Alternative Data News. 28, August 2019

Finding sources and uses for alternative data can be difficult. At CloudQuant we regularly read and search the internet for new sources of data that can be used in our mission to find alpha signals and build quantitative trading strategies. We recognize that we are technology and data junkies so we wrote our own crawler that specifically seeks out web pages, posts, and news articles that give us a snapshot of what is going on in the world of Alt Data. The following is a collection of articles that we think you will find interesting from the past week.


Over-the-Wall Data Science and How to Avoid Its Pitfalls

Over-the-wall data science is a common organizational pattern for deploying data science team output to production systems. A data scientist develops an algorithm, a model, or a machine learning pipeline, and then an engineer, often from another team, is responsible for putting the data scientist’s code in production.

Such a pattern of development attempts to solve for the following:

  • Quality: We want production code to be of high quality and maintained by engineering teams. Since most data scientists are not great software engineers, they are not expected to write end-to-end production-quality code.
  • Resource Allocation: Building and maintaining production systems requires special expertise, and data scientists can contribute more value solving problems for which they were trained rather than spend the time acquiring such expertise.
  • Skills: The programming language used in production may be different from what the data scientist is normally using.

However, there are numerous pitfalls in the over-the-wall development pattern that can be avoided with proper planning and resourcing.

2019-08-28 12:14:05.434000+00:00 Read the full story…
Weighted Interest Score: 4.9955, Raw Interest Score: 2.4782,
Positive Sentiment: 0.1652, Negative Sentiment 0.1802

CloudQuant Thoughts… Here at CloudQuant we have developed a system specifically for trading which solves a number of these issues. First the development is in a purely Python environment which gives excellent flexibility for the Data Scientist to develop and also allows a smooth and straightforward process for idea development, testing and promotion to live. All the AltData and Market data is at the users fingertips and support is a click away. Cutting edge Machine learning introduces its own wrinkles but the process works very well. Being in a trading environment means we have a secondary set of “bumpers” in the form of a Risk Management System which would catch and stop an account if aberrant behavior was detected.

Alternative-data provider Quandl is changing its strategy as industry giants like Bloomberg and S&P push into the $7 billion market

Adapt or die. One of the first alternative-data providers has taken that message to heart, as larger traditional data powerhouses move into its market.

Quandl, one of the early sellers of unique data sets from nontraditional sources, has been forced to rethink its business strategy as industry giants such as Bloomberg and S&P have launched alternative-data offerings of their own.

Tammer Kamel, Quandl’s CEO and cofounder, told Business Insider that the company, which was acquired by Nasdaq in 2018 for an undisclosed amount, now sees more opportunity creating proprietary data sets as opposed to just selling others’ feeds. The change in philosophy comes at a time when larger data companies have gotten into the lucrative business of selling alternative data.

“I don’t want to be offering a data product that’s on eight other platforms,” Kamel said. “What’s my case to the customer that they should buy it from me as opposed to anyone else? … I can’t outcompete Bloomberg at that game, so why would I get into it? I will outcompete Bloomberg by offering better data products.”
2019-08-27 00:00:00 Read the full story…
Weighted Interest Score: 4.0093, Raw Interest Score: 1.6653,
Positive Sentiment: 0.4005, Negative Sentiment 0.1054

CloudQuant Thoughts… Slowly we solve the problems around Alternative Data. Costs to end users are dropping, many ETL issues have been resolved but with so many new datasets coming available every day, how do you know they have value? It is on you to sink significant development and research time into them, often paying for the pleasure of proving they don’t work for you!
CloudQuant is working on the solution. Hosted data sets, ETL already completed, original white papers from the data supplier plus independent white papers from CloudQuant detailing our observations including source code for the backtest used. The code is in Python, ready to be run on our own publicly available backtesting system CloudQuant Mariner.

