WaPo:
One big reason some foods cost so much more than others
“Hint: It’s (mostly) not subsidies. Although they’ve certainly played a role in shaping our food supply such that we have huge quantities of just a few crops — a recipe for low prices — the discrepancy that seems to be at issue is the one between commodity crops such as corn and soy, and the fruits and vegetables that everyone’s trying to get us to eat more of. There’s a factor there that plays a much larger role than subsidies, and it doesn’t get much airtime…Its machines.”
An older article from 2014 in WaPo:
Farm bill: Why don’t taxpayers subsidize the foods that are better for us?
“But we also need to move away from a system that requires taxpayers to spend billions underwriting a system detrimental to public health.”
That last statement needs some qualification. We've seen something similar before from the NYT (see Are Farm Subsidies Making us Fat). Not sure the causal link between subsidies and public health is much to talk about.
Economist Jayson Lusk knocks
it out the the park. He addresses the science behind these specious
connections (and links to a number of related research articles) and
includes some of his work in the area:
"There are actual lots of people who know how much farm subsidies
contribute to food consumption, and they're called agricultural
economists (in fact, McMillian goes on to then cite two prominent food
and agricultural economists on the issue: Parke Wilde and David
Just)…..In the model I used for the forthcoming paper I wrote on the
distributional impacts of crop insurance subsidies, I find that the
complete removal of crop insurance subsidies to farmers would only
increase the price of cereal and bakery products by 0.09% and increase
the price of meat by 0.5%, and would also increase the price of fruits
ad vegetables by 0.7%. So, while these policies may be inefficient,
regressive, and promote regulatory over-reach, their effects on food
prices are tiny, and depending on which policy we're talking about,
could push prices and consumption up or down."
Could you make the argument that simply shifting money toward programs related to fruits and vegetables would have a large enough impact on price to influence consumption? How much money would that take and what would the effect size be?
Wednesday, June 28, 2017
Tuesday, June 27, 2017
Recent News in Agribusiness: Whole Foods and Amazon
Its about the data.
From: https://blog.dataiku.com/big-data-is-the-big-news-in-amazon-whole-foods-deal
"If you consider the use of data and data projects as the end goal, the recent acquisition of Whole Foods makes perfect sense; no one would dispute the fact that Amazon knows the online customer backwards and forwards, but when it comes to understanding the brick-and-mortar shopper, they lack insight. Amazon didn’t buy Whole Foods for the business - they bought it for the data."
"this is almost certainly a win for Whole Foods customers as well…. But Amazon can also bring its data innovation, scientists, labs, and creativity to Whole Foods, which ultimately can mean the next generation of grocery stores even for shoppers that aren’t interested in grocery delivery services. Think Amazon-like optimization of stocking using real-time data or predictive analytics to streamline the experience for shoppers and suppliers alike (and all while potentially helping to eliminate food waste, a massive problem in the United States today)."
Its about platform strategy (which inherently is related to data)
From: http://mitsloan.mit.edu/newsroom/articles/platform-strategy-explained/?utm_source=mitsloantwitter&utm_medium=social&utm_campaign=platformexplainer
"Arguably, your platform strategy is more critical to success than the idea behind the platform itself. Building a platform, especially after a decade of buzzworthy attempts and a few huge successes (Amazon, eBay, Uber, Airbnb), is really, really hard. There are countless ways to flub this. A solid platform strategy will answer two key questions: How will you attract customers? And how will you make your technology the core of an ecosystem?"
Traditional Retailers Will Continue to Thrive
From: https://www.bloomberg.com/view/articles/2017-06-20/the-amazon-approach-to-groceries-won-t-replace-stores
To some extent there may be significant overlap between customers:
“For a certain kind of urban professional, Amazon and Whole Foods are brands that define the consumption of staple goods: the weekly trip to pick up cheese, produce, maybe some pasture-raised organic beef; and the nice UPS man dropping off everything else, from toilet paper to truffle oil. On Friday, those folks learned that they are facing a future of truly one-stop shopping: Amazon.com Inc. plans to acquire Whole Foods Market Inc. for $13.7 billion.”
However, other retailers will maintain an appeal to a much broader customer base:
“the Dollar General customer is, in general, a very, very different kind of person than the folks who regularly shop at Whole Foods, or for that matter, at Amazon.”
Still why Whole Foods? Were they cheap? (their performance has not been that great this last year). Why not purchase a retailer that already has a more mature data strategy (like Kroger and the Kroger Plus Card etc.)? Perhaps as mentioned in the dataiku and BloombergView article, there is some idea that there is significant overlap between Amazon and WholeFood customers. As far as other retailers, they are not standing still. Points and loyalty programs as well as online offerings and ready to pick up grocery services are just some of the ways they are continually innovating to create value for customers.
