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Time Series Analysis & Modelling with Python (Part II) – Data Smoothing

Posted on January 20, 2021January 20, 2021 by Dexlab

Data Smoothing is done to better understand the hidden patterns in the data. In the non- stationary processes, it is very hard to forecast the data as the variance over a period of time changes, therefore data smoothing techniques are used to smooth out the irregular roughness to see a clearer signal.

In this segment we will be discussing two of the most important data smoothing techniques :-

Moving average smoothing
Exponential smoothing

Moving average smoothing

Moving average is a technique where subsets of original data are created and then average of each subset is taken to smooth out the data and find the value in between each subset which better helps to see the trend over a period of time.

Lets take an example to better understand the problem.

Suppose that we have a data of price observed over a period of time and it is a non-stationary data so that the tend is hard to recognize.

QTR (quarter)	Price
1	10
2	11
3	18
4	14
5	15
6	?

In the above data we don’t know the value of the 6^th quarter.

….fig (1)

The plot above shows that there is no trend the data is following so to better understand the pattern we calculate the moving average over three quarter at a time so that we get in between values as well as we get the missing value of the 6^th quarter.

To find the missing value of 6^th quarter we will use previous three quarter’s data i.e.

MAS = = 15.7

QTR (quarter)	Price
1	10
2	11
3	18
4	14
5	15
6	15.7

MAS = = 13

MAS = = 14.33

QTR (quarter)	Price	MAS (Price)
1	10	10
2	11	11
3	18	18
4	14	13
5	15	14.33
6	15.7	15.7

….. fig (2)

In the above graph we can see that after 3^rd quarter there is an upward sloping trend in the data.

Exponential Data Smoothing

In this method a larger weight ( ) which lies between 0 & 1 is given to the most recent observations and as the observation grows more distant the weight decreases exponentially.

The weights are decided on the basis how the data is, in case the data has low movement then we will choose the value of closer to 0 and in case the data has a lot more randomness then in that case we would like to choose the value of closer to 1.

EMA= F_t= F_t-1 + (A_t-1 – F_t-1)

Now lets see a practical example.

For this example we will be taking = 0.5

Taking the same data……

QTR (quarter)	Price (A_t)	EMS Price(F_t)
1	10	10
2	11	?
3	18	?
4	14	?
5	15	?
6	?	?

To find the value of yellow cell we need to find out the value of all the blue cells and since we do not have the initial value of F₁ we will use the value of A_1.Now lets do the calculation:-

F₂=10+0.5(10 – 10) = 10

F₃=10+0.5(11 – 10) = 10.5

F₄=10.5+0.5(18 – 10.5) = 14.25

F₅=14.25+0.5(14 – 14.25) = 14.13

F₆=14.13+0.5(15 – 14.13)= 14.56

QTR (quarter)	Price (A_t)	EMS Price(F_t)
1	10	10
2	11	10
3	18	10.5
4	14	14.25
5	15	14.13
6	14.56	14.56

In the above graph we see that there is a trend now where the data is moving in the upward direction.

So, with that we come to the end of the discussion on the Data smoothing method. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab Analytics DexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.

Digital Transformation Calls for Wider Security Transformation!

Posted on October 25, 2018October 25, 2018 by Dexlab

Digital Transformation Calls for Wider Security Transformation!

Going Digital is the buzzword – conventional businesses are getting transformed, thanks to digital bandwagon! Each day, it’s developing some new ways to engage clients, associate with partners and strike better operational efficiencies. Today’s business houses are using digital power to enhance revenue and reduce cost, and we can’t agree more.

Digital business is generally the implementation of digital technologies to support business models through user behavior evolution and considerable regulation support. For an instance, let’s look at Uber:

New Technology – Transportation technology platform
Business Model – Driver-partners and riders model
User Behavior Norm – Acceptance of non-traditional transportation method
Regulation Support – Cities and countries modify regulation to strengthen models

Today, cyber security and technology risk-management are treasure keys to future business growth and prosperity – security industry has evolved a lot over the years in terms of risk mitigation measures. Digital transformation has made way for security transformation, and in this regard, below we’ve whittled down the elements used for security transformation:

Digital Technologies – Smart watches, smart cars, health bands, voice assistants and smart home devices are some of the latest digital technologies clogging the present industry. These devices are to be supported by robust application platforms using AI, Machine Learning and Big Data.