Finance Quants: Entry-Level Pay with a BA, MA, or PhD

Should you bother getting a master’s if you want to be a quant in the financial services industry? It certainly translates into a pay bump for those trying to break into this sub-field of finance and mathematics. But if you really want to make a difference in your entry-level pay, you’ll need to achieve a PhD. (And keep in mind that quants with the right qualifications and experience can make more than a million dollars per year, so sticking with it is important if you truly want to score the big bucks.)
2019-08-27 00:00:00 Read the full story…
Weighted Interest Score: 4.4835, Raw Interest Score: 2.0445,
Positive Sentiment: 0.1076, Negative Sentiment 0.0359

CloudQuant Thoughts… Feels like a blowing our own trumpet kind of week… but if you have the skills and want to make some moeny on the side, you could develop a trding model on CloudQuant. Our goal is to empower people who have the skills but not the access to quality market data and backtesting systems. We make those systems available for free and if you develop a successfull model we may back it so that it can tade in the market with significant capital. You take on none of the risk but take a share of any profit.

Bloomberg Aims to Expand Early Alerts

Bloomberg has launched Early Alerts, which predicts changes in US dollar corporate investment grade and high yield securities, and aims to expand the machine learning model to other currencies and securities.

Early Alerts, which was launched this month, uses Bloomberg’s proprietary library of fixed income data with machine learning models to develop predictive insights for more than 16,000 US dollar denominated investment grade and high-yield corporate bonds. The model generates scores over 1-day, 5-day, and 20-day horizons. The higher the score, the greater the probability that a corporate bond will have a significant credit spread tightening or widening.

2019-08-27 16:12:56+00:00 Read the full story…
Weighted Interest Score: 4.1501, Raw Interest Score: 2.1579,
Positive Sentiment: 0.2555, Negative Sentiment 0.0568

Guide To Implementing Time Series Analysis: Predicting Bitcoin Price With RNN

In our previous articles, we have talked about Time Series Forecasting and Recurrent Neural Network. We explored what it is and how it is important in the class of Machine Learning algorithms. We even implemented a simple LSTM Network to evaluate its performance on the MNIST dataset. In this tutorial, we will take it a little further by forecasting a real-world data.

The cryptocurrency market has seen its rise and fall in the past few years. With a variety of coins being exchanged for real money, it is important to know the trend in the coin price. In this article, we will build a fairly simple LSTM network to predict or forecast the prices of Bitcoin.

2019-08-28 05:30:43+00:00 Read the full story…
Weighted Interest Score: 4.8996, Raw Interest Score: 2.4257,
Positive Sentiment: 0.0495, Negative Sentiment 0.0990

Artificial Intelligence Gets A Boost With The Latest Generation Intel® XEON® Scalable Processors That Drive Inference At Scale

It is an industry-wide phenomenon that has opened the best opportunities for organisations across the board. Artificial Intelligence has become the growth story for India’s digital natives like Flipkart, Swiggy and Ola. Today, many AI applications — facial recognition, product recommendations, virtual assistants have become rooted in our day-to-day lives. However, these emerging AI applications have one common feature ruling them all — dependence…
2019-08-28 05:47:18+00:00 Read the full story…
Weighted Interest Score: 3.6293, Raw Interest Score: 1.7767,
Positive Sentiment: 0.3322, Negative Sentiment 0.0386

TRADING UP: Instinet Hires Arora for Execution Sales

Russian broker BCS Global Markets has hired Andrew Richards from ING. The firm announced on August 5 that Richards had joined its ranks in London as a sales director covering prime services. He will report to Tim Bevan, chief executive of BCS UK. Most recently, Richards was director of origination global security finance, synthetic prime brokerage at ING Bank in London. Earlier in his career he worked as a vice president covering prime brokerage …
2019-08-26 13:58:23+00:00 Read the full story…
Weighted Interest Score: 3.5855, Raw Interest Score: 1.9458,
Positive Sentiment: 0.1390, Negative Sentiment 0.0232

Claudia Buch: Digitalization, competition, and financial stability

Digital innovations and improvements in information technology have the potential to significantly change the competitive structure of banking markets. They are transforming the way in which information is collected, processed, and analysed. FinTechs and BigTechs potentially have comparative advantages over banks in deploying big data techniques, artificial intelligence, machine learning, or social media data for credit scoring or risk assessments.

