P. Balasubramanian

Measuring stock price and trading volume causality among Nifty50 stocks: The Toda Yamamoto method

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.

Financial Market Prediction Using Feed Forward Neural Network

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.

Price-Volume Relationship: Some Evidence from the Indian Stock Market

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.

Yarn price prediction using advanced analytics model

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.

A study on the impact of macroeconomic factors on S&P BSE Bankex returns

Macro environment plays a major role along with the micro environment in making an impact over the performance of stock market in India. This study attempts to research whether Money Supply, Foreign Direct Investments (FDI), Inflation Rate, Index of Industrial Production (IIP), Foreign Exchange Reserves and Foreign Portfolio Investments (FPI) are making any significant impact on the BSE Bankex returns. The previous studies have taken different variables as macroeconomic factors in order to measure their impact on Bankex. This study is focusing on different macroeconomic factors that have not yet taken earlier in order to understand their impact on Bankex. For that monthly data was collected over a period of 10 years ranging from April 2005 to March 2015 from the websites of Bombay Stock Exchange, Reserve Bank of India etc. Unit root test, multiple regression and multicollinearity test were conducted for making the analysis. The analysis revealed that FDI and Foreign Exchange Reserves have a significant impact on the BSE Bankex returns and there is no multicollinearity exists between the variables in the model.

Factors affecting inflation in India: A cointegration approach

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.

Study on inter sector association rules in national stock exchange, India

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.

Impact of grading of IPOs in short run price performance in India: A regression model approach

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.

Human Error Prediction and Control model using Recursive Partitioning

The problems associated with human error are very complicated with a number of dynamic factors influencing the outcome. Though it has been studied in detail in various industries with various tools and techniques, there is no comprehensive model available considering multiple factors that address the issue. In the field of Banking and Financial services, the problem of human error is more critical since it can lead to operational losses and bad customer experience. Traditionally simple parametric and non - parametric statistical tests of hypotheses are used as standalone tools for analysis and hence improvement. From a real world perspective, control of one factor leading to a trade-off on another resulting in more improvement projects rather than resolving the problems. The specific requirement from a practitioners point of view is also not just identifying factors influencing errors but to what extent. Poisson regression, Negative Binomial regression and Recursive Partitioning are the tools which help us analyze the problem irrespective of the type of data and arrive at controllable thresholds for the Operations Managers. This paper aims at providing a comprehensive and practical error management and control model for human errors in a transaction processing industry.