Qualification: 
Ph.D, MPhil, MA
bala@amrita.edu

Dr. Balasubramanian P. has fifteen years experience in teaching and research and two years of industrial experience. He completed his Ph. D. dissertation from Jawaharlal Nehru University. He teaches core courses in economics and elective courses in finance like financial derivatives, financial modeling and valuation in the MBA programme and behavioral economics for Ph. D. scholars. He has published in academic journals such as Vision, MDI Journal and international conference proceedings. He helped to organize rehabilitation activities for tsunami victims, focusing on education, counseling and support of children. Currently Chairperson Student Welfare, Coimbatore campus.

Publications

Year of Publication
Title

2019

G. Parambalath, Mahesh, E., Dr. P. Balasubramanian, and Kumar, P. N., “Big Data Analytics: A Trading Strategy of NSE Stocks Using Bollinger Bands Analysis”, Data Management, Analytics and Innovation, vol. 839. Springer Singapore, Singapore, pp. 143-154, 2019.[Abstract]

The availability of huge distributed computing power using frameworks like Hadoop and Spark has facilitated algorithmic trading employing technical analysis of Big Data. We used the conventional Bollinger Bands set at two standard deviations based on a band of moving average over 20 minute-by-minute price values. The Nifty 50, a portfolio of blue chip companies, is a stock index of National Stock Exchange (NSE) of India reflecting the overall market sentiment. In this work, we analyze the intraday trading strategy employing the concept of Bollinger Bands to identify stocks that generates maximum profit. We have also examined the profits generated over one trading year. The tick-by-tick stock market data has been sourced from the NSE and was purchased by Amrita School of Business. The tick-by-tick data being typically Big Data was converted to a minute data on a distributed Spark platform prior to the analysis.

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2018

B. Dharshan, Dr. P. Balasubramanian, and Yermal, L., “Momentum Strategy for Making Abnormal Return: Evidence from Power and Telecom Sector”, in 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017, 2018.[Abstract]

This paper aims to study the momentum strategy in Telecom and Power sector in two time windows (1) 5-5 momentum strategy and (2) 8-8 momentum strategy. Thirteen companies in Telecom sector and Fourteen from Power sector that are listed in Bombay Stock Exchange were studied. The adjusted closing price data of these companies was collected for the period of 2009-2017. This study adopted the Jegadeesh and Titman methodology and the empirical result shows that momentum strategies have Positive returns in Telecom sector but, in Power sector momentum strategies have Negative returns. © 2017 IEEE.

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2018

R. Kruthika, Dr. P. Balasubramanian, and Sureshkumar, V., “Relationship between Google Trends Data and Index Returns”, in 7th IEEE International Conference on Computation of Power, Energy, Information and Communication, ICCPEIC 2018, 2018, pp. 42-45.[Abstract]

This paper aims to test the presence or absence of relationship between Google trends data for the words 'bull market' and 'bear market' and the returns of NIFTY. We use Granger causality test to find the relationship between the Google trends data and the index returns. For this study, the adjusted closing prices of Nifty 50 data for period of one year from December 2016 to December 2017 is used to calculate the weekly index return and the Google trend data for India for the keywords 'bull market' and 'bear market' is taken. It was observed that the Google search data for the 'Bull market' is affecting the Nifty 50 returns. © 2018 IEEE.

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2018

L. Yermal and Dr. P. Balasubramanian, “Application of Auto ARIMA Model for Forecasting Returns on Minute Wise Amalgamated Data in NSE”, in 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017, 2018.[Abstract]

Traders dealing in stock markets often apply various means to generate models that can forecast prices of stocks aimed at making abnormal returns on their investment. Such predictive models often takes into account the past price movements and the volatility of the stocks. Earlier studies have used both univariate and multivariate time series methods to generate forecasts. This paper explores the usage of Automatic ARIMA function using Eviews 9.5 for forecasting the stock returns on minute by minute data for 50 stocks in the National Stock Exchange in India. The study finds that Auto ARIMA applied on the sample generates satisfactory forecasts identified by low Mean Absolute Percentage Error (MAPE) only for 3 companies while the forecasts for the others were found to be weak. © 2017 IEEE.

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2018

Dr. P. Balasubramanian, Sureshkumar, V., and Dr. Sangeetha G, “Spatial Proximity and SWB a Study Across Nations”, International Journal of Engineering and Technology (UAE), vol. 33, 2018.

