By Chainika Thakar
Deep Learning plays an important role in Finance and that is the reason we are discussing it in this article. In simple words, Deep Learning is a subfield of Machine Learning. Since they differ with regard to the problems they work on, their abilities vary from each other.
Let us see what all this article will cover ahead:
- A General Overview of Deep Learning
- Uses of Deep Learning in Finance
- Models of Deep Learning
- Application of Deep Learning in Finance
- What is the Future of Deep Learning in Finance?
First, we will see the concept of Deep Learning explained in a structure which it is a part of.
Deep Learning is a part of Artificial Intelligence which provides the output for even extremely complex inputs.
Below, we have made a visual representation in the way of a flowchart to understand where exactly Deep Learning plays a role :
Mainly, as you can see in the image above, it is Artificial intelligence (AI) that consists of Machine Learning, Deep Learning and Neural Networks.
So let us first understand the meaning of Artificial Intelligence. To explain in simple words, AI is a broad concept which means all the learnt concepts by machines that are originally human actions. Making it simpler, AI is any such machine that shows the traits of the human mind such as rationalizing, learning and problem-solving.
Then comes the concept of Machine learning which involves the study of algorithms and stats models. Based on this study the machines or systems perform a specific task and do not need any explicit instructions for the same. This is because the machines rely on the learnt patterns and inferences from the past.
Third, and a deeper concept is Deep Learning. This concept is known as Deep Learning because it utilises a huge amount of data or the complexities of the information available. With that information, the Deep Learning model becomes able enough to identify the errors and correct them on their own without human intervention. Since Machine Learning does not use such in-depth information, it can not identify and correct the errors without human involvement.
Another concept is Neural Networks which is a part of Machine Learning and simply means the concept with which the machines are able to process the information in the same way as humans do for solving various tasks. Hence, it is a concept of an artificial neural network which mimics the biological neural network.
Now, Deep Neural Network is an organization of the artificial neural network which helps to give outputs to extremely complex inputs. Deep Neural Network plays an important role as they deal with extremely complex inputs to provide apt outputs.
Read more on Deep Neural Network here.
Now, coming to Finance, Artificial Intelligence as a whole is applied in the financial industry a lot. For instance, it helps to identify problems like unusual debit card use or huge amounts of deposits in the account. This way, Artificial Intelligence as a whole concept helps save people from fraudulent activities. Also, AI is used to make trading easier and better with a more organized and quick decision making on the basis of various factors in the markets.
Okay! Further, let us move to the uses of Deep Learning in Finance.
As we mentioned above, Deep Learning is a concept which processes complex inputs and provides the output based on them. Also, it has the potential to correct itself since it is designed to be efficient enough to need no human intervention.Hence, this system learns from its own successes and failures after a data is recorded.
In the financial world there are several important areas where AI or, to be more precise, Deep Learning can be applied. So let us walk through those important areas where Deep Learning is used:
- Stock Market Prediction
- Automation of Process
- Analysing Trading Strategies
- Financial Security
- Loan Application Evaluation
- Credit Card Customer Research
Stock Market Prediction
Based on the historical data and different parameters of the current market situation, the neural networks in Deep Learning predict the stock values. As Deep Learning uses the data in detail, taking the hidden layers as well, the accuracy of the prediction improves. Hence, it is observed that with Deep Learning, the prediction accuracy is the maximum.
Automation of Process
This technology helps with processes by providing call-centre automation, paperwork automation and gamification of employee training and much more.
Analysing Trading Strategies
Since the algorithms of Deep Learning can analyze thousands of data sources at the same time, it is much faster than human beings. Based on such analysis, the trading strategies formed are much more profitable.
The surge of online transactions has increased the rate of fraudulent activities too. With Deep Learning algorithms being excellent at detecting frauds, financial security is being achieved simultaneously.
Robo-advisory is nothing but the algorithms at play for advising the clients with regard to financial instruments. For example, to recommend financial products like insurance facility, and for portfolio management, i.e., managing the assets across various investment opportunities.
Loan Application Evaluation
The Deep Neural Networks in Deep Learning help the banks in deciding whether or not to approve a loan application on the basis of learnt patterns for both approving and rejecting the applications.
Credit Card Customer Research
Since the banks need their customers to utilise their credit cards, the Deep Learning system helps find out such customers. Hence, for identifying the right customers, the system provides more meaningful questions to be put on the credit card applications.
We have mentioned most of the areas where automation with Deep Learning has proven to be beneficial but there are many other areas such as Credit approval, Business failure prediction, Bank theft and so on.
There are several premium companies like CRISIL, Titan, JP Morgan Chase, BNY Mello, Swiggy and many more which are using Deep Learning for automating their systems.