From: https://blog.dataiku.com/big-data-is-the-big-news-in-amazon-whole-foods-deal
"If you consider the use of data and data projects as the end goal, the recent acquisition of Whole Foods makes perfect sense; no one would dispute the fact that Amazon knows the online customer backwards and forwards, but when it comes to understanding the brick-and-mortar shopper, they lack insight. Amazon didn’t buy Whole Foods for the business - they bought it for the data."
"this is almost certainly a win for Whole Foods customers as well…. But Amazon can also bring its data innovation, scientists, labs, and creativity to Whole Foods, which ultimately can mean the next generation of grocery stores even for shoppers that aren’t interested in grocery delivery services. Think Amazon-like optimization of stocking using real-time data or predictive analytics to streamline the experience for shoppers and suppliers alike (and all while potentially helping to eliminate food waste, a massive problem in the United States today)."
Its about platform strategy (which inherently is related to data)
From: http://mitsloan.mit.edu/newsroom/articles/platform-strategy-explained/?utm_source=mitsloantwitter&utm_medium=social&utm_campaign=platformexplainer
"Arguably, your platform strategy is more critical to success than the idea behind the platform itself. Building a platform, especially after a decade of buzzworthy attempts and a few huge successes (Amazon, eBay, Uber, Airbnb), is really, really hard. There are countless ways to flub this. A solid platform strategy will answer two key questions: How will you attract customers? And how will you make your technology the core of an ecosystem?"
Traditional Retailers Will Continue to Thrive
From: https://www.bloomberg.com/view/articles/2017-06-20/the-amazon-approach-to-groceries-won-t-replace-stores
To some extent there may be significant overlap between customers:
“For a certain kind of urban professional, Amazon and Whole Foods are brands that define the consumption of staple goods: the weekly trip to pick up cheese, produce, maybe some pasture-raised organic beef; and the nice UPS man dropping off everything else, from toilet paper to truffle oil. On Friday, those folks learned that they are facing a future of truly one-stop shopping: Amazon.com Inc. plans to acquire Whole Foods Market Inc. for $13.7 billion.”
However, other retailers will maintain an appeal to a much broader customer base:
“the Dollar General customer is, in general, a very, very different kind of person than the folks who regularly shop at Whole Foods, or for that matter, at Amazon.”
Still why Whole Foods? Were they cheap? (their performance has not been that great this last year). Why not purchase a retailer that already has a more mature data strategy (like Kroger and the Kroger Plus Card etc.)? Perhaps as mentioned in the dataiku and BloombergView article, there is some idea that there is significant overlap between Amazon and WholeFood customers. As far as other retailers, they are not standing still. Points and loyalty programs as well as online offerings and ready to pick up grocery services are just some of the ways they are continually innovating to create value for customers.
Friday, June 23, 2017
Bursting the Big Data Bubble....with Theory
There was an article in the June 2017 printing of Significance titled Bursting the big data bubble. Unfortunately I don't have paid access but here is the teaser:
"In the financial world, big data is hailed as a potential game changer for predicting stock market performance. But without adequate safeguards, big data analyses may result in spurious correlations, misguided predictions and disappointing returns."
You might not have to know anything about big data to know that building models, developing strategies, or coding algorithms to successfully predict stock returns (at least well enough to consistently earn above average returns in a portfolio long term) is a steep uphill climb against a mountain of economic theory.
Not being a financial economist I'll speak broadly and provide references with more detail below. But, according to the theory of rational expectations and efficient market hypothesis, all unexploited profit opportunities are eliminated because prices reflect all publicly available information. Prices follow a random walk and for all practical purposes are not predictable. Even when prices diverge from fundamental values, according to the theory, the divergence can't be predicted.
One exception is inside information. A trader with insider information (i.e. publicly unavailable, read illegal) would have an edge and could act on it and make profitable trades. I do wonder, however, could a firm have an edge if they developed a proprietary algorithm that makes *better* use of public information? Is novel interpretation of public information the next best thing to insider information?
I'm not sure. Definitely this may have had a chance early on for some quant funds. However, I still think in the long run other firms could replicate the strategy, eliminating unexploited profit opportunities. The citizen data scientist with a good understanding of statistics and willingness to crack a book can learn to implement the same advanced algorithms using open source packages (via R and Python) as someone with 2 PhDs who may have been hired a few years back working for a quant fund coding the algorithms from scratch.
