Python Desktop Application Mock-up on macOS

Before you start, please refer to the following page to set up your Python environment.
http://thefjg.blogspot.com/2018/02/python-machine-learning-deep-learning.html

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Start Terminal on macOS.

Install a light Web framework Flask:
pip3 install Flask

Install the latest version of node.js:
brew uninstall node
brew install node
brew link --overwrite --dry-run node


Check the installation of node.js.
which node
which npm


Install Electron for desktop applications backed by Web technologies like HTML, CSS, JavaScript.
npm install -g electron-prebuilt

Check the installation of Electron.
which electron

Creat a ElectronApp holder.
Move to the holder on Terminal, by using cd commands.
npm init -y

Now package.json file is created.

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Start Atom.

File > Add Project Holder > (Select your ElectronApp holder.)

Update the package.json file as follows:

{
  "name": "ElectronApp",
  "version": "0.0.0",
  "main": "main.js",
  "scripts": {
    "test": "echo \"Error: no test specified\" && exit 1"
  },
  "keywords": [],
  "author": "",
  "license": "ISC",
  "description": ""
}


Create (Atom > File > New File) and save the main.js file (Atom > File > Save As...) as follows:

'use strict';

// Modules for application control
var electron = require('electron');
var app = electron.app;
var BrowserWindow = electron.BrowserWindow;

// to avoid GC (Gabage Collection) of mainWindow, make it global
let mainWindow;

// Terminate if all the windows are closed
app.on('window-all-closed', function() {
  if (process.platform != 'darwin') {
    app.quit();
  }
});

// Execute after the Electron's initialization
app.on('ready', function() {
  // Show main screen
  mainWindow = new BrowserWindow({
    width: 800,
    height: 600
  });
  mainWindow.loadURL('file://' + __dirname + '/index.html');

  //Terminate the application when closing the window
  mainWindow.on('closed', function() {
    mainWindow = null;
  });
});

Create (Atom > File > New File) and save the index.html file (Atom > File > Save As... > index.html) for the desktop application's interface as follows:

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Back to your Terminal on macOS.

Move up the directory and select/run the ElectronApp holder.
cd ..
electron ElectronApp/

Python Machine Learning (Deep Learning) Runtime Environment Set-up on macOS

[1] Intended Readers

This article is for:

• Python beginners
• Python single user (for yourself, but not for a team/project with virtual environments)

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[2] System Environment

macOS High Sierra
Version 10.13.3

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[3] Installing Package Manager: Homebrew

• A package manager enable you to install a package software automatically via the internet.
• You don't need to search the web, download files, and then install them on your computer.

Access the website:
https://brew.sh

Copy a script:
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Start Terminal on your macOS.
Paste the script above and press return.
Enter password of your macOS and press return.

After a few minutes, check whether Homebrew is correctly installed. On Terminal, type and enter:
which brew
It returns:
/usr/local/bin/brew

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[4] Installing Python

which python3
If it returns like the following, then you don't need to re-install Python:
/Library/Frameworks/Python.framework/Versions/3.6/bin/python3

If it does not return anything, then type the following Homebrew command on Terminal to (re-)install Python.
brew install python3

Type on Terminal:
which python3

It reruns:
/Library/Frameworks/Python.framework/Versions/3.6/bin/python3

When you install Python via Homebrew, then you also install pip3,
which pip3

It returns:
/Library/Frameworks/Python.framework/Versions/3.6/bin/pip3

pip3 is a tool to manage libraries for Python.


Homebrew manages software such as python3, pip3, on macOS. pip3 manages libraries for python3.

On Terminal, type and return:
python3 --version
Python 3.6.4

On Terminal, install useful libraries for Python:
pip3 install numpy  # linear algebra
pip3 install scipy  # mathematical formula manipulation
pip3 install matplotlib   # drawing, plotting
pip3 install pandas  # data(base) manipulation

pip3 install scikit-learn # machine learning package

pip3 install tensorflow # Google Tensorflow (deep learning libraries )

python
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))
b'Hello, TensorFlow!'
# TensorFlow successfully installed.
>>> exit()

pip3 install chainer # Chainer (deep learning libraries made in Japan)

pip3 install jupyter # Easy Python running environment

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[5] Installing A Text Editor w/ Runtime Environment: Atom

Access:
https://atom.io

Download an installation file.
Copy the file and paste it to the Applications folder on your macOS.

