Cloud Hosting

Analyst expectations of firms earnings are on average biased upwards, and that bias varies over time and stocks, according to new research by experts at Wharton and elsewhere. They have developed a machine-learning model to generate a statistically optimal and unbiased benchmark for earnings expectations, which is detailed in a new paper titled, Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases. According to the paper, the model has the potential to deliver profitable trading strategies: to buy low and sell high. When analyst expectations are too pessimistic, investors should buy the stock. When analyst expectations are excessively optimistic, investors can sell their holdings or short stocks as price declines are forecasted.

[With the machine-learning model], we can predict how the prices of the stocks will behave based on whether or not the analyst forecast is too optimistic or too pessimistic, said Wharton finance professor Jules H. van Binsbergen, who is one of the papers authors. His co-authors are Xiao Han, a doctoral student at the University of Edinburgh Business School; and Alejandro Lopez-Lira, a finance professor at the BI Norwegian Business School.

The researchers found that the biases of analysts increase in the forecast horizon, or in the period when the earnings announcement date is not anytime soon. However, on average, analysts revise their expectations downwards as the date of the earnings announcement approaches. These revisions induce negative cross-sectional stock predictability, the researchers write, explaining that stocks with more optimistic expectations earn lower subsequent returns. At the same time, corporate managers have more information about their own firms than investors have, and can use that informational advantage by issuing fresh stock, Binsbergen and his co-authors note.

The Opportunity to Profit

Comparing analysts earnings expectations with the benchmarks provided by the machine-learning algorithm reveals the degree of analysts biases, and the window of opportunity it opens. Binsbergen explained how investors could profit from their machine-learning model. With our machine-learning model, we can measure the mistakes that the analysts are making by taking the difference between what theyre forecasting and what our machine-learning forecast estimates, he said.

We can measure the mistakes that the analysts are making by taking the difference between what theyre forecasting and what our machine-learning forecast estimates. Jules H. van Binsbergen

Using that arbitrage opportunity, investors could short-sell stocks for which analysts are overly optimistic, and book their profits when the prices come down to realistic levels as the earnings announcement date approaches, said Binsbergen. Similarly, they could buy stocks for which analysts are overly pessimistic, and sell them for a profit when their prices rise to levels that correspond with earnings that turn out to be higher than forecasted, he added.

Binsbergen identified two main findings of the latest research. One is how optimistic analysts are substantially over time. Sometimes the bias is higher, and sometimes it is lower. That holds for the aggregate, but also for individual stocks, he said. With our method, you can track over time the stocks for which analysts are too optimistic or too pessimistic. That said, there are more stocks for which analysts are optimistic than theyre pessimistic, he added.

The second finding of the study is that there is quite a lot of difference between stocks in how biased the analysts are, said Binsbergen. So, its not that were just making one aggregate statement, that on average for all stocks the analysts are too optimistic.

Capital-raising Window for Corporations

Corporations, too, could use the machine-learning algorithms measure for analysts biases. If you are a manager of a firm who is aware of those biases, then in fact you can benefit from that, said Binsbergen. If the price is high, you can issue stocks and raise money. Conversely, if analysts negative biases push down the price of a stock, they serve as a signal for the firm to avoid issuing fresh stock at that time.

When analysts biases lift or depress a stocks price, it implies that the markets seem to be buying the analysts forecasts and were not correcting them for over-optimism or over-pessimism yet, Binsbergen said. With the machine-learning model that he and his researchers have developed, you can have a profitable investment strategy, he added. That also means that the managers of the firms whose stock prices are overpriced can issue stocks. When the stock is underpriced they can either buy back stocks, or at least refrain from issuing stocks.

For their study, the researchers used information from firms balance sheets, macroeconomic variables, and analysts predictions. They constructed forecasts for annual earnings that are a year and two years ahead for annual earnings; similarly, they used forecasts that were one, two and three quarters ahead for quarterly earnings. With the benchmark expectation provided by their machine-learning algorithm, they then calculated the bias in expectations as the difference between the analysts forecasts and the machine-learning forecasts.