I think this is echoed somewhat in a recent Chat with Traders podcast with Matthew Hoyle when he discussed the fact that strategies have a short shelf life-what is valuable is the ability and energy to look at new and interesting things and put it all together with a sense of business development and desire to explore.
Reference:
Fong, W. M. (2017), Bursting the big data bubble. Significance, 14: 20–23. doi:10.1111/j.1740-9713.2017.01035.x
See also:
Masters in Business with Barry Ritholtz Guest: Andrew Lo of MIT
In Praise of the Citizen Data Scientist
Efficient Capital Markets: A Review of Theory and Empirical Work. Eugene F. Fama
The Journal of Finance. Vol. 25, No. 2, Papers and Proceedings of the Twenty-Eighth Annual Meeting of the American Finance Association New York, N.Y. December, 28-30, 1969 (May, 1970), pp. 383-417
- a classic paper reviewing work related to efficient capital markets theory.
- the above reviews some of the market anomolies literature, finding many studies fall short in terms of methodology.
Abstract:
The anomalies literature is infested with widespread p-hacking. We replicate this literature by compiling a large data library with 447 anomalies. With microcaps alleviated via NYSE breakpoints and value-weighted returns, 286 anomalies (64%) including 95 out of 102 liquidity variables (93%) are insignificant at the 5% level. Imposing the t-cutoff of three raises the number of insignificance to 380 (85%). Even for the 161 significant anomalies, their magnitudes are often much lower than originally reported. Among the 161, the q-factor model leaves 115 alphas insignificant (150 with t < 3). In all, capital markets are more efficient than previously recognized.
"In the financial world, big data is hailed as a potential game changer for predicting stock market performance. But without adequate safeguards, big data analyses may result in spurious correlations, misguided predictions and disappointing returns."
You might not have to know anything about big data to know that building models, developing strategies, or coding algorithms to successfully predict stock returns (at least well enough to consistently earn above average returns in a portfolio long term) is a steep uphill climb against a mountain of economic theory.
Not being a financial economist I'll speak broadly and provide references with more detail below. But, according to the theory of rational expectations and efficient market hypothesis, all unexploited profit opportunities are eliminated because prices reflect all publicly available information. Prices follow a random walk and for all practical purposes are not predictable. Even when prices diverge from fundamental values, according to the theory, the divergence can't be predicted.
One exception is inside information. A trader with insider information (i.e. publicly unavailable, read illegal) would have an edge and could act on it and make profitable trades. I do wonder, however, could a firm have an edge if they developed a proprietary algorithm that makes *better* use of public information? Is novel interpretation of public information the next best thing to insider information?
I'm not sure. Definitely this may have had a chance early on for some quant funds. However, I still think in the long run other firms could replicate the strategy, eliminating unexploited profit opportunities. The citizen data scientist with a good understanding of statistics and willingness to crack a book can learn to implement the same advanced algorithms using open source packages (via R and Python) as someone with 2 PhDs who may have been hired a few years back working for a quant fund coding the algorithms from scratch.
I think this is echoed somewhat in a recent Chat with Traders podcast with Matthew Hoyle when he discussed the fact that strategies have a short shelf life-what is valuable is the ability and energy to look at new and interesting things and put it all together with a sense of business development and desire to explore.
Reference:
Fong, W. M. (2017), Bursting the big data bubble. Significance, 14: 20–23. doi:10.1111/j.1740-9713.2017.01035.x
See also:
Masters in Business with Barry Ritholtz Guest: Andrew Lo of MIT
In Praise of the Citizen Data Scientist
Efficient Capital Markets: A Review of Theory and Empirical Work. Eugene F. Fama
The Journal of Finance. Vol. 25, No. 2, Papers and Proceedings of the Twenty-Eighth Annual Meeting of the American Finance Association New York, N.Y. December, 28-30, 1969 (May, 1970), pp. 383-417
- a classic paper reviewing work related to efficient capital markets theory.
Hou, Kewei and Xue, Chen and Zhang, Lu,
Replicating Anomalies (June 12, 2017). Charles A. Dice Center Working
Paper No. 2017-10; Fisher College of Business Working Paper No.
2017-03-010. Available at SSRN: https://ssrn.com/abstract=2961979 or http://dx.doi.org/10.2139/ssrn.2961979
- the above reviews some of the market anomolies literature, finding many studies fall short in terms of methodology.