Business Models – Risk management techniques are perfect for determining information risks emanating from business processes. In digital businesses, dynamic processes are common and evolving. Traditional risk models can’t handle them.

Evolving User Behaviors – Consumers are king in the digital world. The users are empowered with tools to make their own choices. On the contrary, traditional security processes used to treat users as weak links.

Regulation Support – To manage risk, security and privacy, regulations around the globe are changing and control standards are being updated or modified. For effective adaptability with the relevant changes, compliance assurance and sustenance need to be modified.

A Few Fundamental Design Principles for Control Framework for Security Transformation

Business Accelerator – Only security is not just good enough for smooth digital transformation. Security has to take the role of an accelerator since the fundamental premise of going digital is to be fast in the market and enhance customer satisfaction.

Example – Biometric Authentic – it improves user speed and experience.

Technology Changes and Agile Design – The stream of technology is evolving – AI, ML, Blockchain, Virtual Reality, Internet of Things, etc. – every domain of technology is undergoing a robust transformation. Therefore, security controls have to be adaptable and agile in design.

Customer-oriented – Known to all, customers are the most important element in digital business. In the new digitized world, users are the ones who decide. Two-decade ago rule, ‘deny all, permit some’ is now changed into ‘permit all, deny some’ rule – and we are truly excited!

Automate and Digitize – It’s time security goes digital – automation is the key.

In the near future, risk management through security transformation is going to be the utmost priority for all risk managers –if you are interested in Market Risk Analytics, drop by DexLab Analytics. They are the best in town for recognized and reputable Value at Risk Model online training. For more, check out their official website.

The blog has been sourced from ― www.forbes.com/sites/forbestechcouncil/2018/09/27/the-digital-transformation-demands-large-scale-security-transformation/#64df7fc41892

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Risk Analytics: How to Frame Smarter Insights with Organizational Data

Posted on April 2, 2018April 2, 2018 by Dexlab

Companies are launching cloud-based data analytics solutions with an aim to aid banks improve and manage their risk efficiently and streamline other activities in the most cost-effective ways.

Risk analysis is a major constituent of banking circle. Analytics-intensive operations are being run in almost all banking institutions, including cyber-security, online data theft and third-party management. The concept of risk is not something new. For years, it has been the key responsibility of C-suite professionals, but the extravagant amount of awareness and recognition associated with risk analytics was missing then. Also, the regulatory and economic landscape of the world is changing and becoming more intense – hence, risks need to be managed adequately. The executive teams should make risk analytics their topmost agenda for better organization functioning.

Why risk analytics?

The first and foremost reason to incorporate risk analytics is to measure, quantify and forecast risk with amped certainty. Analytics help in developing a baseline for risk assessment in an organization by working on several dimensions of risk and pulling them in a single unified system for better results.

What are the potential benefits of risk analytics?

Risk analytics help in turning guesswork into meaningful insights by using a number of tools and techniques to draw perspectives, determine calculable scenarios and predict likely-to-happen events.
An organization stay exposed to risk. Why? Because of a pool of structured and unstructured data, including social media, blogs, websites available on both internal and external platforms. With risk analytics, you can integrate all these data into a single perspective offering actionable insights.
Risk is a largely encompassing concept, spilling across several domains of organizational structure that at times it can really be hard to know how to manage risk and pull out meaningful insights. In such situations, risk analytics play a pivotal role in ensuring organizations develop foresight for potential risks and provide answers to difficult questions so as to create a pathway for action.

Things to do now:

Ask the right questions

Analytics means research. It ushers you to ask questions and dig deeper into risk-related stuffs. But framing random questions don’t help. To have a real impact, conjure up a handful of questions that hits the real topic.

Understand interdependencies

Risk pierces into organizational boundaries. And analytics work by offering cross-enterprise insights, by inferring conclusions throughout the business. That makes it effective to tackle far-reaching issues.

Streamline productive programs

Analytics help decision-makers introspect and evaluate risks, as well as rewards – related to operational and strategic decisions. Adding insights into pre-determined actions to determine and curb risks yield sustainable value for the program, which in the end improves overall program performance.

In the end, risk analytics seem to be quite a daunting subject to take up, but the truth is, some organizations are really doing well in managing their risks. If you are frustrated somehow and this whole concept of risk analytics baffles you more, take up SAS risk management certification. DexLab Analytics, a premier market risk training institute offers incredible market risk courses for data-hungry aspirants.

