What is better – factor of sector investing? A recent paper takes a look on this question a offers an advice. Analysis is related to multiple smart beta strategies:

Authors: Briere, Szafarz

Title: Factors vs. Sectors in Asset Allocation: Stronger Together?

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2965346

Abstract:

This paper compares and contrasts factor investing and sector investing, and then seeks a compromise by optimally exploiting the advantages of both styles. Our results show that sector investing is effective for reducing risk through diversification while factor investing is better for capturing risk premia and so pushing up returns. This suggests that there is room for potentially fruitful combinations of the two styles. Presumably, by combining factors and sectors, investors would benefit both from the diversification potential of the former and the risk premia of the latter. The tests reveal that composite strategies are particularly attractive; they confirm that sector investing helps reduce risks during crisis periods, while factor investing can boost returns during quiet times.

Notable quotations from the academic research paper:

"Factor investing has recently become a huge success in asset allocation. But its supposed superiority over other portfolio management techniques has yet to be proven. To fill that gap, we lay down a challenge to factor investing by organizing a contest pitting it against a well established competitor, the classical industry-based approach to asset allocation. We compare the financial performances of factor-based and industry-based asset allocations in the investment universe composed of U.S. equities. We contrast the mean-variance performance of diversified portfolios made up of sectors with diversified portfolios composed of the five factors developed by Fama and French. We
duplicate all the trials for long-only portfolios (no short sales) and long-short ones (unlimited short sales accepted).

Our contest reveals no overall winner. In fact, we find circumstantial evidence of superiority for each style. The alphas of factors with respect to the market inflate expected returns, while sectors reduce risks through high diversification potential. Factor investing tends to dominate when short sales are permitted. By contrast, when short-selling is excluded, industry based allocation is preferable, especially for highly risk-averse investors. These balanced results lead us to conjecture that factors and sectors could be complementary investing styles, and that combining them should help enhance financial performance, at least under some configurations regarding short-selling and/or risk aversion.

Our empirical investigation suggests that composite portfolios made up of sectors and factors are particularly attractive under two types of circumstances:

First, during crisis periods, a mixture of sectors and factors largely dominates whichever style is the best standalone performer.

Second, moderately risk-averse investors will find it best to combine sector and factor investments."

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A recent paper takes a look on a long-term behaviour of momentum portfolios. Related to all equity momentum strategies, mainly to:

#14 – Momentum Effect in Stocks

Authors: Ali, Daniel, Hirshleifer

Title: One Brief Shining Moment(um): Past Momentum Performance and Momentum Reversals

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2956493

Abstract:

Motivated by behavioral theories, we test whether recent past performance of the momentum strategy (Past Momentum Performance–PMP) negatively predicts the performance of stale momentum portfolios. Following periods of top-quintile PMP, momentum portfolios exhibit strong reversals 2-5 years after formation, whereas, following periods of bottom-quintile PMP, stale momentum portfolios earn positive returns. The difference in cumulative five-year Fama-French alphas for momentum portfolios formed in high- and low-PMP months is 40%. A value-weighted trading strategy based on this effect generates an alpha of 0.40% per month (t = 3.74). These patterns are confirmed in international data. These findings present a puzzle for existing theories of momentum.

Notable quotations from the academic research paper:

"A set of studies propose behavioral hypotheses to explain the momentum anomaly. An implication of some of these models is that the momentum phenomenon is a result of delayed overreaction to certain information shocks. This implies that a sufficiently `stale' momentum portfolio, where `stale' refers to a momentum portfolio formed at a lag of twelve months or more, will on average earn negative abnormal returns.

However, to our knowledge, no study has yet examined the conditional variation in the performance of stale momentum strategies, i.e., the performance of momentum portfolios in years 2-5 post-formation. One interesting possibility, motivated by the idea that investors chase past style performance, is that strong recent past performance of the momentum style will cause investors to overvalue new momentum portfolios, resulting in poor subsequent long-run performance of these portfolios. In this paper, we explore this issue by testing whether long horizon performance of momentum portfolios is negatively related to the performance of the momentum strategy in the recent past.

In particular, we examine the relation of stale momentum returns to a measure of the recent performance of the momentum strategy, which we call Past Momentum Performance or PMP. PMP is simply the return of a standard (12,2) momentum strategy over the preceding 2 years (24 months). Our basic finding is that momentum portfolios formed in high PMP months (months when PMP is in the top 20% of all months in our sample) generate strongly negative returns and alphas 2-5 years after formation. Strikingly, momentum portfolios formed in low PMP months continue to (weakly) outperform in post-formation years 2-5. Thus, the momentum reversal documented by Jegadeesh and Titman (2001) is strongly state dependent.