Further, we will see the Models of Deep Learning and the significance of each.
Categorising the models broadly, there are two types, i.e., Supervised Models and Unsupervised Models. Both of these models are trained differently and hold various different features.
Let us first take Supervised Models, which are trained with the examples of a particular dataset. These models are:
- Classical Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
Classical Neural Networks
Classical Neural Networks are also known as Multilayer perceptrons or the Perceptron Model. This model was created by American psychologist in 1958. It can also be termed as A Simple neural network. This is singular in nature and adapts to basic binary patterns with a series of inputs to simulate the learning patterns of human-brain. Its basic condition is to consist of more than 2 layers.
As you can see, it simply has an input layer with a few hidden layers and an output layer.
Convolutional Neural Networks
This is an advanced version after the Classical Neural Networks, since it is designed for taking care of a greater level of complexity with regard to processing and computing output of the data.
Since these Neural Networks were mainly built for image data, they should be the most suited for image classification but gradually, they were made capable of working with non-image data as well.
Let us now discuss how Convolutional Neural Networks are built for an image. So, for the building of model, you first import the input data into the model, which goes through the five steps:
- Input Image - Basically the input data is taken as an image (in pixels).
- Feature detectors and Feature maps - Detectors are basically the identifiers of the characteristics of the image. These are also called filters. Feature maps consist of the information collected by the Feature detectors or filters.
- Max-Pooling - It then enables the model to identify the image presented with modification. Hence, the image may be flipped, mirrored, upside-down etc. Max pooling helps the convolution network to identify all the details of the image by taking matrix of different areas. This makes the network note that they all are the details of the same image.
- Flattening - In this step, the data is flattened into an array so that the model is able to read it. Now, the details are converted or flattened from matrix to vertical column. After this, the input is passed through the Artificial Neural Network to be processed further.
- Full Connection - This is the hidden layer of the data, which is then taken into consideration. This is called a fully connected layer of data, which is the same as the hidden data in Artificial Neural Network. In this step, calculation of error function is also done which is called Loss function in Artificial Neural Network.
Recurrent Neural Networks
“Recurrent Neural Networks” is one of the types of six Neural networks, which consider the data in a sequential manner. This type remembers the previous information in the sequence and helps to interpret elements from the same later in sequence.
For instance, an interpretation of text, which consists of words or characters in a sequence for making the reader understand their intended meaning.
For understanding Recurrent Neural Networks better, let us see the visual representation and understand the types of inputs and outputs it supports:
Okay, so above visual representation shows:
- One to One
This is the basic mode of processing the information from fixed-sized input to fixed-size output. For instance, Image Classification into one category.
- One to Many
This implies processing one information and providing the output with more than one word to the display. For instance, taking one image as the input and creating a caption with a sentence of words as an output.
- Many to One
In this, the input goes in as a sentence of words, which is classified as positive or negative sentiment expression. Following which the output needs to predict the next character. For instance, sequence input (a sentence) and fixed size-output in one word.
- Many to Many
This implies sequence input leads to a sequence output but the output is modified. For instance, Machine translation, which leads to machine translating the English input into French language.
- Many to Many
This is another type of sequence input, which comes out as sequence output and is synced. For instance, video classification where each frame of the video is labelled.
Since you are now clear about Supervised Models of Deep Learning, let us move ahead to the Unsupervised Models. These models are only given input data and do not have any set output to learn from. These models are:
- Self Organizing Maps (SOMs)
- Boltzmann Machines
Self Organizing Maps (SOMs)
SOMs contain unsupervised data and usually reduce the number of random variables in the model. In the Self Organizing Map, output dimension is usually 2-dimensional. So, if we have more than 2 input characteristics, the output will be brought down to 2 dimensions. In this, for each synapse that connects input and output nodes, there is a weight assigned to it. Now the closest node is called the BMU or Best Matching Unit and the SOM shifts its weights to be closer to the BMU. The closer a node is to BMU, the more its weights change.
Now, you must have noticed that in the previous models that all of them go in a particular direction i.e., from Input to Hidden layer to Output. Even SOM, being an Unsupervised Model, goes in the same direction as all others in Supervised Models.
But, with Boltzmann Machines the case is not the same since they do not follow a particular direction. As you can see in the visual representation of the model below, all the nodes are connected to one another in a round shape.
h -> hidden layer
v-> visual layer
It is so because the Boltzmann machine can generate all parameters of the model instead of the fixed inputs. This model is termed as stochastic (random) model while others are deterministic models.
AutoEncoders are basically simple algorithms used for displaying an output which is the same as the input. This mechanism compresses the input data and then reconstructs the output from it.