The article has been sourced from – https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Deloitte-Analytics/dttl-analytics-us-da-oriskanalytics3minguide.pdf

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Now Navigate Through Risks with Better Data, Improved Analytics

Posted on December 19, 2017December 19, 2017 by Dexlab

The treasure trove of data can devise new improved ways to mitigate risks.

How to reduce the range of risks and better grasp the reins of the business? Though data is being gathered, and pushed through the highly advanced risk analytics tools, how do the risk insurers utilize these insights to boost improved decision-making procedures that affect the business future and potential losses?

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Incredible Future Possibilities of Market Risk Analytics

Posted on December 18, 2017May 23, 2020 by Dexlab

Global risks are burgeoning; companies of all sizes are seeking the perks of risk analytics and management. Smart companies are realizing the change is coming from people as well as recent technological breakthroughs, including Big Data and AI. And CEOs are improvising their risk teams, and transforming them into perceptive strategic advisors to address budding dangerous threats like cybercrime.

Modern risk analysts have accurate knowledge about risk, artificial intelligence and cyber security – so, it’s time they get an opportunity to show a greater presence in the stoic boardrooms as strategic advisors. AI, the cutting-edge risk analytics tool surfaced out to enhance the inexorable march of big data. As such, their importance in the organization in assessing risk has greatly increased.

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Market Risk Management 101: Types of Market Risks and How to Manage Them

Posted on November 30, 2017May 23, 2020 by Dexlab

Last year, Britain opted to leave the European Union – and that created spiking fluctuation and acute market uncertainty across the globe.

Most of the investors out there know investment involves risks and rewards, just like head and tail in a coin and so do the analysts. Higher the risk, better are the chances to gain potential rewards. As a result, it is critical for both an investor and analyst to understand the true nature of market risks that influences the market conditions and controls the shooting volatility and the ways to manage those risks.

Data Science Machine Learning Certification

Common Market Risks

Relevant market risks depend largely on the nature of investment as well as geographic boundaries. Some of the key market risks are as follows:

Interest Rate Risk – It is the risk of a decrease in the value of a security owing to changes in interest rates. The rate of change of interest rates is inversely proportional to bonds – based on a rationale that a bond is the future security of a healthy stream of payments – hence as interest rate rises, the price of the issued bonds decreases.

Inflation Risk – It relates to the risk that gets affected as the prices of goods and services increases reducing the value of money. This risk results in affecting the value of investments in a negative way. It decreases the purchasing power of money, thereby reducing the value of investment. Sometimes inflation risk is also known as Purchasing Power Risk.
Currency Risk – This type of risk arises when your money needs to be converted to a different currency for investment purposes. Here, a small change in exchange rates between the home currency and US dollars can affect your investment return.
Liquidity Risk – It refers to the risk of not being able to fulfill certain investment requirements quickly for a price that determines the true value of the asset. Sometimes, one may face difficulties in selling the investment due to a lack of buyers, resulting in a drastic decrease of investment value of that product until someone is ready to pay for it. Foreign investments, over-the-counter markets and small-capitalization stocks are some of the high liquidity risks items.
Sociopolitical Risk – The socio-political environ, such as war, terrorist attack, election and corruption affects the market conditions. They affect investor perceptions, resulting in severe oscillation in stock prices.

Managing Market Risk

Well, you can’t control the market risks from taking a front seat in your financial life, though you can take some steps to manage and mitigate them.

As globalization seeped through all leading economies and market segments, a majority of fintech institutions started realizing the criticality of an enhanced operational risk, especially related to cyber-security, IT failures and data theft. Amid this, cyber risks and data theft issues posed key challenges, followed by IT failures and outsourcing issues. The revolution of digitization did many goods to our society, but the moment banks got dependent on single computer networking setups, the vulnerability of confidential customer data leakage multiplied. As a result, the need for data analysts and market researchers spiked up – they are the trained souls who possess both the experience and expertise to tackle diverse investment portfolios for clients in the best way possible to fetch maximum profits.

For that, affluent market risk courses in Delhi are available around – train your mind well, before taking the big leap in the big field of data analytics. Once you are done, reach DexLab Analytics – their comprehensive Market Risk Modelling using SAS courses are top-of-the-line courses in the industry at present.

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Risk Analytics Market: Serious Growth Rate Projection for 2017-2021

Posted on November 13, 2017May 18, 2020 by Dexlab

Want to get to the core of understanding risk within various business frameworks? The answer is Risk Analytics. This new breed of data analytics facilitates organizations in precisely defining, recognizing and managing their risk, and its need is going to increase in the coming few years. New developments in risk analytics are gaining limelight and bringing a notable transformation in the market, while enhancing its overall capability.

