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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Our favorite research paper about the performance of long-only investment strategies in commodities:

Authors: Erb, Harvey

Title: Conquering Misperceptions about Commodity Futures Investing

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2645444

Abstract:

Long-only commodity futures returns have been very disappointing over the last decade, leading some to wonder if it was a mistake to invest in commodities. The poor performance is the result of poor “income returns” and not of falling commodity prices. This observation may be surprising for many commodity investors who were not aware, who misperceived, they were making a bet on income returns, a return building block similar to a stock’s dividend yield or a bond’s yield. For investors seeking an inflation hedge, it may be surprising that the historical linkage of commodity returns with inflation seems to be the result of a connection between commodity income returns and inflation, not, as commonly misperceived, commodity price returns and inflation. It may be surprising that the value of commodity investments is smaller than the market capitalization of Facebook, a potentially striking misperception for investors seeking a portfolio diversifier with abundant capacity. There has been no change in the way that price returns and income returns drive the total returns of stocks, bond and commodities. What has changed is that maybe a good number of commodity investors now realize that they were operating outside of their “circle of competence” and did not have a sense of what future price and income returns could be and would be.

Notable quotations from the academic research paper:

"The last ten years have been challenging for many long‐only commodity futures investors and given many a reason to question whether a ‘bad’ investment strategy drove a bad outcome or a ‘good’ strategy experienced an unlucky outcome. The total return of the S&P GSCI commodity index was ‐4.6% per year, much lower than the +7.4% return for the S&P 500 stock index and the +4.5% return for the Barclays U.S. Aggregate bond index. The key driver of the poor S&P GSCI performance has been a ‐8.0% income return. The commodity income return is the sum of a collateral return (in this case the three‐month Treasury bill) and a roll return (the cost, or benefit, of staying invested in futures contracts over time).

What has driven commodity portfolio returns? Focusing on the investable commodity index with the longest performance history, the S&P GSCI, Exhibit 3 shows correlations for rolling 10‐year returns for the drivers of the S&P GSCI. What seemingly drove commodity total returns? Interestingly, the first row shows that, historically, there has been little correlation between total return and price return (‐0.07), a high correlation between total return and income return (0.73) and a positive correlation between total return and inflation (0.55). What drove commodity price returns? The third row shows that historically price returns were negatively correlated with income returns (‐0.73), with roll returns (‐0.71), with collateral returns (‐0.63) and with inflation (‐0.26). What drove income returns? The fourth row shows that historically income returns were positively correlated with roll returns (0.97), with collateral returns (0.87) and with inflation (0.55). Finally, which commodity return components were correlated with inflation? The seventh row shows that price returns were negatively correlated with inflation (‐0.26), positively correlated with income returns (0.55), positively correlated with roll returns (0.36) and positively correlated with collateral returns (0.83). In a broad sense, Exhibit 3 suggests:

1) a weak link between commodity price returns and commodity total returns,

2) a negative link between inflation and commodity price returns,

3) a positive link between commodity income returns and commodity total returns,

4) a positive link between inflation and commodity income returns and

5) a negative relationship between income returns and price returns.

There are at least two opposing views to explain the decline in income and roll returns. The first view offered by Bhardwaj, Gorton and Rouwenhorst (2015) is that there is in fact no difference between pre‐2004 commodity performance and post‐2004 commodity performance. Their view is illustrated by looking at the performance of a hypothetical, equally‐weighted paper portfolio created by Gorton and Rouwenhorst (2006). This paper portfolio embeds a common smart beta strategy, rebalancing an equally weighted portfolio. Working with an intuition that commodity futures markets are risk transfer insurance markets for commodity hedgers, Bhardwaj, Gorton and Rouwenhorst also find no evidence that an influx of long‐only financial commodity investors over the last decade has impacted the historical or prospective returns of their hypothetical paper portfolio. Summing up the impact of the last decade, they find “the risk premium has been comparable to its long‐term historical average”.

Norrish (2015) argues that Gorton and Rouwenhorst’s hypothetical paper portfolio “is not a viable option for most investors”, and reflects an alternative view that over the last decade an influx of long‐only financial investors significantly lowered returns for actual and tradable long‐only commodity indices. Echoing the view that commodity futures markets can be viewed as price insurance markets, Norrish’s view is that there has been too much long‐only insurance capital chasing too few insurance opportunities. If too much insurance‐inspired capital has lowered returns then perhaps a contraction in insurance inspired capital might increase returns."

