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"Capital market Computer simulation."
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Global algorithmic capital markets : high frequency trading, dark pools, and regulatory challenges
Global capital markets have undergone fundamental transformations in recent years and, as a result, have become extraordinarily complex and opaque. Trading space is no longer measured in minutes or seconds but in time units beyond human perception: milliseconds, microseconds, and even nanoseconds. Technological advances have thus scaled up imperceptible and previously irrelevant time differences into operationally manageable and enormously profitable business opportunities for those with the proper high-tech trading tools. These tools include the fastest private communication and trading lines, the most powerful computers and sophisticated algorithms capable of speedily analysing incoming news and trading data and determining optimal trading strategies in microseconds, as well as the possession of gigantic collections of historic and real-time market data. 0Fragmented capital markets are also becoming a rapidly growing reality in Europe and Asia, and are an established feature of U.S. trading. This raises urgent market governance issues that have largely been overlooked. Global Algorithmic Capital Markets seeks to understand how recent market transformations are affecting core public policy objectives such as investor protection and reduction of systemic risk, as well as fairness, efficiency, and transparency. 0The operation and health of capital markets affect all of us and have profound implications for equality and justice in society. This unique set of chapters by leading scholars, industry insiders, and regulators discusses ways to strengthen market governance for the benefit of society at whole.
Book
Covariance Regression Analysis
This article introduces covariance regression analysis for a p-dimensional response vector. The proposed method explores the regression relationship between the p-dimensional covariance matrix and auxiliary information. We study three types of estimators: maximum likelihood, ordinary least squares, and feasible generalized least squares estimators. Then, we demonstrate that these regression estimators are consistent and asymptotically normal. Furthermore, we obtain the high dimensional and large sample properties of the corresponding covariance matrix estimators. Simulation experiments are presented to demonstrate the performance of both regression and covariance matrix estimates. An example is analyzed from the Chinese stock market to illustrate the usefulness of the proposed covariance regression model. Supplementary materials for this article are available online.
Journal Article
Predicting Stock Price Using Two-Stage Machine Learning Techniques
Stock market forecasting is considered to be a challenging topic among time series forecasting. This study proposes a novel two-stage ensemble machine learning model named SVR-ENANFIS for stock price prediction by combining features of support vector regression (SVR) and ensemble adaptive neuro fuzzy inference system (ENANFIS). In the first stage, the future values of technical indicators are forecasted by SVR. In the second stage, ENANFIS is utilized to forecast the closing price based on prediction results of first stage. Finally, the proposed model SVR-ENANFIS is tested on 4 securities randomly selected from the Shanghai and Shenzhen Stock Exchanges with data collected from 2012 to 2017, and the predictions are completed 1–10, 15 and 30 days in advance. The experimental results show that the proposed model SVR-ENANFIS has superior prediction performance than single-stage model ENANFIS and several two-stage models such as SVR-Linear, SVR-SVR, and SVR-ANN.
Journal Article
Approximate Bayesian Estimation of Stochastic Volatility in Mean Models Using Hidden Markov Models: Empirical Evidence from Emerging and Developed Markets
The stochastic volatility in mean (SVM) model proposed by Koopman and Uspensky (J Appl Econ 17:667–689, 2002) is revisited. This paper has two goals. The first is to offer a methodology that requires less computational time in simulations and estimates compared with others proposed in the literature as in Abanto-Valle et al. (Q Rev Econ Financ 80:272–286, 2021) and others. To achieve the first goal, we propose to approximate the likelihood function of the model applying Hidden Markov Models machinery to make possible Bayesian inference in real-time. We sample from the posterior distribution of parameters with a multivariate Normal distribution with mean and variance given by the posterior mode and the inverse of the Hessian matrix evaluated at this posterior mode using importance sampling. Further, the frequentist properties of estimators are analyzed conducting a simulation study. The second goal is to provide empirical evidence estimating the SVM model using daily data for five Latin American stock markets, USA, England, Japan and China. The results indicate that volatility negatively impacts returns, suggesting that the volatility feedback effect is stronger than the effect related to the expected volatility. This result is similar to the findings of Koopman and Uspensky (J Appl Econ 17:667–689, 2002), where the respective coefficient is negative but non statistically significant. However, in our case, all countries (except Peru and China) presents negative and statistically significant effects. Our results are similar to those found using Hamiltonian Monte Carlo (HMC) and Riemannian HMC methods based on Abanto-Valle et al. (Q Rev Econ Financ 80:272–286, 2021).
