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Pages:
11 pages/≈3025 words
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5 Sources
Level:
APA
Subject:
IT & Computer Science
Type:
Research Paper
Language:
English (U.S.)
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MS Word
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Topic:

Insights on Investor Decisions in Tesla Stocks (Research Paper Sample)

Instructions:

We were are given tesla data that we seek to build predictive model with and write our findings.

source..
Content:


Insights on Investor Decisions in Tesla Stocks
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Date
Table of Contents
Insights on Investor Decisions in Tesla Shares.....................................1
Table of Contents...................................................................................2
Introduction............................................................................................3
research methods..................................................................................4
Sampling Techniques………...................................................................5
Data Collection Method….......................................................................5
Analysis Method.....................................................................................6
Research Design and Methodology........................................................8
Conclusion…………………………………………………………………….10
References...........................................................................................11
Figures ...................................................................................................12
1.0 Introduction
Tesla shares have had an exponential rise since the introduction of new-generation electric vehicles with improved battery capacities. This has seen a surge in interest of investors in the Tesla shares from different stock markets such as the Nasdaq and S&P 500. By 2022 there is an estimated 50% gain for a long-term investment decision with a quick decision in combating global warming. This issue can be eradicated with the switch to non-fossil engines. With uncertainty in the market, better decisions using software and human-aided decisions will land us with a great deal of minimized inputs as we seek to harness plenty of returns.
The hypothesis on whether to buy or sell the shares will depend on historical data collected from Tesla from the year 2010 to 2021 Feb. This research paper opted for content analysis, survey data analysis, quantitative research, and market research as our research methods. Random sampling technique of the time series data will be used to give and survey techniques from customer churn data set.
The data used online market analytics. Time series analysis method and cross-validation are both used as analysis methods and model validation techniques, respectively. We can give an insight to the investors on the right to either buy or sell tesla stocks and either invest on a short-term or long-term basis.
1.1 Research Method
We followed the methodology of market analysis, content analysis, survey data analysis, and quantitative research with the help of python software to understand the trends in a historical timestamp of 2679 observations. The need to understand quantitative data has risen in the past few decades, and the financial markets are in a state of uncertainty through a financial year. The percentage change calculator can be used to lead us to better decisions at the end of the day as it equals the change in value divided by the absolute value of the original value, multiplied by 100.
Data was scraped from Kaggle and made available to develop the insights, predictions, validation, and future trends noted with high precision and confidence during the collection. With the increased unprecedented deviations in stock prices during the Covid-19 pandemic, the need for price forecasting became more critical. Investigations on Tesla during the COVID-19 pandemic have raised an alarming call time to understand the accuracy and predictability of the models in this highly volatile time region. Model training through all stocks' data are split into train and test datasets (Mentab & Sen, 2020).
The test dataset starts from 2010 to Feb 2021, which covers a great deal of sample data observation which will mean unbiasedness in the estimations. The results show that the pct change and Last value models have higher accuracy in predicting the stock prices because of the strong correlation between the previous day and the next day's price value. This will finally make our research methods suitable for the research topic(Liu, 2021).
1.2 Sampling methods
Through the study we had a random sample of 2679 observations and with nine variables. The need to develop a sampling technique on the data harnessing is to reduce on biasness and ensure all critical aspects of the analyses are captured.
The significant need to take this is by default, and we assume the data follows a normal distribution when the noise terms are plotted.
Surveys from past interviews were analyzed, aided in decision making on the past trends. We deduced a sharp rise in stock profitability with the new exchange method of an untraceable blockchain system from the insights.
Throughout the new scheme, Tesla saw an upward trend until April 2021 when Elon Musk, the Tesla Ceo, opted out from the system, which shows a slow recession but picked again from may after the adaptation. The plentiful nature of the sampling techniques offered plenty of empirical studies through the data analyses; we already know, correlation does not cause causation(Edman & Weishaupt, 2020).
1.3 Data collection Technique.
The research used a lot of random data recorded that could be vital in finding the trend of the share price of Tesla stock. To get this data, we needed a data collection technique that could help us get the correct information for the analyses where online marketing analytics and online surveys proved more than enough in giving a more broad data set that covered a significant period to give the proper insight. The Tesla data is reliable and uncleansed, giving us raw and untampered data that can be deduced to illustrate the real-time analysis of the company trends, hence reducing manipulations on the initial recordings leading to a quantitative report.
1.4 Analysis Methods
Exponential moving averages and a MACD have been employed widely to give clear visuals on the investor's decision. On graph interpretations, once the MACD crosses the signal and an upward trend is recorded, this is the best time to decide on buying the stock and vice-versa. We have a conventional assertion that the past and present readings on the closing prices can barely explain the future trends from past time series.
Arima, a time series model with one AR term and one MA term, is applied to the variable. For a linear trend in the data, a first moving difference can be graphed or computed to aid in convincing the human brain on future trends and preparedness in case of losses;
Zt = Xt − X(t − 1).
These simulations needed for complex and sophisticated analyses may be only applicable for short-term forecasting, and a similar neural network like the Recurrent Neural networks leverages the whole situation.
1.4.1 Stationarity in a time series;
The notion of stationarity is essential in this analysis despite the use of the software. The statistical properties of the process do not change over a given period. For better and consistent estimations, a time series will have to adhere to this assumption. The autocorrelation matrix, a key concept here, shows the correlation between measurements at two different quarters. Stock prices may be correlated from day one day to the next day with a specific lag value. A lag value can be viewed as to be the time interval between values.
In a time-series data analysis, several assumptions can be made. These may include;
* Constant mean;
By a constant mean, we imply no trend and the variations have been ignored.
* Constant Variance.
The distance 

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