Group 11 presented their project on time series analysis and forecasting of large cap mutual funds. They analyzed the daily NAV data of 5 large cap mutual funds (TATA, SBI, NIPPON, INVESCO, EDELWEISS) from 2020-2022 to model and forecast returns using simple moving average, exponential smoothing, and Holt's winter methods. They found that exponential smoothing had the best AIC values while Holt's winter produced the lowest RMSE, indicating both methods provided good fits for forecasting mutual fund returns.

1. Project Presentation
Group 11
Participants:
1) Niranjan D Pawar(9817)
2) Ashwinkumar A Bhagwat(9812)
3) Saurabh Ligade (9814)
4) Vijay V Jaybhaye (9778)
5) Shivshankar R Avhad(9787)
Time series analysis and
forecasting of large cap mutual
funds .
With guidance of DR Sandesh kurade and
DR jaya limbore

2. Our motivations
I. We have selected this project because more and more new investor are
investing in stock market. According to SEBI, there is increase in demat
account total more than 14 million in 2020-21, compares with less than
5 million in 2019-20.
II. Stock market is giving more return to investors ,According The BSE Sensex
has gained 10,054 points between January 1, 2021 to December 29, 2021. This
is the highest year-on-year increase in absolute terms historically.
III. It will also help young generation to start investing.
Objective:
1)To improve the return of mutual funds with help of statistics.
2)To study different factor that affect the mutual funds.
3)To find the stock/company which gives best return to investors.

3. What are mutual funds ?
A mutual fund is a company that pools money from many investors and invests
the money in securities such as stocks, bonds, and short-term debt. The
combined holdings of the mutual fund are known as its portfolio.
In India it is regulated by SEBI.
Companies for forecasting :
• TATA : Assets are worth of rs 62,077.92 cores.
• INVESCO : Assets are worth of Rs. 36,795.25 crores.
• SBI: Assets are worth of Rs. 5,04,455.21 crores
• Edelweiss: Assets worth of Rs. 46,849.31 crores.
• NIPPON :Assets worth of Rs 33,625.32 crores

4. Summary of data
Large cap Avg NAV
(2021-2022)
SD SKEWNESS STD ERROR
TATA 253.7795 55.34634 0.11133 11.29753
SBI 121.4478 29.34327 0.163478 5.989669
NIPPON 16.02859 3.572681 0.113638 0.72927
INVESCO 34.46075 6.989848 0.144863 1.426797
EDLWEISS 42.84179 8.608263 -0.00778 1.757154
We have collected daily NAV’s of 2 years of this 5 Large cap mutual funds companies.
From AMFINDIA.com . The data is from 01-Jan-2020 To 01-Jan-2022. We have taken
Averages of every month in this 2 years of data to make the study of data easier.

5. Graphical Analysis
TATA SBI NIPPON
INVESCO EDELWEISS

6. Time Series Analysis and Forecasting
• Whether we wish to predict the trend in financial markets or electricity consumption, time is an
important factor that must now be considered in our models.
• IN time series. A time series is simply a series of data points ordered in time. In a time series, time
is often the independent variable and the goal is usually to make a forecast for the future.
• Stationarity is an important characteristic of time series. A time series is said to be stationary if its
statistical properties do not change over time. In other words, it has constant mean and variance,
which is independent of time.
• Often, stock prices are not a stationary process, since we might see a growing trend, or its volatility
might increase over time.
• Seasonality refers to periodic fluctuations. For example, electricity consumption is high during the
day and low during night, or online sales increase during Christmas before slowing down again.