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An interesting academic paper about the intristic value of Gold:

Authors: Harris, Shen

Title: The Intrinsic Value of Gold: An Exchange Rate-Free Price Index

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2926454

Abstract:

In this paper, we propose a gold price index that enables market participants to separate the change in the ‘intrinsic’ value of gold from changes in global exchange rates. The index is a geometrically weighted average of the price of gold denominated in different currencies, with weights that are proportional to the market power of each country in the global gold market, where market power is defined as the impact that a change in a country’s exchange rate has on the price of gold expressed in other currencies. We use principal components analysis to reduce the set of global exchange rates to four currency ‘blocs’ representing the U.S. dollar, the euro, the commodity currencies and the Asian currencies. We estimate the weight of each currency bloc in the index in an error correction framework using a broad set of variables to control for the unobserved intrinsic value. We show that the resulting index is less volatile than the USD price of gold and, in contrast with the USD price of gold, has a strong negative relationship with global equities and a strong positive relationship with the VIX index, both of which underline the role of gold as a safe haven asset.

Notable quotations from the academic research paper:

"The market for gold is one of the largest and most liquid in the world, surpassed only by the major currency pairs in terms of daily turnover. The price of gold, like that of many commodities, is conventionally quoted in USD. However, gold is not exclusively a US asset and so the return from an investment in gold, when calculated using the quoted USD price, conflates the change in the value of gold with the change in the value of the USD. In this paper, we develop a gold price index, which when used to compute returns, reflects changes in the intrinsic value of gold independently of concurrent changes in global exchange rates.

Various index-based approaches go some way towards removing the exchange rate component of the gold price, they do not properly reflect the intrinsic value of gold because they use arbitrarily defined weights that do not represent the actual impact that changes in individual exchange rates have on the quoted gold price.

The gold price index that we develop can be thought of as a geometric weighted average of the normalized real (i.e. inflation-adjusted) price of gold in different currencies. The weight of each currency is proportional to that country’s market power in the global gold market, as reflected in the impact that a change in the country’s real exchange rate has on the real price of gold quoted in other currencies. We cast the relationship between the price of gold, exchange rates and a broad set of fundamental variables in a cointegration framework, in which we simultaneously model both the long run relationship between the price of gold and its determinants, and its short run dynamics. We use weekly data from 3 January 1995 to 26 October 2015 for 23 exchange rates against the GBP. In view of the very high correlations between many individual exchange rates, we use principal components analysis to extract the significant underlying exchange rate factors.

Over the full sample, we find that there are four ‘significant’ principal components in real exchange rates, which are shown to represent the USD currency bloc, the commodity currency block, the EUR currency block, and the Asian currency bloc, respectively. The cointegration framework allows us to distinguish between the long run and short run elasticities of the price of gold with respect to exchange rates. In particular, the long run elasticity with respect to an exchange rate (or an exchange rate bloc) partially reflects structural changes and, in particular, the impact that a change in the exchange rate has on fundamentals through, for example, changes in production capacity or demand. In contrast, the short run elasticity reflects the change in the gold price arising directly from changes in the exchange rate, i.e. changes in the price of gold that simply reflect ‘translation’ effects. It is these short run elasticities that are relevant for the construction of the gold price index.

We therefore first estimate the long run relationship between the real price of gold, real exchange rates and proxies for the non-exchange rate related fundamentals, including global equity and bond prices, the oil price and the level of the VIX index of implied volatility, and show that these variables are strongly cointegrated. The gold price has a negative long run relation with global equity prices and a positive long run relation with global bond prices, the price of oil and the VIX index. Gold has a positive long run relationship with the USD bloc, the EUR bloc and the commodity currency bloc, but a somewhat weaker relationship with the Asian currency bloc.

We then estimate the short run dynamics of the change in the gold price as a function of (1) changes in exchange rates, (2) changes in fundamentals and (3) the lagged error correction term that captures the deviation from long run equilibrium. The short run elasticities from this regression are then used as weights in the gold price index. Using the full sample to estimate the model, we show that the (normalized) weights on the USD bloc, the commodity bloc and the EUR bloc are about 21%, 48% and 31%, respectively, reflecting the relative importance of these currency blocks for production, consumption and investment in the global gold market. The weight on the Asian currency bloc is not significantly different from zero. We convert the real gold price index into a nominal USD gold price index to enable a comparison with the USD price of gold. We show that the nominal gold price index is less volatile than the USD gold price and, in contrast with the USD gold price, has a strong negative relationship with global equities and a strong positive relationship with the VIX index, both of which underline the role of gold as a safe haven asset."

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