"Quant Trading for a Living – Lessons from a Life in the Trenches" by Andreas...Quantopian
It takes hard work, skill and time to develop robust trading models, but that is just the beginning of the journey. The question then is what you can do with it, and how to go about building a career in quant finance.
If your plan is to move beyond hobby trading and build a career in in the professional quant trading field, the work is not over once you have a great model.
This presentation will discuss how to leverage your trading models into building a successful career in quant trading. We will look at the various options available, and their respective merits and faults. Whether you want to trade your own money for a living, find a job in the industry or build your own business, your model design will have to be adapted to your aim. We will discuss what type of models and results there is a market for, how to go about finding investors for your trading, and how the real economics of the business look.
"Bayesian Deep Learning: Dealing with Uncertainty and Non-Stationarity" by Dr...Quantopian
Deep Learning continues to build out its dominance over other machine learning approaches on several challenging tasks including image, hand-writing, and speech recognition, image synthesis, as well as playing board and computer games exceeding human expert abilities.
This has generated a lot of interest in the quant finance community to try and mirror Deep Learning's success in the domain of algorithmic trading. Unfortunately, algorithmic trading poses a unique set of challenges. Specifically, the risk (i.e. uncertainty) of certain trading decisions as well as the fact that market behavior changes over time (i.e. non-stationarity) is not handled well by deep learning.
In this talk, I will show how we can embed Deep Learning in the Probabilistic Programming framework PyMC3 and elegantly solve these issues. Expressing neural networks as a Bayesian model naturally instills uncertainty in its predictions. This talk is focused on practitioners and will be introductory and hands-on with many code examples.
Contained within this guide are 13 essential rules for profitable investing. Each rule is easy-to-implement and will bring about a measurable increase in your long-term returns. Check it out now!
Stauth common pitfalls_stock_market_modeling_pqtc_fall2018Quantopian
Data Modeling the Stock Market Today - Common Pitfalls to Avoid
The lure of creating models to predict the stock market has drawn talent from fields beyond finance and economics, reaching into disciplines such as physics, computational chemistry, applied mathematics, electrical engineering and perhaps most recently statistics and what we now refer to as data science. The attraction is clear - the stock market (and the economy/internet at large) throws off massive and ever increasing reams of data from garden variety time-series to complex structured data sets like quarterly financials, to unstructured data sets like conference call transcripts, news articles and of course — tweets! While all this data holds promise - it also holds traps and blind alleys that can be deceptively tricky to avoid. In this session we’ll review some of the common (but not easy!) pitfalls to avoid in creating models for predicting stock returns; overfitting & exploding model complexity, non-stationary processes, time-travel illusions, under-estimation of real-world costs, and as many more as we have time to cover.
"Quant Trading for a Living – Lessons from a Life in the Trenches" by Andreas...Quantopian
It takes hard work, skill and time to develop robust trading models, but that is just the beginning of the journey. The question then is what you can do with it, and how to go about building a career in quant finance.
If your plan is to move beyond hobby trading and build a career in in the professional quant trading field, the work is not over once you have a great model.
This presentation will discuss how to leverage your trading models into building a successful career in quant trading. We will look at the various options available, and their respective merits and faults. Whether you want to trade your own money for a living, find a job in the industry or build your own business, your model design will have to be adapted to your aim. We will discuss what type of models and results there is a market for, how to go about finding investors for your trading, and how the real economics of the business look.
"Bayesian Deep Learning: Dealing with Uncertainty and Non-Stationarity" by Dr...Quantopian
Deep Learning continues to build out its dominance over other machine learning approaches on several challenging tasks including image, hand-writing, and speech recognition, image synthesis, as well as playing board and computer games exceeding human expert abilities.
This has generated a lot of interest in the quant finance community to try and mirror Deep Learning's success in the domain of algorithmic trading. Unfortunately, algorithmic trading poses a unique set of challenges. Specifically, the risk (i.e. uncertainty) of certain trading decisions as well as the fact that market behavior changes over time (i.e. non-stationarity) is not handled well by deep learning.