7. Modelling time series
• There are many ways to model a time series in order to make predictions.
Here, I will present:
1. Simple moving Average
• A simple moving average (SMA) calculates the average of a selected range of prices, usually
closing prices, by the number of periods in that range.
• A simple moving average is a technical indicator that can aid in determining if an asset price
will continue or if it will reverse a bull or bear trend.
2. Exponential Smoothing
• Exponential smoothing is a broadly accurate principle for smoothing time series data using the
exponential window function. The controlling input of the exponential smoothing calculation is
defined as the smoothing factor or the smoothing constant.
• We used Exponential smoothing to smooth out data to make forecast.
3. Holts winter method
• Holt-Winters is a model of time series behavior. Forecasting always requires a model, and Holt-
Winters is a way to model three aspects of the time series: a typical value (average), a slope
(trend) over time, and a cyclical repeating pattern (seasonality).

8. Decomposition of Time series
TATA SBI NIPPON
INVESCO EDELWEISS

9. 1) Simple Moving Average
TATA SBI NIPPON
INVESCO EDELWEISS

10. 2) Exponential Smoothing
TATA SBI NIPPON
INVESCO EDELWEISS

11. 3)Holt’s Winter Forecasted value
TATA SBI NIPPON
INVESCO EDELWEISS

12. Holts Winter Forecasting graph
TATA SBI NIPPON
INVESCO EDELWEISS

13. Holt’s Winter Fitted graph
TATA SBI NIPPON
INVESCO EDELWEISS

14. Models Exponential Smoothing Holts Winter
Companies AIC RMSE AIC RMSE
TATA 210.3315 14.40938 214.4941 14.45827
SBI 175.9158 7.034913 177.5817 6.701008
NIPPON 83.32324 1.022115 86.14630 0.9973579
INVESCO 136.0346 3.06494 138.2743 2.954571
EDELWEISS 121.5668 2.267358 124.7269 2.228026
Comparison between models
• As we can see AIC od Exponential smoothing is less for every company.
• And we can see RMSE of Holts Winters is less for most companies.
1) According to AIC Exponential Smoothing is best fit model .
2) According to RMSE Holts Winter method is best fit model.

15. Limitations of our project
1) When we collected annual data it was not in continuous forms as stock market is closed
on Saturday and Sunday. So we were not able to fit few models on the annual data .
2) In exponential smoothing model we don’t get the forecasted value we get just the
predicted graph. So it wont be possible to calculate future returns.
3) As stock prizes are affected by various parameters like natural and manmade
emergency this prediction may not hold if such emergency occurs.
Future Scope
As in the start we discussed that number of investors are increasing day by day so it is
necessary to have some tools for analysis and prediction of stock market for better
investment. This project will be helpful for those who are new investors and want to
reduce their risk . And it will be beneficial to check which model will be best in future
for the analysis

16. ABSTRACT
As our main objective was to find which is the best model for forecasting of time
series. We can see that according to AIC exponential smoothing is best model and
according to RMSE Holts winter is the best model. So we can say that if you want to
forecast the future value of a mutual fund you can use any of this two models since
this two are good fit. And with help of any one of this models you can calculate the
forecasted returns of a company and invest according to that.
Appreciation
We are very thankful to our Statistics Department Head Mr Sandesh Kurade and our
project guide Dr Jaya Limbore for motivating and guiding us to complete this project.
We would like to thank our friends as well.

17. RETURNS QUATERLY [2020-2021]
QUARTER‘S
FUND’S
SBI TATA INVESCO NIPPON EDELWEISS
JAN-MAR
-28.2525 -29.016738 -26.0462 -33.1584 -26.1987
APRIL-JUNE 22.52044 20.37812588 23.5761 19.56299 21.35455
JULY-SEPT 8.483899 6.026563658 8.814704 4.869001 8.443908
OCT-DEC 8.070022 21.10463658 3.337237 7.672597 3.725446
JAN-MAR
8.584564 9.055890122 8.974001 7.465536 6.741331
APRIL-JUNE
7.120876 7.897128793 14.28571 13.89248 11.18075
JULY-SEPT
3.568015 11.7896314 1.986456 -0.91753 -0.45662
OCT-DEC 7.326789
-0.09451158 9.849736
11.98646
8.56895