We explore a set of behavioral hypotheses for the strong dependence of stale momentum performance on PMP. One of our hypotheses is based upon style chasing.

A basic hypothesis is that the performance of the momentum style will tend to continue in the short run, so that after the momentum strategy has done well, it tends to do well again. The style chasing approach suggests that following high returns on the momentum style, owing to return extrapolation, naive investors switch into this style, meaning that they buy winners and sell losers heavily. This trading pressure reinforces the strong performance of the momentum strategy, and will temporarily cause better-than-usual momentum performance after the conditioning date if such return chasers arrive gradually.

This effect is driven by overreaction in the components of the momentum portfolio. In consequence, the returns on the momentum portfolio will eventually reverse. So after high PMP, there are on average negative returns to a stale momentum strategy of buying firms that were winners at least a year ago and selling firms that were losers at least a year ago.

In contrast, after low PMP, investors switch out of the momentum style. Heavy selling of winners and buying of losers induces underreaction in winner and loser returns. So after low PMP, this hypothesis implies eventual positive returns to a stale momentum strategy. Putting these two cases together, we expect reversal of momentum to be stronger as PMP increases.

Motivated by these ideas, we examine the relationship between PMP and the performance of stale momentum portfolios and find a number of novel effects. We first show that over the full CRSP sample, there is on average very little tendency of momentum to reverse after controlling for the value effect. This finding is in contrast to that of Jegadeesh and Titman (2001) who find, in a shorter sample, that equal-weighted momentum portfolios exhibit strong reversals even after controlling for the value effect.

Then, turning to our main result, we find a strong relationship between PMP and long-run reversal of momentum – reversal is greater after high PMP. Specifically, we rank the months in our sample into quintiles based on PMP and examine the performance of momentum portfolios formed in each category of month (i.e., for months in each PMP quintile) during the five years after formation. Stale momentum performance declines strongly and monotonically with PMP. In Quintile 1, instead of reversal, momentum portfolios exhibit weak continuation in post-formation years 2-5. In sharp contrast, momentum portfolios formed in Quintile 5 months lose 42% of their value over the next five years. We call this strong reversal of momentum after high PMP the PMP effect.

"

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A very interesting example of FX trading strategy created by Richard Olsen (Founder of OANDA):

Authors: Golub, Glattfelder, Olsen

Title: The Alpha Engine: Designing an Automated Trading Algorithm

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2951348

Abstract:

We introduce a new approach to algorithmic investment management that yields profitable automated trading strategies. This trading model design is the result of a path of investigation that was chosen nearly three decades ago. Back then, a paradigm change was proposed for the way time is defined in financial markets, based on intrinsic events. This definition lead to the uncovering of a large set of scaling laws. An additional guiding principle was found by embedding the trading model construction in an agent-base framework, inspired by the study of complex systems. This new approach to designing automated trading algorithms is a parsimonious method for building a new type of investment strategy that not only generates profits, but also provides liquidity to financial markets and does not have a priori restrictions on the amount of assets that are managed.

Notable quotations from the academic research paper:

"To summarize, our aim is to develop trading models based on parsimonious, self-similar, modular, and agent-based behavior, designed for multiple time horizons and not purely driven by trend following action. The intellectual framework unifying these angles of attack is outlined in Section 3 of source research paper. The result of this endeavor are interacting systems that are highly dynamic, robust, and adaptive. In other words, a type of trading model that mirrors the dynamic and complex nature of financial markets. The code can be download from GitHub [The Alpha Engine: Designing an Automated Trading Algorithm Code. https://github.com/AntonVonGolub/Code/blob/master/code.java. Accessed: 2017-01-04. 2017]

The Alpha Engine is a counter-trending trading model algorithm that provides liquidity by opening a position when markets overshoot, and manages positions by cascading and de-cascading during the evolution of the long coastline of prices, until it closes in a prot. The building blocks of the trading model are:

– an endogenous time scale called intrinsic time that dissects the price curve into directional changes and overshoots;
- patterns, called scaling laws that hold over several orders of magnitude, providing an analytical relationship between price overshoots and directional change reversals;
- coastline trading agents operating at intrinsic events, defined by the event based language;
- a probability indicator that determines the sizing of positions, by identifying periods of market activity that deviate from normal behavior;
- skewing of cascading and de-cascading designed to mitigate the accumulation of large inventory sizes during trending markets;
- the splitting of directional change and, consequently, overshoot thresholds into upwards and downwards components, i.e., the introduction of asymmetric thresholds."

