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| 1 | Indian stock market prediction using artificial neural networks on tick data显示文摘Introduction:Nowadays,the most significant challenges in the stock market is to predict the stock prices.The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature.Case description:Support Vector Machines(SVM)and Artificial Neural Networks(ANN)are widely used for prediction of stock prices and its movements.Every algorithm has its way of learning patterns and then predicting.Artificial Neural Network(ANN)is a popular method which also incorporate technical analysis for making predictions in financial markets.Discussion and evaluation:Most common techniques used in the forecasting of financial time series are Support Vector Machine(SVM),Support Vector Regression(SVR)and Back Propagation Neural Network(BPNN).In this article,we use neural networks based on three different learning algorithms,i.e.,Levenberg-Marquardt,Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared.Conclusion:All three algorithms provide an accuracy of 99.9%using tick data.The accuracy over 15-min dataset drops to 96.2%,97.0%and 98.9%for LM,SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data. | Dharmaraja Selvamuthu Vineet Kumar Abhishek Mishra | 2019 | Financial Innovation2019，5，1： | 2 |
| 2 | Spectral characterization, cyclic voltammetry , morphology, biological activities and DNA cleaving studies of amino acid Schiff base metal( Ⅱ ) complexes显示文摘 | Neelakantan M A Rusalraj F Dharmaraja J | 2008 | Spectrochimica Acta Part A2008，71，4： | 1 |
| 3 | Extraction of fission palladium (II) from nitric acid by benzoylmethylenetriphenylphosphorane (BMTPP) 显示文摘 | Mohan Raj M Dharmaraja A Panchantheswaran K | 2006 | Hydrometailurgy2006，84，： | 1 |
| 4 | Optimal stabilityfor trapezoidal - backward difference split - steps 显示文摘 | DHARMARAJA S WANG Y STRANG G | 2010 | IMAJournal of Numerical Analysis2010，30，1： | 1 |
| 5 | Spec- tral characterization, cyclic voltammetry, morphology bio- logical activities and DNA cleaving studies of amino acid Schiff base mate1 ( U ) complexes 显示文摘 | Neelakantana M A Rusalraj F Dharmaraja J | 2008 | Spectrochim Aeta Part A : Mol Biomol SPectro2008，71，4： | 1 |
| 6 | Robotic Partial Nephrectomy with Superselective Versus Main Artery Clamping: A Retrospective Comparison显示文摘 | Mihir M. Desai Andre Luis de Castro Abreu Scott Leslie Jei Cai Eric Yi-Hsiu Huang Pierre-Marie Lewandowski Dennis Lee Arjuna Dharmaraja Andre K. Berger Alvin Goh Osamu Ukimura Monish Aron Inderbir S. Gill | 2014 | European Urology2014，，： | 1 |
| 7 | Portfolio optimization of credit risky bonds: a semi-Markov process approach显示文摘This article presents a semi-Markov process based approach to optimally select a portfolio consisting of credit risky bonds.The criteria to optimize the credit portfolio is based on l_(∞)-norm risk measure and the proposed optimization model is formulated as a linear programming problem.The input parameters to the optimization model are rate of returns of bonds which are obtained using credit ratings assuming that credit ratings of bonds follow a semi-Markov process.Modeling credit ratings by semi-Markov processes has several advantages over Markov chain models,i.e.,it addresses the ageing effect present in the credit rating dynamics.The transition probability matrices generated by semi-Markov process and initial credit ratings are used to generate rate of returns of bonds.The empirical performance of the proposed model is analyzed using the real data.Further,comparison of the proposed approach with the Markov chain approach is performed by obtaining the efficient frontiers for the two models. | Puneet Pasricha Dharmaraja Selvamuthu Guglielmo D’Amico Raimondo Manca | 2020 | Financial Innovation2020，6，1： | 1 |
| 8 | FDG PET/CT images demonstrating epididymo-orchitis in a patient with HIV,acute kidney injury and known epididymo-orchitis on scrotal ultrasound显示文摘 | Chopra S Dharmaraja A Mehta P | 2015 | Clin Nucl Med2015，40，2： | 1 |
| 9 | Mycobacterium tuberculosis has diminished capacity to counteract redox stress induced by elevated levels of endogenous superoxide显示文摘 | Tyagi P Dharmaraja A T Bhaskar A | 2015 | Free Radic Biol Med2015，36，5： | 1 |
| 10 | A Markov regenerative process with recurrence time and its application显示文摘This study proposes a non-homogeneous continuous-time Markov regenerative process with recurrence times,in particular,forward and backward recurrence processes.We obtain the transient solution of the process in the form of a generalized Markov renewal equation.A distinguishing feature is that Markov and semi-Markov processes result as special cases of the proposed model.To model the credit rating dynamics to demonstrate its applicability,we apply the proposed stochastic process to Standard and Poor’s rating agency’s data.Further,statistical tests confirm that the proposed model captures the rating dynamics better than the existing models,and the inclusion of recurrence times significantly impacts the transition probabilities. | Puneet Pasricha Dharmaraja Selvamuthu | 2021 | Financial Innovation2021，7，1： | 0 |
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