Quantpedia Premium Update – 22th August

New strategies:

#773 – Changes in Ownership Breadth Predict Performance of Equity Factors

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1981-2018
Indicative performance: 8.34%
Estimated volatility: 17.03%

Source paper:

Wu & Xu: Changes in Ownership Breadth and Capital Market Anomalies.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2988428
Abstract:
We investigate how the interaction of entries and exits of informed institutional investors with market anomaly signals affects strategy performance. The long legs of anomalies earn more positive alphas following entries, while the short legs earn more negative alphas following exits. The enhanced anomaly-based strategies of buying stocks in the long legs of anomalies with entries and shorting stocks in the short legs with exits outperform the original anomalies with an increase of 19-54 bps per month in the Fama-French (2015) five-factor alpha. The entries and exits of institutional investors capture informed trading and earnings surprises thereby enhancing the anomalies.

#774 – Afternoon Reversal Trading Strategy

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 1993-2018
Indicative performance: 9.85%
Estimated volatility: 8.44%

Source paper:

Xu, Haoyu: Intraday Market Timing of Liquidity Trading and its Implication for Asset Pricing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4123107
Abstract
I investigate liquidity traders’ market timing behavior across regular trading hours. Both morning and afternoon stock markets have large volume, but afternoon has much lower bid-ask spread. It incentivizes discretionary liquidity traders to concentrate in the afternoon. Consistent with the idea, stocks experiencing mutual fund fire sale exhibit increased abnormal afternoon trading activity. The magnitude of the increase positively correlates with morningafternoon bid-ask spread difference. More generally, price movement in the afternoon is temporary and reverses quickly. Afternoon return is the main driver behind daily and monthly reversal. In contrast, morning return positively predicts future total return.

#775 – Hierarchical Stock Momentum

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex strategy
Backtest period: 1997-2021
Indicative performance: 10.58%
Estimated volatility: 12.31%

Source paper:

Cirulli, Antonello and Kobak, Michal and Ulrych, Urban: Portfolio Construction with Hierarchical Momentum
https://ssrn.com/abstract=4125072
Abstract:
This paper presents a portfolio construction approach that combines the hierarchical clustering of a large asset universe with the stock price momentum. On the one hand, investing in high-momentum stocks stabilizes portfolio performance across economic regimes and enhances risk-adjusted returns. On the other hand, hierarchical clustering of a high-dimensional asset universe ensures sparse diversification and mitigates the problems of increased drawdowns and large turnovers typically present in momentum portfolios. Moreover, the proposed portfolio construction approach avoids the covariance matrix inversion. An out-of-sample backtest on a non-survivorship-biased dataset of international stocks shows that hierarchical-momentum portfolios achieve substantially improved cumulative and risk-adjusted portfolio returns as well as decreased portfolio drawdowns compared to the model-free benchmarks net of transaction costs. Furthermore, we demonstrate that the unique characteristics of the hierarchical-momentum portfolios arise due to both dimensionality reduction via clustering and momentum-based stock selection.

#776 – Max Pain Strategy – Stock Return Predictability at Options Expiration

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1996-2021
Indicative performance: 4.91%
Estimated volatility: 7.97%

Source paper:

Filippou, Ilias and Garcia-Ares, Pedro Angel and Zapatero, Fernando: No Max Pain, No Max Gain: Stock Return Predictability at Options Expiration
https://ssrn.com/abstract=4140487
Abstract:
Max Pain price is the strike price at which the total payoff of all options (calls and puts) written on a particular stock, and with the same expiration date, is the lowest. We construct a measure of (potential) Max Pain gain/loss, sort stock prices according to this measure, and find that a spread portfolio that buys high Max Pain stocks and sells low Max Pain stocks generates large, positive and statistically significant returns and alphas. Our results provide strong evidence of stock return predictability at the expiration of the options. We also find that there is significantly higher stock trading volume and order imbalances for these Max Pain gain/loss portfolios. The strategy is not related to reversals of price trends that might have explained initial options volume. The results are especially strong for relatively small and illiquid stocks.

#777 – Combined Momentum and Nearness to 52-week High

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1963-2020
Indicative performance: 31.3%
Estimated volatility: 31.08%

Source paper:

Chen, Chen and Stivers, Chris and Sun, Licheng: Short-term Relative-Strength Strategies, Turnover, and the Connection between Winner Returns and the 52-week High
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4122300
Abstract:
We contribute with two principal findings that suggest a material role for 52-week-high price anchors in understanding the short-run behavior of one-month stock returns. First, we find that short-term momentum in high-turnover stocks is only evident for stocks whose prices are relatively close to their 52-week high. Conversely, strong reversals are evident for high-turnover stocks whose prices are relatively far from their 52-week high. Second, we find that the apparent price-to-52- week-high (PTH) anchoring biases are asymmetric with a concentration in past winners. HighPTH winners strongly outperform low-PTH winners, across all the different turnover and size segmentations that we examine. Conversely, a comparable PTH-based performance variation in past losers is not evident. This asymmetry is behind our first principal finding. We conduct three supplemental investigations that support a PTH anchoring-bias interpretation for the winnerPTH relation; also evaluating market sentiment, dispersion in analyst forecasted earnings, and firm size.

New research papers related to existing strategies:

#210 – Adaptive Asset Allocation
#220 – Momentum and Trend Following in Global Asset Allocation
#341 – Global Cross-Asset Time Series Momentum in Bond and Equity Markets

Keller, Wouter J.: Relative and Absolute Momentum in Times of Rising/Low Yields: Bold Asset Allocation (BAA)
https://ssrn.com/abstract=4166845
Abstract:
Our aim is to develop a very offensive (‘aggressive’) tactical asset allocation strategy, by combining some of our previous models like Protected- (PAA), Vigilant- (VAA) and Defensive (DAA) Asset Allocation. We will call this new strategy the ‘Bold Asset Allocation’ (BAA). BAA combines a slow relative momentum with a fast absolute momentum and crash protection, based on the concept of the ‘canary’ universe, where we switch from our offensive to the defensive universe when any of the assets in the canary universe has negative absolute momentum. As a result, BAA spends ca 60% in the defensive universe. By enhancing this defensive universe beyond cash, we find very impressive returns (>=20%) with low monthly max drawdowns (<=15%) over Dec 1970 – Jun 2022.

#650 – Volatility Effect in Cryptos

Moreno, Ester Aguayo and Garcia Medina, Andres: LSTM-GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios
https://ssrn.com/abstract=4132498
Abstract:
Cryptocurrencies are based on a decentralised network, which allows them to exist outside the control of central banks. One of their main characteristics is their high volatility. In the present work, the volatility of the main cryptocurrencies is predicted through generalized autoregressive conditional heteroskedasticity (GARCH) models, long short-term memory (LSTM) deep learning models, and hybrid models of the type LSTM and GARCH, where parameters of the GARCH family are included as features of LSTM models. The different forecasts are evaluated through the heteroscedastic mean absolute error (HMAE), the heteroscedastic mean quadratic error (HMSE), the Sharpe ratio (SR), and the value at risk (VaR), and the accuracy of the prediction is measured through the Diebold-Mariano (DM) test. The study period covered the scenario of the pandemic declaration by the World Health Organization (WHO) and included high-frequency data from 01/01/2020 to 06/30/2020. Evidence is presented showing that the asymmetric univariate models gjrGARCH and gjrGARCH with volume as an exogenous variable present better performance in terms of HMAE and HMSE. The asymmetric univariate model eGARCH presents the highest SR. On the other hand, the multivariate models DCC-GARCH and DCC-GARCH-Vol with volume as an exogenous variable present the best results with respect to VaR. In all cases, the GARCH(1,1) model family was considered. In summary, it was found that the deep learning models and their different variants better recover the structure of the realized variance, and there was evidence of these models being the most accurate according to the DM test. An unexpected finding is the minimum variance portfolio shows the highest capital allocation in the Tether cryptocurrency. Therefore, it could be considered the free risk asset for this new class of financial instruments.

#660 – ESG in Currencies

Baker, Edward and Braga-Alves, Marcus V. and Morey, Matthew R.: Exchange Rate Changes and ESG: Predicting Exchange Rate Changes Using Country Environmental, Social, and Governance Ratings
https://ssrn.com/abstract=4114906
Abstract:
We examine the ability of country ESG ratings, which assess a country’s performance on environmental, social, and governance (ESG) risk factors, to predict future one-year and two-year exchange rate changes. To conduct the study, we used the annual MSCI ESG Government Ratings from 2008 to 2018 for forty-two countries. Using statistical analysis, we find a significant relationship between a country’s ESG ratings and the future change in the local currency. More specifically, we find that countries with high ESG ratings have significantly better-performing currencies than countries with lower ESG ratings. We find this result when predicting exchange rates one year and two years into the future using a host of controls.

