Text Mining on Financial News

As discussed previously, an analyst should give specific attention to problem representation particularly when we are dealing with text data. A way to do this will be discussed below, however something has to give and there is no perfect solution for such a task.

First of all we have to find the source of the news : It could be financial news sites such as Bloomberg, Financial Times, or RSS Feeds URLs such as the ones provided by MarketWatch. RSS Feeds might be a better solution because there is already some predetermined categorization of news according to the feed type and this can be great help for some analysts.

After finding the news sources and making the necessary code to get the actual information we could end up with the following text file :



You can see that i use a '^' separator to differentiate between :

1) A date stamp,
2) A date string
3) The news string
4) A characterization of the news (important or unimportant)
5) A categorization of the financial news.


This simple file could provide the basis for a training file for text categorization. Assuming that we have trained algorithms to automatically classify news, we could use a news classifier to first categorize news to important or unimportant and pass only the important news to a second classifier which will do the detailed classification of the news.

Another option is to use clustering : You can imagine that the solution detailed above has a tremendous amount of work depending on how much data you are planning to collect...so too much data means too much work, less data could mean -usually but not always- less accuracy.

But how could clustering be performed on such data? Simply, we just use field number (4) on our training text file to train a clustering algorithm and then see what 'classes' the algorithm has come up with.


So let's see a small example about clustering : This is a capture from WEKA just before the clustering process :


I have produced a training file which essentially contains the 'buzzwords' of financial news : barrel, recession, Yen, Euro, ECB, price, consumer, etc. The file is then analyzed by K-means algorithm to extract clusters of the same 'buzzwords'. Each cluster is assigned a number so each news header ultimately falls onto one cluster number.


After running the K-Means algorithm i ended up with 16 clusters. Let's see two instances that K-Means decided that they should fall under cluster '6' :


Instance_number : 130.0

Fear
Decrease
US
Economy
Futures

and


Instance_number : 174.0

Fear
Decrease
US
Price
Oil
Banking
Recession


So the first instance is about fears for the US Economy which results in US Futures dropping and the second instance must be -something about- a decrease of Oil prices and Banking stocks because of the fear of US recession. Not bad at all...

But not so fast : Clustering presents a lot of problems later in the process. Remember that what we are after, is to combine text mining and data mining together to better understand how the markets react. Should one use classification or clustering? There are many more things to take under consideration and for obvious reasons i cannot disclose all the details of such a project...but i am hoping to give to the interested reader a good enough introduction on the subject.

Capturing the Financial Facts

So far, we have seen the data mining part on analyzing the financial markets and some of the problems that arise during such an analysis : Data have to be collected and pre-processed accordingly. There are dangers of over-fitting and the analyst must make sure that the model(s) created have the expected quality. The analyst has also to choose relevant attributes with which the analysis will be performed and how the training of the algorithms will be made.

The markets react to financial news and there is no question about this. Of course there are other factors that make people buy or sell : For example if a stock price has hit a support or resistance level then some investors are going to either buy or sell when such a price level is reached. Investors are also going to buy or sell when specific technical indicators such as MACD or oscillators show the signals to do so. Even when bad news are out, markets after an -unknown- number of consecutive drops will go up by an -unknown- percentage and vice-versa.

People that are involved with Machine Learning know that the representation of the problem at hand is of high importance...so first we are going to see how financial news can be represented in a helpful way for the analysis.

We have to see with what we are dealing here. To do this, we have to analyze and categorize accordingly the financial information as this is created. Financial News can be news about a number of things :

1) The number of jobless claims in US is higher than last year.
2) Automotive company's XYZ sales were dropped by 15%
3) Oil prices hit -yet- another record high
4) The dollar is dropping


....and the list goes on.