In this talk, I will show how we can embed Deep Learning in the Probabilistic Programming framework PyMC3 and elegantly solve these issues. Expressing neural networks as a Bayesian model naturally instills uncertainty in its predictions. This talk is focused on practitioners and will be introductory and hands-on with many code examples.
Contained within this guide are 13 essential rules for profitable investing. Each rule is easy-to-implement and will bring about a measurable increase in your long-term returns. Check it out now!
Stauth common pitfalls_stock_market_modeling_pqtc_fall2018Quantopian
Data Modeling the Stock Market Today - Common Pitfalls to Avoid
The lure of creating models to predict the stock market has drawn talent from fields beyond finance and economics, reaching into disciplines such as physics, computational chemistry, applied mathematics, electrical engineering and perhaps most recently statistics and what we now refer to as data science. The attraction is clear - the stock market (and the economy/internet at large) throws off massive and ever increasing reams of data from garden variety time-series to complex structured data sets like quarterly financials, to unstructured data sets like conference call transcripts, news articles and of course — tweets! While all this data holds promise - it also holds traps and blind alleys that can be deceptively tricky to avoid. In this session we’ll review some of the common (but not easy!) pitfalls to avoid in creating models for predicting stock returns; overfitting & exploding model complexity, non-stationary processes, time-travel illusions, under-estimation of real-world costs, and as many more as we have time to cover.
Gerstein Fisher’s new video about Multi-Factor® investing takes viewers through a brief history of factor-based investing and explains Gerstein Fisher’s own Multi-Factor® approach in easy-to-understand terms, reflecting our commitment to investor education and transparency of process.
China's Subnational Debts: Problems and Suggestions: Liu Shangxi, Research I...World Bank Publications
Presentation at Ministry of Finance, P.R. China-World Bank Summit on Subnational Debt Management and Restructuring, Nanning, Guangxi Province, P.R. China. October 22, 2015.
The Chinese market is an obvious source of inspiration, talent, and opportunity for Australian businesses. But tackling such a complex market needs considerable context, insight, and cultural understanding.
At ThoughtWorks Live Australia 2016, Angela Ferguson and Hu Kai shared stories and learnings around the level of upfront preparation, commitment, and assessment needed to ensure the best chance of success in the Chinese market.
This presentation of the economic outlook for the coming decade highlights the key findings from CBO’s report The Budget and Economic Outlook: 2016 to 2026, which was released in January.
I express my sincere respect to the authors and my teachers from whom I remain updated in this segment. Due care have been taken so as not to violate the copyright issues.
Smart Beta ia a rather elusive term in modern finance sometimes know.pdfnipuns1983
Smart Beta ia a rather elusive term in modern finance sometimes known as advanced
beta,alternate beta or startegy indices.firstly theorized by Harry Markowitz , the first smart beta
ETF was launched in 2003.A smart Beta ETF is a type of exchange-traded fund that uses
alternative index construction rules instead of the typical cap-weighted index strategy, in a
transparent way. It takes into account factors such as size, value and volatility. It utilizes both
passive and active methods of investing ... passive because it follows an index, but active
because it considers alternative factors. Smart Beta ETFs are ideal for investors hoping to
maximize their income and returns and minimize risk.
\"Smart\" refers to the use of an alternative methodology rather than following an index\'s size-
based (market-cap) allocations.Its Evolution began with its namer\' Professional services firm
Towers Watson .\"Smart beta strategies have seen huge inflows in recent years. According to
ETF provider Invesco Powershares, from 2010 to 2017, Smart Beta ETFs took in more than 21%
of U.S. equity ETF inflows, representing 12% of total ETF industry assets.
Shifting On to the barriers for allocating smart beta in a investor`s portfolio
investors have consistently reported that \"determining the best strategy or combination of
strategies and managing unintended factor biases\" are the biggest concerns relating to
implementation of a smart beta strategy.Finding the best variation is the issue of major concern
for investors.
Stating that excess returns mean extra risks for a smart beta strategy is not really a criticism of
smart beta. Rather, it is a criticism of smart beta systems that claim to produce excess returns
without assuming extra risks
Smart beta is no different, regardless of how it is marketed.
smart beta managers don\'t investigate the underlying economic logic behind performance, which
can lead to problematic allocations.A smart beta construction is likely to be focused on past
performance, because that is how factors are identified. Factors that show the strongest
correlation with active management performance tend to be favored. That is as deep as the
diagnosis goes with many smart beta mutual funds and exchange-traded funds (ETFs).