#558 – Quality Strategy in the Indian Market
#620 – Long Term Time-series Momentum in India
#740 – Low Value Factor in India

Raju, Rajan and Krishnan, Harish, Factor Indices in India: Factor Exposures and Style Analysis
https://ssrn.com/abstract=4133389
Abstract:
Factor investing in India has seen much recent interest – primarily due to strong returns seen in the momentum factor, compared to traditional choices for an investor. We present a framework to evaluate factor indices through the lens of the academic framework and show how much of the “academic factor” is captured by a sample of 16 indices in India. While deliberating on factor exposure that each factor index seems to capture, we present a framework to evaluate indices based on factor regressions, returns style analysis and tracking errors. Recognising the liquidity and short-selling constraints in India, we use Big long-only legs of factors in our methodology. Indian factor index options capture only a part of the academic factors and, to a greater extent, the Big long-only legs. Multi-factor and complex factor indices show that the underlying factor exposures are generally uneven and occasionally nonexistent for one of the claimed factors. Choices of the universe, rebalance, and weighting schemes can create further divergence from theoretical factors, thus providing more options for asset managers and investors to innovate more “true-to-label” factor indices.

#4 – Overnight Anomaly

Haghani, Victor and Ragulin, Vladimir V and Dewey, Richard: Night Moves: Is the Overnight Drift the Grandmother of All Market Anomalies
https://ssrn.com/abstract=4139328
Abstract:
The bedrock of financial economics is that there should be a tradeoff between risk and reward: an investment with low risk should have a low expected return, while one that could make you rich should also be one which could lose you a lot of money. A lot of research in finance is focused on finding deviations to this risk-reward tradeoff, which are called “market anomalies” in deference to the idea that they are exceptions to this fundamental law of finance. Discovery of an anomaly is usually followed by frenzied debate and research that tries to explain it away as: 1) a statistical fluke, 2) compensation for some hitherto overlooked risk, or 3) some friction in the market which when fixed will make the anomaly go away.

The “overnight effect” is one such anomaly, which has been uncovered recently enough that its causes are still being hotly debated among researchers and practitioners. The overnight effect refers to the fact that, over at least the past three decades, investors have earned 100% or more of the return on a wide range of risky assets when the markets are closed, and, as sure as day follows night, have earned zero or negative returns for bearing the risk of owning those assets during the daytime, when markets are open. The effect is seen over a wide range of assets, including the broad stock market, individual stocks (particularly those popular with retail investors, and Meme stocks most of all), many ETFs, and cryptocurrencies.

In this article we briefly review the dozen or so papers which have explored this phenomenon to date, which have mostly focused on returns at the level of the broad stock market. We then take a closer look at the behavior of individual US stocks for clues about aggregate stock market behavior. We found that not only did the effect exist at the index level as previously reported, but it also shows up in a suggestively clustered pattern in individual stocks returns, and is particularly strong in “Meme” stocks. We find that a simple long-short portfolio that only takes exposure when the market is closed would have earned a return of 38% per annum (importantly, ignoring transactions costs) with an annualized Sharpe Ratio of about 3.

There are good reasons to care about this market anomaly, namely, 1) retail traders are potentially missing out on billions of dollars of returns due to mistimed trades, which should concern investors and market regulators alike, 2) there is speculation that the overnight effect might have implications for the long-term valuation of the entire equity market, and, 3) a better understanding of this phenomenon can contribute to our understanding of the limits of market efficiency.

And several interesting free blog posts have been published during last 2 weeks:

ETFs: What’s Better? Full Replication vs. Representative Sampling?

ETFs employ two fundamentally distinct methods to replicate their underlying benchmark index. The more conventional method, physical replication, involves holding all constituent securities (full replication) or a representative sample (representative sampling) of the benchmark index. In contrast, the synthetic replication achieves the benchmark return by entering into a total return swap or another derivative contract with a counterparty, typically a large investment bank. As we have previously discussed, there is no significant difference in the tracking ability between the physical and synthetic ETFs in the long term. And while our article compares physical and synthetic ETFs, it does not address the differences between the full replication ETFs and sampling ETFs. Therefore, one may ask a question: “When selecting a physically replicated ETF, which replication method is better, full replication or representative sampling?”

100-Years of the United States Dollar Factor

Finding high-quality data with a long history can be challenging. We have already examined How To Extend Historical Daily Bond Data To 100 years, How To Extend Daily Commodities Data To 100 years, and How To Build a Multi-Asset Trend-Following Strategy With a 100-year Daily History. Following the theme of our previous articles, we decided to extend historical data of a new factor, the Dollar Factor. This article explains how to combine multiple data sources to create a 100-year daily data history for the Dollar Factor (the value of the United States Dollar relative to its most important trading partners’ currencies), introduces data sources, and explains the methodology.