Quantpedia Premium Update – 1st March

New strategies:

#723 – The Realized Jumps Predict Cryptocurrency Returns

Period of rebalancing: Daily
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Moderately complex strategy
Backtest period: 2017-2021
Indicative performance: 54.81%
Estimated volatility: 12.36%

Source paper:

Zhang, Zehua and Zhao, Ran: Good Volatility, Bad Volatility, and the Cross Section of Cryptocurrency Returns
https://ssrn.com/abstract=3910202
Abstract:
This paper examines the distributional properties of cryptocurrency realized variation measures (RVM) and the predictability of RVM on future returns. We show the cryptocurrency volatility persistence and the importance of the asymmetry on volatility forecasting. Signed jumps variations contribute around 18% of the cryptocurrency return quadratic variations. The realized signed jump (RSJ) strongly predicts the cross-sectional future excess returns. Sorting the cryptocurrencies into portfolios sorted by RSJ yields statistically and economically significant differences in future excess returns. This jump risk premium remains significant after controlling for cryptocurrency market characteristics and existing risk factors. The standard cross-sectional regression convinces the cryptocurrency return predictability from RSJ by controlling multiple cryptocurrency characteristics. The investor attention explains the predictability of realized jump risk in future cryptocurrency returns.

#724 –Geopolitical Risk and Commodities

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 1982-2018
Indicative performance: 20.27%
Estimated volatility: 19.14%

Source paper:

Xiao Han, Nikolai Roussanov, and Hongxun Ruan: Mutual Fund Risk Shifting and Risk Anomalies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3931449
Abstract:
Risk-shifting by underperforming funds increases their demand for risky stocks. We investigate its contribution to the well known risk anomalies: the apparent overvaluation of stocks with high beta, idiosyncratic volatility, and skewness. We show that these anomalies are concentrated among stocks mainly held by laggard funds. Exploiting the Morningstar methodology change in 2002, whereby its star” ratings became based on relative performance within a style category rather than across the entire fund universe, we show that the beta anomaly is signi cant only when beta is measured against the S&P 500 index for the pre-2002 period and against the relevant category index for the post-2002 period. Using a demand system approach we nd that removing holdings of the bottom performance quintile of funds substantially reduces the beta anomaly returns.

#725 – Implied Volatility Effect in Corporate Bonds

Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: bonds
Complexity: Simple strategy
Backtest period: 2002-2017
Indicative performance: 7.44%
Estimated volatility: 7.7%

Source paper:

Jie Cao, Amit Goyal, Xiao Xiao, Xintong Zhan, Implied Volatility Changes and Corporate Bond Returns
https://ssrn.com/abstract=3400694
Abstract:
Corporate bonds with large increases in implied volatility over the past month underperform those with large decreases in implied volatility by 0.6% per month. In contrast to An, Ang, Bali, and Cakici (2014) who show that implied volatility changes carry information about fundamental news, our evidence suggests that implied volatility changes contain information about uncertainty shocks to the firm. Our results are consistent with the notion that informed traders with new information about firm risk prefer to trade in the option market, and that the corporate bond market under-reacts to this information.

#726 –Technical Indicators Predict Cross-Sectional Expected Stock Returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1932-2020
Indicative performance: 41.91%
Estimated volatility: 11.78%

Source paper:

Zeng, Hui and Marshall, Ben R. and Nguyen, Nhut H. and Visaltanachoti, Nuttawat, Technical Indicators and Cross-Sectional Expected Returns
https://ssrn.com/abstract=3992035
Abstract:
This study shows that 14 widely documented technical indicators explain cross-sectional stock expected returns. The technical indicators have lower estimation errors than the three factor Fama-French model and the historical mean. The long-short portfolios based on the crosssectional estimated returns generate substantial profits consistently across the entire period. The well-known cross-sectional expected return determinants, including momentum, size, book-to-market, investment, and profitability, do not explain the explanatory power of the technical indicators. Our findings suggest that the technical indicators play an important role in determining the variation in cross-sectional expected returns in addition to the five-factor model.

