Business problem:
The challenge of the stock price forecast is the most crucial component for companies and equity
traders to predict future revenues. A successful and accurate prediction of future stock prices
ultimately results in profit maximization.
The stock market is one of the major fields that investors are dedicated to, thus stock market price
trend prediction is always a hot topic for researchers from both financial and technical domains. In
this research, our objective is to build a state-of-art prediction model for price trend prediction,
which focuses on short-term price trend prediction.
Background / History:
Stock market prediction is the act of trying to determine the future value of company stock or other
financial instruments traded on an exchange. The successful prediction of a stock’s future price could
yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently
available information and any price changes that are not based on newly revealed information thus
are inherently unpredictable.
Data Explanation:
The datasets are fetched from the National Stock Exchange website. For this project, we are
planning to predict two random stocks and the dataset will have the following fields:
- Date – Trade date.
- Symbol – Tick value of the stock.
- Prev. Close – Previous day’s close price.
- Open – Open price of the day.
- High – The highest price in a day.
- Low – Lowest price in a day.
- Last – Last traded price of the day.
- Close – Close price of the day.
Methods:
For this project, I am planning to use time series methods because it is recorded at regular time
intervals, and the order of these data points is important. Therefore, any predictive model based on
time series data will have time as an independent variable. The output of a model would be the
predicted value or classification at a specific time.
For a new investor general research that is associated with the stock or share market is not enough
to make the decision. The common trend towards the stock market among the society is highly risky
for investment so most of the people are not able to make decisions based on common trends. The
seasonal variance and steady flow of any index will help both existing and new investors to
understand and make a decision to invest in the share market.
Analysis:
Stock and financial markets tend to be unpredictable and even illogical. Due to these characteristics,
financial data should necessarily possess a rather turbulent structure which often makes it hard to
find reliable patterns. Modeling turbulent structures requires machine learning algorithms capable
of finding hidden structures within the data and predicting how they will affect them in the future.
Stock prices are not randomly generated values instead they can be treated as a discrete-time series
model which is based on a set of well-defined numerical data items collected at successive points at
regular intervals of time.
Conclusion:
As of now, to conclude we have changed the method from linear regression to time series as
directed. We will split the training dataset into train and test sets and we will use the train set to fit
the model and generate a prediction for each element on the test set. Finally, we will track of all
observations in a list called history that is seeded with the training data and to which new
observations are appended at each iteration.
Assumptions:
As mentioned early, we are going to implement various time series methods to achieve the results. It
works perfectly to predict the results based on time. We are assuming here none other factors are
affecting the results except the time. Apart from this assumption, there is no assumption made and
the values which we are going to implement in this project are real-time values only.
Limitations:
In this project, we don’t know the exact accuracy until we complete the coding part. And there are
some other factors like company’s revenue, products they realize, management people will directly
affect the stock price which is not considered here. So, probably the accuracy of the results may be
less in this approach which can’t be mentioned now.
Challenges:
With the resurgence of machine learning and artificial intelligence, never has it been easier to
implement predictive algorithms both new and old. With just a few lines of code, state-of-the-art
models can be readily accessible at the fingertips of the budding data enthusiast, ready to conquer
whatever insurmountable digital task may lay at hand. But a little bit of knowledge can be a
dangerous thing. While much of machine learning can be attributed to statistics and programming
what is equally important, but often skipped over in favor of instant gratification, is domain
knowledge. But there are reasons for the project might fail which are listed as follows:
- Selection Bias – This is problematic as the stock selection is not an arbitrary process, it is part
of the investment decision-making process that requires a model in itself. - Incorrect correct application of pre-processing – Standard rinse, wash and repeat data pre-processing techniques like standardization cannot be directly applied to stock prices.
- Look ahead bias – Frequently, observations associated with particular dates would not have
been available at that date.
Future Uses:
Stock market prediction aims to determine the future movement of the stock value of a financial
exchange. The accurate prediction of share price movement will lead to more profit investors can
make. If the predictions came well with accuracy, we can implement it as a mobile app with a good
User Interface for public use.
Recommendations:
As per websites like Kaggle and other data science websites, the recommended model for stock
market prediction is the ARIMA model. A famous and widely used forecasting method for timeseries prediction is the AutoRegressive Integrated Moving Average (ARIMA) model. ARIMA models
are capable of capturing a suite of different standard temporal structures in time-series data.
Implementation Plan:
We can use a library called “nsepy” to extract the historical data for the Indian stock companies.
Then will create a few visualizations to show per day close price of a stock which we are selecting for
analysis. Then, we need to check if a series is stationary or not because time series analysis only
works with stationary data. If we fail to reject the null hypothesis, we can say that the series is nonstationary. This means that the series can be linear. If both mean and standard deviation are flat
lines(constant mean and constant variance), the series becomes stationary. Then, we are going to
create an ARIMA model and will train it with the closing price of the stock on the train data.
Ethical Assessment:
In this project, we are going to use the data which is available for public use from websites like the
national stock exchange and money control. So, there are no ethical issues in handling data. But the
ethical issue might raise when we release the results of this project because it’s totally experimental
and we don’t know exactly how the model will behave for each stock. So, there are some potential
threats that the result may mislead the investors. Thus, to avoid this kind of issue, we need to test
the model thoroughly with different stocks.
Reference:
- 🤔Stock Market📈 Prediction with Linear Regression | Kaggle
- Time-Series Forecasting: Predicting Stock Prices Using An ARIMA Model | by Serafeim Loukas | Towards Data Science
- Stock Price Prediction and Stock Price Forecasting using Stacked LSTM (analyticsvidhya.com)
Data visualization:
