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Deep Learning in Finance: Is This The Future of the Financial Industry?

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Deep Learning in Finance: Is This The Future of the Financial Industry?

Get a handle on how deep learning is affecting the finance industry, and identify resources to further this understanding and increase your knowledge of the various aspects.


Accelerating Growth in the Financial Industry Using Deep Learning

 
Is Deep Learning now leading the charge for innovation in finance? Computational Finance, Machine Learning, and Deep Learning have been essential components of the finance sector for many years. The development of these techniques, technologies, and skills have enabled the financial industry to achieve explosive growth over the decades and become more efficient, sharp, and lucrative for its participants. Will this continue to be what drives the future of the financial industry?

How Do You Use Deep Learning in Finance?

 
Deep Learning for finance is the art of using neural network methods in various parts of the finance sector such as:

  • customer service
  • price forecasting
  • portfolio management
  • fraud detection
  • algorithmic trading
  • high performance computing
  • risk management
  • credit assessment
  • and operations

With the newer deep learning focus, people driving the financial industry have had to adapt by branching out from an understanding of theoretical financial knowledge. They are now forced to learn how to use Python, Cloud Computing, Mathematics & Statistics, and also adopt the use of GPUs (Graphical Processing Units) in order to process data faster.

In this post we will highlight:

  • Algorithmic Trading in Finance
  • Price Forecasting in Finance
  • Fraud Detection in Finance

Each section also includes a helpful link to a tutorial.

Algorithmic Trading in Finance

 
Algorithmic Trading is the process of creating a computational model to implement buy-sell decisions in the financial market. Other than being based on mathematical models, a trader can use deep learning techniques that use approximation models to implement buy and sell trades.

Algorithmic Trading Strategies

  • Trend Following
    • This is the most common type of strategy where investors will follow patterns in the price movements, moving averages, breakouts, etc. There are no predictions made on the price, instead the aim is to execute buy-sell strategies based on logical instruction provided by the investor.
  • Arbitrage Opportunities
    • Profiting off the price differential of a financial asset is known as “Financial Arbitrage”. This is basically when you buy a cheaper asset and sell it at a higher price in a different market, thereby taking a profit without any net cash flow. If the investor is able to successfully execute a strategy taking advantage of price differentials, there is opportunity for profitable trading.
  • Mathematical Modeling
    • Mean Reversion – This is based on the idea that high and low prices of an asset will revert back to its mean (average) value. Once its price is below the mean, it is seen as an opportunity to buy the asset in hopes of the price going above its average. The average value of an asset constantly changes, so it requires constant monitoring.
    • Volume-Weighted Average Price (VWAP) – This strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using stock specific historical volume profiles. The aim is to execute the order close to the VWAP, thereby benefiting on the average price.
    • Time weighted Average Price (TWAP) – Time-weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using evenly divided time slots between a start and end time. The aim is to execute the order close to the average price between the start and end times, thereby minimizing market impact.

See this tutorial on Programming For Finance With Python Python, Zipline and Quantopian to learn how to use Quantitative Trading with Python.

Price Forecasting in Finance

 
Traders and experts in the financial industry have relied heavily on computers over the decades, but have been able to take it to the next level with high performance computing (HPC) running GPUs. These new workstations and servers offer large storage options for massive datasets. These systems also allow people to execute complex, memory heavy algorithms that require millions or even billions of data points on their local machine to execute financial trading strategies, as well as price forecasting using deep learning techniques.

Techniques Used for Deep Learning Price Forecasting

  • Recurrent Neural Network (RNN) – Short time horizon
    • RNN is used for data with a sequential order, such as a time series database.
  • Long Short Term Memory Models (LSTM) – Longer time horizon compared to RNN
    • LSTM is a variation of RNN with added parameters in order to support longer memory so that the forecasted time horizon can be longer.
  • Multilayer Perceptron (MLP)
    • MLP is a class of feed-forward neural networks that consists of an Input layer, Hidden layer and Output layer. This is also suitable for time series forecasting because it is:
      • Robust to outliers, noisy data and missing values
      • Non-linear modeling
      • Support for Multivariate forecasting
      • Multi-step forecasting

This tutorial can take you through Financial Asset Price Prediction using Python and TensorFlow 2 and Keras

Fraud Detection in Finance

 
The world of finance is riddled with fraud and deception. Hackers and scammers are forever trying to steal confidential personal information and internal company information to sell. Firms are under major scrutiny by governments worldwide to upgrade their cybersecurity and fraud detection systems. Cybersecurity is also one of the most sought after positions in the job market in 2020.