There are different channels through which digital innovations can impact banking markets, with different implications for competition and financial stability. Banks can develop new technologies in-house, FinTechs can provide specific financial services, or BigTechs can use their existing network structures to provide a large range of financial services to customers (FSB 2019). New market entrants may provide services to incumbent financial firms or cooperate with existing financial firms such as banks in order to offer new or enhanced financial products to customers. By providing services to incumbents, technology can be improved and costs can be reduced. But new market entrants may also target limited, high value added elements of the value chain of banks and financial firms.
2019-08-28 21:32:17+10:00 Read the full story…
Weighted Interest Score: 3.4397, Raw Interest Score: 1.5398,
Positive Sentiment: 0.3608, Negative Sentiment 0.2675

Huawei launches A.I. chip as it looks to defy US pressure, pitting it against giants like Qualcomm and Nvidia

Huawei announced the commercial availability of an artificial intelligence (AI) chip Friday, pitting it against major American giants like Qualcomm and Nvidia, as it looks to defy continued U.S. pressure and prove it can still bring out core technology.

The chip, called the Ascend 910, was first unveiled in October last year and is aimed at data centers. Companies using AI applications require huge amounts of data to train smart algorithms, which can take several days or weeks. Huawei claims that its chip can process more data in a faster amount of time than its competitors and help train networks in a matter of minutes.

2019-08-23 00:00:00 Read the full story…
Weighted Interest Score: 3.4282, Raw Interest Score: 1.4599,
Positive Sentiment: 0.0730, Negative Sentiment 0.1460

Choosing the right algorithm for your real-world problem

You import your data. You clean your data. You make your baseline model.

Then, you tune your hyperparameters. You go back and forth from random forests to XGBoost, add feature selection, and tune some more. Your model’s performance goes up, and up, and up.
And eventually, the thought occurs to you: when do I stop?

Most data scientists struggle with this question on a regular basis, and from what I’ve seen working with SharpestMinds, the vast majority of aspiring data scientists get the answer wrong. That’s why we sat down with Tan Vachiramon, a member of the Spatial AI team Oculus, and former data scientist at Airbnb.

2019-08-27 18:12:41.353000+00:00 Read the full story…
Weighted Interest Score: 3.2669, Raw Interest Score: 1.6094,
Positive Sentiment: 0.1201, Negative Sentiment 0.2162

Migrating market data to the cloud

Market data is the lifeblood of financial institutions (FIs) with banks, brokers, wealth managers, asset managers and hedge funds relying on quality pricing and trade-related data to power their business applications. But today customers expect to access to data on their personal devices and FIs are stepping up their digital transformation game.

Developers are demanding easier access to this data for app development, data usage is increasing, C-…
2019-08-27 11:15:00 Read the full story…
Weighted Interest Score: 3.2514, Raw Interest Score: 1.5501,
Positive Sentiment: 0.3781, Negative Sentiment 0.0756

Madrona leads $10M round for Tesorio, a fintech startup that automates cash flow analysis

Hope Cochran knows about managing cash flow. As the former chief financial officer at Candy Crush maker King Digital and telecom company Clearwire, she’s familiar with inefficiencies facing those responsible for managing dollars coming in and out of an organization.

That’s why she’s excited about Tesorio, a Burlingame, Calif.-based fintech startup that just raised a $10 million round led by Seattle-based Madrona Venture Group.

Founded in 2015, …
2019-08-27 15:24:15-07:00 Read the full story…
Weighted Interest Score: 3.2292, Raw Interest Score: 1.6389,
Positive Sentiment: 0.1612, Negative Sentiment 0.1343

Neptune Networks Expands In Data And Analytics

Data and analytics are becoming increasingly important to Neptune Networks, the consortium which was launched to reduce costs by allowing institutional investors to access real-time bond pricing from banks in a standard format.

