Monday, January 16, 2017

Big Data for the next green revolution


It is clear that the projected population growth and urbanisation rates will have dramatic impacts on food security across the world by 2050. The impacts are multi-sectoral and extend well beyond food into infrastructure, healthcare, and technology.
However, technology has the potential to re-shape these trends for the benefit of society. Technology is disrupting all areas of agricultural value chain, driving countless opportunities and challenges particularly around profitably feeding the 9.6 billion people on Earth by 2050.
At the same time, the growing demand for food and shifting food security needs are driving innovation in the resource space. World is now more inter-connected, spawning massive data and exploration of these data can help to drive decision making that can transform the farm source-to-consumer value chain. Agri-businesses are subject to numerous regulations and consumer requirements across their supply chain. Of the several touchpoints along the agri-value chain, each hold critical information that can help businesses make the most of their resources, provide greater transparency in their processes and protect consumers.
Big Data has the potential to add value across each touchpoints starting from selection of right agri-inputs, monitoring the soil moisture, tracking prices of markets, controlling irrigations, finding the right selling point and getting the right price.
What data can do
Big-data businesses can analyse varieties of seeds across numerous fields, soil types, and climates. Similar to the way in which Google can identify flu outbreaks based on where web searches are originating, analysing crops across farms helps identify diseases that could ruin a potential harvest. The challenges and opportunities of data is immense in a country like India with 638,000 villages and 130 million farmers speaking around 800 languages with 140 million hectares of cultivable land under 127 agro climatic regions capable of supporting 3,000 different crops and one million varieties.
Self-driven vehicles can already drive themselves across fields using Global Positioning System (GPS) signals accurate to less than inch of error thus helping farmers plant more accurately, but the real potential is what happens when this data from thousands of tractors on thousands of farms is collected, grouped and analysed in real time.
Precision agriculture aids farmers in tailored and effective water management, helping in production, improving economic efficiency and minimising waste and environmental impact. Recent progress in Big Data and advanced analytics capabilities and agri-robotics such as aerial imagery, sensors, and sophisticated local weather forecasts can truly transform the agri-scape and thus holds promise for increasing global agricultural productivity over the next few decades.
Right information
Farmers need accurate weather forecasts and accurate information on the inputs they can use. Optimising input factors (e.g., nutrients, irrigation, and pest control) can help protect natural resources. The use of granular data (for example, data for every 100 meter square of a field) and analytical capability to integrate various sources of information (such as weather, soil, and market prices) will help in increasing crop yield and optimising resource usage, lowering cost. Since, climate change and extreme weather events will demand proactive measures to adapt or develop resiliency, Big Data can bring in the right information to take informed decisions.
Big Data and advanced analytics are streamlining food processing value chains by finding the core determinants of process performance, and taking action to continually improve the accuracy, quality and yield of production. Big Data is already being used for optimising production schedules based on supplier, customer, machine availability and cost constraints.
It can provide agri-business with greater visibility into supplier quality levels, and greater accuracy in predicting supplier performance over time. In India, every year 21 million tons of wheat is lost, primarily due to scare cold-storage centres and refrigerated vehicles, poor transportation facilities and unreliable electricity supply. Big Data has the potential of systematisation of demand forecasting thus reducing such losses.
Connecting the dots
A trading platform for agricultural commodities that links small-scale producers to retailers and bulk purchasers via mobile phone messaging can help send up-to-date market prices via an app or SMS and connect farmers with buyers, offering collective bargaining opportunities for small and marginal farmers.
India should look at establishing a systematic mechanism to capture the data that could offer additional value-creating opportunities. In particular, rapid proliferation of mobile technologies in rural populations could let farmers in these areas to improve productivity based on decision made backed by better information grounded on Big Data. It also has the potential to change the agri-business models including revenue models, as businesses will have the opportunity to offer new products and services thus developing sustainable revenue streams.
Proliferation of data offers unprecedented opportunities to understand consumer needs and preferences of farmers, and to deliver tailored services and products for organisations that can make sense of this data.
Given all this, today is right time for agri-businesses to lead on defining what better practices on data use are available. There is need to formulate a business model wherein value can be captured from the scale of data being captured by different players in the agri-supply chain. Companies must act now to focus, simplify and standardise big data through an enterprise-wide data management strategy as Big Data poise to deliver the next revolution of farming.