Machine learning and deep learning is now used to automate the process of searching data streams for anomalies that could be a security threat.

Autoencoders

 
A Deep Learning algorithm for anomaly detection is an Autoencoder. An Autoencoder neural network is an unsupervised learning algorithm that applies backpropagation, setting the target values to be equal to the inputs which essentially encodes and compresses the data and reconstructs the data to as close of a representation to the original data as possible.

An Autoencoder consists of 2 parts:

  • Encoder – Takes input data and compresses it into a vector of quantities.
  • Decoder – Takes the data from the encoder and reconstructs the original input.

This tutorial will take you through Autoencoders with Keras, TensorFlow and Deep Learning

Scratching the Surface of Deep Learning’s Potential in Finance

 
The finance industry is one of the most influential industries impacted by new findings in AI (artificial intelligence). Forecasting opportunities to increase returns and protecting data using AI are two areas seeing growth due to the higher volatility in markets in recent years and the increased threat of cybercrime.

The industry generates trillions of data points that need innovative solutions to process and analyze this data. Tighter regulation and increasing pressure from governments, industry and consumers force players in the finance industry to protect data while still increasing returns to investors.

Who Will Run Your Deep Learning Projects?

 
The financial industry used to be dominated by MBA’s from the most prestigious schools in the world. Now the shift in focus is toward tech talent with knowledge of programming languages like Python, along with cloud computing and deep learning.

Engineers also play an important role in setting up and managing GPU-powered hardware to meet new challenges. Understanding what data you are working with, the deep learning applications and frameworks you need to use, and the results you want to get, requires everyone to work together. If you’re missing engineers in your mix, finding a company like Exxact can help with understanding your requirements and delivering a solution that is pre-configured, set up and ready to go as soon as you plug it in. You just need to make sure you have the technical staff on hand to then use it, or gain the requisite knowledge to run it yourself.

 
Original. Reposted with permission.

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Source: https://www.kdnuggets.com/2020/07/deep-learning-finance-future-financial-industry.html

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Join Hands with Instagram’s New Algorithm to Boost Your Business’s User Engagement

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👉 Most people are not at all happy with the latest 📸 Instagram algorithm. However, if you want to make the most of your business, you should understand how it works. The trick here is you must work with its algorithm and not go against it here; it is easy for your know-how. 👇🔻

🚩This post will guide you on how the 🆕 new Instagram algorithm works and how you can use it for the advantage of your business-

How does the new Instagram algorithm work?

The new 📸 Instagram ♀️ algorithm is a mystery to many users on the platform. It no longer functions at a slow pace as it did in the past. To score high on Instagram today, you need to be extremely diligent as a business owner. The algorithm needs to be fully optimized so that you get success in the platform. So, read and remember the following factors to help you determine how your posts can perform well to boost user engagement and sales revenue for your business with success-

The volume of social engagement you receive -: Note, your posts with the maximum number of shares, views, comments, etc., will rank high in the Instagram feed over others. When your post receives a lot of comments and likes, the Instagram platform gets a signal that it is engaging to users and has high-quality content. It wants more users to see it, and the algorithm on the platform will display it to other users online.

However, here again, there is a catch. It is not only the engagement that Instagram considers now; it is how fast your Instagram post engages the readers in some cases. Trending hashtags on Instagram, for instance, is the best-known example of the above. The volume of engagement you get for your business posts is important; however, how quickly you get this engagement is even more important!

👉▶Tip- You should discover when the best time of the day to post is. In this way, you can schedule posts targeted at that time only. You should post when most users can see it. This increases your chances of boosting engagement faster, and the Instagram algorithm as it knows what somebody likes on Instagram and will take over from here to share it with those users who will ✌ ⚡ like, share and comment on your post.