Banks previously sent bond inventory information in multiple formats to investors. In 2014 a group of buy-side and sell-side institutions began discussing how best to distribute real-time axes in a standard open source fo…
2019-08-20 17:18:47+00:00 Read the full story…
Weighted Interest Score: 3.2224, Raw Interest Score: 1.7964,
Positive Sentiment: 0.3443, Negative Sentiment 0.0000

When NVMe is Simply Not Enough: The Future of Storage for Edge Workloads

Click to learn more about author Scott Shadley.

SSDs have evolved over the past decade to meet the growing demand of AI and Edge-Related workloads and now computational storage takes intelligent storage to the next level.

Today the big buzz words like “AI” and “Edge Computing” have taken the technology industry by storm and to the casual observer it all seems very cool and “fashion-forward.” Truth is, what it really means is that a tsunami of d…
2019-08-28 07:30:06+00:00 Read the full story…
Weighted Interest Score: 3.0710, Raw Interest Score: 1.5447,
Positive Sentiment: 0.2391, Negative Sentiment 0.1839

Finance for the Non-Financial Leader

Harness the power of financial data to make better decisions for your organization. Designed for the non-financial leader, this program provides foundational principles in accounting and finance and teaches how to effectively use financial information to make a meaningful impact.

Understanding financial fundamentals is essential to direct your company in strategic decisions. This knowledge allows you to make better use of your inf…
2019-09-28 00:00:00 Read the full story…
Weighted Interest Score: 3.0686, Raw Interest Score: 1.5343,
Positive Sentiment: 0.4513, Negative Sentiment 0.0000

A 2019 Guide to Semantic Segmentation

Semantic segmentation refers to the process of linking each pixel in an image to a class label. These labels could include a person, car, flower, piece of furniture, etc., just to mention a few. We’ll now look at a number of research papers on covering state-of-the-art approaches to building semantic segmentation models.

By Derrick Mwiti, Data Analyst.

Semantic segmentation refers to the process of linking each pixel in an image to a class …
2019-08-20 00:00:00 Read the full story…
Weighted Interest Score: 3.0009, Raw Interest Score: 1.6966,
Positive Sentiment: 0.2030, Negative Sentiment 0.0870

Standard Bank leads $4 million round in mobile finance startup Nomanini

Standard Bank has led a $4 million investment in Nomanini, a mobile pint-of-sale and data analytics service for merchants operating in typically cash-based economies.

Standard Bank intends to use Nomanini’s platform to unearth previously ‘invisible’ data on the informal retail economy, offering a mobile application which provides access to new lines of business, credit and savings services for millions of informal merchants across 14 African cou…
2019-08-27 09:54:00 Read the full story…
Weighted Interest Score: 2.9664, Raw Interest Score: 1.7955,
Positive Sentiment: 0.0781, Negative Sentiment 0.1561

A 2019 Guide to Object Detection

Object detection has been applied widely in video surveillance, self-driving cars, and object/people tracking. In this piece, we’ll look at the basics of object detection and review some of the most commonly-used algorithms and a few brand new approaches, as well.

By Derrick Mwiti, Data Analyst.

Object detection is a computer vision technique whose aim is to detect objects such as cars, buildings, and human beings, just to mention a few. Th…
2019-08-20 00:00:00 Read the full story…
Weighted Interest Score: 2.9539, Raw Interest Score: 1.7723,
Positive Sentiment: 0.1248, Negative Sentiment 0.1082

Osaka Exchange imposes JPY20m fine on Citigroup Global Markets Japan

An investigation has shown that the company accepted and executed orders of spoofing transactions in the 10-year JGB futures market.

Osaka Exchange, Inc. has imposed a fine of JPY 20 million on Citigroup Global Markets Japan Inc. and has requested that the company submit a business improvement report.

During market surveillance in the OSE market, Japan Exchange Regulation (JPX-R) has detected activity suspected to constitute market manipulation…
2019-08-28 11:31:40+03:00 Read the full story…
Weighted Interest Score: 2.9531, Raw Interest Score: 1.6779,
Positive Sentiment: 0.0271, Negative Sentiment 0.8660

3 Specialty Retail Stocks Ready to Bounce

Specialty retail stocks rely heavily on the purchasing power of consumers, a favorable labor market, and rising wages. Therefore, it’s no surprise that the industry has underperformed the S&P 500 Index by about 7.5% over the past month after the June jobs report disappointed. Like most other sectors, talk about a looming recession and constant trade war developments dominating news feeds haven’t helped the space either.