Open Atom.

Install a Package. Or
Menu > Atom > Preferences > Install


Enter terminal on the "Search packages" textbook and press Packages.
Find "platformio-ide-terminal" and press Install. (Note: You need to install Xcode before this.)

Now you can start Terminal within the Atom text editor; you are able to write codes and run them within Atom. It boosts your productivity.
Press + sign in the bottom left corner of the screen.


Similarly, enter script on the "Search packages" textbook and press Packages.
Find "script" and press Install.

Now you can run your code within the Atom text editor; you are able to write codes and run them within Atom. It boosts your productivity.
Press + sign in the bottom left corner of the screen.


To create a Python executable project folder,
File > Add Project Folder...
Select your folder that contains py script files.


To create a new Python (py) file,
File > New File
File > Save As...
test.py

Write a very simple code:
print('Hello, Python!')

To run the script above,
Packages > Script > Run Script

You can see the result:
Hello, Python!
[Finished in 0.084s]

[AQR] Momentum Crashes

• Despite their strong positive average returns across numerous asset classes, momentum strategies can experience infrequent and persistent strings of negative returns. These momentum crashes are partly forecastable. They occur in panic states, following market declines and when market volatility is high, and are contemporaneous with market rebounds.The low ex ante expected returns in panic states are consistent with a conditionally high premium attached to the option like payoffs of past losers. An implementable dynamic momentum strategy based on forecasts of momentum’s mean and variance approximately doubles the alpha and Sharpe ratio of a static momentum strategy and is not explained by other factors. These results are robust across multiple time periods, international equity markets, and other asset classes.
• A momentum strategy is a bet on past returns predicting the cross section of future returns, typically implemented by buying past winners and selling past losers.
• Momentum is pervasive: the academic literature shows the efficacy of momentum strategies across multiple time periods, in many markets, and in numerous asset classes.
• In a bear market, the loser portfolio implies that momentum strategies behave like written (=short) call options on the market; that is, when the market falls, they gain a little, but when the market rises, they lose much.
• In normal environments, consistent price momentum is both statistically and economically strong and manifest itself across numerous equity markets and a wide range of diverse asset classes.
• However, in panic states, following multi year market drawdowns and in periods of high market volatility, the prices of past losers embody a high premium. When poor marketconditions ameliorate and the market starts to rebound, the losers experience strong gains, resulting in a momentum crash as momentum strategies short these assets.
• We find that, in bear market states, and in particular when market volatility is high, the down-market betas of the past losers are low, but the up-market betas are very large. (like short call options) Consequently, the expected returns of the past losers are very high, and the momentum effect is reversed during these times.
• This feature does not apply equally to winners during good times, however, resulting in any asymmetry in the winner and loser exposure to market returns during extreme times. 
• These crash periods are predictable. We use bear market indicators and ex ante volatility estimates to forecast the conditional mean and variance of momentum signals.
• What can explain these findings?
• Equity: a Merton '1974) story for the option-like payoffs of equities could make sense, but the existence of the same phenomena and option-like features for futures, bonds, currencies, and commodities makes this story more challenging.
• Alternatively, these effects can be loosely consistent with several behavioralfindings, in which in extreme situations individuals tend to be fearful and appear to focus on losses, largely ignoring probabilities.

