New strategies:
#384 – Time-Series Momentum and Carry
Period of rebalancing: Monthly
Markets traded: equities, commodities, bonds, currencies
Instruments used for trading: futures, CFDs
Complexity: Simple strategy
Bactest period: 1975 – 2016
Indicative performance: 9.20%
Estimated volatility: 10.00%
Source paper:
Molyboga, Marat and Qian, Junkai and He, Chaohua: Time-Series Momentum, Carry and Hedging Premium
https://ssrn.com/abstract=3075650
Abstract:
This paper examines the performance of time-series momentum across 65 futures markets from all major asset classes, including equity indices, fixed income, currencies and commodities, for the period between January 1975 and December 2016. We find that the basis between spot and futures contracts explains approximately 36% of the performance of time-series momentum indicating that time-series momentum and carry are related. Conditioning trading signals on the sign of the basis improves the Sharpe ratio of time-series momentum by approximately 0.17 and is robust across sub-periods, choice of position-sizing in the implementation of time-series momentum and the lookback period used in the calculation of the basis. The improvement in performance is particularly strong during the early stages of recessions that tend to exhibit very poor stock market performance. Therefore, our strategy can substantially improve investors' welfare. We investigate whether time-series momentum and carry are related to hedging premium by examining the positions of hedgers in the Commitment of Traders (COT) reports. We find strong evidence that indicates that time-series momentum is capturing hedging premium whereas the carry trade is only weakly related to hedging premium. Thus, time-series momentum and carry are related because both strategies benefit from the time-series and cross-sectional variability in basis, and yet they are distinct because time-series momentum alone is linked to hedging premium.
#385 – Weather-Based Equity Trading Strategy
Period of rebalancing: Daily
Markets traded: equties
Instruments used for trading: futures, ETFs
Complexity: Very complex strategy
Bactest period: 1993 – 2012
Indicative performance: 16.78%
Estimated volatility: 23.31%
Source paper:
Dong, Ming and Tremblay, Andreanne: Are Weather-Based Trading Strategies Profitable?
https://ssrn.com/abstract=3111467
Abstract:
We estimate the profitability of global index-level trading strategies formed on daily weather across 49 countries. We use ex ante weather information combined with the statistical relationship between daily weather and country index returns to predict index returns on each day. We then form a long-short hedge strategy and a long-only strategy, and find that both strategies generate substantially out-of-sample gross profits. The long-only strategy produces more consistent annualized profits of up to 20.5%, as opposed to a mean world index return of 3.75% during 1993-2012. These findings help solidify the claim that weather exerts economically important impact on mood, and investors can trade profitably on daily weather.
New research papers related to existing strategies:
#12 – Pairs Trading with Stocks
Psaradellis, Laws, Pantelous, Sermpinis: Pairs Trading, Technical Analysis and Data Snooping: Mean Reversion vs Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3128788
Abstract:
We examine the technical trading rules performance on the statistical arbitrage investment strategy using daily data from 1990 to 2016 for 15 commodity, equity and currency pairs. Adopting the false discovery rate method to control for data snooping bias and exercising 18,412 technical trading rules, we find evidence of significant predictability and excess profitability, especially for commodity spreads, in which the best performing strategy generates an annualized mean excess return of 17.6%. In addition, we perform an out-of-sample analysis to cross-validate our results in different subperiods. We find that whilst the profitability of rules based on technical analysis exhibits a downward trend over the sample, the opportunities for pairs trading returns have been increased in certain cases.
