Stock market trend prediction using k nearest neighbor(knn) algorithm

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Stock Market Trend PredictionUsing K-Nearest
Neighbor(KNN) Algorithm
In this paper author is evaluating performance of KNN(K-Nearest Neighbor)
supervised machine learning algorithm. In the finance world stocktrading is one
of the most important activities. Stock market prediction is an act of trying to
determine the future value of a stock other financial instrument traded on a
financial exchange. The programming language is used to predict the stock
market using machine learning is Python. In this paper we propose a Machine
Learning (ML) approach that will be trained from the available stocks data and
gain intelligence and then uses the acquired knowledge foran accurate prediction.
In this context this study uses a machine learning technique called K-Nearest
Neighbor to predict stockprices for the large and small capitalizations and in the
three different markets, employing prices with both daily and up-to-the-minute
frequencies.
Predicting the Stock Market has been the bane and goal of investors since its
existence. Everyday billions of dollars are traded on the exchange, and behind
each dollar is an investor hoping to profit in oneway oranother. Entire companies
rise and fall daily based on the behaviour of the market. Should an investor be
able to accurately predict market movements, it offers a tantalizing promises of
wealth and influence. It is no wonderthen that the StockMarket and its associated
challenges find their way into the public imagination every time it misbehaves.
The 2008 financial crisis was no different, as evidenced by the flood of films and
documentaries based on the crash. If there was a common theme among those
productions, it was that few people knew how the market worked or reacted.
Perhaps a better understanding of stockmarket prediction might help in the case
of similar events in the future.
Despite its prevalence, StockMarket prediction remains a secretive and empirical
art. Few people, if any, are willing to share what successfulstrategies they have.
A chief goal of this project is to add to the academic understanding of stock
market prediction. The hope is that with a greater understanding of how the
market moves, investors will be better equipped to prevent another financial

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crisis. The projectwill evaluate someexisting strategies from a rigorous scientific
perspective and provide a quantitative evaluation of new strategies.
There are several data mining algorithms that can be used for prediction purposes
in the field of finance. Some examples would be the naive Bayes classifier, the k
nearest neighbour (KNN) algorithm and the classification and the regression tree
algorithm (Wu et al. 2007). All the mentioned algorithms could fill the purpose
ofthe paper butit will center around the kNN algorithm as a method ofpredicting
stock market movements as well as the MA formula. The movements will be
detected by looking at a large amount of historical data and finding patterns to
establish a well estimated forecast. This specific algorithm was chosen as it is a
simple but a very effective algorithm to implement when looking at large amounts
of data (Berson et al. 1999).The KNN algorithm simply states: "Objects that are
’near’ to each other will have similar prediction values as well. Thus if you know
the prediction value of one of the objects you can predict it for its nearest
neighbours" (Berson et al. 1999). As a comparison with the KNN algorithm, the
MA formula was chosen. The MA formula has its simplicity as a commonfactor
with the KNN algorithm, but it is a statistical method used frequently by traders
(Interactive Data Corp, 2014).
There are existing techniques when it comes to stock prediction, some of them
are multispectral prediction, distortion controlled prediction and lempel-ziv based
prediction. These are based on the fact that the data representation is more
compact by removing redundancy while the essential information is kept in
format that is accessible (Azhar et al. 1994). Due to the scope of the project the
techniques that were the most suitable to work with were the KNN algorithm and
the MA formula despite the existing techniques listed above.
To conductexperiment author has used Yahoo Finance stock Dataset and below
is some example records of that dataset which contains request signatures. I have
also used same dataset and this dataset is available inside ‘dataset’ folder.
Dataset example
['High', 'Low', 'Open', 'Close', 'Volume', 'Adj Close']
Above list are the columns of Yahoo finance

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201
7-
01-
05
116.8600
01
115.8099
98
115.9199
98
116.6100
01
22193600
.0
111.3933
03
201
7-
01-
09
119.4300
00
117.9400
02
117.9499
97
118.9899
98
33561900
.0
113.6668
24
Above two records are the APPLE stock form the Yahoo Finance Dataset. For
the rest of analysis, we will use the Closing Price which remarks the final price in
which the stocks are traded by the end of the day.
we analyse stocks using two key measurements: Rolling Mean and Return Rate
Rolling Mean:
Rolling mean/Moving Average (MA) smooths out price data by creating a
constantly updated average price. This is useful to cut down “noise” in our price
chart. Furthermore, this Moving Average could act as “Resistance” meaning from
the downtrend and uptrend of stocks you could expect it will follow the trend and
less likely to deviate outside its resistance point.
The Moving Average makes the line smooth and showcase the increasing or
decreasing trend of stocks price.

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ReturnDeviation— to determineriskandreturn
Expected Return measuresthe mean, or expected value, of the probability
distribution of investment returns. Theexpected return of a portfolio is
calculated by multiplying theweight of each asset by its expected return and
addingthevalues for each investment — Investopedia.
Following is the formula you could refer to:

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Before running code execute below two commands
Screen shots

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Double click on ‘run.bat’ file to get below screen
In above screen click on ‘Download Button’ download the Apple Stock and
competitors data from Yahoo Finance Dataset

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In above screen I am Downloading of Apple Stock and Apple competitor Stock
Data from Yahoo Finance Dataset.
Now click on ‘Correlation Data’ Button to find the correlation between Apple
and Competitor Stock market Dataset. show the trend in the technology industry
rather than show how competing stocks affect each other.
Now click on‘Data PreProcessingbutton to drop missing values, split labels split
train and test

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After pre-processing all missing values are dropped, Separating the label here,
Scalling of X, find Data Series of late X and early X (train) for model generation
and evaluation, Separatelabeland identifyit as y and Separationof training and
testing of model.
In above screen we can see dataset contains total 1752 records and 1226 used for
training and 526 used for testing.
Now click on ‘Run KNN with Uniform Weights’ to generate KNN model with
uniform weights and calculate its model accuracy

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In above screen we can see with KNN with uniform weights got 96.8% accuracy,
now click on ‘Run KNN with distance weights’ to calculate accuracy

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In above screen we got 100% accuracy, now we will click on ‘Predict Test
Data’button to upload test data and to predict whether test data stockmarket for
both models.
accuracy score (>0.95) for most of the models.

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In above screenfor each test data we got forecastvalues for Apple Stock for each
test record. Now click on ‘KNN Accuracy’ button to save the predicted values
for each model save in the local directory and Accuracy comparison to both the
models
From above graph we can see that distance weights has little bit better better
accuracy compare to Uniform weights, in above graph x-axis contains algorithm
name and y-axis represents accuracy of that algorithms

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Plotting the PredictionforKNN withUniformWeights:

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Plotting the PredictionforKNN withDistanceWeights:

Stock market trend prediction using k nearest neighbor(knn) algorithm

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Stock market trend prediction using k nearest neighbor(knn) algorithm