2017

M. A. Nair, Dr. P. Balasubramanian, and Yermal, L., “Factors Influencing Herding Behavior among Indian Stock Investors”, in 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), 2017, pp. 326-329.[Abstract]

Behavioral finance proposes that cognitive traits of investors impact their investment decisions which are not always rational, in contradiction to traditional finance. These cognitive traits of stock investors are influenced by their demographical profile and the financial information that they receive from various sources which in turn influences their stock investment decisions. Investors with similar demographic profile tend to follow a similar pattern with regard to their investment behavior biases. The main objective of this study is to analyze the impact of Indian stock investors' demographics and various sources of financial information on their cognitive biases. Various behavioral biases like herding, loss aversion, regret aversion, market information; mental accounting, price change, and price anchoring were studied but herding behavior has been taken into consideration for analysis in this study. A questionnaire was floated by using quota sampling. Stata software was used for analysis, by using ordered logistic regression on the conceived model. Gender, age, marital status and word of mouth are found to have significant impact on the herding behavior of stock investors.

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2017

K. Dhamodharan, Dr. P. Balasubramanian, and Mohan, D., “Influence of Yamaganda on Stock Trading Behaviour among Nifty200 Stocks: Using Minute-Wise Data”, in 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), 2017, pp. 321-325.[Abstract]

This paper analyses the influence of Yamaganda period in investor's decision to trade, based on the relationship between Yamaganda timing and Stock trading volume using minuteby minute data(combined fromtick data) of Nifty 200 companies traded at India's National Stock Exchange (NSE) for the period of one year from July 1, 2014 to June 30, 2015 (This period consists of 245 Trading days) and is done using Big data tool Spark. Yamaganda is an Indian cultural variable; it is a time period defined in the astrology which is considered inauspicious. The study is done on Yamaganda period, Non-Yamaganda period, Pre-Yamaganda period (half an hour before Yamaganda Period) & Post-Yamaganda Period (half an hour after Yamaganda period). The analysis shows that this cultural variable has very less impact on the Trading decisions of investors. A separate test done only on Tuesday (in order to eliminate day of the week effect and further the absence of Yamaganda period in the trading hours of Wednesday and Thursday) also shows that this cultural variable has very less impact on the Trading decisions of the investors.

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2017

B. Nikita, Dr. P. Balasubramanian, and Yermal, L., “Impact of Key Macroeconomic Variables of India and USA on Movement of the Indian Stock Return in Case of S & P CNX Nifty”, in 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), 2017.[Abstract]

The stock market is referred as the barometer of Indian economy; it is the indicator of the country's economic condition.
Many studies have established the relationship between Indian stock returns and macro economic variables such as gold
price, oil price, exchange rate, etc. This study investigates the relationship between the Indian stock returns and the Macro
economic variables viz interest rate of India, interest rate of USA, inflation rate of India, inflation rate of USA, GDP growth
rate of India and GDP growth rate of USA. Quarterly data was collected for a period from January, 2000 to December, 2015
for all the macro economic variables. Regression Model was used to analyze the data, the variables were tested for
stationarity, serial correlation, heteroscedasticity and normality. The study found that the GDP growth rate of India and USA
are the significant predictors of S&P CNX Nifty return

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2017

P. Krishnamurthy, Dr. P. Balasubramanian, and Mohan, D., “Study on Relationship between Exchange Rate Return and Various Stock Indices Returns”, in 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), 2017.[Abstract]

The Indian stock market is affected by many factors such as monetary policy, oil prices etc. This paper studies the relationship between Exchange rate return of Rupee-Dollar and various Stock Indices returns of India namely Nifty IT, Nifty Pharma, Nifty MNC, Nifty Commodity Index, Nifty Energy Index and Nifty Metal Index, for the time period December 2011-December 2016. The study also takes into account the volatility of returns of these indices and that of exchange rate returns to study the relationship between these two variables using Granger Causality Test. It was observed that Commodity Index return, IT index return, MNC Index return, Energy Index return, Sigma Commodity Index return and Sigma IT index return granger cause the Exchange rate return.