However, encouraging July…
2019-08-19 14:40:42.007000+00:00 Read the full story…
Weighted Interest Score: 2.8939, Raw Interest Score: 1.6467,
Positive Sentiment: 0.4117, Negative Sentiment 0.2148

The Data Warehouse, the Data Lake, and the Future of Analytics

Data lakes were created in response to the need for Big Data analytics that has been largely unmet by data warehousing. The pendulum swing toward data lake technology provides some remarkable new capabilities, but can be problematic if the swing goes too far in the other direction. Far from being at the end of this evolutionary process, we are in the middle of it, said Anthony Algmin, CEO of Algmin Data Leadership, during his presentation titled …
2019-08-27 07:35:36+00:00 Read the full story…
Weighted Interest Score: 2.8802, Raw Interest Score: 1.5513,
Positive Sentiment: 0.2998, Negative Sentiment 0.2477

25 Tricks for Pandas

Check out this video (and Jupyter notebook) which outlines a number of Pandas tricks for working with and manipulating data, covering topics such as string manipulations, splitting and filtering DataFrames, combining and aggregating data, and more.

By Matthew Mayo, KDnuggets.

Last week, Kevin Markham (@justmarkham) of DataSchool.io posted a handy video and an companion Jupyter notebook titled “My top 25 pandas tricks.” I found the collection of…
2019-08-25 00:00:00 Read the full story…
Weighted Interest Score: 2.7652, Raw Interest Score: 1.4044,
Positive Sentiment: 0.0000, Negative Sentiment 0.1652

Deep Dive: RBS India Taps Into Analytics To Deliver Personalized Customer Offerings

When it comes to banking, there is a major disruption happening. The FinTech ecosystem in the nation has matured considerably and banks like RBS have taken a lead in staying abreast of all the advancements in analytics and AI.

To know more about the bank’s analytics unit and its roadmap for the coming year, we got in touch with Arun Mehta, Head of Data & Analytics – Digital Engineering Services at Royal Bank of Scotland (RBS). In this article, w…
2019-08-28 08:30:41+00:00 Read the full story…
Weighted Interest Score: 2.7469, Raw Interest Score: 1.4929,
Positive Sentiment: 0.4502, Negative Sentiment 0.0237

Australia and the FX leverage laws: Full report on current legal and consultative status

We talk to lawyers, FX industry executives, compliance managers and academics in Australia and North America to look at how the current stance on leveraged FX and CFD products is following last week’s proposals on retail products by ASIC

Last week, FinanceFeeds provided a unique and detailed report into the proposed new rulings by the Australian Securities and Investment Commission (ASIC) which pertain to how the esteemed regulatory authority wi…
2019-08-27 12:38:40+03:00 Read the full story…
Weighted Interest Score: 2.5512, Raw Interest Score: 1.2631,
Positive Sentiment: 0.1451, Negative Sentiment 0.1109

Estimating The Fair Value Of Enphase Energy, Inc. (NASDAQ:ENPH)

How far off is Enphase Energy, Inc. (NASDAQ:ENPH) from its intrinsic value? Using the most recent financial data, we’ll take a look at whether the stock is fairly priced by taking the expected future cash flows and discounting them to today’s value. I will use the Discounted Cash Flow (DCF) model. It may sound complicated, but actually it is quite simple!