---

Source
https://www.aqr.com/library/journal-articles/momentum-crashes

[AQR] Market Timing: Sin a Little

• Is market timing a useful source of added value or a sin to be avoided?
• We propose an interpretation that offers a practical enhancement to value timing strategies: adding a dose of momentum.
• By that, one can improve the risk-adjusted performance of contrarian market timing strategies.
• Valuations can drift higher or lower for years or decades, making it difficult to categorize the current market confidently as "cheap" or "expensive" without hindsight calibration, and therefore difficult to profit from such categorizations.
• Combining value and momentum has been shown to be very effective in stock selection and cross-sectional strategies, and the combined signal intuitively represents "value with a catalyst," or patient contrarian investing, with a supportive momentum signal potentially reducing the risk of value traps or premium signals.
• If market timing is a sin, perhaps you could--or even should--sin a little.
• Value investors looking for a less explicit way to reduce negative exposure to short-term momentum could simply rebalance their tilts less frequently.
• A better variant of this approach is to "slow down" the value signal using a moving average, which is equivalent to making a sequence of overlapping value bets that are each "locked in" for a long period
• In this paper, we have examined how drifting valuations contribute to the initially puzzling gap between encouraging in-sample evidence and disappointing out-of-sample performance of value timing strategies. We have shown that the challenge of such long-term drift has been compounded by persistent negative exposure to a shorter-term time series momentum factor, which offers a practical and intuitive avenue for improving value-based tactical timing signals.
• Our results suggest that even a small does of momentum, whether applied explicitly or just by rebalancing less frequently or smoothing valuation signals, may improve market timing decisions.
• In summary, attractive predictive correlations do not always translate to successful timing out of sample. This has occurred both because of long-drifts in average weight (as stocks become more or less cheap for very long periods) and because a pure value strategy effectively "shorts" the successful momentum strategy. We have no cure for long drifts in average weights other than our view it's unlikely to be as bad for valuation timing in the next half century as in the last. Adding a momentum component may help investors to become more patient and successful long-term value investors.
• Finally, we note that even a completely blend of value and momentum timing signals produces only modest long-term outperformance. Thus: sin (only) a little.
• Timing two assets is a broader and potentially more promising strategy, as well as providing independent supporting evidence.

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Source

https://www.aqr.com/library/journal-articles/market-timing-sin-a-little

[AQR] A Century of Evidence on Trend-Following Investing

• Momentum (trend-following) strategy
• Time-series (directional, asset-by-asset)
• Going long a market with recent positive returns
• Going short a market with recent negative returns
• Cross-sectional (non-directional / market-neutral, assets as a whole)
• Going long markets with recent relatively better returns
• Going short markets with recent relatively worse returns
• Time-series momentum strategy all the way back to 1880
• Consistently profitable throughout the past 137 years.
• How the strategy performs across various macroeconomic environments:
• recession versus booms
• war versus peacetime
• high- versus low-interest-rate regimes
• high- versus low-volatility periods
• high- versus low-inflation periods
• high- versus low-correlation periods
• The characteristics that appears to have affected the performance the most is correlation -- the strategy has performed the best during low-correlation environments.
• Constructing the time-series momentum strategy
• Equal-weighted combination of 1, 3, and 12-month time-series momentum strategies for the 67 markets (four asset classes) from January 1800 to December 2016.
• Rebalanced each month as follows.
• Each strategy always holds either a long or short position in every market.
• Each position is sized to target the same amount of volatility, both to provide diversification and to limit the portfolio risk from any one market.
• The position across the three strategies are aggregated each month and scaled such that the combined portfolio has an annualized ex ante volatility target of 10% annualized.
• Transaction costs (not for costs of "rolling" futures contracts) and fees (2% management, 20% performance subject to HWM) subtracted
• Long-term out-of-sample evidence suggests that it is unlikely that the existence of price trends in markets is a product of statistical randomness or data mining.
• Price trends exists in part because of long-standing behavioral biases exhibited by investors, such as anchoring and herding, as well as the trading activity of non-profit-seeking participants, such as central banks and corporate hedging programs.
• The time-series momentum strategy has performed the best during low-correlationenvironments. Said differently, when correlations are high, there are fewer truly different trends to bet on.
• Conclusion: Trend-following investing has performed well in each decade for more than a century, as far back as we can get reliable return data for several markets. Our analysis provides significant out-of-sample evidence across markets and asset classes beyond the substantial evidence already in the literature. Further, we find that a trend-following strategy performed relatively similarly across a variety of economic environments and provided significant diversification benefits to a traditional allocation. This consistent long-term evidence suggests that trends are pervasive features of global markets.

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Source

https://www.aqr.com/library/journal-articles/a-century-of-evidence-on-trend-following-investing

[AQR] Carry

Summary (w/ my own comments)

• An asset’s “carry” - expected return assuming that market conditions, including its price, stays the same
• A security’s expected return = expected “carry” + expected price return, where carry can be measured in advance (i.g., ex-ante) without an asset pricing model, namely model-free.
• Carry predicts returns both in the cross section and time series for a variety of different asset classes that include global equities, global bonds, currencies, commodities, U.S. Treasuries, credit and equity index options.
• This predictability underlies the strong returns to “carry trades” that go long high-carry and short low-carry securities, a strategy applied almost exclusively to currencies but shown here to be a robust feature of many assets.
• We decompose carry returns into static and dynamic components and analyze the economic exposures. Despite unconditionally low correlations across asset classes, we find times when carry strategies across all asset classes do poorly, and show that these episodes coincide with global recessions.
• Carry strategies are commonly exposed to global recession, liquidity, and volatility risks though none fully explains carry's premium.
• A security’s realized return = expected “carry” + expected price return + unexpected price shock = (expected “carry” & price return) + (unexpected price shock)
• Carry is a model-free characteristic directly observable ex ante from futures, synthetic futures, or forward prices makes it special.
• Expected price return must be estimated using an asset pricing model.
• Seemingly unrelated predictors of returns across different assets can be bonded together through the concept of carry.
• For instance, the carry for bond is closely related to the slope of the yield curve studied in the bond literature, plus "roll down" component that captures the price change that occurs as the bond moves along the yield curve as time passes.
• The commodity carry is akin to the basis or convenience yield.
• Equity carry is a forward-looking measure of dividend yields.
• While carry predicts future returns in every asset class with a positive coefficient, the magnitude of the predictive coefficient differs across asset classes, indicating whether carry is positively or negatively related to future price appreciation.
• In global equities, global bonds, and credit markets, the predictive coefficient is greater than one, implying that carry predicts positive future price changes that add to returns, over and above the carry itself.
• In commodity and option markets, the estimated predictive coefficient is less than one, implying that the market takes back part of the carry (although not all, as implied by Uncovered Interest Parity and the Expectation Hypothesis).
• Hence, there are commnly shared features across different carry strategies and also interesting differences.
• Carry timing strategies buy a security when the carry is positive or above its historical mean.
• Theory suggests that expected returns can vary due to macroeconomic risk, limited arbitrage, market liquidity risk, funding liquidity risk, volatility risk, and downside risk exposure. Further, we examine whether carry can be explained by other predictors of returns across global asset classes such as value and momentum.
• The returns to carry strategies cannot be explained other known global return factors such as value, momentum, and time series momentum within each asset class as well as across all asset classes. The relation between carry and these factors varies across asset classes, where carry is positively related to value and momentum in some asset classes and negatively in others. However. none of the carry exposures to value, momentum, or time series momentum is large in any asset class, and carry consistently produces positive alpha with respect to these factors. Hence, carry represents a different return predictor within and across asset classes, adding to the list of factors that drive returns across many markets.
• Crash risk cannot explain the ubiquitous returns to carry strategies as suggested by the literature on currecy carry trades (Brunnermeier et al., 2008). While it is well documented that the currency carry trade has negative skewness, this cannot be said for all carry strategies. In fact, several of the carry strategies have positive skewness and the across-all-asset-class global carry factor has negligible skewness. All carry strategies have excess kurtosis, however, indicating fat-tailed returns with large occasional profits and losses. Crash risk theories (for currencies) are unlikely to explain the general carry premium.
• Carry strategies generally tend to incur losses during times of worsened liquidy and heightened volatility. (US Treasuries of different maturities has the opposite loadings and thus, acts as a hedge against the other carry strategies during these times.) Carry returns tend to be lower during global recessions, which appears to hold uniformly across asset classes.
• Part of return premium earned on average for going long carry could be compensation for exposure that generates large losses during extreme times of global recessions. Whether these extreme times are related to macroeconomic risks and heightened risk aversion or are times of limited capital and arbitrage and funding squeezes remains an open question.
• Carry factor is a set of compensations for macro/economic (global business cycle), margin requirements and funding costs (liquidity), (downside) volatility, and limited arbitrage risks.
• Carry in various asset classes
• Carry across asset classes
• high Sharpe ratio, exposure to recessions, liquidity risk, and volatility risk
• Currency
• Yield spread. The foreign interest rate in excess of the local risk-free rate because the forward contract is a zero-cost instrument whose return is an excess return. The historical positive return to currency carry trade is a well-known violation of the uncovered interest rate parity (UIP). The UIP is based on the simple assumption that all currencies should have the same expected return, but many economic settings would imply differences in expected currency returns could arise from differences in consumption risk, crash risk, liquidity risk, and country size, where a country with more exposure to consumption or liqudity risk could have both a high interest rate and a cheaper currency.