China’s embrace of a new electricity-transmission technology holds lessons for others


YOU cannot negotiate with nature. From the offshore wind farms of the North Sea to the solar panels glittering in the Atacama desert, renewable energy is often generated in places far from the cities and industrial centres that consume it. To boost renewables and drive down carbon-dioxide emissions, a way must be found to send energy over long distances efficiently.
The technology already exists (see article). Most electricity is transmitted today as alternating current (AC), which works well over short and medium distances. But transmission over long distances requires very high voltages, which can be tricky for AC systems. Ultra-high-voltage direct-current (UHVDC) connectors are better suited to such spans. These high-capacity links not only make the grid greener, but also make it more stable by balancing supply. The same UHVDC links that send power from distant hydroelectric plants, say, can be run in reverse when their output is not needed, pumping water back above the turbines. 
Boosters of UHVDC lines envisage a supergrid capable of moving energy around the planet. That is wildly premature. But one country has grasped the potential of these high-capacity links. State Grid, China’s state-owned electricity utility, is halfway through a plan to spend $88bn on UHVDC lines between 2009 and 2020. It wants 23 lines in operation by 2030.
That China has gone furthest in this direction is no surprise. From railways to cities, China’s appetite for big infrastructure projects is legendary (see article). China’s deepest wells of renewable energy are remote—think of the sun-baked Gobi desert, the windswept plains of Xinjiang and the mountain ranges of Tibet where rivers drop precipitously. Concerns over pollution give the government an additional incentive to locate coal-fired plants away from population centres. But its embrace of the technology holds two big lessons for others. The first is a demonstration effect. China shows that UHVDC lines can be built on a massive scale. The largest, already under construction, will have the capacity to power Greater London almost three times over, and will span more than 3,000km.
The second lesson concerns the co-ordination problems that come with long-distance transmission. UHVDCs are as much about balancing interests as grids. The costs of construction are hefty. Utilities that already sell electricity at high prices are unlikely to welcome competition from suppliers of renewable energy; consumers in renewables-rich areas who buy electricity at low prices may balk at the idea of paying more because power is being exported elsewhere. Reconciling such interests is easier the fewer the utilities involved—and in China, State Grid has a monopoly.
That suggests it will be simpler for some countries than others to follow China’s lead. Developing economies that lack an established electricity infrastructure have an advantage. Solar farms on Africa’s plains and hydroplants on its powerful rivers can use UHVDC lines to get energy to growing cities. India has two lines on the drawing-board, and should have more.
Things are more complicated in the rich world. Europe’s utilities work pretty well together but a cross-border UHVDC grid will require a harmonised regulatory framework. America is the biggest anomaly. It is a continental-sized economy with the wherewithal to finance UHVDCs. It is also horribly fragmented. There are 3,000 utilities, each focused on supplying power to its own customers. Consumers a few states away are not a priority, no matter how much sense it might make to send them electricity. A scheme to connect the three regional grids in America is stuck. The only way that America will create a green national grid will be if the federal government throws its weight behind it.
Live wire
Building a UHVDC network does not solve every energy problem. Security of supply remains an issue, even within national borders: any attacker who wants to disrupt the electricity supply to China’s east coast will soon have a 3,000km-long cable to strike. Other routes to a cleaner grid are possible, such as distributed solar power and battery storage. But to bring about a zero-carbon grid, UHVDC lines will play a role. China has its foot on the gas. Others should follow.

