社内勉強会資料_Object Recognition as Next Token Prediction
JPX TOKYO STOCK EXCHANGE TOP PERFORMING STOCKS
1. JPX TOKYO STOCK EXCHANGE - STOCK PRICE PREDICTION
Presented By:
PREPARED BY:
o DILIP KUMAR (DBI001_15)
o KAUSHIK PRASAD DEY (DBI001_19)
o MANSI KARWANI (DBI001_23)
o MAYURKUMAR SURANI (DBI001_24)
o SUMIT SHARMA (DBI001_57)
GROUP-09
INSTITUTE: INDIAN INSTITUTE OF MANAGEMENT – NAGPUR
BATCH: DBI001
YEAR: 2021-22
2. Introduction
Japan Exchange Group, Inc. (JPX) established in Jan’2013 is a holding company operating one of the
largest stock exchanges in the world,
• Tokyo Stock Exchange (TSE)
• Osaka Security Exchange Co (OSE)
• Tokyo Commodity Exchange (TOCOM).
JPX has experienced financial market professionals to provide the best return on investments from their past experiences. While
these finance decisions were historically made manually by professionals, technology has ushered in new opportunities.
The Purpose of this project is how we can open the new horizon with help of technology and incurred chances of ROI maximization
and Loss minimization using the right methodology, tools and model.
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4.54 4.38 4.89 4.95
6.22
5.3
6.2 6.72
0
2
4
6
8
2013 2014 2015 2016 2017 2018 2019 2020
USD ( In Trillion)
3. Problem Statement
We are interested to explore quantitative trading models, where decisions are executed programmatically based on predictions from trained
models. To do so, we need to build portfolios of top 50 & bottom 50 stock performance & returns from 2000 stocks.
-Also, we can access financial data such as stock information and historical stock prices to train and test models.
Significance of Problem on Hand:
Below are some important aspects of problem in hand:
• Effective market study and past performance of stock
• Investment in right stocks
• ROI maximization
• Loss Minimization
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4. DATA SET SPECIFICATION
The dataset used is “Stock price” having 25 variable out of which we have utilized 6 important variables being used to derive the
recommendation for top companies to invest
Variable used for recommendation:
• Date: Trade date. Align with stock_price's TradeDate
• Target: Change ratio of adjusted closing price between t+2 and t+1 where t+0 is TradeDate.
• Close: last traded price on a day.
• Volume: number of traded stocks on a day.
• Securities Code: Local securities code.
• 17SectorName: Sector Name
• Name: Name of security.
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5. TOOLS AND METHEDOLOGY
Tools:
- Python is used as a programming language in the capstone project
- For Exploratory Data Analysis data set duration (2017 - 2021)
- For ML Model data set duration is (251 days prior to 6-Dec)
Methodologies:
Machine Learning Models
- LIGHT GBM
- Feature Engineering and Training
- Joblib Library
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7. EXPLORATORY DATA ANALYSIS
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• In 2021, nearly all industries saw a
positive return on average, with the
highest in Energy Resources at about
0.13% overall, while in 2018, all sectors
saw a negative return except for Electric
Power & Gas.
• Since some of the stocks were added in
December 2020, We will use the data
filtered after this date so that the data
will consist of 231 days of stock prices for
all 2,000 stocks
AVERAGE RETURN BY STOCKS
8. INDEX FLUCTUATION
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- Most sectors have variance between 10% and -10%, there are quite a few outliers across all industries.
- some variance as high as 62% in Commercial & Wholesale Trade and others as low as -31% in IT & Services sector.
9. STOCK PRICE MOVEMENT
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• In the candlestick charts above, the boxes
represent the daily spread between the
open and close prices and the lines
represent the spread between the low and
high prices.
• The color of the boxes indicates whether the
close price was greater or lower than the
open price, with green indicating a higher
closing price on that day and red indicating a
lower closing price.
• In late August, the market saw a consecutive
14-day period where the close price was
greater than the open price.
10. 10
• Among stocks with the highest return on
average since December 2020, 6 of the 7
were in the IT & Services sector and one
was in the Pharmaceutical sector. The IT
& Services and Pharmaceutical sectors
also make up 6 of the stocks with the
lowest returns on average.
• The side graph shows the relationships
between the top 5 stocks with the
highest average returns, Exchange Ltd.
• For Startups, Inc., Symbio
Pharmaceuticals, Fronteo, Inc., and
Emnet Japan Co. Ltd.
HIGH AND LOW PERFORMING STOCKS
11. CORRELATION BETWEEN STOCK AND SECTOR
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All the companies (stocks) are correlated to different sectors and get impacted by any events with which it is closely
correlated and hence this correlation analysis gives interesting insights of the same.
12. RETURNS AND VOLATALITY
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• In the graphs of the stock price Moving
Averages (MA) and Exponential Moving
Averages (EMA), when the shorter period,
the 10-Day average, crosses above the
longer period, the 50-day average, the
closing price tends to decrease, which is
typically indicative of a sell signal.
• Conversely, when the 10-Day MA/EMA
crosses the 50-Day MA/EMA from below,
the closing price increases, which typically
indicates a buy signal.
• There is greater fluctuation between
longer periods, while Stock Volatility tends
to be more stable over time, with more
fluctuation in shorter periods
13. - Use Machine Learning is a most accurate way to drive prediction, but we should also understand that it is driven by
human behavior as well and there is always an certain element of risk.
- Using ML can certainly help you save lot of men hours which is certainly the case in manual analysis.
- This can certainly help you save time and investment by not investing in non-performing assets and open the horizon by
investing in high performing assets.
- Using Moving Average methodology ensure to evaluate the performance over a longer period and also consider uncertain
factors as well which can provide better results.
- Based on EDA performed by us the Top performing sector in JPX (Tokyo Stock Exchange) are: (Data used is from (2017 -
2021))
o Energy Resources
o Automobiles and Transportation Equipment’s
o Steel and Non Ferrous Metals
- Based on EDA performed by us the worst performing sector in JPX (Tokyo Stock Exchange) are:
o Pharmaceutical
o Electric Power and Gas
o Foods
RECOMMENDATIONS
14. - We have used the Light GBM model to drive insights, check model accuracy and provide recommendation as a part of
our exercise. The data used within the model is of past 251 days as of Dec 2021.
- Reason for using only 251 days of data is because of resource limitation.
Why Light GBM Model
- Light GBM model is popular for time series analysis, and we were finding it widely suitable for our stock market analysis.
- Light GBM Computation speed is better than XG boost.
- Light GBM reduces loss function and gives higher accuracy at scale
LIGHT GBM MODEL