How long are people looking at your post on Instagram -: If you see the algorithm that Facebook uses, it takes into account how long users look at your post and spend time interacting with its content. Instagram is no different! Its algorithm will take a look at the duration of time that users spend on your post too. This is again, another important factor that Instagram uses to boost posts.

👉▶Tip- Here, you should craft a great 📸 Instagram caption for your post to boost user engagement. If your Instagram caption is engaging, users will want to read it or click on the button that says “more” to spend more time on your post, and in this way, you can boost more engagement with success on  📸 Instagram.

This is why Boomerangs and videos are generally posted in the 📹 video format. This makes them perform well with the Instagram algorithm. Users take more time to watch them toll the end. Another great way for you to make users stay on your posts is to swipe up CTA action for them to view more. This is another great strategy you can use for your business to boost engagement.

When did you post your photo?-: This is another factor that influences how the 📸 Instagram algorithm works for your business is determining the time of the post. It depends on how often you use the 📲 app. In the past, the algorithm used to give you insights into the recent posts; however, if you tend to log on to Instagram just a few times in one week, it will show posts that have been posted recently. You might even get likes from a previous post that you published a few days ago. The target here is to keep users updated on the posts they might have missed due to the fact that they did not log in regularly.

👉 This means that users can see your posts now for longer periods.

The sort of content you post also influences engagement – Think about if 📸 Instagram only focused on content with the optimal engagement; users would only see that content every time they logged into 📸 Instagram. However, this is not the case with the Instagram algorithm.

For instance, the content 👥 users’ genre searches for in the platform also influence the algorithm of how Instagram works. If a user is a fan of sports, says the NBA and sees that genre of content regularly, Instagram will immediately catch on to this and bring the user more similar content related to sports and the NBA. It knows that the user will be interested in such posts and perhaps bring news and updates of the LA Lakers to boost 👤 user engagement and satisfaction.

Accounts that users search for -: Like the above, the accounts that users search for also determines how the 📸  Instagram algorithm works. This is why when users search a specific 🧾 account many times; Instagram will bring more such content from other accounts to that user. This is why they see it often in their Instagram feeds.

From the above, it is evident that if you want to work with the new Instagram algorithm, you must understand how it works and optimize your posts to help it boost your business. In the past, the feed on Instagram was chronological; however, things have changed now.

🟥 So, ensure that your CTA is strong; you use the right hashtags, post at the best time, and make your feed on Instagram as attractive as possible. In this way, you can boost user engagement, lead conversions, sales, and of course, gain a strategic edge in the market as well.

↘ Source: Ron Johnson. Ron is a Marketer. He always shares his tips on trendy marketing tricks. He always implements new tricks in his field.

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Top 10 Big Data trends of 2020

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Top 10 Big Data trends of 2020

By Priya Dialani

During the last few decades, Big Data has become an insightful idea in all the significant technical terms. Additionally, the accessibility of wireless connections and different advances have facilitated the analysis of large data sets. Organizations and huge companies are picking up strength consistently by improving their data analytics and platforms.

2019 was a major year over the big data landscape. In the wake of beginning the year with the Cloudera and Hortonworks merger, we’ve seen huge upticks in Big Data use across the world, with organizations running to embrace the significance of data operations and orchestration to their business success. The big data industry is presently worth $189 Billion, an expansion of $20 Billion more than 2018, and is set to proceed with its rapid growth and reach $247 Billion by 2022.

It’s the ideal opportunity for us to look at Big Data trends for 2020.

Chief Data Officers (CDOs) will be the Center of Attraction

The positions of Data Scientists and Chief Data Officers (CDOs) are modestly new, anyway, the prerequisite for these experts on the work is currently high. As the volume of data continues developing, the requirement for data professionals additionally arrives at a specific limit of business requirements.

CDO is a C-level authority at risk for data availability, integrity, and security in a company. As more businessmen comprehend the noteworthiness of this job, enlisting a CDO is transforming into the norm. The prerequisite for these experts will stay to be in big data trends for quite a long time.

Investment in Big Data Analytics

Analytics gives an upper hand to organizations. Gartner is foreseeing that organizations that aren’t putting intensely in analytics by the end of 2020 may not be ready to go in 2021. (It is expected that private ventures, for example, self-employed handymen, gardeners, and many artists, are excluded from this forecast.)