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2017

K. K. Pranesh, Dr. P. Balasubramanian, and Mohan, D., “The Determinants of India's Implied Volatility Index”, in 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), 2017.[Abstract]

This study examines the determinants of India's implied volatility index (VIX). The factors considered are Purchasing Managers Index (PMI), Business Confidence Index (BCI), Net activity of Foreign Institutional Investors (FII) and Net activity of Domestic Institutional Investors (DII). In this study Granger causality is used to find whether these factors cause IndiaVIX. This study confirms that only BCI has significant and positive impact with IndiaVIX and other factors such as PMI, FII and DII do not have any significant impact on India VIX. The results show that FII has a significant and negative impact on DII and hence these two factors do not have a significant impact on IndiaVIX.

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2017

Dr. P. Balasubramanian, Kannadhasan, M., Aramvalarthan, S., and Gopika, A., “Determinants of Dividend Policy of Indian Manufacturing Companies: Panel Autoregressive Distributed Lag Analysis”, Academy of Accounting and Financial Studies Journal , vol. 21, no. 2, pp. 1-12, 2017.[Abstract]

Corporate dividend policy has been an area of concern in financial literature for quite a long time. Substantial research has
been carried out on dividend policy leading to the emergence of various theories. Majority of this research has been carried
out with respect to developed countries. There are only a limited number of empirical investigations on the dividend policy of
companies in emerging economies such as India. Identifying and understanding the key factors that motivate the managers
to distribute dividends is important for investors. The study analyses the determinants of dividend policy of manufacturing
companies in India using panel data. Financial leverage, profitability, and firm size determine the dividend policy of the firm.
Growth of the firm has an effect on dividend policy in the short run. To best of the knowledge, no study has done on this topic
using Panel ARDL in Indian context.

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2016

S. Madhavan, iv, Dr. Amalendu Jyotishi, and Dr. P. Balasubramanian, “Planning Fallacy: A Case of Task Planning in IT Project Support Services”, Purushartha: A Journal of Management Ethics and Spirituality, vol. 9, 2016.[Abstract]

Schedule and effort slippages are measures that practitioners in the Information Technology (IT) industry are all too familiar with. While we accept the fact that these slippages are realities of our day-to-day life, we put continual efforts to overcome or reduce the impact of these deviations. Our propensity to lose sense of time-taken and become over optimistic and thereby skew our planning is termed as planning fallacy.

This research is to study the planning errors, the reason for such behavior, its ubiquity in IT industry and how remedial actions may reduce planning errors. The intent is to approach the problem from a behavioral economics point of view, on the irrational approach followed by individuals that lead to planning fallacy. The research methodology adopted was experimental design with random samples chosen as control and treatment group. The results of the study and experiments establish the g presence of planning fallacy in many areas of task planning. Our results on the treatment group demonstrate that this judgment bias could be reduced to a large extent by periodic monitoring and facilitation.

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2016

S. Sudhakaran and Dr. P. Balasubramanian, “A study on the impact of macro economic factors on S&P BSE bankex returns”, in International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016, 2016.

2016

A. P, Kumar, V. Suresh, Dr. P. Balasubramanian, and Vijay Krishna Menon, “Measuring stock price and trading volume causality among Nifty 50 stocks:The Toda Yamamoto Method”, in International Conference on Advances in Computing, Communications and Informatics, ICACCI, Jaipur, Rajasthan, 2016.[Abstract]

This paper analyzes the existence of a Granger causality relationship between stock prices and trading volume using minute by minute data (transformed from tick by tick data) of Nifty 50 companies traded at the National Stock Exchange, India for the period of one year from July 2014 to June 2015. Since the time series data taken is not integrated in of the same order, the Toda-Yamamoto methodology was applied to test for causality. The results show that 29 companies out 50 companies have two-way (bi-directional) causality between price and volume and 15 companies have one way (unidirectional) causal relationship where price causes volume and volume does not cause price and 6 other companies have no causal relationship in either way. The study suggests that the Efficient Markets Hypothesis does not hold true for these 29 companies during the period of this study.

