trading using neural networks

Expert Systems With Applications, Feb 1, 2008

It has been long recognized that trading volume provides valuable information for understanding s... more It has been long recognized that trading volume provides valuable information for understanding stock price movement. As such, equivolume charting was developed to consider how stocks appear to move in a volume frame of reference as opposed to a time frame of reference. Two technical indicators, namely the volume adjusted moving average (VAMA) and the ease of movement (EMV) indicator, are developed from equivolume charting. This paper explores the profitability of stock trading by using a neural network model developed to assist the trading decisions of the VAMA and EMV. The generalized regression neural network (GRNN) is chosen and utilized on past S&P 500 index data. For the VAMA, the GRNN is used to predict the future stock prices, as well as the future width size of the equivolume boxes typically utilized on an equivolume chart, for calculating the future value of the VAMA. For the EMV, the GRNN is also used to predict the future value of the EMV. The idea is to further exploit the equivolume potential by using a forecasting system to predict the future equivolume measurements, allowing investors to enter or exit trades earlier. The results show that the stock trading using the neural network with the VAMA and EMV outperforms the results of stock trading generated from the VAMA and EMV without neural network assistance, the simple moving averages (MA) in isolation, and the buy-and-hold trading strategy.

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Using Data Processing Algorithms and Neural Networks to Forecast One-Month Price Moves of the S&P 500 Index

ASME eBooks, Jun 30, 2010

Concepts on Market-Oriented Transmission Investment

International Journal of Power and Energy Systems, 2007

For several years, much of the deregulated power industry has appeared to be in "repair&quot... more For several years, much of the deregulated power industry has appeared to be in "repair" mode; in other words, the general approach seem to be "fix a problem if it shows up and continue to function until another problem is encountered." Although a free market should generate enough signals within the confines of the market rules to sustain itself in the long run, such is not the norm in the case of the electricity industry. In this paper, market incentives for transmission capacity expansion are explored. A real options framework is presented whereby transmission investment can be funded and financial risks can be minimized. A game theory strategy that can be combined with real options is then introduced for calculating the value and cost of transmission investment. As an example, the use of game theory for allocating cost is presented with a simple three-generator, two-bus example. The proposed method has the advantage of considering the social welfare of all participants in order to ensure that the costs are being fairly allocated to those market participants that will realize the most benefit.

Using Neural Networks to Forecast Volatility for an Asset Allocation Strategy Based on the Target Volatility

Procedia Computer Science, 2016

The objective of this study is to use artificial neural networks for volatility forecasting to en... more The objective of this study is to use artificial neural networks for volatility forecasting to enhance the ability of an asset allocation strategy based on the target volatility. The target volatility level is achieved by dynamically allocating between a risky asset and a risk-free cash position. However, a challenge to data-driven approaches is the limited availability of data since periods of high volatility, such as during financial crises, are relatively rare. To resolve this issue, we apply a stability-oriented approach to compare data for the current period to a past set of data for a period of low volatility, providing a much more abundant source of data for comparison. In order to explore the impact of the proposed model, the results of this approach will be compared to different volatility forecast methodologies, such as the volatility index, the historical volatility, the exponentially weighted moving average (EWMA), and the generalized autoregressive conditional heteroskedasticity (GARCH) model. Trading measures are used to evaluate the performance of the models for forecasting volatility. An empirical study of the proposed model is conducted using the Korea Composite Stock Price Index 200 (KOSPI 200) and certificate of deposit interest rates from January, 2006 to February, 2016.

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The adaptive selection of financial and economic variables for use with artificial neural networks

Neurocomputing, 2004

It has been widely accepted that predicting stock returns is not a simple task since many market ... more It has been widely accepted that predicting stock returns is not a simple task since many market factors are involved and their structural relationships are not perfectly linear. Recently, a promising data mining technique in machine learning has been proposed to uncover the predictive relationships of numerous ÿnancial and economic variables. Inspired by the fact that the determinant between these variables and their interrelationships over stock returns changes over time, we explore this issue further by using data mining to uncover the recent relevant variables with the greatest predictive ability. The objective is to examine whether using the recent relevant variables leads to additional improvements in stock return forecasting. Given evidence of non-linearity in the ÿnancial market, the resulting variables are then provided to neural networks, including probabilistic and feed-forward neural networks, for predicting the directions of future excess stock return. The results show that redeveloped neural network models that use the recent relevant variables generate higher proÿts with lower risks than the buy-and-hold strategy, conventional linear regression, and the random walk model, as well as the neural network models that use constant relevant variables.