Journal Article
Mental Simulation and Product Evaluation: The Affective and Cognitive Dimensions of Process Versus Outcome Simulation
In this research, the authors examine the role of process versus outcome simulation in product evaluation and demonstrate how manipulating the type of information-processing mode (cognitive vs. affective) leads to unique effects in process and outcome simulation. The article begins with the premise that when consumers do not have well-formed preferences for a product, they tend to focus on the usage process. The authors predict and find that outcome simulation is more effective than process simulation in increasing product evaluation under a cognitive mode, whereas process simulation is more effective than outcome simulation under an affective mode. Establishing boundary conditions, the authors further show the effect of two important moderators that alter consumers' focus on/away from the product's usage process. Specifically, they show a reversal of the effect for each type of mental simulation for hedonic products, for which product benefits are the more salient aspect (vs. the usage process). Furthermore, a distant-future (vs. near-future) evaluation frame shifts people's focus away from the usage process toward product benefits and reverses the effect of each type of simulation. The authors conclude with a discussion of theoretical and managerial implications.
Journal Article
An Application of the IFM Method for the Risk Assessment of Financial Instruments
External influences or behavioral biases can affect the way risk is perceived. This paper studies the prediction of VaR (Value at Risk) as a measure of the risk of loss for investments on financial products. Our aim is to predict the percentage of loss that a financial product would have in the future to assess the risks and determine the potential loss of a security in the stock market, thus reducing reasoning influenced by feelings for bank and financial firms seeking to deploy AI and advanced automation. We used the IFM (inference function for margins) method in different market scenarios, with particular emphasis on the strengths and weaknesses of it. The study is assessed on single product level with the skewed studen-t GARCH(1,1) model and portfolio level with t-copulas for the inter-dependencies. It has been shown that under normal market conditions the risk is predicted properly for both levels. However, when an unexpected market event occurs, the prediction fails. To address this limitation, a combined model with sentiment analysis and regression is proposed for further investigation as a future work.
Journal Article
An Automatic Incident Detection Method for a Vehicle-to-Infrastructure Communication Environment: Case Study of Interstate 64 in Missouri
Transportation agencies continuously and consistently work to improve the processes and systems for mitigating the impacts of roadway incidents. Such efforts include utilizing emerging technologies to reduce the detection and response time to roadway incidents. Vehicle-to-infrastructure (V2I) communication is an emerging transportation technology that enables communication between a vehicle and the infrastructure. This paper proposes an algorithm that utilizes V2I probe data to automatically detect roadway incidents. A simulation testbed was developed for a segment of Interstate 64 in St. Louis, Missouri to evaluate the performance of the V2I-based automatic incident detection algorithm. The proposed algorithm was assessed during peak and off-peak periods with various incident durations, under several market penetration rates for V2I technology, and with different spatial resolutions for incident detection. The performance of the proposed algorithm was assessed on the basis of the detection rate, time to detect, detection accuracy, and false alarm rate. The performance measures obtained for the V2I-based automatic incident detection algorithm were compared with California #7 algorithm performance measures. The California #7 algorithm is a traditional automatic incident detection algorithm that utilizes traffic sensors data, such as inductive loop detectors, to identify roadway events. The California #7 algorithm was implemented in the Interstate 64 simulation testbed. The case study results indicated that the proposed V2I-based algorithm outperformed the California #7 algorithm. The detection rate for the proposed V2I-based incident detection algorithm was 100% in market penetrations of 50%, 80%, and 100%. However, the California #7 algorithm’s detection rate was 71%.
Journal Article
An agent based decentralized matching macroeconomic model
In this paper we present a macroeconomic microfounded framework with heterogeneous agents—individuals, firms, banks—which interact through a decentralized matching process presenting common features across four markets—goods, labor, credit and deposit. We study the dynamics of the model by means of computer simulation. Some macroeconomic properties emerge such as endogenous business cycles, nominal GDP growth, unemployment rate fluctuations, the Phillips curve, leverage cycles and credit constraints, bank defaults and financial instability, and the importance of government as an acyclical sector which stabilize the economy. The model highlights that even extended crises can endogenously emerge. In these cases, the system may remain trapped in a large unemployment status, without the possibility to quickly recover unless an exogenous intervention takes place.
Journal Article
Reproductive Failure and Long-Term Profitability of Spring- and Fall-Calving Beef Cows
We determined how reproductive failure impacts the long-term profitability of beef cows in spring- and fall-calving herds. Simulation models were established to generate distributions of net present value, payback periods, and breakeven prices of calves when a dam fails to wean zero, one, or two calves over her life. Results indicate that giving a dam another calving opportunity after failing to wean a calf would likely result in her being unprofitable. A producer would be better off selling the open dam than giving her another chance to breed. This illustrates the value in selecting replacement heifers based on fertility.
Journal Article