#7 – Volatility Effect in Stocks – Long-Only Version
#14 – Momentum Effect in Stocks
Berghorn, Vogl, Schultz, Otto: Trend Momentum II: Driving Forces of Low Volatility and Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3120232
Abstract:
In discussions and critiques on the validity of the Efficient Market Hypothesis, there are two important research focuses: statistical analyses showing that the basic assumption of statistical independence in price series is violated and empirical findings that show that significant market anomalies exist. In this work, we combine both viewpoints by analyzing two important mathematical factor anomalies: low volatility and momentum. By applying an explicit trend model, we show that both anomalies require trending. Additionally, we show that the trend model used exhibits log-normal trend characteristics. Furthermore, the model allows us to describe how low volatility uses implicitly asymmetric trend characteristics while momentum directly exploits trends. Using Mandelbrot’s model of fractional Brownian Motions, we can finally link statistical analyses (measuring the Hurst exponent and persistence in returns) to the empirically observed momentum factor. Experimentally, the Hurst exponent in itself allows for a momentum strategy, and it can also be utilized to significantly improve low volatility strategies. In contrast to Mandelbrot’s approach, we offer a non-stationary view that allows us to describe both investment strategies using the trend model.
Three additional related research papers have been included into existing free strategy reviews during last 2 weeks:
#13 – Short Term Reversal in Stocks
Drechsler, Moreira, Savov: Liquidity Creation As Volatility Risk
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3133291
Abstract:
We show, both theoretically and empirically, that liquidity creation induces negative exposure to volatility risk. Intuitively, liquidity creation involves taking positions that can be exploited by privately informed investors. These investors' ability to predict future price changes makes their payoff resemble a straddle (a combination of a call and a put). By taking the other side, liquidity providers are implicitly short a straddle, suffering losses when volatility spikes. Empirically, we show that short-term reversal strategies, which mimic liquidity creation by buying stocks that go down and selling stocks that go up, have a large negative exposure to volatility shocks. This exposure, together with the large premium investors demand for bearing volatility risk, explains why liquidity creation earns a premium, why this premium is strongly increasing in volatility, and why times of high volatility like the 2008 financial crisis trigger a contraction in liquidity. Taken together, these results provide a new, asset-pricing view of the risks and rewards to financial intermediation.
A recent academic research looks at effects of algorithmic trading during turbulent times:
Breedon, Chen, Ranaldo, Vause: Judgement Day: Algorithmic Trading Around the Swiss Franc Cap Removal
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3126136
Abstract:
A key issue raised by the rapid growth of computerised algorithmic trading is how it responds in extreme situations. Using data on foreign exchange orders and transactions that includes identification of algorithmic trading, we find that this type of trading contributed to the deterioration of market quality following the removal of the cap on the Swiss franc on 15 January 2015, which was an event that came as a complete surprise to market participants. In particular, we find that algorithmic traders withdrew liquidity and generated uninformative volatility in Swiss franc currency pairs, while human traders did the opposite. However, we find no evidence that algorithmic trading propagated these adverse effects on market quality to other currency pairs.
Nice academic paper uses Metcalfe’s law to estimate Bitcoin's fundamental value. A really recommended read … :
Wheatley, Sornette, Huber, Reppen, Gantner: Are Bitcoin Bubbles Predictable? Combining a Generalized Metcalfe's Law and the LPPLS Model
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3141050
Abstract:
We develop a strong diagnostic for bubbles and crashes in bitcoin, by analyzing the coincidence (and its absence) of fundamental and technical indicators. Using a generalized Metcalfe’s law based on network properties, a fundamental value is quantified and shown to be heavily exceeded, on at least four occasions, by bubbles that grow and burst. In these bubbles, we detect a universal super-exponential unsustainable growth. We model this universal pattern with the Log-Periodic Power Law Singularity (LPPLS) model, which parsimoniously captures diverse positive feedback phenomena, such as herding and imitation. The LPPLS model is shown to provide an ex-ante warning of market instabilities, quantifying a high crash hazard and probabilistic bracket of the crash time consistent with the actual corrections; although, as always, the precise time and trigger (which straw breaks the camel’s back) being exogenous and unpredictable. Looking forward, our analysis identifies a substantial but not unprecedented overvaluation in the price of bitcoin, suggesting many months of volatile sideways bitcoin prices ahead (from the time of writing, March 2018).