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2016

Dr. Venkataraman D., Vinay, N., Vardhan, T. V. V., Boppudi, S. P., Reddy, R. Y., and Dr. P. Balasubramanian, “Yarn Price Prediction using Advanced Analytics Model”, in 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, India, 2016.[Abstract]

Making profits and investing the capital in an efficient way is really a tough task in the sectors like textiles and spinning, as the Cotton Yarn prices highly fluctuate. So, the main goal of this project is to forecast the yarn price (40s Karded HANK, 40s Combed CONE) using cotton price (Shankar-6) forecast and also other attributes that influence the yarn price. The unique aspect of this paper is that the accuracy is achieved by integrating seasonality, ARIMA and KNN models (i.e. values predicted using Seasonality and ARIMA are future used to predict yarn price using KNN Algorithm). Using the integrated model stated above, finally we are able to achieve the accuracy of 97% in yarn price prediction. Also the results are tabled in [TABLE XII.] and [TABLE XIV.] for the period of 3 months from Oct 2015 to Dec 2015. By knowing the future trends of the yarn price, the industry will decide to stock or sell the products to gain profits.

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2015

K. Kalyanasundaram and Dr. P. Balasubramanian, “Effect of Spirituality on Human Performance-A Myth or Reality?”, Purushartha: A Journal of Management Ethics and Spirituality, vol. 7, pp. 63-78, 2015.[Abstract]

There are many researches conducted in the area of factors influencing human performance in various industries. Many improvement projects using lean and six sigma techniques have been applied in the past to overcome the problem of human errors. Factors can be broadly classified into Individual and Organisational. The topic of human performance has been heavily researched in industries such as Nuclear, Aviation, Healthcare etc...Studies have been conducted depending upon the type of tasks considering Physical Quotient, Intelligent Quotient and Emotional Quotient. However, the effect of human task performance as a result of Spiritual Quotient is a very unique study and first of its kind. This paper aims to test if the ultimate knowledge of spiritual intelligence has any influence on the human performance. A controlled experiment was conducted. The dependent variable is Accuracy of the transaction processed with independent factors as Gender and Spiritual Intelligence. It was a 22 experiment, i.e. 2 factors (Gender and SQ) and 2 levels (male/female and high/low). 63 MBA students (41 males and 22 females) of a Business School were involved in this experiment. They were asked to fill up a 24 items questionnaire on Spiritual Intelligence. Later they were asked to perform a task to understand their performance. The task involved reading a passage and entering data both numeric and alphabetic into a standard template.The participants were given sufficient time as per the industrial standards just to simulate the work environment pressure. There were 59 fields of data entry and the performance was calculated by measuring the accuracy of data entry. That is, the ratio of number of fields entered correctly to the total number of fields to be entered. (defects per opportunity). The results show that human performance is significantly related to Spiritual Intelligence irrespective of the Gender. More »»

2015

K. Kalyanasundaram and Dr. P. Balasubramanian, “Evaluation of Human performance in Data Entry tasks: Effect of Time Pressure and Complexity”, in Fifth International Conference on Industrial Engineering and Operations Management (IEOM ), 2015.

2015

V. Rajmohan, Kalyanasundaram, K., and Dr. P. Balasubramanian, “Effect of Religious Propaganda on Commercial Interaction between Believers and Non-believers of a Particular Religious Ideology”, Purushartha: A Journal of Management Ethics and Spirituality, vol. 7, 2015.[Abstract]

The paper is an attempt to understand the impact of religious propaganda on believers and evaluating their attitudinal changes, both personal and trade-related towards nonbelievers. To analyse the impact of propaganda, a controlled experiment was undertaken with 86 post graduate students and 3 faculty members from a College in Tamilnadu and subjecting them to narratives based on three different forms of propaganda - fundamentalist, moderate and secular. After each propaganda, the responses were taken through a questionnaire to measure the impact of propaganda on personal relations and commercial relations: 1) with believers or people following the same set of beliefs, 2) with non-believers or people projected as enemies of the community, and 3) with intermediaries or people who are deemed as tolerable. The results were statistically analysed and interpreted. The results provide insights into the value of different types of propaganda on commercial and personal interactions between those subjected to the propaganda and the community targeted through such propaganda (believers and non-believers). The results show that moderate propaganda is not a full-fledged antidote to fundamentalist propaganda; it only improves the commercial relationships. Only secular propaganda can withstand the onslaught of fundamentalist propaganda in both personal and commercial interactions. The results also reveal that creating a set of intermediaries would be helpful in increasing the commercial interaction among believers and non-believers. More »»

2015

Dr. P. Balasubramanian, Kalyanasundaram, K., and Aravindhan, S., “Sunk Cost Fallacy: Effect of Situational Knowledge on Irrational Choices”, Singapore Economic Review conference 2015 . Mandarin Orchard Singapore, 2015.