One of the glorious things about market-cap-weighted indexes is that they\'re comparatively easy
to run. Once you own all the stocks you want in their market weights, you generally don\'t need
to touch the portfolio except to reinvest dividends, handle corporate actions and deal with index
changes.
Smart-beta strategies are holding securities in different weights, adjusting their portfolios to
match their \"smart\" strategies, and often rebalancing regularly. All of that means that ETFs
tracking smart-beta indexes often have a much more difficult time than their vanilla brethren.
Apart from these short comings, investors need to have a deep insight of these matters while
adding advanced beta to their portfolios.
1.. Not all s.
4 active vs passive advisor insert funds flows dfa (advisor present) p. 1-3, ...Weydert Wealth Management
This excellent article contains three key graphics illustrating how average investors flow into and out of investments at the wrong times and contrasts this with the average DFA investor who remains much more consistent and disciplined.
There are multiple niches in the microcap space where GeoInvesting’s track record has proven that consistent alpha can be achieved. Each strategy provides favorable percentage returns, but is limited in size. A combination of well-defined strategies can enhance portfolio returns by offering the benefit of diversifying into uncrowded situations with low market correlation without overexposing to a single stock.
We believe the best way for a company of your size to approach a microcap strategy would be to deploy a target capital amount across a few basket portfolios of around 5 stocks each. These baskets can vary by strategy and time horizon. Around these baskets, we can implement one-off ideas as they emerge based on very high probability special situations
Richard D'Ambola • Questar Capital Corporation (QCC)
- When history rhymes: Identifying realistic estimates of future investment strategy performance by Dave Walton
- Buybacks slowing while CEO confidence remains high
- Outsourcing to increase productivity (Steve Miller, Transamerica Financial Advisors)
Carla Zevnik-Seufzer • The Strategic Financial Alliance
- The problem with pie charts by Greg Gann
- Oil price surge troubling, but still within ranges
- Serving special needs (Russell Luce, Foresters Equity Services, Inc.)
Gerstein Fisher’s new video about Multi-Factor® investing takes viewers through a brief history of factor-based investing and explains Gerstein Fisher’s own Multi-Factor® approach in easy-to-understand terms, reflecting our commitment to investor education and transparency of process.
China's Subnational Debts: Problems and Suggestions: Liu Shangxi, Research I...World Bank Publications
Presentation at Ministry of Finance, P.R. China-World Bank Summit on Subnational Debt Management and Restructuring, Nanning, Guangxi Province, P.R. China. October 22, 2015.
The Chinese market is an obvious source of inspiration, talent, and opportunity for Australian businesses. But tackling such a complex market needs considerable context, insight, and cultural understanding.
At ThoughtWorks Live Australia 2016, Angela Ferguson and Hu Kai shared stories and learnings around the level of upfront preparation, commitment, and assessment needed to ensure the best chance of success in the Chinese market.
This presentation of the economic outlook for the coming decade highlights the key findings from CBO’s report The Budget and Economic Outlook: 2016 to 2026, which was released in January.
I express my sincere respect to the authors and my teachers from whom I remain updated in this segment. Due care have been taken so as not to violate the copyright issues.
Smart Beta ia a rather elusive term in modern finance sometimes know.pdfnipuns1983
Smart Beta ia a rather elusive term in modern finance sometimes known as advanced
beta,alternate beta or startegy indices.firstly theorized by Harry Markowitz , the first smart beta
ETF was launched in 2003.A smart Beta ETF is a type of exchange-traded fund that uses
alternative index construction rules instead of the typical cap-weighted index strategy, in a
transparent way. It takes into account factors such as size, value and volatility. It utilizes both
passive and active methods of investing ... passive because it follows an index, but active
because it considers alternative factors. Smart Beta ETFs are ideal for investors hoping to
maximize their income and returns and minimize risk.