Recently, a team of analysts had eureka moment – they introduced a new concept of real-time risk analytics – it is nothing but a modern, more advanced version of traditional risk analytics methods. Here, the prediction is based on real-time data – it processes, examines and determines risk all on a real-time basis – hence top notch financial institutions are putting real-time risk analytics to best use to manage and mitigate associated risks. Several asset management, portfolio management and hedge fund firms, and investment banks are relying on this mode of risk analytics to modify their operating principles to play in accordance with investment and market shifts.

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Market Risk Analytics: How Top Notch Companies Are Assessing Intricate Risks

Posted on November 10, 2017May 23, 2020 by Dexlab

Risk analytics tools boost operational efficiency. But do you know what tools to implement to derive the best results?

With the burgeoning demand for big data all over the world, major corporate houses are taking risk analytics – the process of collecting, analyzing and measuring real-time data to forecast future risk for improved decision-making – to a new high.

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The Future of Risk Management: Triggering a Technology Dividend

Posted on October 30, 2017September 27, 2018 by Dexlab

Many factors are constantly shaping and reshaping the structure of risk management today – including global geopolitical inconsistency, macroeconomic headwinds and increasing number of cyber activities – which is extensively damaging and recurring. All this is leading to elevated risk perceptions.

The nature of risks has changed over the years too, as well as the manner of addressing them. Today, to mitigate risk issues, technology plays a crucial role. Headwinds like global and Asian accelerating debt levels, lower projection of productivity growth, increasing levels of policy uncertainty and constant increase of US interest have created a lot of prominent macroeconomic challenges, especially in export-oriented Asian economies. Topping that, budding risks from technological advancements are on the rise, exposing industries to newer challenges like cybersecurity and data fraud.

Explaining the Everlasting Bond between Data and Risk Analytics – @Dexlabanalytics.

As a result, the regulatory scenario of the world is also changing, especially after the global financial crisis. With a wide array of regulations introduced, the issue of risk management has started getting the desired prominence. These increasing regulations have compelled banks to accelerate their compliance activities, while giving increasing pressure on risk-management policymaking. The risk management teams now need to be constantly on a lookout for newer uncertainties – the key to address this concern remains productivity gains, but for that technology needs to be employed to the vast extent.

Cyber Value-at-Risk Model: Quantifying the Value-at-Risk – @Dexlabanalytics.

Hitting a technology dividend

Advanced data analytics, contemporary data and NLP coupled with process digitization offers new robust opportunities for effective market risk management. The technological opportunities can be realized throughout various key functions and levels, but it is the duty of the risk professionals to chalk out a more affordable and fruitful approach to address risk-related issues.

A New Course Alert! DexLab Analytics Launches Market Risk Analytics and Modelling – @Dexlabanalytics.

Check out these 3 principal levers to nab potential opportunities:

Data – Data is the new powerful combat weapon. Financial institutions consist of huge piles of data, where internal and external sources of data continuously pour in at an accelerating rate. Data, in every form – including transaction, social media, and other sources helps discover real-time customer insights and generate dividends thereafter.

Analytics – Nowadays, machine learning, NLP, advanced analytics and self-learning algorithms are widely available and at achievable prices. The best example to show how advanced analytics is boosting risk management is improving debt collection.

As per conventional debt repayment collection procedure, a lot many calls were asked to make, out of which very few turned out to be successful. But now, with advanced analytics, a set of high-end predictive models are developed to fire up decision-making process. After this, an improved insight about customers can be curated, which can further be developed with better prediction quality.

Processes – With digitization, one gets the opportunity to automate and design risk-monitoring processes to mitigate emerging risks. Nowadays, several financial institutions are implementing machine learning and transaction data to automate monitoring of conduct risk.

Subject to the extent of digitization, the change in factors for risk organization is proposed – in the beginning of digitization, one expects 15-20 percent efficiency gains, while a 60-70% improvement is to be expected in case of a fully digitized risk function, which is quite a show!

Market Risk Analytics: What It is All About – @Dexlabanalytics.

Do you want to know more about market risk modelling techniques? Drop by DexLab Analytics; being a one-stop-destination for Market Risk Modelling using SAS, it boasts of superior training and well-researched study materials.

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