2015

S. Ulagapriya and Dr. P. Balasubramanian, “Study on inter sector association rules in national stock exchange, India”, in 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, 2015, pp. 859-865.[Abstract]

In this paper, the stocks grouped under different sector indices under National Stock Exchange (NSE) of India are analyzed to identify any interesting relations among the sectors. Concept of Association rules is used for this analysis. Stocks are grouped into sectors based on their operations/industrial classification. Owing to their similarity in every context stock prices within a sector generally vary in the same direction. This study examines inter sector relations using association rules. Daily closing prices are used to identify the trend of stock price variation which are in turn processed using Apriori algorithm [1] to get association rules and those spanning across sectors are separated and their behavior is analyzed. © 2015 IEEE.

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2015

S. Neeraja and Dr. P. Balasubramanian, “Impact of grading of IPOs in short run price performance in India: A regression model approach”, in 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, 2015, pp. 866-868.[Abstract]

Capital markets all over the world are subject to information asymmetry where the potential investors have inferior knowledge about the company. As a step to make markets efficient SEBI introduced a new mechanism of grading of IPOs in 2006. Grades assigned by different credit rating agencies acts as signal of quality of the company. The objective of this study is to analyze the impact of grading of IPOs in short run price performance. Price performance is one indicator of market efficiency. Using sample of 121 IPOs listed on NSE from 2006 to 2013, IPO returns for 6 months post offer day is calculated. Control variables Beta and 6 months market return are also introduced. Statistical tool multiple linear dummy variable regression analysis is used to understand the dependence of returns from IPO to the grades assigned taking market fluctuation and sensitivity of stock returns to market fluctuations as control variables. © 2015 IEEE.

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2015

A. Mohan and Dr. P. Balasubramanian, “Factors affecting inflation in India: A cointegration approach”, in 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, 2015, pp. 855-858.[Abstract]

This study is an empirical analysis to find out the major factors that determine inflation in India. The long run and short run relationships between inflation and other macroeconomic indicators such as per capita GDP, money supply, international oil price and exchange rate are determined using Cointegration method and Vector Auto regression model (VAR) respectively. The annual data of these variables from 1980 to 2013 is used for the study. The study finds that there is a long term as well as short term relationship between Inflation (measured using CPI) and exchange rate where as there is a short term relationship between Inflation and per capita GDP. © 2015 IEEE.

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2014

Dr. P. Balasubramanian and Kalyanasundaram, K., “Human Error Prediction and Control Model Using Recursive Partitioning”, in Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing, Amrita Vishwa Vidyapeetham, 2014.

2013

Dr. P. Balasubramanian, “Fairness, efficiency and effectiveness in panchyat-based Dispute Resolution among Pattinavar in Nagapattinam District, Tamil Nadu, India”, 17th World Congress of the IUAES2013, University of Manchester. 2013.

2012

Dr. P. Balasubramanian, “Precedence of indigenous law over state law among Pattinavar caste in Nagapattinam District, Tamilnadu, India: An economic perspective”, in The international conference on legal pluralism in natural resource management, Amrita School of Business, Amrita Vishwa Vidyapeetham, Ettimadai, Coimbatore , 2012.

2011

P. Na Kumar, Seshadri, G. Ra, Hariharan, Aa, Mohandas, V. Pb, and Dr. P. Balasubramanian, “Financial market prediction using feed forward neural network”, Communications in Computer and Information Science, vol. 145 CCIS, pp. 77-84, 2011.[Abstract]

This paper outlines a methodology for aiding the decision making process for investment between two financial market assets (eg a risky asset versus a risk-free asset or between two risky assets itself), using neural network architecture. A Feed Forward Neural Network (FFNN) and a Radial Basis Function (RBF) Network has been evaluated. The model is employed for arriving at a decision as to where to invest in the next time step, given data from the current time step. The time step could be chosen on daily/weekly/monthly basis, based on the investment requirement. In this study, the FFNN has yielded good results over RBF. Consequently two such FFNN have been developed to enable us make a decision on investment in the next time step to decide between two risky assets. The prediction made by the two FFNN models has been validated from the actual market data. © 2011 Springer-Verlag.