#727 –VIX Put-Call Volume Ratio

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: futures
Complexity: Moderately complex strategy
Backtest period: 2015-2020
Indicative performance: 50.02%
Estimated volatility: 43.8%

Source paper:

Gu, Chen and Guo, Xu and Kurov, Alexander and Stan, Raluca: The Information Content of the VIX Options Trading Volume
https://ssrn.com/abstract=3975868
Abstract:
This paper investigates the predictive content of the VIX options trading volume for the future dynamics of the underlying VIX index. Using a novel dataset from the Chicago Board Options Exchange, we calculate the put-call ratio based on the VIX option volume initiated by buyers to open new positions. We show that the put-call ratio negatively predicts the subsequent changes in the VIX index. The predictability is stronger during periods of elevated VIX levels and for short-dated contracts. These results support the hypothesis that informed traders use the VIX option market as a venue for their trading.

New research papers related to existing strategies:

#35 – Insiders Trading Effect in Stocks

Ma, Rui and Marshall, Ben R. and Nguyen, Harvey and Nguyen, Nhut H. and Visaltanachoti, Nuttawat: Climate Disasters and Insider Trading
https://ssrn.com/abstract=4008601
Abstract:
The monthly value of insider trades increases over 200% in firms headquartered in counties with a climate disaster. Climate-induced insider trading holds in general but is stronger when investors are distracted and less prevalent when insiders face higher litigation risk. Firm fundamentals decline following disasters, and insiders benefit by selling prior to this decline being priced. Insiders living in disaster counties do not trade more than those in unaffected counties, which indicates against a personal liquidity motivation. Our paper documents a new way through which climate impacts investor behavior and financial markets.

#14 – Momentum Factor Effect in Stocks

Goyal, Amit and Jegadeesh, Narasimhan and Subrahmanyam, Avanidhar: What Explains Momentum? A Perspective From International Data
https://ssrn.com/abstract=4012902
Abstract:
There is as yet no consensus on why equity markets permit momentum, although the literature proposes several explanations. Our analysis uses out-of-sample international data to consider a “horse race” across existing empirical proxies for momentum rationales used by earlier studies. Our central finding in cross-sectional analyses is that the proxy for thefrog-in-the-pan (FIP) hypothesis, which posits that due to limited attention, investors underreact to information that arrives gradually rather than in concentrated doses, consistently wins. Also, internationally, momentum is stronger in less volatile markets and in up-markets. The FIP proxy indicates that information flows more gradually during these market states, implying additional support for the hypothesis.

#5 – FX Carry Trade
#129 – Dollar Carry Trade

Kim, Sun Yong and Saxena, Konark: Long Run Risks in FX Markets: Are They There?
https://ssrn.com/abstract=3950981
Abstract:
This paper documents a tight connection between long run consumption risks (LRRs), currency excess returns and traditional global currency risk factors. We adopt a novel identification strategy that estimates country level LRRs using asset market data alone. With this identification strategy in hand, we find that: (1) currencies that suffer a bad relative LRR shock appreciate on impact before depreciating over the long run, (2) the High-Minus-Low (HML) carry trade sorts currencies on the basis of global LRR exposures, (3) the dollar carry trade outperforms on impact before underperforming over the long run in response to positive US relative LRR shocks, (4) US relative LRR shocks drive global currency risk factors. We interpret these facts as evidence in favour of an international LRR model where US LRRs drives the global exchange rate factor structure.