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We are really happy to see that guys from QuantInsti did a new independent analysis of a strategy we have in our database. An article is written by Milind Paradkar and is focused on 52-Weeks High Effect in Stocks (Strategy #18) using Indian stocks as an investment universe:

https://www.quantinsti.com/blog/trading-strategy-52-weeks-high-effect-in-stocks/

QuantInsti™ is one of the pioneer algorithmic trading research and training institutes across the globe. With its educational initiatives, QuantInsti™ is preparing financial market professionals for the contemporary field of algorithmic and quantitative trading. They offer a really well-prepared professional training course EPAT™ (Executive Programme in Algorithmic Trading) which is Asia's first algorithmic trading education program. This comprehensive course exposes its participants to various strategy paradigms and enables them to build an algorithmic trading system. QuantInsti™ also offers Quantra™ which is an e-learning portal that specializes in short self-paced courses on algorithmic and quantitative Trading. Quantra™ offers an interactive environment which supports 'learning by doing' through guided coding exercises, videos and presentations.

The original academic paper (“Industry Information and the 52-Week High Effect”) has been authored by Xin Hong, Bradford D. Jordan, and Mark H. Liu. They propose a modified rotational momentum strategy which uses a 52-Week High as a predictor of cross-sectional equity performance to select top performing industries.

Milind Paradkar from QuantInsti performed an independent analysis of a resultant strategy during last 3 years (an out of sample period from 2014 until 2017) on Indian stocks. Overall, the performance isn't very stellar and we can say that Indian market hasn't been very generous for this strategy (total performance has been only 17% flat over those 3 years with a Sharpe ratio around 0.4). But we are really glad for this analysis as it offers a valuable look on a strategy on different universe as most trading strategies are usually academically researched only on US equities.

The final OOS equity curve:

Thanks for nice analysis Milind…

Are portfolio managers skilled in stock-picking? It is a popular subject for academic research and majority of papers show that active funds underperform their respective benchmarks. But… It doesn't mean professionals do not know how to pick stocks. It can simply mean that a lot of managers are too afraid (or are limited by risk or fund size) to increase their funds' active share. Seems like there is a subset of stocks where fund managers picks tend to outperform the rest of the market – the lottery stocks – low price, high idiosyncratic risk and skewness stocks :

Authors: Stein

Title: Are Mutual Fund Managers Good Gamblers?

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2919410

Abstract:

I investigate the skill of mutual fund managers by focusing in their holdings of a special type of stock. Kumar (2009) classifies low price, high idiosyncratic risk and skewness stocks as ‘Lottery Stocks’, and shows that these securities severely under-perform. I look at the effect that these investments have on the performance of U.S. equity mutual funds, and how they reflect on the skill of the manager. As part of this analysis I introduce the ‘Lottery Score’, the percentage of equity assets invested in Lottery Stocks. I find that the Lottery Stocks that fund managers pick tend to outperform the rest of the market, and the funds themselves persistently outperform similar funds that don’t invest in these stocks. An investable strategy that buys Lottery Stocks held by the funds and sells those ignored by them attains a monthly alpha of 2%. The Lottery Score is shown to be a good predictor of fund performance, even after controlling for a number of previously introduced measures of skill. Since the funds’ out-performance cannot be fully explained by their allocation to Lottery Stocks, this behavior uncovers a more general ability for asset management.

Notable quotations from the academic research paper:

"Following the literature that analyses which securities fund managers hold and which they avoid, I focus in a particular type of stock labeled by Kumar (2009) ‘Lottery Stocks’. Compared to the median of all stocks that trade in the U.S. equity market, Lottery Stocks have a lower price, higher idiosyncratic volatility and idiosyncratic skewness. Kumar describes Lottery Stocks as ‘long shots’ which are similar to lottery tickets, in that they offer a risky investment opportunity at a relatively low cost and, should the gamble pay off, a high reward as well. He shows that retail investors who prefer these stocks also have a higher demand for lotteries. Unfortunately for these investors, Kumar shows that the average Lottery Stock underperforms other stocks by about 66 bps per month. While Kumar focuses on retail investors, I look at the ‘gambling’ behavior of mutual fund managers in terms of their investments in Lottery Stocks.