Go here to see the original:
How to Beat Analysts and the Stock Market with Machine Learning - Knowledge@Wharton

These would affect all aspects of HR functions such as the way HR professionals on-board and hire people, and the way they train them

Stay informed and join our daily newsletter now!

October12, 20204 min read

Opinions expressed by Entrepreneur contributors are their own.

You're reading Entrepreneur India, an international franchise of Entrepreneur Media.

Artificial intelligence (AI) is changing all aspects of our lives and that too at a rapid pace. This includes our professional lives, too. Experts expect that in the days ahead, AI would become a greater part of our careers as all companies are moving ahead with adopting such technology. They are using more machines that use AI technology that would affect our daily professional activities. Soon enough, we would seemachine learning and deep learning in HRtoo. It would affect all aspects of HR (human resources) such as the way HR professionals on-board and hire people, and the way they train them.

Impact on onboarding and recruitment

These days, companies are usingrobotics in HRto make sure they have found the right people for particular job profiles. This means that even before you have stepped into your new office, your company already knows that you are the best person for the job thanks to such technology. They are using AI to pre-screen candidates before they invite the best candidates for interviews. This especially applies to large companies that offer thousands of new jobs each year and where millions of applicants go looking for jobs.

Impact on training on the job

Companies are also usingmachine learning and deep learning in HRto help provide on-the-job training to employees. Just because you have landed a job and settled in it, it does not mean that you know it all. You need to get job-related training so you can keep getting better. This is where experts expect that AI would play a major role in the coming years. It will also help one generation of professionals in an organization transfer its skills to its successors. This will make sure that no company would ever suffer from skill gaps.

Workforce augmentation

Robotics in HRwill play a major role in improving the people working in organizations where the management implements such technology. A major reason why people are so apprehensive about using AI in an organization is that they feel it would replace them and do all that they can do now. This will consequently lead to job losses. However, in the present scenario, AI is all about augmenting such a workforce. This means that it would help you perform your job with greater efficiency. Contrary to popular opinion, it would not replace you.

Workplace surveillance

Companies can also usemachine learning and deep learning in HRto improve their workforce surveillance work. This is uncomfortable for several employees as they feel that such technology would encroach on their workplace privacy. Gartner recently did a survey where it found that more than half of the companies that had a yearly turnover over $750 million use digital tools to get data on the activities of their employees and monitor their overall performance. As part of this, they analyze their emails to find out how engaged and content they are with their work.

Usage of workplace robots

Apart fromrobotics in HR,companies these days are also using physical robots that can move around on their own. This is especially true for the warehousing and manufacturing companies. Experts expect that soon this would become a common feature in a lot of other workplaces too. Companies specializing in mobility are creating delivery robots that can move around the workplace and deliver items straight to your desk. Tech companies are also developing security robots. Experts believe they would become commonplace because they can assure the safety of commercial properties from trespassers. Companies are also developing software to help you park your cars in your office.

Go here to read the rest:
Robotic Interviews, Machine Learning And the Future Of Workforce Recruitment - Entrepreneur

Artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used by Fintech platform developers to make more intelligent or informed decisions regarding key processes. This may include using AI to identify potentially fraudulent transactions, determining the creditworthiness of a borrower applying for a loan, and many other use cases.

Research conducted by Accenture found that 87% of business owners in the United Kingdom claim that theyre struggling with finding the best ways to adopt AI or ML technologies. Three out of four or 75% of C-Suite executives responding to Accentures survey said they really need to effectively adopt AI solutions within 5 years, so that they dont lose business to competitors.

As reported by IT Pro Portal, theres currently a gap between what may be considered just hype and actual or practical implementation of AI technologies and platforms.

Less than 5% of firms have actually managed to effectively apply Ai, meanwhile, more than 80% are currently just exploring basic proof of concepts for applying AL or ML algorithms. Many firms are also not familiar or dont have the expertise to figure out how to best apply these technologies to specific business use cases.

Yann Stadnicki, an experienced technologist and research engineer, argues that these technologies can play a key role in streamlining business operations. For example, they can help Fintech firms with lowering their operational costs while boosting their overall efficiency. They can also make it easier for a companys CFO to do their job and become a key player when it comes to supporting the growth of their firm.