#49 – S&P 500 Index Addition Effect

Chinco, Alexander and Sammon, Marco: Excess Reconstitution-Day Volume
https://ssrn.com/abstract=3991200
Abstract:
FTSE/Russell reconstitutes its Russell 1000 and 2000 indexes on the last Friday of June each year. While exchange-traded funds (ETF) linked to either index must rebalance on Russell’s reconstitution day, FTSE/Russell announces which stocks will switch between indexes roughly two weeks in advance in order to give other kinds of funds (mutual funds, pension funds, endowments, etc) a chance to gradually rebalance their holdings. Textbook models suggest that at least some of these non-ETFs should take advantage of this opportunity. Yet we document that index-switcher volume does not increase at all during the two weeks prior to Russell’s reconstitution day. It suddenly spikes on reconstitution day with 3.15x more volume than can be explained by ETF rebalancing alone. It is as if all funds that rebalance in response to Russell reconstitution trade like ETFs in spite of the fact that they do not face the same end-of-day portfolio constraints.

#642 – Cashflow to Price in Indian Market

Preet, Simmar and Gulati, Ankita and Gupta, Arnav and Aggarwal, Aadit: Back Testing Magic Formula on Indian Stock Markets: An Analysis of Magic Formula Strategy
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3945468
Abstract:
The research tried to simulate functioning and evaluate results of a value investing strategy known as Magic formula developed by Joel Greenblatt. This strategy involves ranking a set of companies based on their Return on Capital Employed (ROCE) and Price-Earning (P/E) ratio, then adding these ranks to obtain a combined score and then choosing 30 companies with least joint score to form an equally weighted portfolio. In this research we show the working of this strategy for a period of 8 years starting from 1st July, 2012 till 1st February, 2020. Through this research we have tried to find answers to two important questions. First, whether the returns of this strategy outperforms the returns generated by broad market indices. In this research the broad market index taken into consideration for comparison is BSE Sensex. Second, whether the returns of this strategy improves on increasing/decreasing the number of companies to be included in the portfolio. To check this we have formed 10-stock and 50-stock portfolios and compared their returns and other variables with the primary 30-stock portfolio. For finding the answers to above questions, this strategy was used on the dataset of BSE 500 companies excluding financial companies and companies with negative earnings. The results obtained from the application of the Magic Formula have provided the following answers to the above mentioned questions. First, the risk-unadjusted returns generated exceeded market returns in 5 out of 8 years. CAGR of BSE Sensex is 9.31% against 13.89% of 30-stock Magic Formula portfolio. Thus the Magic Formula outperformed the market significantly. Second, CAGR of 30-stock portfolio exceeded CAGR of both of 10- stock and 50-stock portfolios and thus it is evident that 30-stock Magic Formula portfolio provides the best possible returns. Thus, the results of this research bolsters the application of value investing strategy professed by Joel Greenblatt named as “The Magic Formula” in the Indian Markets. It not only reaffirms the use of Magic Formula ideology but also the adoption of elements such as the number of companies to be included in the portfolio.

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

Are There Seasonal Intraday or Overnight Anomalies in Bitcoin?

At Quantpedia, we love seasonality effects, and our screener includes several strategies that exploit them. These anomalies are fascinating since they usually offer a favorable risk and reward ratio and are commonly invested only during short periods. Frequently, these strategies are valuable additions to portfolios because they are not that sensitive to overall market performance. This short article presents a brief examination of some possible Bitcoin seasonalities.

What’s the Relation Between Grid Trading and Delta Hedging?

Delta hedging is a trading strategy that aims to reduce the directional risk of short option strategy and reach a so-called delta-neutral position. It does so by buying or selling small increments of the underlying asset. Similarly, grid trading is a trading strategy that buys/sells an asset depending on its price moves. When the price falls, it buys and sells when the price rises a certain amount above the buying price. This article examines the similarities between delta hedging and grid trading. Additionally, it analyzes numerous versions of grid trading strategies and compares their advantages and disadvantages.

Beware of Excessive Leverage – Introduction to Kelly and Optimal F

Most investors focus solely on the profitability of their investment strategy. And, even though having a profitable strategy is important, it is not everything. There are still numerous other things to consider. One of them is the size of the investment. The investment size can increase or decrease the profitability of a strategy, so it is essential to choose it right. The following article is our introduction to Kelly and Optimal F methodologies, that underlies our upcoming Quantpedia Pro report.