Pages

Tuesday, December 25, 2018

Onion Farming Price vs Production


Onion is one of the main ingredient of Indian diet and is known to have changed Govts when the price goes up as it is consumed daily by majority households.Even after having such robust market, farmers still are not getting the right price for their produce and end up getting stuck in the period of high/low productions.

Below is the data which i have taken from Govt website to show how this pattern is leading to mismatch b/w Demand & Supply.

Notice the spike in prices of 2010 when the production was lesser (only by 10%), main reason was not enough remuneration of the produce in previous year 2009.

After price went up in 2010, there was an increase in area under production which further resulted in record production of 15,118 thousand tons and brought the prices down to 12.

Is contract farming is a better solution to plan supply against demand?
Or our(whole world) production levels have gone high and there isn't enough demand? given the fact that Indian Productivity levels are still much lesser than developed countries or China?


Production Data India


AR AREA(000’ ha) PRODUCTION (000’tons)
1996-97 404 4180
2000-01 450 4721
2006-07 768 10847
2007-08 821 13900
2008-09 834 13565
2009-10 756.2 12158.8
2010-11 1064 15118


Production Data Bhopal


District_Name Crop_Year Season Crop Area Production
BHOPAL 1999 Whole Year Onion 581 6850
BHOPAL 2000 Whole Year Onion 564 6677
BHOPAL 2001 Whole Year Onion 638 6407
BHOPAL 2002 Whole Year Onion 615 5932
BHOPAL 2003 Whole Year Onion 748 8103
BHOPAL 2004 Whole Year Onion 832 11195
BHOPAL 2005 Whole Year Onion 852 10042
BHOPAL 2006 Whole Year Onion 1036 13434
BHOPAL 2007 Whole Year Onion 941 9048
BHOPAL 2008 Whole Year Onion 829 8898
BHOPAL 2009 Whole Year Onion 805 9499
BHOPAL 2010 Whole Year Onion 644 3397
BHOPAL 2011 Whole Year Onion 691 15643
BHOPAL 2012 Whole Year Onion 733 22922
BHOPAL 2013 Whole Year Onion 696 10216


Price Data Bhopal


Date Centre_Name Commodity_Name Price
31-12-99 BHOPAL Onion 4
29-12-00 BHOPAL Onion 6
31-12-01 BHOPAL Onion 5
31-12-02 BHOPAL Onion 4
31-12-03 BHOPAL Onion 12
31-12-04 BHOPAL Onion 6
30-12-05 BHOPAL Onion 10
29-12-06 BHOPAL Onion 4
31-12-07 BHOPAL Onion 10
31-12-08 BHOPAL Onion 6
31-12-09 BHOPAL Onion 10
31-12-10 BHOPAL Onion 30
31-12-11 BHOPAL Onion 12
31-12-12 BHOPAL Onion 12
31-12-13 BHOPAL Onion 18






https://data.gov.in/catalog/district-wise-season-wise-crop-production-statistics

Sunday, December 16, 2018

Bull Call Spread strategy on Karnataka Bank


Bull call spread helps you take a directional call and it has lesser cost as compare to buying a naked call since you get some cash flow by selling a higher strike call.

In the below table, we are buying a 105 strike call for KTK Bank and selling 112.5 strike call. View we have taken is that stock price will go up which will increase the value of 105 while the increase in the value of 112.5 will be lower thus increasing our profit.

In the cost column, we can further analyze that the strategy is cheaper when the stock price is away from the strike price however we can't wait for that to happen and get into the order as soon as we have the confidence about the direction.