Companies can be valued in a lot of ways, so we would point out that a DCF is not perfect for every situation. If you want to learn more about discounted…
2019-08-27 20:24:13+10:00 Read the full story…
Weighted Interest Score: 2.5108, Raw Interest Score: 1.7821,
Positive Sentiment: 0.0631, Negative Sentiment 0.1419

Challenges In Analytics Sector: The Industry Perspective

Analytics industry has witnessed significant growth over the years but is still prone to a lot of challenges in terms of talent, reaching the right consumers, cumulating data points, among others. This month we reached out to the analytics leaders to understand the challenges that they most often face in the industry. We asked them questions around challenges of analytics adoption, challenges of moving from pilot to production, challenges specifi…
2019-08-28 04:49:26+00:00 Read the full story…
Weighted Interest Score: 2.5049, Raw Interest Score: 1.2793,
Positive Sentiment: 0.1672, Negative Sentiment 0.5016

Reliability Of Neural Networks Against Unforeseen Adversarial Attacks

The reliability of a machine learning model is assessed based on how erroneous it is. Lesser the number of errors, better the prediction. In theory, ML models should be able to predict, classify and recommend right every single time. However, when deployed in the real world, the model has a very good chance of running into information that has never appeared during training. To prepare the model for such untimely adversities, adversarial techniqu…
2019-08-28 09:30:28+00:00 Read the full story…
Weighted Interest Score: 2.4854, Raw Interest Score: 1.2148,
Positive Sentiment: 0.1620, Negative Sentiment 1.1338

Python MANOVA Made Easy using Statsmodels

Python MANOVA Made Easy using Statsmodels

In previous posts, we learned how to use Python to detect group differences on a single dependent variable. However, there may be situations in which we are interested in several dependent variables. In these situations, the simple ANOVA model is inadequate.

One way to examine multiple dependent variables using Python would, of course, be to carry out multiple ANOVA. That is, one ANOVA for each of these…
2019-08-26 10:01:13+00:00 Read the full story…
Weighted Interest Score: 2.4753, Raw Interest Score: 1.1410,
Positive Sentiment: 0.0967, Negative Sentiment 0.0580

Why Do People Lie With Charts?

In Three Ways To Lie With Charts, we saw three common ways in which people lie with charts.

To recap, they are:

Slippery Slope Slide Floating Origin Fraud Dual Axis Deception

In this post, I’ll speculate on the reasons for lying with charts.

The most obvious trigger for lying with charts is that, people are out to sell some product, service or point-of-view and run into facts that threaten to derail their pitch.

But, after reading my previou…
2019-08-26 14:07:44 Read the full story…
Weighted Interest Score: 2.4489, Raw Interest Score: 0.9609,
Positive Sentiment: 0.0620, Negative Sentiment 1.2089

Guide to R and Python in a Single Jupyter Notebook

Linear/Polynomial Regression, but make it R

After this section, we’ll know everything we need to in order to work with R models. The rest of the lab is just applying these concepts to run particular models. This section, therefore, is your ‘cheat sheet’ for working in R.

What we need to know:

Importing (base) R functions

Importing R Library functions

Populating vectors R understands

Populating DataFrames R understands

Populating Formulas R…
2019-08-26 13:59:38.616000+00:00 Read the full story…
Weighted Interest Score: 2.4015, Raw Interest Score: 1.5517,
Positive Sentiment: 0.0246, Negative Sentiment 0.1355

Ascend Introduces Industry’s First Queryable Dataflows for Faster Pipeline Development

chnical preview of Queryable Dataflows, a new capability to accelerate end-to-end data development that brings the interactivity of data warehouses to the scale of data pipelines. For the first time, data engineers can directly query incremental stages of any Dataflow without changing tools or disrupting the development process. Powered by the Dataflow Control Plane, interactive queries are not only optimized for any scale of data but can be immediately productionized as new Dataflow stages in a single click. With the ability to explore, profile, and prototype now seamlessly integrated within the data deve…
2019-08-28 07:05:49+00:00 Read the full story…
Weighted Interest Score: 2.3562, Raw Interest Score: 1.3699,
Positive Sentiment: 0.0000, Negative Sentiment 0.3836

ABN Amro partners Ecochain to help biz clients go green

ABN Amro is teaming up with Dutch firm Ecochain to use sustainability and financial data to help business clients go green.

The bank argues that while many firms are eager to become more sustainable, they often lack objective insight into the environmental impact they make.

Ecochain helps with this through software that captures the link between sustainability and financial data, giving clients insight in their company’s footprint, including contributing processes and products.


