Parmod Kumar Paul

Bio: Parmod Kumar Paul is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Statistics & Technical analysis. The author has an hindex of 1, co-authored 2 publications receiving 1 citations.

Analyzing Returns and Pattern of Financial Data Using Log-linear Modeling

Abstract: Technical analysis is useful for forecasting the price movement through the analysis of historic data. This sort of movement has Turn of the year effect also and useful for short term prediction. If the direction of price of two or more assets is same, it becomes necessary to analyze the returns also. We first use optimal band to predict the direction of price and create a contingency table of the data to analyze the pattern (movement) against returns. We use log-linear modeling for the analysis of the contingency table. We next include the volume of transactions as one more variable in the contingency table. The table consisting of three variables, Pattern, Returns and Volume is further analyzed by using log-linear modeling. We test various hypotheses of association for these variables by using Chi-square test for contingency tables.

An Optimal Band for Prediction of Buy and Sell Signals and Forecasting of States: Optimal Band for Buy and Sell Signals

Abstract: A trading band, based on historical movements of a security price, suggests buy or sell pattern. Bollinger band is one of the most famous bands based on moving average and volatility of the security. The authors define a new trading band, namely Optimal Band, to forecast the buy or sell signals. This optimal band uses a linear function of local and absolute extrema of a given financial time series. The parameters of this linear function are then estimated by simple linear optimization technique. The authors then define different states using various upper and lower values of Bollinger band and the optimal band. The approach of Markov and Hidden Markov Models are used to forecast the future states of given time series. The authors apply all the techniques on the closing price of Bombay stock exchange and intra-day price series of crude oil and Nifty stock exchange. An Optimal Band for Prediction of Buy and Sell Signals and Forecasting of States: Optimal Band for Buy and Sell Signals

Analyzing the Association between Pattern and Returns Using Goodman-Kruskal Prediction Error Reduction Index (λ)

Abstract: For selecting and interpreting appropriate behaviour of proportion between buy/neutral/sell patterns and high/moderate/low returns, the prediction error reduction index is a very useful tool. It is operationally interpretable in terms of the proportional reduction in error of estimation. We first obtain the buy/sell pattern using an Optimal Band. The analysis of the association between patterns and returns is based on the Goodman–Kruskal prediction error reduction index ( λ ). Empirical analysis suggests that the prediction of returns from patterns is more impressive or of less error as compared to the prediction of patterns from returns. We demonstrated the prediction index for Index NIFTY 50, BANK-NIFTY, and NIFTY-IT of NSE (National Stock Exchange), for the period 2010–2020.

The Analysis of Discrete Data

Abstract: One consideration which has a very large influence on the methods of analysis which we adopt is the question of the type of measurement of which our observations are composed. Some possibilities are as follows: (i) Continuous data. This includes data such as the weight per unit length of plastic pipe, Example 1.3; measurements of height such as in Example 1.6, etc. In these cases it is convenient to think of the variables as being continuous, even though they must be recorded to a finite number of decimal places. (ii) Discrete data. This is when the observed values of our variables can only be one of a discrete set of values. Examples include the number of seeds germinating out of a box of 100, Example 1.1; the number of particles emitted from a radioactive source in a 5-second period, Example 1.2, etc. (iii) Ranked data. This is when, for example, a subject is asked to rank some drawings in order of preference; if the subject is asked to rank n items the observation on any item is one of the numbers, 1, 2,..., n, representing the rank, and each of the numbers 1, 2,..., n appears just once in any ranking of n items. Methods of analysis for ranked data include those given in Chapter 7. (The methods of Chapter 7 can also be used on continuous data.)

Analyzing the Association between Pattern and Returns Using Goodman-Kruskal Prediction Error Reduction Index (λ)

Abstract: For selecting and interpreting appropriate behaviour of proportion between buy/neutral/sell patterns and high/moderate/low returns, the prediction error reduction index is a very useful tool. It is operationally interpretable in terms of the proportional reduction in error of estimation. We first obtain the buy/sell pattern using an Optimal Band. The analysis of the association between patterns and returns is based on the Goodman–Kruskal prediction error reduction index ( λ ). Empirical analysis suggests that the prediction of returns from patterns is more impressive or of less error as compared to the prediction of patterns from returns. We demonstrated the prediction index for Index NIFTY 50, BANK-NIFTY, and NIFTY-IT of NSE (National Stock Exchange), for the period 2010–2020.