\"Smart\" refers to the use of an alternative methodology rather than following an index\'s size-
based (market-cap) allocations.Its Evolution began with its namer\' Professional services firm
Towers Watson .\"Smart beta strategies have seen huge inflows in recent years. According to
ETF provider Invesco Powershares, from 2010 to 2017, Smart Beta ETFs took in more than 21%
of U.S. equity ETF inflows, representing 12% of total ETF industry assets.
Shifting On to the barriers for allocating smart beta in a investor`s portfolio
investors have consistently reported that \"determining the best strategy or combination of
strategies and managing unintended factor biases\" are the biggest concerns relating to
implementation of a smart beta strategy.Finding the best variation is the issue of major concern
for investors.
Stating that excess returns mean extra risks for a smart beta strategy is not really a criticism of
smart beta. Rather, it is a criticism of smart beta systems that claim to produce excess returns
without assuming extra risks
Smart beta is no different, regardless of how it is marketed.
smart beta managers don\'t investigate the underlying economic logic behind performance, which
can lead to problematic allocations.A smart beta construction is likely to be focused on past
performance, because that is how factors are identified. Factors that show the strongest
correlation with active management performance tend to be favored. That is as deep as the
diagnosis goes with many smart beta mutual funds and exchange-traded funds (ETFs).
One of the glorious things about market-cap-weighted indexes is that they\'re comparatively easy
to run. Once you own all the stocks you want in their market weights, you generally don\'t need
to touch the portfolio except to reinvest dividends, handle corporate actions and deal with index
changes.
Smart-beta strategies are holding securities in different weights, adjusting their portfolios to
match their \"smart\" strategies, and often rebalancing regularly. All of that means that ETFs
tracking smart-beta indexes often have a much more difficult time than their vanilla brethren.
Apart from these short comings, investors need to have a deep insight of these matters while
adding advanced beta to their portfolios.
1.. Not all s.
4 active vs passive advisor insert funds flows dfa (advisor present) p. 1-3, ...Weydert Wealth Management
This excellent article contains three key graphics illustrating how average investors flow into and out of investments at the wrong times and contrasts this with the average DFA investor who remains much more consistent and disciplined.
There are multiple niches in the microcap space where GeoInvesting’s track record has proven that consistent alpha can be achieved. Each strategy provides favorable percentage returns, but is limited in size. A combination of well-defined strategies can enhance portfolio returns by offering the benefit of diversifying into uncrowded situations with low market correlation without overexposing to a single stock.
We believe the best way for a company of your size to approach a microcap strategy would be to deploy a target capital amount across a few basket portfolios of around 5 stocks each. These baskets can vary by strategy and time horizon. Around these baskets, we can implement one-off ideas as they emerge based on very high probability special situations
Richard D'Ambola • Questar Capital Corporation (QCC)
- When history rhymes: Identifying realistic estimates of future investment strategy performance by Dave Walton
- Buybacks slowing while CEO confidence remains high
- Outsourcing to increase productivity (Steve Miller, Transamerica Financial Advisors)
Carla Zevnik-Seufzer • The Strategic Financial Alliance
- The problem with pie charts by Greg Gann
- Oil price surge troubling, but still within ranges
- Serving special needs (Russell Luce, Foresters Equity Services, Inc.)
Effective Corporate Tax Rates - A Global ComparisonEric J. Weigel
A look at current and historical median tax rates for a sample of close to 14,000 global companies. Tax rates are calculated from financial statements rather than tax forms. Significant tax arbitrage opportunities exist leading to a fertile environment for tax inversions. Legislation to close the "tax gap" is likely to lead to a competitive response by low-tax countries
1. 1
Is Loving My Smart Beta All That Wrong?
Investors are madly in love with “smart” beta strategies and asset managers offering such products have been
shown the love. It was almost inevitable that as active management has gotten a bad rap investors would
flock to the next great thing. Just like in the comedy “If Loving You is Wrong” by Tyler Perry there is the
potential for significant turbulence ahead when falling head over heels in love with these “smart” beta
strategies.