For instance, images as inputs help the system learn about the particular figure or structure.
Hence, the input is compressed into a few categories. In autoencoding, the data is compressed with the help of the functions which are:
- Data specific, which means that the system once trained with human faces would not be able to perform well with the images of buildings.
- Self-performers, i.e., if there is appropriate amount of data for training, then the system will keep performing well on that specific type of input.
In the visual representation below, input X is the image input and with the help of encoder and decoder in the system, it presents output X’. Here, the output is the same as the input as the system stores particular characteristics of the same.
Okay now, let us go ahead and see the applications of Deep Learning in Finance with the python code.
In this code below, we try to predict the direction of market movement using a set of features. Since it can either be an uptrend or downtrend it's a binary classification problem.
Epoch 1/10 548/548
7s 13ms/step - loss: 0.2832 - acc: 0.4854
Epoch 2/10 548/548
- 4s 8ms/step - loss: 0.2523 - acc: 0.5365
Epoch 3/10 548/548
- 4s 8ms/step - loss: 0.2474 - acc: 0.5547
Epoch 4/10 548/548
- 4s 8ms/step - loss: 0.2558 - acc: 0.5146
Epoch 5/10 548/548
- 4s 7ms/step - loss: 0.2445 - acc: 0.5474
Epoch 6/10 548/548
- 4s 7ms/step - loss: 0.2496 - acc: 0.5274
Epoch 7/10 548/548
- 4s 7ms/step - loss: 0.2535 - acc: 0.5237
Epoch 8/10 548/548
- 4s 7ms/step - loss: 0.2502 - acc: 0.5292
Epoch 9/10 548/548
- 4s 7ms/step - loss: 0.2564 - acc: 0.5036
Epoch 10/10 548/548
- 4s 7ms/step - loss: 0.2519 - acc: 0.5146 array([[15, 71], [ 9, 88]])
The short code snippet uses LSTM from the Keras package to predict the direction of market movement. The prediction is done using 3 features:
- The mean of adjusted OHLC (Open, High, Low and Close values).
- Adjusted close (For different values in the dataset).
- The percentage change on Adjusted Close.
- Return values from a day before.
For distinguishing between upward and downward trend appropriately, we then create a feature matrix-X with all the features merged in it. After this, we convert the matrix to a numpy array. Then we take the corresponding binary levels for upward(1) and downward trend(0) and we scale the features, stack the features with the labels as mentioned earlier. After this, we test-train the split of dataset, separate the labels and features before reshaping the test and train sets for making them compatible with the model. Further we go on to define the sequential objects by adding conditions and values and finally, train the model followed by testing the predictions and getting the confusion matrix for binary classifications.
It is important to note that, in the LSTM classifier we use dropout to avoid over fitting and the confusion matrix is plotted to show the results of the prediction.
Perfect! Since you are through with the application of python code in Deep Learning, let us see what the future holds for Deep Learning in Finance.
With so many applications of Deep Learning in Finance, its future is nothing but Great!
Going by the recent market evaluation report, according to openpr.com, Machine Learning and Deep Learning in Finance market will continue to expand for the period 2020-2027.
With this study, you must have got a great idea about the importance of Deep Learning in Finance since it shapes up the understanding of its scope ahead.
The presence of machines has made trading much faster since High Frequency Trading makes billions of trades possible every microsecond.
It is seen that almost 73%of trading everyday is done by machines and every well-known financial firm is investing in machines and Deep Learning. For instance, CRISIL has recently revealed in Economic Times that it keeps investing in Deep Learning and plans to go ahead with the same.
According to Economic Times, ‘To improve its research reports and analytics, Crisil has been adopting automated data extraction including extraction of unstructured paragraphs, tables, etc which are automated‘ and ‘almost 90 percent of Crisil’s key processes are data-driven’.
Since these automated systems make operations of the firms faster and more accurate with regard to real-time trade decisions, they also maximize the returns. It is very well known that the market is becoming more and more sophisticated day by day with artificial trading systems. Hence, with the advancements taking place, market participants are always trying their best to make their operations faster, more accurate and more profitable.
Since machines are even processing and taking actions on the news information faster than any human, we can expect automated systems to help more in the coming time.
In this article, we covered a brief overview of Deep Learning and its uses in the financial world. Then we understood the models of Deep Learning and their classification into Supervised Models and Unsupervised Models. These models hold significance in their respective ways in accordance with the inputs. Also, the application of Deep Learning in Finance along with its future was covered.
Disclaimer: All data and information provided in this article are for informational purposes only. QuantInsti® makes no representations as to accuracy, completeness, currentness, suitability, or validity of any information in this article and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use. All information is provided on an as-is basis.