The real-time speech analytics market has seen its previously sustained adoption cycle beginning in 2019. The idea of customer journey analytics is anticipated to grow consistently, with the objective of improving enterprise productivity and the client experience. Real-time speech analytics and customer journey analytics will increase its popularity in 2020.

Multi-cloud and Hybrid are Setting Deep Roots

As cloud-based advances keep on developing, organizations are progressively liable to want a spot in the cloud. Notwithstanding, the process of moving your data integration and preparation from an on-premises solution to the cloud is more confounded and tedious than most care to concede. Additionally, to relocate huge amounts of existing data, organizations should match up to their data sources and platforms for a little while to months before the shift is complete.

In 2020, we hope to see later adopters arrive at a conclusion of having multi-cloud deployment, bringing the hybrid and multi-cloud philosophy to the front line of data ecosystem strategies.

Actionable Data will Grow

Another development concerning big data trends 2020 recognized to be actionable data for faster processing. This data indicates the missing connection between business prepositions and big data. As it was referred before, big data in itself is futile without assessment since it is unreasonably stunning, multi-organized, and voluminous. As opposed to big data patterns, ordinarily relying upon Hadoop and NoSQL databases to look at data in the clump mode, speedy data mulls over planning continuous streams.

Because of this data stream handling, data can be separated immediately, within a brief period in only a single millisecond. This conveys more value to companies that can make business decisions and start processes all the more immediately when data is cleaned up.

Continuous Intelligence

Continuous Intelligence is a framework that has integrated real-time analytics with business operations. It measures recorded and current data to give decision-making automation or decision-making support. Continuous intelligence uses several technologies such as optimization, business rule management, event stream processing, augmented analytics, and machine learning. It suggests activities dependent on both historical and real-time data.

Gartner predicts more than 50% of new business systems will utilize continuous intelligence by 2022. This move has begun, and numerous companies will fuse continuous intelligence during 2020 to pick up or keep up a serious edge.

Machine Learning will Continue to be in Focus

Being a significant innovation in big data trends 2020, machine learning (ML) is another development expected to affect our future fundamentally. ML is a rapidly developing advancement that used to expand regular activities and business processes

ML projects have gotten the most investments in 2019, stood out from all other AI systems joined. Automated ML tools help in making pieces of knowledge that would be difficult to separate by various methods, even by expert analysts. This big data innovation stack gives faster results and lifts both general productivity and response times.

Abandon Hadoop for Spark and Databricks

Since showing up in the market, Hadoop has been criticized by numerous individuals in the network for its multifaceted nature. Spark and managed Spark solutions like Databricks are the “new and glossy” player and have accordingly been picking up a foothold as data science workers consider them to be as an answer to all that they disdain about Hadoop.

However, running a Spark or Databricks work in data science sandbox and then promoting it into full production will keep on facing challenges. Data engineers will keep on requiring more fit and finish for Spark with regards to enterprise-class data operations and orchestration. Most importantly there are a ton of options to consider between the two platforms, and companies will benefit themselves from that decision for favored abilities and economic worth.

In-Memory Computing

In-memory computing has the additional advantage of helping business clients (counting banks, retailers, and utilities) to identify patterns rapidly and break down huge amounts of data without any problem. The dropping of costs for memory is a major factor in the growing enthusiasm for in-memory computing innovation.

In-memory innovation is utilized to perform complex data analyses in real time. It permits its clients to work with huge data sets with a lot more prominent agility. In 2020, in-memory computing will pick up fame because of the decreases in expenses of memory.

IoT and Big Data

There are such enormous numbers of advancements that expect to change the current business situations in 2020. It is hard to be aware of all that, however, IoT and digital gadgets are required to get a balance in big data trends 2020.

The function of IoT in healthcare can be seen today, likewise, the innovation joining with gig data is pushing companies to get better outcomes. It is expected that 42% of companies that have IoT solutions in progress or IoT creation in progress are expecting to use digitized portables within the following three years.

Digital Transformation Will Be a Key Component

Digital transformation goes together with the Internet of Things (IoT), artificial intelligence (AI), machine learning and big data. With IoT connected devices expected to arrive at a stunning 75 billion devices in 2025 from 26.7 billion presently, it’s easy to see where that big data is originating from. Digital transformation as IoT, IaaS, AI and machine learning is taking care of big data and pushing it to regions inconceivable in mankind’s history.