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Forecasting daily stock market return using dimensionality reduction

Expert Systems With Applications, 2017

In financial markets, it is both important and challenging to forecast the daily direction of the... more In financial markets, it is both important and challenging to forecast the daily direction of the stock market return. Among the few studies that focus on predicting daily stock market returns, the data mining procedures utilized are either incomplete or inefficient, especially when a large amount of features are involved. This paper presents a complete and efficient data mining process to forecast the daily direction of the S&P 500 Index ETF (SPY) return based on 60 financial and economic features. Three mature dimensionality reduction techniques, including principal component analysis (PCA), fuzzy robust principal component analysis (FRPCA), and kernel-based principal component analysis (KPCA) are applied to the whole data set to simplify and rearrange the original data structure. Corresponding to different levels of the dimensionality reduction, twelve new data sets are generated from the entire cleaned data using each of the three different dimensionality reduction methods. Artificial neural networks (ANNs) are then used with the thirty-six transformed data sets for classification to forecast the daily direction of future market returns. Moreover, the three different dimensionality reduction methods are compared with respect to the natural data set. A group of hypothesis tests are then performed over the classification and simulation results to show that combining the ANNs with the PCA gives slightly higher classification accuracy than the other two combinations, and that the trading strategies guided by the comprehensive classification mining procedures based on PCA and ANNs gain significantly higher risk-adjusted profits than the comparison benchmarks, while also being slightly higher than those strategies guided by the forecasts based on the FRPCA and KPCA models.

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Financial Engineering: The Savior Or End Of Engineering Economy?

2007 Annual Conference & Exposition Proceedings, Sep 3, 2020

There has been a major push into the field of "financial engineering" in the last few years, alth... more There has been a major push into the field of "financial engineering" in the last few years, although the field has been growing in both research and education for over two decades. Initially, the field was described as the design (engineering) of financial products (i.e., derivatives), but now it has a much broader interpretation, including the application of advanced tools (i.e., mathematical programming or stochastic processes) to the analysis of financial instruments with the goal of increasing profits and/or decreasing risk. As this field has grown, so have the employment opportunities for engineers in the financial sector, including jobs as analysts with investment banks, hedge funds, brokers, and insurance companies. This has resulted in a number of schools offering graduate programs in Financial Engineering (or similar areas, such as Computational Finance, Mathematical Finance, Quantitative Finance, and Analytical Finance). Inevitably, and in our experience, demand is growing at the undergraduate level for such a major. If offered, it would seem natural that the degree would come from an industrial engineering, engineering management, operations research or systems engineering department. We examine how schools are addressing this issue and also ponder the requirements for such a degree and how it may or may not impact the "traditional" offering of engineering economy. Introduction: Trends and Needs Engineering economy is a mature field, which draws its roots back to the turn of the century. Broadly defined, engineering economy is concerned with the time value of money and economic decision analysis. Studies are generally tied to capital budgeting decisions and may utilize decision criteria, such as present worth or internal rate of return, sensitivity analysis, simulation, and even mathematical programming (advanced studies) in order to make investment decisions.

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Valuation for the Strategic Management of Research and Development Projects: The Deferral Option

Engineering management journal, Dec 1, 2004

Abstract: The most widely used financial technique for evaluating projects is discounted cash flo... more Abstract: The most widely used financial technique for evaluating projects is discounted cash flow; however, discounted cash flow analysis fails to consider flexibility. Real options analysis offers an alternative technique that provides value for the managerial flexibility that is inherent in most R&D projects. This article investigates the deferral option using computer simulation. There are five variables that determine the value of the deferral option, and simulations analyze these variables over a wide range of conditions. Sensitivity analysis on the five variables is performed and the results are discussed.

Case Study: Applying a Regional Cge Model for Estimation of Indirect Economic Losses Due to Damaged Highway Bridges

The Engineering Economist, Nov 26, 2007

ABSTRACT Destruction from natural and man-made disasters can result in extensive damage to the af... more ABSTRACT Destruction from natural and man-made disasters can result in extensive damage to the affected area&#39;s infrastructure. While the destruction results in costs that are necessary to restore the physical destruction and repair of existing infrastructure, a wider economic impact is often indirectly measured and felt. Policy-makers generally focus only on losses that are directly caused by the destruction, such as the replacement of roads and bridges, yet tend to overlook the consequences from indirect economic losses. This study proposes a framework to estimate the indirect economic loss due to damaged bridges within the highway system of a major metropolitan area. For the research, a simulated earthquake within the St. Louis metropolitan region is selected as a case study. The computable general equilibrium (CGE) model is applied as the loss estimation tool for modeling the indirect cost. The study results show that the indirect loss is significant when compared to the direct loss and should therefore be considered by policy-makers when making both pre- and post-disaster infrastructure decisions.

Stock Market Prediction Using a Combination of Stepwise Regression Analysis, Differential Evolution-based Fuzzy Clustering, and a Fuzzy Inference Neural Network

Intelligent Automation and Soft Computing, Dec 1, 2013

ABSTRACT This paper discusses a hybrid prediction model that combines differential evolution-base... more ABSTRACT This paper discusses a hybrid prediction model that combines differential evolution-based fuzzy clustering with a fuzzy inference neural network for performing an index level forecast. In the first phase of the proposed model, stepwise regression analysis is implemented to determine the combination of inputs that have the strongest forecasting ability. Next, the selected variables are grouped by means of a differential evolution-based fuzzy clustering method, allowing the extraction rules to be determined. For the final stage, a fuzzy inference neural network is implemented to predict the market prices by using the extraction rules from the previous stage.

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