M. Thenmozhi

Bio: M. Thenmozhi is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topic(s): Stock market & Volatility (finance). The author has an hindex of 12, co-authored 62 publication(s) receiving 512 citation(s).

Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest

Abstract: There exists vast research articles which predict the stock market as well pricing of stock index financial instruments but most of the proposed models focus on the accurate forecasting of the levels (i.e. value) of the underlying stock index. There is a lack of studies examining the predictability of the direction/sign of stock index movement. Given the notion that a prediction with little forecast error does not necessarily translate into capital gain, this study is an attempt to predict the direction of S&P CNX NIFTY Market Index of the National Stock Exchange, one of the fastest growing financial exchanges in developing Asian countries. Random forest and Support Vector Machines (SVM) are very specific type of machine learning method, and are promising tools for the prediction of financial time series. The tested classification models, which predict direction, include linear discriminant analysis, logit, artificial neural network, random forest and SVM. Empirical experimentation suggests that the SVM outperforms the other classification methods in terms of predicting the direction of the stock market movement and random forest method outperforms neural network, discriminant analysis and logit model used in this study.

Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models

Abstract: The purpose of this paper is to develop and identify the best hybrid model to predict stock index returns. We develop three different hybrid models combining linear ARIMA and non-linear models such as support vector machines (SVM), artificial neural network (ANN) and random forest (RF) models to predict the stock index returns. The performance of ARIMA-SVM, ARIMA-ANN and ARIMA-RF are compared with performance of ARIMA, SVM, ANN and RF models. The various competing models are evaluated in terms of statistical metrics and trading performance criteria via a trading strategy. The analysis shows that the hybrid ARIMA-SVM model is the best forecasting model to achieve high forecast accuracy and better returns.

Does quality of innovation, culture and governance drive FDI?: Evidence from emerging markets

Abstract: This study examines how technology, culture and corporate governance drive inward FDI in emerging economies. A study of 22 emerging economies shows that technology is the major attractive factor influencing inward FDI. Further, FDI increases as technology absorption and innovation capacity increase. The greater the quality of country governance, the greater the influence of corporate governance on FDI. Cultural dimensions such as individualism, masculinity and uncertainty avoidance exhibit a weaker influence on inward FDI, while power distance and indulgence have a stronger influence on inward FDI. Our results support the leapfrogging approach of emerging economies towards promoting innovation and enhancing technology adoption to drive FDI. Interaction effect of country governance further highlighted that the better the governance of a country the impact of technology, innovation, corporate governance and culture in attracting inward FDI also increases.

Forecasting stock returns based on information transmission across global markets using support vector machines

Abstract: This paper provides evidence that forecasts based on global stock returns transmission yield better returns in day trading, for both developed and emerging stock markets. The study investigates the performance of global stock market price transmission information in forecasting stock prices using support vector regression for six global markets--USA (Dow Jones, S&P500), UK (FTSE-100), India (NSE), Singapore (SGX), Hong Kong (Hang Seng) and China (Shanghai Stock Exchange) over the period 1999---2011. The empirical analysis shows that models with other global market price information outperform forecast models based merely on auto-regressive past lags and technical indicators. Shanghai stock index movement was predicted best by Hang Seng Index opening price (57.69), Hang Seng Index by previous day's S&P500 closing price (54.34), FTSE by previous day's S&P500 closing price (57.94), Straits Times Index by previous day's Dow Jones closing price (54.44), Nifty by HSI opening price (60), S&P500 by STI closing price (55.31) and DJIA by HSI opening price (55.22), and Nifty was found to be the most predictable stock index. Trading using global cues-based forecast model generates greater returns than other models in all the markets. The study provides evidence that stock markets across the globe are integrated and the information on price transmission across markets, including emerging markets, can induce better returns in day trading.

Do cross-border acquisitions involving emerging market firms create value: Impact of deal characteristics

Abstract: Purpose – The purpose of this paper is to contribute to M&A literature by explicitly investigating whether cross-border acquisitions involving emerging markets, either as acquirers or as targets, create value and how is the performance outcome in such acquisitions impacted by deal-specific characteristics. Design/methodology/approach – This study uses industry-adjusted operating performance to measure acquisition gains, the Wilcoxon signed rank test to examine value creation potential and OLS regression to evaluate the impact of deal characteristics on acquisition gains. Findings – The authors find very pronounced value destruction when emerging market firms acquire targets in developed markets, the adverse outcome being further aggravated when the mode of acquisition is “tender offer” rather than a “negotiated deal”. On the other hand, when developed market firms acquire targets from emerging markets, there is an even chance of value creation, the outcome being favourably influenced by the pre-acquisitio...

Market Liquidity: Asset Pricing with Liquidity Risk

Abstract: This paper solves explicitly a simple equilibrium model with liquidity risk. In our liquidityadjusted capital asset pricing model, a security s required return depends on its expected liquidity as well as on the covariances of its own return and liquidity with the market return and liquidity. In addition, a persistent negative shock to a security s liquidity results in low contemporaneous returns and high predicted future returns. The model provides a unified framework for understanding the various channels through which liquidity risk may affect asset prices. Our empirical results shed light on the total and relative economic significance of these channels and provide evidence of flight to liquidity. r 2005 Elsevier B.V. All rights reserved.