2019-08-28 00:01:00 Read the full story…
Weighted Interest Score: 2.3482, Raw Interest Score: 1.6194,
Positive Sentiment: 0.1619, Negative Sentiment 0.0810

Here’s How to Create a Successful DataOps Framework for Your Business

to the data world with DataOps so that we can achieve agile data mastering:

Agile Data Mastering (n): A modern Data Management technique to rapidly and repeatedly unlock value from a wide variety of data sources by combining highly automated, scalable, cross-domain data unification technologies with a rapid, iterative, collaborative process.

In order to put a DataOps framework into place, you need to structure your organization around three key components: technology, organization, and process. Let’s explore each component in detail to understand how to set your business up for long-term data mastering …
2019-08-27 07:30:46+00:00 Read the full story…
Weighted Interest Score: 2.3188, Raw Interest Score: 1.3253,
Positive Sentiment: 0.3414, Negative Sentiment 0.1807

A Brief History of Master Data

Master data is generally described as essential business data about people, places, and things. Master Data Management or MDM describes a system for managing the data, which is different than the actual data. Master data is not typically transactional data, but in some situations transactional data can be treated as master data. For instance, if information about products, suppliers, and vendors is contained only within the transactional data (or…
2019-08-20 07:35:05+00:00 Read the full story…
Weighted Interest Score: 2.2566, Raw Interest Score: 1.3455,
Positive Sentiment: 0.1377, Negative Sentiment 0.1165

Mind The Gap!” – The life and times of a man on the move Episode 60

Use your data wisely – and your wallet; AFX Group’s $28 million black hole, and Australia’s CFD providers do the right thing

In this weekly series, I look back on what stood out, what was bemusing, amusing and interesting during my weekly travels, interesting findings within the FX industry and interaction with an ever-shrinking big wide world. This is purely observational and for your enjoyment.

Monday: Data, its value and the hot air that sur…
2019-08-25 09:46:08+03:00 Read the full story…
Weighted Interest Score: 2.1761, Raw Interest Score: 1.0417,
Positive Sentiment: 0.1736, Negative Sentiment 0.1881

ABN Amro Partners With Ecochain For Impact-Based Banking

hip with Ecochain, ABN AMRO has gained access for its business clients to a unique tool. Ecochain is an innovative technology company that leverages unique software to combine sustainability data and financial data.

After a successful pilot project involving clients from a range of industries, ABN AMRO signed the contract with its new strategic partner Ecochain on 23 August 2019.

Fred Bos, Managing Director of Advisory Banking, says: “Ecochain has developed an exciting tool mapping a company’s entire life cycle, including its environmental footprint. This is then followed up by a review of financial optim…
2019-08-28 08:32:10+00:00 Read the full story…
Weighted Interest Score: 2.1168, Raw Interest Score: 1.3139,
Positive Sentiment: 0.4745, Negative Sentiment 0.0365

Case Study: Semantic Web Ontologies and Geoscience Collaboration Helps the Planet

In the geoscience community, collaboration is critical. Different disciplines — engineering geologists, geochemists, hydrologists — need to share their findings with each other to address big questions about the earth.

Take climate change. What factors contribute to it? What impact will it have? Oceanographers who study the dynamics of oceans do their work individually from atmospheric scientists who study the global dynamics of climate. A study…
2019-08-22 07:35:37+00:00 Read the full story…
Weighted Interest Score: 2.0946, Raw Interest Score: 1.3679,
Positive Sentiment: 0.2707, Negative Sentiment 0.1282

Digital Singularity: The Digital Transformation Guide for C-level Executives and Investors

If you are a C-level executive, how do you steer your organization through a voyage of digital transformation? If you are an investor, how do you identify the right company in which to invest? Why did IBM, a thoroughly transformed digital company, layoff over 50% of its Watson health division in 2018? How did Philip Morris International (PMI) achieve a market monopoly in Japan in less than two years? How did Cambridge Analytica affect the 2016 US…
2019-08-21 00:00:00 Read the full story…
Weighted Interest Score: 1.9129, Raw Interest Score: 1.3653,
Positive Sentiment: 0.2870, Negative Sentiment 0.2087

U.S. Pension Risk Transfer Sales Slow: Survey

Sales of group annuities for the U.S. pension risk transfer market may be cooling.