Stadnicki points out that a research study suggests that company executives werent struggling to adopt AI solutions due to budgetary constraints or limitations. He adds that the study shows there may be certain operational challenges when it comes to effectively integrating AI and ML technologies.

He also mentions:

The inability to set up a supportive organizational structure, the absence of foundational data capabilities, and the lack of employee adoption are barriers to harnessing AI and machine learning within an organization.

He adds:

For businesses to harness the benefits of AI and machine learning, there needs to be a move away from an overhyped theoretical narrative towards practical implementation.It is important to formulate a plan and integration strategy of how your business will use AI and ML, to both mitigate the risks of cybercrime and fraud, while embracing the opportunity of tangible business impact.

Fintech firms and organizations across the globe are now leveraging AI and ML technologies to improve their products and services. In a recent interview with Crowdfund Insider, Michael Rennie, a U.K.-based product manager for Mendix, a Siemens business and the global leader in enterprise low-code, explained how emerging tech can be used to enhance business processes.

He noted:

Prior to low-code, the application and use of cutting-edge technologies within the banking sector have been more academic than actual. But low-code now enables you to apply emerging technologies like AI in a practical way so that they actually make an impact. For example, you could pair a customer-focused banking application built with low-code with a machine learning (ML) engine to identify user behaviors. Then you could make more informed decisions about where to invest in customer experience and most benefit your business.

He added:

Its easy to see the value in this. The problem is that without the correct technology, its too difficult to integrate traditional customer-facing applications with new technology systems. Such integrations typically require millions of dollars in investment and years of work. By the time an organization finishes that intensive work, the market may have moved on. Low-code eliminates that problem, makes integration easy and your business more agile.

The rest is here:
AI and Machine Learning Can Help Fintechs if We Focus on Practical Implementation and Move Away from Overhyped Narratives, Researcher Says - Crowdfund...

Repeat offenders will be put through a simulation ride and will not be let off until they complete it without violations. Calling your influential friends is not an option. So beware

Heard that Eagles classic song that goes, You can check out any time you like but you can never leave? Well that is what is going to happen to habitual traffic offenders at this new Traffic training School.

Tired of imposing fines on people who repeatedly jump signals, drive on pavements and commit many traffic offences despite being penalised, the Bangalore Traffic Police (BTP) has started this pilot project.

Habitual offenders think it is okay to commit such offences. This needs to be corrected

M Narayan, DCP Traffic East Division

The East Division Traffic police have started this initiative as a pilot project along with Honda at the Traffic Training and Road Safety Institute in Thanisandra. Policemen are looking out for people who have committed the same kind of traffic offences five times or more.

We are working with habitual offenders. In such offenders, committing a traffic offence has become a habit-forming behaviour, and they think that it is alright to commit such offences. This needs to be corrected, said M Narayan, DCP Traffic East Division.

.

This is similar to what happens when one applies for a driving license, Narayan said.This is a long term process and BTP will observe and track repeat offenders over a period of time to identify if they have changed their ways. We are also considering writing to the RTO to cancel driving licenses if the riders/drivers continue to commit violations repeatedly, Narayan added.

More:
Machine learning for rowdy roadies: Cops and tech to rein in traffic offenders - Bangalore Mirror

Leveraging innovation in technology to provide access to credit during uncertain times to populations underserved by formal financial services.

This social impact was made possible by the Bank's digital first strategy and Experian's best-in-class decisioning platform. Experian's software enables the Bank to analyse a high volume of alternative data and execute machine learning models for better decision-making and risk management.

Since the first pilot implementation in India in December 2019, the Bank saw an improvement in approvals by increasing overall acceptance rates using big data and artificial intelligence. This enhanced risk management capabilities to test and learn, helping to expand access to crucial credit and financial services.

The Bank and Experian are committed to financial inclusion, with plans for rollouts across 6 more markets across Asia, Africa and the Middle East.