Equipping people to stay ahead of technological change


WHEN education fails to keep pace with technology, the result is inequality. Without the skills to stay useful as innovations arrive, workers suffer—and if enough of them fall behind, society starts to fall apart. That fundamental insight seized reformers in the Industrial Revolution, heralding state-funded universal schooling. Later, automation in factories and offices called forth a surge in college graduates. The combination of education and innovation, spread over decades, led to a remarkable flowering of prosperity.
Today robotics and artificial intelligence call for another education revolution. This time, however, working lives are so lengthy and so fast-changing that simply cramming more schooling in at the start is not enough. People must also be able to acquire new skills throughout their careers. 
Unfortunately, as our special report in this issue sets out, the lifelong learning that exists today mainly benefits high achievers—and is therefore more likely to exacerbate inequality than diminish it. If 21st-century economies are not to create a massive underclass, policymakers urgently need to work out how to help all their citizens learn while they earn. So far, their ambition has fallen pitifully short.
Machines or learning
The classic model of education—a burst at the start and top-ups through company training—is breaking down. One reason is the need for new, and constantly updated, skills. Manufacturing increasingly calls for brain work rather than metal-bashing (see Briefing). The share of the American workforce employed in routine office jobs declined from 25.5% to 21% between 1996 and 2015. The single, stable career has gone the way of the Rolodex.
Pushing people into ever-higher levels of formal education at the start of their lives is not the way to cope. Just 16% of Americans think that a four-year college degree prepares students very well for a good job. Although a vocational education promises that vital first hire, those with specialised training tend to withdraw from the labour force earlier than those with general education—perhaps because they are less adaptable.
At the same time on-the-job training is shrinking. In America and Britain it has fallen by roughly half in the past two decades. Self-employment is spreading, leaving more people to take responsibility for their own skills. Taking time out later in life to pursue a formal qualification is an option, but it costs money and most colleges are geared towards youngsters.
The market is innovating to enable workers to learn and earn in new ways. Providers from General Assembly to Pluralsight are building businesses on the promise of boosting and rebooting careers. Massive open online courses (MOOCs) have veered away from lectures on Plato or black holes in favour of courses that make their students more employable. At Udacity and Coursera self-improvers pay for cheap, short programmes that bestow “microcredentials” and “nanodegrees” in, say, self-driving cars or the Android operating system. By offering degrees online, universities are making it easier for professionals to burnish their skills. A single master’s programme from Georgia Tech could expand the annual output of computer-science master’s degrees in America by close to 10%.
Such efforts demonstrate how to interleave careers and learning. But left to its own devices, this nascent market will mainly serve those who already have advantages. It is easier to learn later in life if you enjoyed the classroom first time around: about 80% of the learners on Coursera already have degrees. Online learning requires some IT literacy, yet one in four adults in the OECD has no or limited experience of computers. Skills atrophy unless they are used, but many low-end jobs give workers little chance to practise them.
Shampoo technician wanted
If new ways of learning are to help those who need them most, policymakers should be aiming for something far more radical. Because education is a public good whose benefits spill over to all of society, governments have a vital role to play—not just by spending more, but also by spending wisely.
Lifelong learning starts at school. As a rule, education should not be narrowly vocational. The curriculum needs to teach children how to study and think. A focus on “metacognition” will make them better at picking up skills later in life.
But the biggest change is to make adult learning routinely accessible to all. One way is for citizens to receive vouchers that they can use to pay for training. Singapore has such “individual learning accounts”; it has given money to everyone over 25 to spend on courses from 500 approved providers. So far each citizen has only a few hundred dollars, but it is early days.
Courses paid for by taxpayers risk being wasteful. But industry can help by steering people towards the skills it wants and by working with MOOCs and colleges to design courses that are relevant. Companies can also encourage their staff to learn. AT&T, a telecoms firm which wants to equip its workforce with digital skills, spends $30m a year on reimbursing employees’ tuition costs. Trade unions can play a useful role as organisers of lifelong learning, particularly for those—workers in small firms or the self-employed—for whom company-provided training is unlikely. A union-run training programme in Britain has support from political parties on the right and left.
To make all this training worthwhile, governments need to slash the licensing requirements and other barriers that make it hard for newcomers to enter occupations. Rather than asking for 300 hours’ practice to qualify to wash hair, for instance, the state of Tennessee should let hairdressers decide for themselves who is the best person to hire.
Not everyone will successfully navigate the shifting jobs market. Those most at risk of technological disruption are men in blue-collar jobs, many of whom reject taking less “masculine” roles in fast-growing areas such as health care. But to keep the numbers of those left behind to a minimum, all adults must have access to flexible, affordable training. The 19th and 20th centuries saw stunning advances in education. That should be the scale of the ambition today.