Abstract:
The anomalies literature is infested with widespread p-hacking. We replicate this literature by compiling a large data library with 447 anomalies. With microcaps alleviated via NYSE breakpoints and value-weighted returns, 286 anomalies (64%) including 95 out of 102 liquidity variables (93%) are insignificant at the 5% level. Imposing the t-cutoff of three raises the number of insignificance to 380 (85%). Even for the 161 significant anomalies, their magnitudes are often much lower than originally reported. Among the 161, the q-factor model leaves 115 alphas insignificant (150 with t < 3). In all, capital markets are more efficient than previously recognized.
Friday, June 9, 2017
December 2017 Corn Market Technicals and Fundamentals (6/9/17)
This week there was some excitement in the new crop corn market. We saw a price breakout from the sideways pattern that we've been following since the beginning of the year reaching a 6 month high of $4.09 on Thursday (June 8) with heavy trading volume behind it. With a current RSI indicating this is not yet a top on the daily chart (although the 9 day RSI could indicate an overbought signal). The MACD shows the beginnings of a bullish crossover starting just at the start of the month. With the wet spring, significant replantings, and possibilities of some dryer weather ahead (adding stress to later planted corn with shallow roots due to earlier wet weather as well as patchiness in stands related to replants, compacted sidewalls, and nitrogen loss) we could get some fundamental weather related support behind this, although there were no surprises in Friday's WASDE report. The latest crop progress reports indicate a stark contrast in conditions for good to excellent ratings when you compare the top corn producing states like Iowa, Indiana, and Illinois vs states like Kentucky.
Additionally funds have been very short on corn and could be poised to start covering as we go. With just a few days into this trend, the ADX is still indicating weak trend. We'll see where we are sitting with the weekly and monthly charts in the coming weeks, as well as June acreage report and crop conditions as the season progresses.
***This commentary is provided for descriptive and entertainment purposes only and is not intended to be used for specific trading strategies or interpreted to be investment advice. *****
| Crop Conditions | % Good to Excellent |
| IL | 59 |
| IN | 46 |
| IA | 77 |
| MI | 77 |
| KY | 82 |
Additionally funds have been very short on corn and could be poised to start covering as we go. With just a few days into this trend, the ADX is still indicating weak trend. We'll see where we are sitting with the weekly and monthly charts in the coming weeks, as well as June acreage report and crop conditions as the season progresses.
***This commentary is provided for descriptive and entertainment purposes only and is not intended to be used for specific trading strategies or interpreted to be investment advice. *****
Saturday, June 3, 2017
In Praise of Finance, Futures, and Trading
"Finance can be used to achieve some of the greatest challenges that are facing mankind, including things like dealing with cancer, Alzheimer's, energy, all sorts of societal challenges that require large amounts of financing" -Andrew Lo with Barry Ritholtz (Masters in Business podcast).
"No other industry is so fundamentally tied to our human nature. It is creative in the truest sense of the word-by growing plants we create and sustain life. And no other industry ties the global population together so inescapably. All life on earth depends on agriculture, how well we distribute agriculture's products-how well we trade grain-determines how Earth's population gains access to its most fundamental needs." -Elaine Kub, Mastering the Grain Markets
“Reining in speculators seems politically expedient. But we live in complex times. Throwing darts becomes perilous when policy makers begin to advocate (and worse yet, actually believe) that speculators should be removed from ag / food markets. Such a move would dismantle futures markets. Imagine what the world might look like a without market liquidity, price discovery and risk mitigation; not to mention the inability to establish pricing plans, attract new capital investment and stimulate innovation across the food business. The absence of those influences, facilitated by futures markets, would ultimately lead to less food production, availability and security – NOT the other way around. Taking speculators out of the mix would be devastating.” Dr. Nevil Speer, No Speculators? No thanks!, Drovers Cattle Network Agsight, March 2011
"No other industry is so fundamentally tied to our human nature. It is creative in the truest sense of the word-by growing plants we create and sustain life. And no other industry ties the global population together so inescapably. All life on earth depends on agriculture, how well we distribute agriculture's products-how well we trade grain-determines how Earth's population gains access to its most fundamental needs." -Elaine Kub, Mastering the Grain Markets
“Reining in speculators seems politically expedient. But we live in complex times. Throwing darts becomes perilous when policy makers begin to advocate (and worse yet, actually believe) that speculators should be removed from ag / food markets. Such a move would dismantle futures markets. Imagine what the world might look like a without market liquidity, price discovery and risk mitigation; not to mention the inability to establish pricing plans, attract new capital investment and stimulate innovation across the food business. The absence of those influences, facilitated by futures markets, would ultimately lead to less food production, availability and security – NOT the other way around. Taking speculators out of the mix would be devastating.” Dr. Nevil Speer, No Speculators? No thanks!, Drovers Cattle Network Agsight, March 2011