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2010

Dr. (Col.) Kumar P. N., G. Seshadri, R., Hariharan, A., Mohandas, V. P., and Dr. P. Balasubramanian, “Agent based Modeling of Financial Markets”, IEEE International Conference on Computational Intelligence and Computing Research (Best paper award), vol. 2. IEEE Explore , 2010.

2010

Dr. (Col.) Kumar P. N., G. Seshadri, R., Hariharan, A., Mohandas, V. P., and Dr. P. Balasubramanian, “Financial Market Analysis of Bombay Stock Exchange using an Agent Based Model”, International Journal of Imaging Science and Engineering, vol. 8, 2010.[Abstract]

Returns on stocks have traditionally been modelled by fitting a suitable statistical process to empirical returns. Studies on agent based models of stock market have been carried out by researchers, primarily on US markets. This paper analyzes the empirical features generated using historical data from the Bombay Stock Exchange (BSE), employing the concept of agent based model proposed by LeBaron[2,3,8]. Agent-based approach to stock market considers stock prices as arising from the interaction of a number of individual investors. These investors are modeled as intelligent agents, using differing lengths of past information, each trading with its own rules adapting and evolving over time, and this in turn determines the market prices. It is seen that the model generates some features that are similar to those from actual data of the BSE. More »»

2010

Dr. (Col.) Kumar P. N., G. Seshadri, R., Hariharan, A., Mohandas, V. P., and Dr. P. Balasubramanian, “A Methodology for Aiding Investment Decision between Assets in Stock Markets Using Artificial Neural Network”, International Journal of Computer Science Issues (IJCSI ), vol. 7, no. 6, 2010.[Abstract]

This paper outlines a methodology for aiding the decision making process for investment between two financial market assets (eg a risky asset versus a risk-free asset), using neural network architecture. A Feed Forward Neural Network (FFNN) and a Radial Basis Function (RBF) Network have been evaluated. The model is employed for arriving at a decision as to where to invest in the next time step, given data from the current time step. The time step could be chosen on daily/weekly/monthly basis, based on the investment requirement. In this study, the FFNN has yielded good results over RBF. Consequently the FFNN developed enable us make a decision on investment in the next time step between a risky asset (eg the BSE Sensex itself or a single share) versus a riskfree asset (eg Securities like Govt Bonds, Public Provident Funds etc).The FFNN is trained with a set of data which helps in under standing the market behaviour. The input parameters or the information set consisting of six items is arrived at by considering important empirical features acting on real markets. These are designed to allow both passive and active, fundamental and technical trading strategies, and combinations of these. Using just six items simplifies the decision making process by extracting potentially useful information from the large quantity of historic data. The prediction made by the FFNN model has been validated from the actual market data. This model can be further extended to choose between any two categories of assets whose historical data is available. More »»

2010

G. Kalyanaram and Dr. P. Balasubramanian, “The Effect of Direct Advertising to Consumers (DTCA) In Prescription Pharmaceutical Drugs, and Consumer Welfare”, Proceedings of the Northeast Business & Economics Association, pp. 423 - 427, 2010.[Abstract]

The empirical results of this study suggest that there is a statistically significant effect of direct advertising to consumers (DTCA) on market share of four pharmaceutical prescription drugs in the US. These results provide some support for the argument that the consumers engage in search and brand switching among the plausible brandchoices, and augment the empirical database of knowledge in this domain. More »»

2007

S. Achath and Dr. P. Balasubramanian, “Predicting Futures Contracts Using Local Data and Non-Parametric Regression”, in 5th International Conference of Association of Indian Management Scholars International, ICFAI Business School, Hyderabad , 2007.

2000

D. Singh and Dr. P. Balasubramanian, “Price-Volume Relationship: Some Evidence from the Indian Stock Market”, Vision: The Journal of Business Perspective, vol. 4, pp. 17–28, 2000.[Abstract]

A contemporaneous relation between price and volume has been established by the present study which proves contrary to the Efficient Market Hypothesis. The methodology adopted for the study is that of Granger-Causality Test which investigates the dynamic relationship between price and volume between two time series. The study tests whether the knowledge of the behaviour of past volume improves conditional price forecasts over price forecast based on past price alone. The information could be considered to be either simultaneous or sequential. The result of the study supports the sequential information arrival hypothesis which states that knowledge of the behaviour of past volume improves the price forecasts. Of the twenty shares studied, 17 shares support the leading and lagging relation between price and volume. More »»