How Amazon innovates in ways that Google and Apple can't


The Echo, Amazon’s voice-controlled speaker, was a big hit this holiday season. Amazon is keeping specific sales figures under wraps, but the company says it sold nine times as many Echo devices during the holidays as it did a year earlier.
It’s the latest example of Amazon pioneering a new product category and then going on to dominate it. Amazon has become the leader in the e-book market on the strength of its Kindle line of e-readers. And it dominates an important segment of the cloud computing market; Amazon Web Services is expected to generate $12 billion in revenue this year.
Next year, Amazon is hoping to start doing something similar for brick-and-mortar retailing. The company recently unveiled Amazon Go, a convenience store whose no-checkout technology could revolutionize the retail sector.
In short, Amazon has shown a remarkable ability to succeed in a wide variety of different product categories. That’s a contrast to most other high-profile tech companies that are really good in one area — Google’s dominant online services or Apple’s extraordinarily profitable hardware — but struggle when the quest for growth pushes them outside their zone of core competency.
“There's an opportunity to do innovation in big companies,” says author and startup guru Eric Ries. “But very few big companies have done this really well. Amazon is one of them.”
Amazon has figured out how to combine the entrepreneurial culture of a small company with the financial resources of a large one. And that allows it tackle problems most other companies can’t.

Most tech companies struggle outside their comfort zones

Google created or acquired a remarkable string of hit products during the 2000s, including Gmail, Google Maps, Google Docs, YouTube, Android, and Chrome. These products have a lot in common. Most are online services like Google’s original search engine. The two major exceptions — Android and Chrome — are software that help people access Google’s online services.
During the 2010s, Google has gotten more ambitious, funding a series of “moonshots” that aim to solve big technology problems far removed from the company’s traditional focus on online services. Indeed, Google co-founders Larry Page and Sergey Brin felt so strongly about this mission that they re-organized Google itself, creating a new parent company called Alphabet to serve as an umbrella for moonshot projects.
But so far, Google has had little to show for these efforts. Google Glass was a flop. The company has developed some impressive self-driving technology over the past six years but has still not turned it into a commercial product. Google bought Nest in 2014, but the company has struggled to expand beyond smart thermostats. Google acquired some robotics startups in 2014, but hasn’t figured out what to do with them and wound up putting one up for sale.
One of the big problems here seems to be that Page and company are a little too focused on solving hard technical problems. Creating a new computer platform around a pair of glasses is a hard and interesting technical challenge, for example. But it was never clear that solving it would produce to a viable product.
Google’s most promising “moonshot” is its self-driving car project, which is widely regarded as the technology leader. But top engineers on the project have grown impatient with the company’s slow pace in getting to market. A team of Google engineers left Google to found Otto, a self-driving truck company acquired by Uber earlier this year. The leader of Google’s self-driving car project, Chris Urmson, recently quit to create a self-driving car startup of his own.
Similarly, robotics is an important area of computer science research. But it was never clear what Google was going to do with a menagerie of robot startups. The thinking seemed to be that Google would acquire the best talent first and come up with a plan to make money later.
Of course, it’s possible that Google’s self-driving car project or one of the other moonshots will produce a spectacular business success that will justify the costs of the other failures. But so far, at least, Google’s efforts to branch out into markets other than online services haven’t borne much fruit.