But before we become all judgmental, what is “smart” beta? I think that there are
as many versions and interpretations as colors in a rainbow. If there is one thing you can always count on it
is the creativity of marketers in creating a buzz about a concept old in origin but in need of a fresh coat of
paint. What I am referring to is plain old “factor” investing as practiced by quantitative portfolio managers
over the last half a century. 1
Now one thing that quantitative investors are not known for is an abundance of marketing pizzazz and loud
proclamations of having found the next great thing in investing. Rather the process of quantitative investing
generally tends to slowly digest lots of academic research and then quietly “borrow” ideas deemed worthy of
implementation and palatable for broad investor consumption.
Factor investing has been a staple of investment approaches for decades starting with the focus on
inexpensive stocks (referred to as the valuation factor) as well as a tilt toward lower capitalization equities (the
size factor). Other typical factors almost always part of quantitative approaches are momentum and quality.
Some of these concepts are a bit fuzzy to define, but, in general, the investment world seems to have settled
on an extension of the three factor model of Nobel Prize winner Eugene Fama and Ken French2 with the
addition of a momentum factor suggested by Mark Carhart3 – the market (plain old beta), value, size and
momentum. Index provider MSCI (www.msci.com) also adds low volatility, high quality and high yield as
legitimate equity factors.
“Smart” beta strategies play on these academic findings and there is nothing wrong in principle with falling in
love with these strategies. The idea behind emphasizing these equity factors is simple. These are all stock
characteristics that when found in above average amounts in a portfolio are expected to translate into higher
1 In this note the focus will be on the more commonly used versions of “smart” beta based on specific single factors of
return. In a subsequent note we will explore another form of “smart” investing related directly to the weighting scheme
of the portfolio.
2
Fama, E. F.; French, K. R. (1992). "The Cross-Section of Expected Stock Returns". The Journal of Finance 47 (2):
427. doi:10.1111/j.1540-6261.1992.tb04398.x. JSTOR 2329112
3
Carhart, M. M. (1997). "On Persistence in Mutual Fund Performance". The Journal of Finance 52: 57–
82. doi:10.1111/j.1540-6261.1997.tb03808.x. JSTOR 2329556
2. 2
returns. Buying inexpensive stocks will, for example, have a higher probability of superior returns down the
road.
While academics will heatedly debate until dawn which of these factors are really legit and which are bogus or
transitory, savvy investors will universally agree on one thing – that is, that none of these factors will result in
superior performance at all times and in all market environments. Indeed, the search for the Holy Grail of
investing continues unabated to this day!
In fact, this is why most investment approaches emphasize various combinations of several of these factors.
As most people know, relying on one factor only, as well researched as it may be, frequently results in painful
periods of under-performance. Most investors simply can’t stomach the drawdowns. Think back, for
example, to the fate of value and small cap stocks in the late 1990’s as the TMT bubble was at its peak. All the
above mentioned factors show up well in back-tests performed over long-periods of time, but none of the
single factor strategies is immune to drawdowns sometimes extending over long periods of time.
So, that leads me to the next question which is, how do I know whether
falling in love with “smart” beta will be a good decision? The answer, as all know
from experience, is that there is no free lunch either in love or investing. Every single strategy suffers at times
from disappointing returns even though over long holding periods the performance may prove to be market
beating.
The conceptual rationale for owning these “smart” beta strategies is rooted in solid academic research, but
nobody guaranteed an uninterrupted path to wealth, did they? Take a look at the Fama-French annual factor
returns (the market, capitalization and value) for US stocks.
Figure 1
Source: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
-50
-40
-30
-20
-10
0
10
20
30
40
50
Fama/French Factor Returns
Market Size Value
3. 3
Clearly, none of these three factors works in every single year. There are several periods of significantly
negative returns. Being plain old “dumb” would have worked better in those years, but over longer holding
periods being “smart” would still have worked better.
If nothing works all the time, what realistic options do we have? Well, the
investor considering investing in “smart” beta strategies either has to have a crystal ball and forecast when the
specific factor underlying the strategy will out-perform, or be willing to buy into the long-term prospects of
superior returns and accept interim results that may be disappointing.