Source: https://www.fintechnews.org/top-10-big-data-trends-of-2020/

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What are the differences between Data Lake and Data Warehouse?

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Overview

  • Understand the meaning of data lake and data warehouse
  • We will see what are the key differences between Data Warehouse and Data Lake
  • Understand which one is better for the organization

Introduction

From processing to storing, every aspect of data has become important for an organization just due to the sheer volume of data we produce in this era. When it comes to storing big data you might have come across the terms with Data Lake and Data Warehouse. These are the 2 most popular options for storing big data.

Having been in the data industry for a long time, I can vouch for the fact that a data warehouse and data lake are 2 different things. Yet I see many people using them interchangeably. As a data engineer understanding data lake and data warehouse along with its differences and usage are very crucial as then only will you understand if data lake fits your organization or data warehouse?

So in this article, let satiate your curiosity by explaining what data lake and warehousing are and highlight the difference between them.

Table of Contents

  1. What is a Data Lake?
  2. What is a Data Warehouse?
  3. What are the differences between Data Lake and Data Warehouse?
  4. Which one to use?

What is a Data Lake?

A Data Lake is a common repository that is capable to store a huge amount of data without maintaining any specified structure of the data. You can store data whose purpose may or may not yet be defined. Its purposes include- building dashboards, machine learning, or real-time analytics.

data warehouse data lake

Now, when you store a huge amount of data at a single place from multiple sources, it is important that it should be in a usable form. It should have some rules and regulations so as to maintain data security and data accessibility.

Otherwise, only the team who designed the data lake knows how to access a particular type of data. Without proper information, it would be very difficult to distinguish between the data you want and the data you are retrieving. So it is important that your data lake does not turn into a data swamp.

data warehouse data lake

Image Source: here

What is a Data Warehouse?

A Data Warehouse is another database that only stores the pre-processed data. Here the structure of the data is well-defined, optimized for SQL queries, and ready to be used for analytics purposes. Some of the other names of the Data Warehouse are Business Intelligence Solution and Decision Support System.

What are the differences between Data Lake and Data Warehouse?

Data Lake Data Warehouse
Data Storage and Quality The Data Lake captures all types of data like structure, unstructured in their raw format. It contains the data which might be useful in some current use-case and also that is likely to be used in the future. It contains only high-quality data that is already pre-processed and ready to be used by the team.
Purpose The purpose of the Data Lake is not fixed. Sometimes organizations have a future use-case in mind. Its general uses include data discovery, user profiling, and machine learning. The data warehouse has data that has already been designed for some use-case. Its uses include Business Intelligence, Visualizations, and Batch Reporting.
Users Data Scientists use data lakes to find out the patterns and useful information that can help businesses. Business Analysts use data warehouses to create visualizations and reports.
Pricing It is comparatively low-cost storage as we do not give much attention to storing in the structured format. Storing data is a bit costlier and also a time-consuming process.

Which one to use?

We have seen what are the differences between a data lake and a data warehouse. Now, we will see which one should we use?

If your organization deals with healthcare or social media, the data you capture will be mostly unstructured (documents, images). The volume of structured data is very less. So here, the data lake is a good fit as it can handle both types of data and will give more flexibility for analysis.

If your online business is divided into multiple pillars, you obviously want to get summarized dashboards of all of them. The data warehouses will be helpful in this case in making informed decisions. It will maintain the data quality, consistency, and accuracy of the data.

Most of the time organizations use a combination of both. They do the data exploration and analysis over the data lake and move the rich data to the data warehouses for quick and advance reporting.

End Notes

In this article, we have seen the differences between data lake and data warehouse on the basis of data storage, purpose to use, which one to use. Understanding this concept will help the big data engineer choose the right data storage mechanism and thus optimize the cost and processes of the organization.

The following are some additional data engineering resources that I strongly recommend you go through-

If you find this article informative, then please share it with your friends and comment below your queries and feedback.

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Source: https://www.analyticsvidhya.com/blog/2020/10/what-is-the-difference-between-data-lake-and-data-warehouse/

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