• Only curerncy carry - negative skewness
• Global equity
• The equity carry is simply the expected dividend yield minus the local risk-free rate, multiplied by a scaling factor, which is close to one.
• Commodity
• Basis trade. The commodity carry is the expected convenience yield of the commodity in excess of the risk-free rate (adjusted for a scaling factor that is close to one).
• Global bond
• Bond carry = (yield spread to the risk-free rate) + (roll down) = (the bond's yield spread to the risk-free rate, which is also called the slope of the term structure) + (the "roll down," which captures the price increase due to the fact the bond rolls down the yield curve)
• Credit
• The definition of carry is the credit spread (the yield over the risk-free rate) plus the roll down on the credit curve.
• Option
• Shorting volatility. The size of the carry is driven by the time decay, which often leads to a negative carry, and the "roll down" on the implied volatility curve.
• A carry trade is a trading strategy that goes long high-carry securities and shorts low-carry securities. Various ways exist of choosing the exact carry-trade portfolio weights:
• A top minus bottom x% approach. Rank assets by their carry and go long the top x% of securities and short the bottom x%, with equal weights applied to all securities within the two groups, and ignore (e.g., place zero weights on) the securities in between these two extremes.
• A rank-based weighting scheme. Signal-weighted scheme that can place considerable weight on the extremes (i.e., highest and lowest rankings). Takes a position in all securities weighted by their carry ranking. This weighting scheme accounts for differences across carry signals even within the top and bottom x% and does not ignore the securities in the middle. Yet, by using ranks instead of weights that are linear in the signals, avoid the impact of outliners in the signals.
• Skewness in carry
• Negative: (strong) currency, options; (some) commodity, fixed-income
• Positive: equities, US Treasuries, and credit
• Excess Kurtosis (fat-tailed positive and negative returns) in carry
• All asset classes
• Carry is a strong predictor of expected returns, with consistently positive and statistically significant coefficients on carry, save for the commodity strategy, which can be tainted by strong seasonal effects in carry for commodities, and for call options.
• c in (23)
• c>1 (when carry is high, prices tend to appreciate more than usual)
• equities, global bond levels and slope, and credit
• c~1 (high interest rate currencies neither depreciate nor appreciate on average; hence, the currency investor earns the interest rate differential, on average)
• currencies
• c<1 (when carry is high, prices tend to appreciate more than usual as yields tend to fall)
• US Treasuries, commodities, and options.
• When a commodity has a high spot price relative to its futures price, implying a high carry, the spot price tends to depreciate on average, thus lowering the realized return on average below the carry.
• In every asset class (global equities, global fixed income, US Treasuries, commodities, currencies, credits, call options, and put options), a carry strategy provides abnormal returns above and beyond simple passive exposures to that asset class. Put differently, carry trades offer excess returns over the "local" market return in each asset class. Further, the betas are often not significantly different from zero. Hence, carry strategies provide sizeable return premia without much market exposure to the asset class itself.
• Carry vs Value and Momentum
• Equities
• Carry: positive exposure to Value
• Carry: no exposure to (cross-sectional and time series) Momentum
• Fixed Income
• Carry: positive exposure to (cross-sectional and time series) Momentum
• Commodities
• Carry: negtive exposure to Value
• Carry: positive exposure to cross-sectional Momentum
• Carry: little (almost no) exposure to time series Momentum
• Currencies
• Currency carry strategies exhibit no reliable loading on value, cross-sectional momentum, or time series momentum. Consequently, the alpha of the currency carry portfolio remains large and significant.
• Credit
• Similarly, for credit, no reliable loadings on these other factors are present and, hence, a significant carry alpha remains.
• Options
• For call options, the loadings of the carry strategies on value, momentum, and TSMOM (time series momentum) are all negative, making the alphas even larger.
• For puts, there are no reliable loadings on these other factors.
• Value, cross-sectional momentum, and time series momentum, do not capture the returns to carry.
• Trading costs: Our results cannot be explained by, and are not subsumed by, trading costs.
• Risks
• Crash and downside (business cycle) risk exposure
• some component of global carry returns can be explained by downside risk
• currencies, commodities, call and put options 
• Global liquidity and volatility risk
• Liquidity and volatility risk explain part of the carry premia across asset classes.
• On the contrary to other asset classes, US Treasuries carry strategy provides a hedge against liquidity and volatility risk, suggesting that liquidity and volatility risk are an incomplete explanation for the cross section of carry strategy returns (or, alternatively, this could be due to trandom chance or noise, which investors might not have expected ex ante)
• An aggressive interpretation concludes that carry is unexplained by downside, liquidity, or volatility risks and presents a substantiall asset pricing puzzle that rejects many theories, posibly offering a wildly profitable investment opportunity.
• A cautious interpretation can conclude that carry strategies almost uniformly load significantly on these downside, liquidity, or volatility risks that partially explains their returns and that, perhaps if better measures of these risks were available, carry's exposure to them, and if risk premia estimats were more precise, most of hte returns to carry through risk could be explained.