Amazon focuses on meeting user needs

Google’s approach — solve the hard technical problems first, worry about the business model later — is rooted in the engineering background of Google Founders Larry Page and Sergey Brin. In contrast, Amazon CEO Jeff Bezos spent almost a decade working for several Wall Street firms before starting Amazon — a background that gives him a more pragmatic outlook that’s more focused on developing products customers will actually want to pay for.
Bezos has worked to create a culture at Amazon that’s hospitable to experimentation.
“I know examples where a random Amazon engineer mentions ‘Hey I read about an idea in a blog post, we should do that,’” Eric Ries says. “The next thing he knows, the engineer is being asked to pitch it to the executive committee. Jeff Bezos decides on the spot.”
A key factor in making this work, Ries says, is that experiments start small and grow over time. At a normal company, when the CEO endorses an idea, it becomes a focus for the whole company, which is a recipe for wasting a lot of resources on ideas that don’t pan out. In contrast, Amazon creates a small team to experiment with the idea and find out if it’s viable. Bezos famously instituted the “two-pizza team” rule, which says that teams should be small enough to be fed with two pizzas.
Ries says that new teams get limited funding and clear milestones; if a team succeeds in smaller challenges, it’s given more resources and a larger challenge to tackle.
But Amazon doesn’t spend too much time on internal testing. “They prioritize launching early over everything else,” one engineer wrote in an epic 2011 rant comparing Amazon’s culture to other technology companies. Launching early with what Ries has dubbed a “minimum viable product” allows Amazon to learn as quickly as possible whether an idea that sounds good on paper is actually a good idea in the real world.
Of course, this method isn’t foolproof; Amazon has had plenty of failures, like its disastrous foray into the smartphone market. But by getting a product into the hands of paying customers as quickly as possible and taking their feedback seriously, Amazon avoids wasting years working on products that don’t serve the needs of real customers.
This seems to be the approach Amazon is taking with Amazon Go, its new convenience store concept. It’s a technology that could work in many different types of retail stores, but Amazon’s initial approach is modest: a single, relatively small convenience store. Media reports suggest that Amazon plans to open 2,000 retail stores, but the company disputesthis. The Amazon way, after all, isn’t to open one store because there’s a plan for 2,000. It’s to open one store and then open thousands more if the first one is a big success.

Why big resources often don’t lead to innovation

In the abstract this approach — minimize bureaucracy, start out with small experiments, expand them if they’re successful — sounds so good that it’s almost banal. But it’s surprisingly difficult for big companies to do this, especially when they’re entering new markets.
Over time, big companies develop cultures and processes optimized for the market where they had their original success. Companies have a natural tendency to establish uniform standards across the enterprise. When I spent a summer as an engineering intern at Google in 2010, much of my time was spent learning how to use the company’s powerful suite of proprietary software tools. Google employees are expected to use these tools across a wide variety of projects. That approach works great when it’s creating a new online service that’s similar to early hits like search or Gmail. But the tools can become a hindrance if a Google team is trying to build something that’s very different from a search engine.
You can make a similar point about Apple. The company is famous for its elegant user interface, a reflection of the central role of designers — rather than engineers or project managers — in the company’s development process. That’s a good way to develop user-friendly gadgets like the iPhone or Apple Watch, but it doesn’t necessarily work well for other product categories. Apple struggled for years to make its iCloud service (and predecessors like .Mac and MobileMe) reliable.
In contrast, Jeff Bezos has been fanatical about letting teams operate independently of one another. “It doesn't matter what technology” teams use at Amazon, one of the company’s former engineers wrote in 2011. Bezos has explicitly discouraged the kind of standardization you see at companies like Google and Apple, encouraging teams to operate independently using whatever technology makes the most sense.
Bezos has worked hard to make Amazon a modular, flexible organization with a minimum of company-wide policies. That has made Amazon’s internal culture somewhat chaotic and balkanized. An engineer on one Amazon project can’t easily jump to another the way they can at Google or Apple. Friction between teams with different cultures may explain why some people find Amazon a stressful place to work.
But this chaotic culture is also hospitable to innovation. A new team can use the tools and processes that make the most sense instead of feeling pressure to conform to company-wide standards.