Insurers completed $5.8 billion in pension risk transfer transactions during second quarter, down from $8.2 billion in the second quarter of 2018, according to a new batch of group annuity issuer survey data from the LIMRA Secure Retirement Institute.

(Related: Pension Risk Transfer Volume Continues to Head Toward Sky)

Seventeen insurers participated in the late…
2019-08-28 00:00:00 Read the full story…
Weighted Interest Score: 1.8519, Raw Interest Score: 1.5964,
Positive Sentiment: 0.0639, Negative Sentiment 0.0000

Python for NLP: Creating Multi-Data-Type Classification Models with Keras

Python for NLP: Creating Multi-Data-Type Classification Models with Keras

This is the 18th article in my series of articles on Python for NLP. In my previous article, I explained how to create a deep learning-based movie sentiment analysis model using Python’s Keras library. In that article, we saw how we can perform sentiment analysis of user reviews regarding different movies on IMDB. We used the text of the review the review to predict the se…
2019-08-22 12:51:00+00:00 Read the full story…
Weighted Interest Score: 1.8019, Raw Interest Score: 0.9585,
Positive Sentiment: 0.0720, Negative Sentiment 0.1813

Python for NLP: Multi-label Text Classification with Keras

Python for NLP: Multi-label Text Classification with Keras

Introduction

This is the 19th article in my series of articles on Python for NLP. From the last few articles, we have been exploring fairly advanced NLP concepts based on deep learning techniques. In the last article, we saw how to create a text classification model trained using multiple inputs of varying data types. We developed a text sentiment predictor using textual inputs plus met…
2019-08-27 15:06:00+00:00 Read the full story…
Weighted Interest Score: 1.7226, Raw Interest Score: 0.8195,
Positive Sentiment: 0.0517, Negative Sentiment 0.2069

How to create a production-ready Recommender System

Well, solving the number 2, 3, and 4 is the reason for this post.

While the problem no. 1 is easily solved with going hybrid, the other ones will still be a headache.

While it has an extremely good performance. It has several serious issues. More importantly when you are trying to create a production-ready system.

So, why waiting? Let’s just start creating Collaborative filtering system in your production environment!

That was, the so-called,…
2019-08-26 14:12:25.604000+00:00 Read the full story…
Weighted Interest Score: 1.7084, Raw Interest Score: 0.8969,
Positive Sentiment: 0.2601, Negative Sentiment 0.0987

Credit card privacy matters: Apple Card vs. Chase Amazon Prime Rewards Visa

A single swipe of your credit card hands over data to at least a half-dozen kinds of companies. {Matt Chinworth for The Washington Post)

Technology columnist

I recently used my credit card to buy a banana. Then I tried to figure out how my credit card let companies buy me.

You might think my 29-cent swipe at Target would be just between me and my bank. Heavens, no. My banana generated data that’s probably worth more than the banana itself. I…
2019-08-26 00:00:00 Read the full story…
Weighted Interest Score: 1.6363, Raw Interest Score: 1.0472,
Positive Sentiment: 0.1113, Negative Sentiment 0.1571

GANs Can Swap Faces Now With FSGAN: A New Deep Learning Approach To Face Swapping

From Snapchat face swap to Mona Lisa talking, DeepFake has come a long way in the field of technology. This creepy technology has given us some chills since its advancements in the face-swapping phenomenon. Recently, we have witnessed DeepFaking Jim Carrey into Jack Nicholson’s most popular cult classic The Shining, swapping Nicolas Cage’s face with John Travolta’s 1997 blockbuster Face/Off and much more.

Recently, researchers from Bar-Ilan Univ…
2019-08-28 10:30:23+00:00 Read the full story…
Weighted Interest Score: 1.4597, Raw Interest Score: 0.7897,
Positive Sentiment: 0.1436, Negative Sentiment 0.3350


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