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M

M. Thenmozhi

Researcher at Indian Institute of Technology Madras

Publications -  70
Citations -  719

M. Thenmozhi is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Autoregressive integrated moving average & Stock market. The author has an hindex of 12, co-authored 62 publications receiving 512 citations.

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Trading and forecasting performance of different hybrid ARIMA – neural network models for stock returns

TL;DR: In this article, the authors examined the performance of different hybrid methodologies that combine ARIMA and ANN to forecast stock market returns and showed that hybrid models outperform independent models and the hybrid ARIMABP model achieves greater accuracy.
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Determinants of Aggregate Credit Flows to U.S. Corporate and Noncorporate Sector

TL;DR: In this article, the authors explore the financial accounts of the United States to analyze the synchronicity in bank and nonbank credit flows with the fund flow patterns of U.S. nonfinancial corporate and non-corporate entities.

Performance implications of diversification strategies during institutional transformations: an emerging market study

TL;DR: In this paper, the authors developed a comprehensive model that examines the impact of corporate diversification, and its interaction effects with ownership structure, industry structure and firm size, in explaining firm performance for three distinct phases of institutional development in an Indian context.

Market value added and share price behaviour an empirical study of bse sensex companies

M. Thenmozhi
TL;DR: In this article, the relationship between share price and market value added in relation to other performance measures like Return on Investment, Return of Net Worth and Earnings per share with particular reference to BSE Sensex companies.
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Information quality and banking success: a theoretical model with empirical validation

TL;DR: The results suggest that information quality has a positive and significant effect on banking success from an internal user perspective.