Amazon is well positioned for the next decade of innovation

The reason Amazon’s organizational choices are significant is that there are a lot of opportunities for big companies that can emulate the best characteristics of a startup.
Amazon Go is a good example. It’s hard to imagine a small startup pulling this off. The technology that makes the store work — using cameras and other sensors to track a customer’s every step and instantly detect when he takes an item off a shelf — is a sophisticated feat of computer science that undoubtedly cost millions of dollars to develop. To recoup those investments, Amazon is going to have to spend years — and millions more — opening stores in an industry not known for its fat profit margins. That requires the kind of deep pockets and longtime horizons that startups rarely have.
Back in the 1990s and early 2000s, the high-tech frontier was on the web. Scaling a website from hundreds of users to millions is technically challenging, but it doesn’t necessarily require a huge team or a ton of physical infrastructure. That’s why companies like Google and Facebook grew from nothing to billion-dollar companies.
In contrast, the next generation of innovations is likely to be more tied to the physical world and to conventional industries: apartment sharing, self-driving cars, retail stores, health care innovations, and so forth.
Google, Uber, Tesla, and conventional car companies are all working on self-driving technology. To succeed in this market, companies are going to have to bring together software, hardware, sophisticated maps, and a strategy for navigating complex regulatory issues — a combination that could be too difficult for an independent startup to manage.
Amazon and Google are both working on drone delivery technology, which has a similar mix of hardware, software, and regulatory challenges. The companies’ existing financial, technical, and lobbying infrastructure will give them a big leg up in these markets.
One way to deal with the conundrum is for big tech companies to acquire startups early in their growth. That allows a startup’s innovations to be combined with the resources of a big company. Uber acquired the self-driving truck startup Otto less than a year after it was founded. GM paid a billion dollars for the self-driving car startup Cruise in March.
But acquiring fast-growing startups is a very expensive way for a big company like Google or Uber to stay on the cutting edge. And in many cases, this strategy doesn’t even work. Google’s $2 billion acquisition of Nest was supposed to accelerate the company’s growth, but instead the company has struggled under the Alphabet umbrella.
This is what makes Amazon’s evident success at nurturing entrepreneurial projects internally so significant. Amazon doesn’t need to rely so heavily on expensive and risky acquisitions because it has developed a system for nurturing entrepreneurial projects internally. And as technology invades the real world, there are going to be more and more opportunities for these kinds of entrepreneurial projects.

Automation having an adverse effect on the job market : Stephen Hawking


Artificial intelligence and increasing automation is going to decimate middle class jobs, worsening inequality and risking significant political upheaval, Stephen Hawking has warned.
In a column in The Guardian, the world-famous physicist wrote that"the automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining."
He adds his voice to a growing chorus of experts concerned about the effects that technology will have on workforce in the coming years and decades. The fear is that while artificial intelligence will bring radical increases in efficiency in industry, for ordinary people this will translate into unemployment and uncertainty, as their human jobs are replaced by machines.
Technology has already gutted many traditional manufacturing and working class jobs — but now it may be poised to wreak similar havoc with the middle classes.
A report put out in February 2016 by Citibank in partnership with the University of Oxford predicted that 47% of US jobs are at risk of automation. In the UK, 35% are. In China, it's a whopping 77% — while across the OECD it's an average of 57%.
 Computerizable Jobs
Bloomberg News & Dave Merrill
And three of the world's 10 largest employers are now replacing their workers with robots.
Automation will, "in turn will accelerate the already widening economic inequality around the world," Hawking wrote. "The internet and the platforms that it makes possible allow very small groups of individuals to make enormous profits while employing very few people. This is inevitable, it is progress, but it is also socially destructive."
He frames this economic anxiety as a reason for the rise in right-wing, populist politics in the West: "We are living in a world of widening, not diminishing, financial inequality, in which many people can see not just their standard of living, but their ability to earn a living at all, disappearing. It is no wonder then that they are searching for a new deal, which Trump and Brexit might have appeared to represent."
Combined with other issues — overpopulation, climate change, disease — we are, Hawking warns ominously, at "the most dangerous moment in the development of humanity." Humanity must come together if we are to overcome these challenges, he says.
Stephen Hawking has previously expressed concerns about artificial intelligence for a different reason — that it might overtake and replace humans. "The development of artificial intelligence could spell the end of the human race," he said in late 2014. "It would take off on its own, and redesign itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded."

Sunday, January 15, 2017

The Art of Looking Stupid


The investment management industry is filled with thousands of extremely smart people. Top in their class smart. It could easily be argued that there is an oversupply of smart investors. Throughout their lives they’ve received accolades and pats on the back reconfirming what they already know – they’re extraordinarily smart. One thing I’ve learned over the years is smart investors – understandably – don’t like to look stupid.
While I have absolutely no data to support this, with an investment world overflowing with smart people, I believe there is a shortage of professional investors willing to look stupid.
That’s a shame as looking stupid is often necessary when practicing absolute return investing. I have a long track record that proves I’m more than willing to look dumb. There have been times when my relative performance was so bad, Bloomberg ranked it “N.A.” Embarrassing to most managers, I view my “stupid” positioning as a necessary part of my process, providing me with the ability to generate attractive absolute returns over a complete market cycle (my investment objective). As long as performance deviations aren’t due to valuation errors and permanent losses to capital, investing differently during periods of inflated valuations may not be stupid at all, but a sign of discipline, perseverance, and even intelligence. In other words, looking stupid is not the same as being stupid.
Instead of being ashamed or embarrassed, I view my ability and willingness to look stupid as a competitive advantage. If there was a market leader in looking stupid during irrational markets, I would like to think I’d be on the short list. I’m sure my current positioning doesn’t look very bright either. But this isn’t new for me. Patient positioning almost always looks questionable or unintelligent during periods of sharply rising asset prices and inflated valuations.
The secret of looking stupid, is not caring what other people think. Perception risk is a very real and underappreciated risk in the investment management industry. In my opinion, it’s one of the leading threats to investment discipline and one of the reasons so many active funds look the same as their peers and benchmarks. If you’re constantly concerned about what your boss, peers, and clients think about you and your positioning, you’ll never master the art of looking stupid.