So the first problem arises : Should we categorize the information according to its content and present it to the algorithms? We could do that by having a boolean field for each type of news on our training file and set it accordingly to TRUE or FALSE values. By using this method we could easily reach thousands of input fields, since for the "jobless claims" news type we could have the following variants :

-A specific country for the jobless claim report (not only the US, it could be any country)

-Jobless claims could be higher than expected or higher than last year or the highest in the last decade.


It is easy to see that this gets way too fast out of control. Perhaps a better solution would be to try to create clusters of (more or less) the same news. The idea of clustering the financial news might seem an interesting one and an analyst could define a number of clusters -say he is after 100- and let the clustering process categorize accordingly all the news. But is clustering the solution? More on this on the next post...

Model Testing

Once a model has been created (such as the decision tree for our example), the analyst is required to test the model. During model testing, an analyst performs specific tests that show the actual predictive power of a model.

Many methods can be used for model testing, depending on the problem. For our example and since the available volume of data is sufficiently large, the model training and testing methodology i used was as follows :

1) 50% of data were used for model training
2) 25% of data were used for model validation - fine tuning
3) 25% were used for testing of the model.

In other words, 75% of the data were used for training the algorithm and assessing the impact that changes on algorithm parameters have on the accuracy of the model. For a decision tree algorithm (and depending on the type of decision tree used) an analyst might try different settings for splitting criteria and/or number of minimum cases per branch, etc.

Unfortunately, numerous times an analyst finds that the predicted accuracy of the model given during training - model validation phases (ie steps 1 and 2 shown above) is in no way representative when the model is tested on unseen cases ( Step 3).

During my analysis, numerous models were showing an estimated accuracy of 85% or more but when they were presented on actual data, the accuracy was dropping down to 50-53%, suggesting that overfitting was present. Consequently, the use of these biased models to predict new cases would have detrimental effects in actual stock trading.

When all models are built, the analyst should choose a model (when there is a requirement to use only one model) according to :

1) (Statistically significant) best accuracy.
2) Misclassification costs, if these are not taken into account during the model building process.


On the next post we will see how text mining may help us in making better predictions for the markets.

Decision Tree Interpretation

On the previous post i went through some basic steps required for predicting the price changes of a specific stock of the Greek stock exchange market. As a result of this process, the following decision tree was generated :

To interpret a decision tree, the analyst starts from the root of the tree and reads through it until a leaf node is reached. For example a rule that can be extracted from the decision tree above is the following:

"IF aseStockExchange > 0.360 AND aseStockExchange > 1.985 THEN price>+2"

The rule above can be found by starting from the root of the tree, moving on the left branch and then continuing to the right sub-branch. In the same way an analyst is able to find the rest of the rules identified by the decision tree.

When using decision tree learners or rule extractors, analysts record the precision and recall of a rule which are not shown in the decision tree above. However, for matters of simplicity i will omit this information and describe the insights provided from the analysis. Decision Trees possess the two following qualities :


1) They provide easy model interpretation

and

2) They show us the relevant importance of the variables

When confronted with many variables, analysts usually start by building a decision tree and then using the variables which the decision tree algorithm has selected with other methods that suffer from the complexity of many variables, such as neural networks. However, decision trees perform worse when the problem at hand is not linearly separable. For the purpose of our example though, a decision tree 'explains' the behavior of the stock nicely.

It should be noted that during the Feature Selection analysis of our stock example we have found that features 'aseStockExchange' and 'DAX' are important. Other features such as 'xaaPersonalHouseProducts' were flagged as important from the Feature Selection algorithm and were not used in the decision tree. Different feature selection methods produce different results (and one might say that this is not very assuring) but usually most methods produce a common feature subset that is of high predictive value.

The importance of the attributes can be seen from the level that they appear on the decision tree (the higher the level, the better is the prediction power of the attribute). So in our example, the 'aseStockExchange' feature is the most important (since it is the attribute with which the decision tree starts) and less important attributes seem to be 'xaaLeisure' and 'xaaBenefit'.