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Sources

https://www.aqr.com/library/journal-articles/carry
https://eorder.sheridan.com/3_0/app/orders/7342/article.php

[AQR] Asset Allocation in a Low Yield Environment

Summary (w/ my own comments)

• For asset allocation decisions, what matters is expected return in excess of the investor’s risk-free rate, i.e., excess return, not expected total return.
• Mechanically and empirically, positive long term excess returns in bond markets are not generated by high (or low) yield levels but rather the average upward slope of yield curves.
• Some measures of expected excess returns are low relative to history for bonds, as well as for equities. But tactical timing has an unimpressive track record, especially when based solely on valuation, and humility is therefore warranted in sizing tactical tilts. Even in a low yield environment, there are plausible scenarios where yields could go much lower.
• While bonds should not be considered risk-reducing hedges, evidence does suggest they can remain useful diversifiers in many market environments. Investors should be cautious about forgoing potential diversification benefits, both within bond portfolios and across asset classes.
• Unexplored Territory for Bond Yields - Are the near-zero or negative yields we observe just a short-term aberration? Do they imply that owning bonds, or at least some bonds, is pointless or a guaranteed loss? Can yields only go up from here or is it possible for yields to go even lower? We examine the implications of this peculiar situation for asset allocators.
• Do Low Yields = Low Expected Returns for Bonds? - Total Return = Risk-Free Rate + Excess Return. Excess return is the return for taking the risk associated with investing, and also potentially the return on investment insight or acumen. Since excess return is the only part of the equation which differs among assets, it is also the key consideration when allocating among assets. All else equal, if either the risk-free rate or excess returns are particularly low, then it’s likely that the total return on the asset will be low as well.
• Investors can only earn the risk-free rate of their home currency. When investing in an asset denominated in a foreign currency, the investor either hedges the currency risk, thereby transparently earning interest at a rate close to their home currency risk-free rate, or the investor doesn’t hedge and any increase (or decrease) in expected return is accompanied by currency risk (and thus not risk-free); either way, the investor’s risk-free return is the same — it’s the risk-free rate of their home currency.
• Lower yields result in lower local total returns; markets with lower average yield levels have not delivered lower excess returns.
• Asset allocation decisions affect only excess returns. Recent low yields don’t mechanically imply a low Sharpe ratio (and hence reduced allocation) for fixed income. But, if yield levels aren’t the source of excess returns for bonds, what is?
• The Term Premium as the Source of Excess Return - Bond excess returns = term premium + capital gains/losses from unexpected changes in yields. The term premium is the excess return bond investors expect to earn for taking duration risk ‒ that is for holding a long-term asset whose price can rise and fall with yield levels, rather than just buying a near-riskless asset like a 3-month Treasury bill.
• Over the long term, we expect changes in short rates to average out to zero. To be precise, we are assuming that market participants’ expected changes in short rates averages out to zero. In so much as investors overestimated future rate increases on average, both the slope of the curve and excess returns would increase, but due to beneficial unexpected yield changes rather than a larger risk premium. In any case, the average shape of the curve (rather than the yield level) would be the explanatory factor for bond excess returns. So our estimate of the long-term average term premium is just the long-term average slope of the yield curve.
• Bonds’ positive long-term excess returns (their risk premium) originate from the average upward slope of yield curves, not the level of yields.
• Both our economic intuition and empirical studies imply that a structurally flat or inverted yield curve over the long term would reduce expected excess returns.
• While the average slope of the yield curve explains average excess returns, year-on-year volatility is driven almost entirely by changes in the level of interest rates. Changes in yields have contributed almost nothing to average excess returns (as we would expect since these yield changes have averaged out to about zero), but they have driven almost all the volatility.
• Real bond yield levels that are high or low compared to their own history have often preceded the opposite return outcome, and an inverted yield curve (the most bearish carry signal) has often been followed by strong returns.
• When Yields Are Low, Can Bonds Still Be Diversifying in a Portfolio? - Yields could conceivably move up or down even from low levels (e.g., negative yields). It follows that bonds can still be useful diversifiers.
• We expect the correlation between bonds and other asset classes to average about zero — which is plenty diversifying (and consistent with long-term historical averages — substantial negative correlations are not the norm)
• Conclusion
• Low yields don’t mechanically imply a low risk premium or low excess returns.
• Risk premium for bonds, the term premium, has been related to yield curve slope rather than to yield level.
• Yields can still move in either direction, and could potentially go negative again in certain environments.
• Bonds have been diversifying to stocks and commodities, even in rising rate environments.
• No tactical signal is powerful enough to warrant wholesale changes to a well-balanced strategic asset allocation.
• Asset allocation affects only excess returns, about which the low yield environment says little.

