The financial market in itself is highly dynamic in
nature and is affected by factors that are multifaceted. Human
beings have the ability to monitor and assess several of these
factors simultaneously. However, similar versatility is not easily
achieved by software. Our aim is to create a analysis system that
showcases some of this desired versatility, with the help of several
algorithms and techniques, that will help capture more than one
factors needed for efficient and thorough analysis of such a
complex market. Moreover, the precision and robustness required
to analyze the mammoth amount of financial data available is
beyond human capacity. A combination of a holistic approach
along with an added layer of efficiency will create a system capable
of much more than mere analysis of the facts.
The key component of the financial market in India is the stock
market. The stock market is represented by the securities, in the
form of company shares, debt securities, etc, traded on the two
exchanges - BSE and NSE. Analysis of the prices and other factors
related to these securities gives a general idea of the economy of the
country with respect to its financial status.
Participants of this industry must adhere to certain rules laid down
by the Securities Exchange Board of India(SEBI) and trade
according to the established guidelines. Additionally, trades are a
way of making profits and to do so, lot of research and analysis is
required. This is done mainly by trained individuals, but in today’s
age of automatization, we would want this process performed by a
computer software that will enable us to tap into unventured
avenues that could not have been explored before due to a lack of
human cognition. Machines, if developed properly, can achieve a
level of precision that open up new ways of analysis.
Intelligent Systems for Stock Market Analysis”,Springer-Verlag
2. Kyoung-jae Kim, “Financial time series forecasting using support
vector machines”,Elsevier B.V.
3. Rohit Choudhry, and Kumkum Garg, “A Hybrid Machine Learning
System for Stock Market Forecasting”,International Scholarly and
Scientific Research & Innovation.
4. Web Source- Investopedia
5. Ping-Feng Pai∗, Chih-Sheng Lin, “A hybrid ARIMA
and support vector machines model in stock price
6. Ahmad Kazem, Ebrahim Sharifi, Farookh Khadeer
Hussain,∗, Morteza Saberi, Omar Khadeer Hussain,
“Support vector regression with chaos-based firefly
algorithm for stock market price
7. Xiao Ding†∗, Yue Zhang‡, Ting Liu, Junwen Duan,
“Deep Learning for Event-Driven Stock Prediction”,
8. Robert P. Schumaker and Hsinchun Chen, “Textual Analysis of
Stock Market Prediction Using Breaking Financial News: The
AZFinText System”,12th Americas Conference on Information
9. Web Source- Investopedia
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