Predicting the
stock market has never been easy, even for the professionals
who continuously research new methods to forecast the stock
market's future. The only characteristic all stock markets share is
uncertainty. And while millions of stock market experts try
their hand at stock market prediction techniques, no one method has
yet been proven to be 100% accurate.
It's relatively easy for us to see how major economic developments,
like wars, or natural events like hurricanes and earthquakes, could
indicate that the stock market might fall. What is harder to
do, is predict how the subtle changes in the economy and normal
everyday trends, will affect the
market.
Researchers and academics fall into two camps when it comes to
stock market predictions: those who believe we can predict the
market and those who maintain that the market is so efficient and
self-correcting that there is no space for any form of prediction
at all. Experts call this latter line of thinking the
"random walk" hypothesis, which purports that the best prediction
you can have about tomorrow's value is today's value.
Many believe you first have to understand how the market is shaped
before you can effectively try your hand at predicting the market's
future. In the simplest terms, the stock market is shaped by
how investors think and react. This process regulates
how much capital
goes in and comes of the stock market, thereby determining its
level. This market prediction methodology stipulates
that the masses base their investment strategies upon what
information they hold. Therefore, the factors of
"information" and how the "investor" reacts equals "market
level."
Methods of stock prediction can be categorized into four major
categories: 1) Technical Analysis, 2) Fundamental
Analysis, 3) Traditional Time Series Forecasting and 4) Machine
Learning.
Technical Analysis traces patterns on charts, which describe
historic market data, thus extrapolating a price using only earlier
values of that same price.
Fundamental Analysis studies a company's financial statements,
interest rates, volumes, competitors' prices, prices of raw
materials or other variables known to affect a stock's intrinsic
value and estimates if its current value is lower that its
intrinsic value.
Traditional Time Series Forecasting attempts to formulate linear
prediction models of stocks to trace patterns thus enabling market
predictions.
Machine Learning forecasting uses a set of sample market data to
generate an understanding of the underlying function that generated
the data.
While these methods are complex, they're also the most
scientific. Some investors play the stock market using a hunch
or a gut feeling. You can go that route if you're feeling
lucky. Otherwise, explore one of these four methods or get
yourself a good broker.
Learn more about how to
invest in the stock market from expert investors.