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Sources

https://www.aqr.com/library/journal-articles/asset-allocation-in-a-low-yield-environment

[AQR] Implementing Momentum: What Have We Learned?

Executive Summary (w/ my own comments)

• Momentum is the phenomenon that securities that have performed well relative to their peers (“winners”) over the recent period tend to continue to outperform, and securities that have performed relatively poorly (“losers”) tend to continue to underperform.
• This is so-called a "trend-following" strategy, not "reversal" strategy to use mean-reversion.
• An academic-style portfolio, such as Fama-French’s “Up-Minus-Down” (UMD) long/short factor is constructed by (1) ranking stocks (or any other investment instruments like currencies) on total returns over the past year skipping the most recent month, (2) selecting the top third of stocks (it depends on your preference), (3) weighting them in proportion to market capitalization (equal-weight or inclination distribution system is also an option), and then (4) rebalancing on a monthly/quarterly basis.
• Two types of Momentum
• cross-sectional momentum
• winners and losers are defined relative to their peers
• time-series momentum
• trend-following for a single asset, where a winner or loser is defined based on its own return being positive or negative

• Behavioral theories for momentum posit short-term underreaction to new information due to anchoring or inattention, and/or overreaction to price moves in the medium-term due to feedback trading and investor herding.
• The authors remain advocates of momentum investing and believe it is implementable in practice as a stand-alone strategy. Further, alongside other themes like value and profitability, momentum is not only more valuable from a portfolio efficiency standpoint, but may also be more implementable when combined with other themes and integrated into a multi-style portfolio.

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Sources

https://www.aqr.com/library/working-papers/implementing-momentum-what-have-we-learned
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3081165

Ross, Adrienne and Moskowitz, Tobias J. and Israel, Ronen and Serban, Laura, Implementing Momentum: What Have We Learned? (December 1, 2017). Available at SSRN: https://ssrn.com/abstract=3081165 or http://dx.doi.org/10.2139/ssrn.3081165

Data matters in Machine Learning

"It's not who has the best algorithm that wins. It's who has the most data."