Analysing the Effect Prediction Has on Investing

Analysing the Effect Prediction Has on Investing
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Analysing the Effect Prediction Has on Investing2
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.

Analysing the Effect Prediction Has on Investing3
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.