To be clear, looking stupid indefinitely is not a smart long-term strategy. For instance, an absolute return investor shouldn’t remain patient or overly conservative throughout the entire market cycle. The goal of positioning differently, or remaining patient during market extremes, is to ultimately take advantage of the inefficiencies and distortions created by conformity and group-think. Eventually cycles end and the mispricing and extrapolation of market extremes unwind. This unwind can create tremendous opportunity, which should allow flexible absolute return investors to shift from patient to aggressive positioning. In effect, there should a period each market cycle when opportunistic and aggressive positioning is necessary and ultimately beneficial.
The purpose of looking stupid is to look smart over a complete cycle. While I’ve had some of the worst in-between cycle performance, I’ve also had some of the best complete cycle returns. To successfully look stupid and generate attractive full-cycle absolute returns, investors might consider setting their egos aside, think independently, and eliminate the importance placed on perception. Good luck!
Looking stupid isn’t as easy as it appears!

Friday, January 13, 2017

With sown area rising, bumper wheat crop expected this year


The prospects for a bumper wheat crop this year brightened after farmers brought more acreage under the cereal crop in the ongoing rabi season.
The increase in acreage was mainly seen in the major producing States of Uttar Pradesh, Madhya Pradesh and Rajasthan.
Apart from favourable weather, the increase in sown area in Uttar Pradesh, the largest wheat-producing State, where planting is still going on, is seen aiding the crop output.
As per the latest data released by the Agriculture Ministry, wheat acreage stands at 309.60 lakh hectares, a 7.1 per cent increase over the corresponding year-ago period.
“We have exceeded last year’s acreage by over one million hectares and are progressing towards an all-time high. The prevailing low temperatures are favourable for the crop’s growth. Considering an average yield of over 3.1 tonnes per hectare, we could be meeting the targeted 96.5 million tonnes,” said GP Singh, Director of the Karnal-based Indian Institute of Wheat and Barley Research.
Singh said weather conditions during February-March will play a crucial role in deciding the crop yield. Last year, the wheat acreage stood at 297 lakh hectares.
Temperatures have been plummeting across the northern parts of the country in recent days, aiding the wheat crop which is in the tillering stages, Singh said.
“There is another round of rainfall forecast in the Indo-Gangetic plains, which is seen as favourable for the crop,” he added. Further, there is no outbreak of a major pest or diseases across the key growing regions. Though there was an instance of yellow rust in a farmer’s field in Gurdaspur, Punjab, it was not on a major scale. Adequate precaution has been taken to contain the spread of the disease, Singh added.
“Although the government’s target of 96.5 million tonnes is quite optimistic, there is no room for pessimism at this point in time because of the increased acreage and prevailing favourable climate.
“However, this year’s crop size will depend on the weather conditions during February-April,” said MK Dattaraj, Managing Director, Krishna Flour Mills, Bengaluru, and past-president of Roller Flour Millers Federation of India.
Though the government had estimated the 2015-16 crop at 93.5 million tonnes, the trade felt that the actual output was lower and the shortages have led to prices rising in the recent months.
The government removed the duty on imports last month to boost supplies of the cereal. So far, wheat imports into the country have been estimated at over two million tonnes from countries such as Australia, Ukraine, Russia, and Bulgaria.
Trade sources estimate that another 1-1.5 million tonnes of wheat could come into the country over the next couple of months.