SINGAPORE, Oct. 15, 2020 /PRNewswire/ -- Experian a leading global information services company has announced a partnership with leading international banking group Standard Chartered to drive financial access across key markets in Asia, Africa and the Middle East by leveraging the latest technology innovation in credit decisioning. Without enough credit bureau data for financial institutions to determine their credit worthiness, especially in this time of unprecedented volatility, many underbanked communities are facing difficulties securing access to loans.

The collaboration involves Experian's leading global decisioning solution, PowerCurve Strategy Manager, integrated with machine learning capabilities that will enable deployment of advanced analytics to help organisations make the most of their data. In support of Standard Chartered's digital-first transformation strategy, this state-of-the-art machine learning capability provides the Bank with the ability to ingest and analyse a high volume of non-Bank or, with client consent, alternative data, enabling faster, more effective and accurate credit decisioning, resulting in better risk management for the Bank and better outcomes from clients.

Story continues

Launched in India in December 2019, Standard Chartered registered positive business outcomes such as increased acceptance rates and reduced overall delinquencies. The success in India meant that Standard Chartered is now able to improve risk management for more clients who previously would have been underbanked, empowering them with access to crucial credit and financial services in their time of need.

Beyond benefits to consumers, access to credit is vital for overall economic growth, with consumer spending helping businesses continue to operate during these difficult times.

"Social and economic growth in developing markets, especially in the coming period, will be driven by progress in financial inclusion. Experian strongly believes that a technology, advanced analytics and data-driven approach can address this opportunity and we remain deeply committed to the vision of progressing financial inclusion for the world's underserved and underbanked population. Our long-standing collaboration with Standard Chartered across our PowerCurve decisioning suite of solutions, leveraging machine learning and big data to advance to the next generation of credit decisioning, is focused on empowering these underbanked communities to access credit," said Mohan Jayaraman, Managing Director, Southeast Asia & Regional Innovation, Experian Asia Pacific.

Reaffirming a commitment towards financial inclusion, Experian and Standard Chartered are working on plans to deploy the solution to its retail franchise across Asia, Africa and the Middle East, in addition to India.

"We're committed to supporting sustainable social and economic development through our business, operations and communities. This partnership helps the Bank manage risk more effectively with a more robust data-driven credit decisioning which in turn enables more clients to gain access to financial services at a time when they need it the most," said Vishu Ramachandran, Group Head, Retail Banking, Standard Chartered.

"Partnerships are central to our digital banking strategy and how we better serve our clients. Experian was a natural choice as a partner given their strong track record in innovation and in driving financial inclusion," said Aalishaan Zaidi, Global Head, Client Experience, Channels & Digital Banking, Standard Chartered.

For more information, please visit Experian's Decisioning & Credit Risk Management solution.

For further information, please contact:

About Experian

Experian is the world's leading global information services company. During life's big moments from buying a home or a car, to sending a child to college, to growing a business by connecting with new customers we empower consumers and our clients to manage their data with confidence. We help individuals to take financial control and access financial services, businesses to make smarter decisions and thrive, lenders to lend more responsibly, and organisations to prevent identity fraud and crime.

We have 17,800 people operating across 45 countries and every day we're investing in new technologies, talented people and innovation to help all our clients maximise every opportunity. We are listed on the London Stock Exchange (EXPN) and are a constituent of the FTSE 100 Index.

Learn more at http://www.experian.com.sg or visit our global content hub at our global news blog for the latest news and insights from the Group.

About Standard Chartered

We are a leading international banking group, with a presence in 60 of the world's most dynamic markets, and serving clients in a further 85. Our purpose is to drive commerce and prosperity through our unique diversity, and our heritage and values are expressed in our brand promise, Here for good.

Standard Chartered PLC is listed on the London and Hong Kong Stock Exchanges.

For more stories and expert opinions please visit Insights at sc.com. Follow Standard Chartered on Twitter, LinkedIn and Facebook.

Logo - https://photos.prnasia.com/prnh/20201013/2947870-1LOGO-a Logo - https://photos.prnasia.com/prnh/20201013/2947870-1LOGO-b

SOURCE Experian

View original post here:
Experian partners with Standard Chartered to drive Financial Inclusion with Machine Learning, powering the next generation of Decisioning - Yahoo...