Insights from a Decision Tree

Assuming that an analyst has made all necessary pre-processing tasks prior to the data mining phase, we are ready to deploy analytical methods such as decision tree learners that can classify unseen cases. For the goal of stock prediction we assume that we have the following data collected :

The column named as XAACLASS is the target column that we wish to classify. Essentially here we have the following classes :

-price change percentage greater than 2%
-price change percentage less than -2%
-price change percentage greater than 0% and +2% inclusive
-price change percentage between -2% inclusive and 0% inclusive

In other words, each line shows us the state of the stock we wish to predict, that occurs given the rest of the market indices (such as realTimeFTSE, realTimeDAX, etc).

So, let us assume that we are ready to build such a model. However, we have to decide the time window that our predictions will be made for...do we wish to predict what the stock price change will be 2 hours ahead? How about 1 day ahead?

Before dealing with this issue, i wanted to see how good a predictive model is by predicting the stock price percentage change right now, based on the current market conditions. Here is a decision tree that is created from such data:

More to come on the next post where the model seen above will be explained in detail. Until then please read the post from this blog about the same problem. If you can, read Fooled By Randomness also...

So...What's important??

A step of a Knowledge Discovery Process is to perform what is known as Feature Selection, which essentially is the identification of a subset of features with high predictive value.

Feature selection can potentially help in increasing the accuracy of prediction models. Methods such as Naive Bayes can perform better when presented with a subset of selected features, rather than the whole feature set (because of feature redundancy).

Even if feature selection does not prove to help too much, it is important to know the predictive power of each feature. There are numerous methods to do this and -as normally is the case- there is no universally better method to perform an optimal feature selection. The following is a representation of all available Feature Selection methods in WEKA:




Let us stick to our example with stocks, to make things more clear. Suppose that i would like to know which features seem to be important for predicting the behavior of a stock. For our example we will try to find out about how the stock of NBG reacts.

By using a feature selection method we extract the following information :



The feature selection method above shows us how many times each attribute was selected during a 10-fold cross validation. We can see that some attributes are used more times than other attributes during each cross validation . For example :

realTimeDax
aseStockExchangeIndex
xaaPersonalHouseProducts
xaaTechnology
bankAgrotiki
bankAlpha
bankPiraeus
bankEuro


are present in all 10 folds of our cross-validation and hence the 10(100%) entry. xaaFinancialServices index has been selected fewer times (8 out of 10) and hence the 8(80%) entry. Other features never appear to any of the cross validation folds.

Of course feature selection does not stop here and there are many ways to enhance the process. Data Mining is both an art and a science. However for our purpose, we were able to identify those attributes that seem to be important in the prediction of the NBG stock. We immediately see for example that DAX index and the Athens Stock Exchange Index are two important features, plus the stocks of four specific banks. Other methods of feature selection produce weights that essentially rank the importance of each attribute for class prediction.

Always know your data!

Before rushing in analyzing and predicting the Financial Markets (and actually anything else) it is essential that we get an idea about the data at hand. So after data collection (ie getting values of different market indices) i wanted to understand first what is going on to the markets. And a correlation Matrix tells us just that. Let's see what happens on the Greek Stock Exchange :







By looking at the matrix we can immediately see some interesting things :

1) There is a high correlation (=0.847) between DAX index and the Greek stock exchange index (marked as aseStockExchangeIndex)

2) The Insurance index sector (xaaInsurance) and the Media sector (xaaMedia) have a low correlation with the aseStockExchangeIndex. Consider the following scatter chart that shows the poor correlation between Insurance sector stocks and the aseStockExchangeIndex :




Those two facts alone can help significantly in trading: For example if an investor's trading decision is heavily based on aseStockExchangeIndex then the investor should also keep a close look on the DAX Index as opposed to other European indices (such as FTSE,CAC40,etc).

A lot of problems later in the analysis can be prevented if one pays attention to the "Data Understanding" phase. Plus, we also get an insight as to what kind of results should we expect from the learning algorithms.