Advanced Data Analytics: Terminologies, Methods, and Importance

Advanced Data Analytics 1
Advanced Data Analytics
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Advanced Data Analytics 2
Advanced-Data Analytics
Terminologies
1. Trend analysis
Trend analysis uses past data to make predictions on the market behaviors in the
future1. Every data analytics specialists need to have knowledge of the analysis data trends in
the market and make a rational financial decision. Therefore, it is crucial to be well-oriented
with how and why trend analysis in identifying and predicting market trends.
Generally, trend analysis is a comparative analysis of data over some time to predict
future behavior. The primary trend analysis in the stock market is to forecast the movements
of stock prices. A stock analyst uses past information and data to give an idea of what to
expect from stocks in the future. As such, a consultant can use trend analysis to advice a
client when to enter the market and when to exit. The analysis can also help the trader spot
potential problems in stock before they occur.
2. Behavioral analytics
Involve an analysis of people’s actions in the buying and selling of stocks. Consumer
behavior helps in the prediction and optimization of stock trading2. The presence of big data
has facilitated the analysis of people behavior and hence, likely to provide an accurate
prediction on market performance and other issues. A data analyst takes a keen interest in
what people say and do regarding the stock market because their actions and communications
have an impact on the stock exchange decisions.
1 J. Patel et al., "Predicting stock and stock price index movement using trend deterministic
data preparation and machine learning techniques." Expert Systems with Applications vol.
42, no. 1, 2015, p. 259-268.
2 G. Nabieu, "Model Behavior Analysis of Stock Market Indicators and Listed Companies:
Evidence from the Ghana Stock Exchange: Automated versus Floor Trading." International
Journal of Business and Management vol. 9, no. 11, 2014, p.234.

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3. Contextual data
This is the data that provide stock market context. The data is used to give a broader
understanding of the specific stock and placing it in a larger picture. Contextual data gives the
historical performance of certain stock to facilitate future decision.
4. Data visualization
It involves the use of graphical representation to present information and data about
stock market performance. Visualization is used to make it easy for the user to recognize
patterns and exceptions and hence make quick interpretations.
5. Risk analysis
It is the process of identifying and quantifying issues with the stock market and
making estimations about their impact. Risk assessment in the stock market is a critical
function of a data analyst to a client for there to be an informed judgment.
6. Financial volatility
It is the statistical measure of the dispersion of returns of a specific stock in the
market. A data analyst tries to measure whether the stock volatility is high or low and advice
the client accordingly. High volatility is associated with high-risk stocks.
Critical Evaluation of Common Data Analytics Methods
1. Mean
It is the average where the observations are divided by the number of observations
made, as shown in the formula below. Although it is an essential tool in data analysis, it is not
sufficient to facilitate rational decision. Mean is used together with mode and median to give
more information about the data set. Also, mean does not provide an accurate answer for
nuanced decision in extensive data which is likely to be skewed.

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2. Content analysis
The method is used to measure the overall theme(s) in qualitative data. The analysis
helps parse textual data such as questionnaires, interviews, and open-ended surveys to find
the most common thread. The method is most appropriate in quantitative data but not
qualitative data. Therefore, it cannot provide numerical measures that assist in the decision.
The method mostly biased as it is based on user opinion and feelings which might not
represent the population.
3. Regression analysis
The method is used to measure the statistical relationship between two or more
variables. It is used to measure a trend that might exist in a set of data, as shown below. But,
the method is not very nuanced since it may ignore essential outliers.
4. Monte Carlo Simulation
The technique is used to make predictions on the effect of unpredictable variables on