Predictive Analysis and Its Impact on Stock Market Investment

This report analyzes the application of predictive analysis in the stock market, emphasizing the use of machine learning techniques to forecast market trends and inform investment strategies. The abstract highlights the challenges of stock market prediction, influenced by various factors like psychological and irrational behaviors. The report reviews two peer-reviewed articles, discussing the use of algorithms like ARIMA, Perceptually Important Points (PIP), and sentimental analysis for prediction. It focuses on the effectiveness of Support Vector Machine (SVM) classifiers and other machine learning methods such as neural networks and ensemble learning. The study concludes that SVM achieves high accuracy in stock market prediction, providing investors with valuable insights to mitigate risks. Furthermore, the report discusses the potential of predictive analysis to enhance trading strategies and attract investors. Future research directions include incorporating macroeconomic factors and exploring advanced machine learning algorithms. Desklib offers similar resources for students.

Analysing the Effect Prediction Has on Investing
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2Analysing the Effect Prediction Has on Investing
Abstract
Predicting how the stock market will perform is not simple instead a huge challenging tasks to do for the analyst or
data scientist. Enormous factors are involved in the predictive analysis process few are physical factors vs
psychological then irrational behaviour and many more. Many analyst and researchers found interest in stock market
prediction. For short term the market behaves like a voting machine as one can invest depending upon person choice
but for long term it behaves like a weighing machine and thus there are various scope of predicting the market
movement for long time frame which will be beneficial. Nowadays data analyst or data scientist uses machine
learning algorithm to predict the next step and forecasting is a field where machine learning is deeply involved.
Predictive analysis consisting of different statistical techniques, data mining and machine learning technique which
builds models to predict the future instances. In this analysis review we used two peer reviewed article/journals to
review the author’s works and review it in short.

3Analysing the Effect Prediction Has on Investing
One of the most fascinating market nowadays can be termed as financial market. It has a great impact on
business, education, economy and many other fields. Learning from previous experience to predict the future
possibilities in order to sustain better decision is termed to be as predictive analysis.
The purpose of the study is get a brief overview of the steps involved in predictive analysis of stock market.
Learning itself to predict the data is called the machine learning which are used widely nowadays. Most of the
prediction and forecasting are carried out by machine learning algorithms (Dhruv,Hima & Prayag 2018). The real
aim of machine learning is to predict. Several researcher and data analyst applied different machine learning
algorithm to get the best outcomes so that the it attract more investor to invest through which there will be a financial
balance will be maintained.
The main objective of the article is to predict the best technique to analyse the stock market data which
achieved the best accuracy. A drastic change has been seen with predictive analysis which results in healthy
financial condition all over the globe. It has also been seen that most of the existing work focuses on technical
analysis and short term stock market prediction which totally changes the view of the stock market.
Fig 1
Few improvements of predictive analysis over the stock markets are:
 To forecast points the best way is to use the Arima model from the machine learning algorithms to fit to the
time series which give better accuracy.
 One most common and widely used pattern recognition is Perceptually Important Points (PIP), which has the
capability to reduce the time-series dimensions.
 Sentimental analysis is another machine learning technique to predict the stock trends via automatic analysis
of the text corpuses such as news feeds or tweets specific to stock markets and public companies.
It uses predictive models for estimation of the number of outcomes and returns on investment. With the
increase in access of these results and increase in the level of accuracy of data analysis and Data predictions,
investors can leverage this information and prediction towards mitigating their risk associated in trading on the stock
market.
The major conclusion to this study is that the Support vector machine classifier algorithm achieved the best
accuracy to predict the accuracy of the stock market. Most researches showed that an optimized version of the SVM
was best suited for the job. For stock market predictive analysis machine learning comes into play through some
machine learning techniques such as feature selection and sentimental analysis. Despite with the risk involved in
stock prediction machine learning algorithm has given the investor a big relief to invest in the right option with the
results of the prediction.

4Analysing the Effect Prediction Has on Investing
However in this study the risk factors were not discussed. A comprehensive study through machine learning
techniques has been depicted. Also few algorithm where introduced and tested to find the best prediction model in
recent times. Also for future scope there will be involvement of other intrinsic macro-economic factors like interest
rate and GDP will be there for further analysis.

5Analysing the Effect Prediction Has on Investing
Reference
Dhruv Pathak, Hima Karan Kadali, Prayag Saraiya (2018). “A Machine Learning Approach to Stock Forecasting”
IJIRCCE, Vol. 6, Issue 1, January 2018.

6Analysing the Effect Prediction Has on Investing
The purpose of this study is to analyse whether predictive analysis is the best way to predict the stock market
and generates the best return in turn. The research mainly focused in the next ten year prediction of the stock market.
Here trading strategies have been discussed after prediction analysis, as trading strategies is an important factors of
stock market analysis. The main discussion was about ups and down market period of the future stock market. Also
there is a brief understanding on how machine learning lifts the stock market analysis trend to a new high.
Here in this paper its main focus is predicting the stock market with machine learning techniques such as
SVM, neural network and many more. Machine learning a part of predicting analysis is well known and is used to
predict stock market. Three most prevalent machine learning algorithm are Neural network, support vector machine
and ensemble learning (Macchiarulo 2019). An interesting fact is that ensemble learning has the ability to confirm
two machine into one prediction which has been described here briefly. Stock market investment and PA continues
to be a challenging problem for all. With increase in data new challenges will be faced to process the data to extract
knowledge and analyse the effects which will directly impact on the stock prices. Few challenges which can be faced
in future are like Sentimental analysis, live testing, self-defeating, long-term predictions, algorithmic trading on
company filings. With new machine learning algorithm, it can be possible to gain more benefits like no dependence
on sentiments, reduced cost and reduce latency.
The study focuses on the machine learning trading strategy. The SVM is termed to be as the best predictive
analysis. A very low margin of error is observed in the study with SVM and neural network taken to consider. This
type of knowledge is very powerful and useful to profit in finance. Also with the analysis it has shown that machine
learning one of the predictive analysis method has achieved the highest overall average monthly return
The major conclusion to this study is that machine learning as a trading strategy will positive impact the
return generated in comparisons with other technical indicator. Also a huge number of investors are attracted to
invest in the stock market due to the recent progress of predictive analysis in the stock market. In future similar
research should be performed over a longer period for better prediction system. For now predictive analysis changed
the word of prediction at a new high which is currently trending on all market.
However a lot of analysis has been performed. But not much future scope has been told in this analysis. Also
not much is explained for the betterment of the investors to invest on the stock market. But a thoroughly analysis is
been performed with the recent stock market data, and how different predictive analysis technique lift the quality of
prediction in stock market in recent times.

7Analysing the Effect Prediction Has on Investing
Reference
MACCHIARULO, A. (2019). PREDICTING AND BEATING THE STOCK MARKET WITH MACHINE
LEARNING AND TECHNICAL ANALYSIS. Retrieved 22 September 2019, from
http://www.icommercecentral.com/open-access/predicting-and-beating-the-stock-market-with-machine-
learning-and-technical-analysis.php?aid=86901.