Given that the average Lottery Stock is an inferior pick, I investigate two general questions:

First, do professional investors, such as mutual fund managers, invest in Lottery Stocks?

Second, what impact do these investments have on the performance of the fund?

I study these questions by looking at the portfolio holdings of a large sample of actively managed mutual funds that invest mostly in U.S. equities, and I introduce the ‘Lottery Score’ which is the percentage of a fund’s equity capital invested in these Lottery Stocks.

I find that a relatively large number of mutual funds report at least some investments in Lottery Stocks, from a low of 50% of all funds in the mid-1990’s to more than 85% in recent years. For most funds the capital devoted to these securities is minute, with the average Lottery Score of the sample below 4% at its highest. However, managers of riskier funds (micro and small cap funds, growth funds) invest larger portions of their capital in these stocks, sometimes topping 10% of assets.

Unlike Kumar’s (2009) results for the full sample of Lottery Stocks, I find that the average Lottery Stocks held by a mutual fund consistently outperform all other stocks in the market by 62 bps per month, in terms of a four-factor alpha. Mutual funds that invest in Lottery Stocks outperform those that do not by 10 bps per month. The preference of fund managers for investing in Lottery Stocks, their ‘gambling’ behavior, is persistent in time, as is their outperformance with respect to their peers.

There is a stark difference between the performance of Lottery Stocks held by fund managers, and the more modest outperformance of their funds. This is due to the small portion of assets allocated on average to these long-shot bets. Risk-taking and short-selling constraints might be the cause of the small effect of Lottery Stocks in mutual fund performance."

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An important academic paper which raises several interesting questions about suspicious behavior of VIX Index:

Authors: Griffin, Shams

Title: Manipulation in the VIX ?

Link: https://westernfinance-portal.org/viewpaper.php?n=491456

Abstract:

At the settlement time of the VIX Volatility Index, volume spikes on S&P 500 Index (SPX) options, but only in the out-of-the-money options that are used to calculate the VIX, and more so for options with a higher and discontinuous influence on VIX. We investigate alternative explanations of coordinated liquidity trading and hedging. Tests including those utilizing differences in put and call options, open interest around the settlement, and a similar volatility contract with an entirely different settlement procedure are inconsistent with these explanations, but consistent with market manipulation. Size and liquidity differences between the SPX and VIX markets may facilitate the sizeable settlement deviations.

Notable quotations from the academic research paper:

"The VIX setting is one with two markets with different liquidities and transactions costs: SPX options market with large bid-ask spreads that make it difficult to arbitrage away price deviations, and large and liquid upper-level market tied to it that translates such deviations into a sizable potential payout.

The Chicago Board Options Exchange Volatility Index (VIX) is a widely tracked index that gauges the thirty-day, forward-looking volatility implied in the market, often referred to as a market `fear-gauge'. Anderson, Bondarenko, and Gonzalez-Perez (2015) demonstrate that the VIX index can exhibit deviations from true volatility due to the inclusion criteria of illiquid options. Futures and options on the VIX have a relatively large volume. Every month, a settlement occurs where the value of VIX derivatives is set equal to the VIX value calculated from SPX options. This settlement value is calculated using the VIX formula from a full range of out-of-the-money (OTM) SPX put and call options with various exercise prices. A manipulator would need to move the price of these lower-level SPX options to influence the VIX settlement calculation and the value of expiring upper-level VIX derivatives. But, manipulators could leave footprints in the data.

Several interesting data patterns emerge:

First, at the exact time of monthly VIX settlement, highly statistically and economically significant trading volume spikes occur in the underlying SPX options.

Second, the spike occurs only in the OTM SPX options that are included in the VIX settlement calculation and not in the excluded in-the-money (ITM) SPX options.

Third, there is no spike in volume for similar S&P 100 Index (OEX) or SPDR S&P 500 ETF (SPY) options that are unconnected to volatility index derivatives.

Fourth, the VIX calculation is more sensitive to price changes of deeper OTM SPX put options. If traders sought to manipulate the VIX settlement, they would want to move the prices by optimally spreading their trades across the SPX strikes and increasing the number of trades in the far OTM put options. Trading volume at settlement follows this pattern, whereas normally far OTM options are rarely traded.

Fifth, there are certain options that exhibit discontinuously higher weighting in the settlement but are otherwise very similar to other OTM options. These options, weighted higher in the VIX calculation, exhibit a jumps in trading volume at settlement."

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