Symbol Date Expiry Option Strike Price Strike Price LTP – 105 LTP – 112.5 LTP – Stock Cost
KTKBANK 30-Nov-2018 27-Dec-2018 CE 105 112.5 3.9 1.55 103.55 2.35
KTKBANK 03-Dec-2018 27-Dec-2018 CE 105 112.5 4.15 1.75 104.85 2.4
KTKBANK 04-Dec-2018 27-Dec-2018 CE 105 112.5 3.55 1.25 103.7 2.3
KTKBANK 05-Dec-2018 27-Dec-2018 CE 105 112.5 2.6 0.9 102.15 1.7
KTKBANK 06-Dec-2018 27-Dec-2018 CE 105 112.5 2.7 0.95 103 1.75
KTKBANK 07-Dec-2018 27-Dec-2018 CE 105 112.5 3.2 0.85 103.1 2.35
KTKBANK 10-Dec-2018 27-Dec-2018 CE 105 112.5 2.2 0.5 101.2 1.7
KTKBANK 11-Dec-2018 27-Dec-2018 CE 105 112.5 2.55 0.5 103.35 2.05
KTKBANK 12-Dec-2018 27-Dec-2018 CE 105 112.5 4.85 1.35 107.2 3.5
KTKBANK 13-Dec-2018 27-Dec-2018 CE 105 112.5 5.1 1.4 108.05 3.7
KTKBANK 14-Dec-2018 27-Dec-2018 CE 105 112.5 5.2 1.3 108.55 3.9

Having a put call strategy would have yielded almost similar result however liquidity of the options is a concern for KTK Bank.


Symbol Date Expiry Option
type
Strike Price Strike Price LTP-105 LTP-112.5 Underlying Value Inflow
KTKBANK 30-Nov-2018 27-Dec-2018 PE 105 112.5 4.65 9.25 103.55 4.6
KTKBANK 03-Dec-2018 27-Dec-2018 PE 105 112.5 3.7 8.1 104.85 4.4
KTKBANK 04-Dec-2018 27-Dec-2018 PE 105 112.5 4.4 9 103.7 4.6
KTKBANK 05-Dec-2018 27-Dec-2018 PE 105 112.5 4.4 10.3 102.15 5.9
KTKBANK 06-Dec-2018 27-Dec-2018 PE 105 112.5 4.75 9.5 103 4.75
KTKBANK 07-Dec-2018 27-Dec-2018 PE 105 112.5 4.5 9.35 103.1 4.85
KTKBANK 10-Dec-2018 27-Dec-2018 PE 105 112.5 6.65 11.1 101.2 4.45
KTKBANK 11-Dec-2018 27-Dec-2018 PE 105 112.5 3.75 9.1 103.35 5.35
KTKBANK 12-Dec-2018 27-Dec-2018 PE 105 112.5 1.7 6.1 107.2 4.4
KTKBANK 13-Dec-2018 27-Dec-2018 PE 105 112.5 1.35 5.4 108.05 4.05
KTKBANK 14-Dec-2018 27-Dec-2018 PE 105 112.5 1.1 4.85 108.55 3.75




Thursday, December 6, 2018

Regression Derivation & Understanding


Regression model uses least square model to fit the equation for independent and dependent variables.

Link for Equation Derivation - https://www.youtube.com/watch?v=DSQ2plMtbLc

It also calculate P value for each variable (Independent) which will be used to check if the independent variable has any impact on the output.

Regression equation can be used to test the hypothesis as well, Hypothesis will be usually the impact of X variable on Y variable.

One very good example has been discussed in below link -

https://www.coursera.org/lecture/linear-regression-business-statistics/hypothesis-testing-in-a-linear-regression-gqxpq

Central Limit Theorem and t-Distribution for Hypothesis Testing

Central Limit Theorem

The Central Limit Theorem state that as the sample size (i.i the number of values in each sample) gets large enough, the sampling distribution of the mean is approximately normally distributed. This is true regardless of the shape of the distribution of the individual values in the population.

Below statistic is used in testing of Hypothesis is σ is known.

Z-Score = (x̅ - μ)/(σ/sqrt(N))

x̅ is sample mean
μ is population mean
σ is population standard deviation
N is number of samples

t-Distribution & its Properties

if the random variable is normally distributed then t statistic value will follow t-distribution with (n-1) degrees of freedom and can be used for hypothesis testing.

t statistic = (x̅ - μ)/(S/sqrt(N))

x̅ is sample mean
μ is population mean
σ is sample standard deviation

N is number of samples