Timing the performance of the typical set of factors used in “smart” beta strategies is not easy even for
professionals. While factors tend to exhibit performance tendencies especially in relation to the business
cycle and the balance of fear and greed among capital market participants the ability to time when these
“smart” beta strategies will outperform the broad equity market will disappoint most investors. Being “right”
60 percent of the time is deemed outstanding by professionals, but I doubt that most investors would be
amused after a long sequence of strike outs.
A more realistic option for investors is to extend their time horizons when buying into the “smart” beta
concept, but given human nature this requires significant mental fortitude. Unfortunately, while we would all
like to believe that we are in it for the long-term, we are by nature inquisitive and we take peeks at our
investment accounts from time to time. We may even check in daily just to make sure that everything is on
course! Some may even see a resemblance to the process of falling in love!
If you are lacking a crystal ball and you know that periods of under-performance make you nervous and
question your decision-making the solution may be a variant of “smart” beta. Instead of focusing on one or
two “smart” factors why not play it a bit safer and buy into a “smart” portfolio instead?
What do I mean by a “smart” portfolio? Simply put such a portfolio emphasizes several of
these “smart” factors all at once. In a sense it is a package of “smart” strategies bundled in one. For
example, the portfolio could emphasize inexpensive, high growth, low capitalization stocks with superior
price momentum. I know this sounds like a mouthful, but to come back full circle these “smart” portfolios
are exactly what quantitative professionals have been offering over the last few decades. The difference is that
in recent times marketing people have applied a fresh coat of paint and some pizzazz to a tried and tested
approach.
The key idea is that by having a portfolio of “smart” factor exposures with a high probability of long-term
success we can trade off “smart” betas that are working as expected against those with current disappointing
performance. As an example value strategies are frequently negatively correlated with the performance of
momentum strategies. Both value and momentum approaches exhibit superior long-term performance, but
that performance occurs at different points in time. Mixing the two approaches has historically resulted in a
smoother path to out-performance.
Not that a portfolio of “smart” factor exposures is a panacea for all ills or that under-performance may not
occur (for example if all “smart” factors stop working at once), but if you are looking for exposure to “smart”
beta why not do it in a more certain way and not rely on timing factors or having to hold forever?
4. 4
Where does all of this leave us? Too many of the “smart” beta strategies sold to investors are
positioned as something akin to the Holy Grail allowing investors to go on cruise control. But just like it is
not advisable to drive in cruise control through road construction areas as we are bound to hit potholes,
investors facing ever-changing capital market conditions are advised to refrain from thinking that there is a
magical “smart” factor just waiting to be plucked.
“Smart” factors do exist as academic research has shown, but we can never be sure which one of these
factors will work at any given point in time and we are therefore better off diversifying our exposure to
several of these time-tested concepts. Buying the package of “smart” beta exposures may lead toward longer
lasting love.
Investors are also reminded that the overwhelming risk permeating all of the “smart” beta strategies whether
packaged as a single factor or a portfolio of factor exposures is what happens to the overall equity market.
All equity strategies whether smart or dumb are highly correlated with each other. An equity is an equity is an
equity is an appropriate way to think of the pre-eminent role played by the overall market itself. In a bull
market all boats will rise with the tide and in a bear market “smart” beta investors will still feel significant
pain.
Nothing substitutes for properly aligning your portfolio asset allocation
with your goals and ability to withstand periods of stress. At Global Focus Capital
our bottom-up stock selection and top-down regional/sector methodologies are all rooted in the academic
concepts imbedded in “smart” beta but we prefer to love these concepts in moderation and as part of an
overall package.
This note has been focused on the conceptual nature of “smart” beta investing and its roots in academic
research. A key message has been a preference for investment approaches mixing several of these “smart”
factors together rather than putting all your eggs into one “smart” beta basket. A subsequent note will
address the specifics of popular “smart” beta strategies highlighting some of the “unintended” exposures that
these products typically possess and the welcome emergence of strategies incorporating a package of “smart”
beta strategies.
Eric J. Weigel
Managing Partner
Feel free to contact us at Global Focus Capital LLC (info@gf-cap.com) or visit our website at www.gf-
cap.com to find out more about our stock selection and asset allocation strategies and their fit with your
overall investment needs.
For those investors wishing to implement their own strategies we offer subscription-based research
publications as well as in-demand consulting services.