New strategies:
#796 – Inflation Hedging Using Online Prices
Period of rebalancing: Daily
Markets traded: bonds
Instruments used for trading: bonds, ETFs, swaps
Complexity: Complex strategy
Backtest period: 2009-2022
Indicative performance: 2.6%
Estimated volatility: 2.4%
Source paper:
Cavallo, Alberto and Czasonis, Megan and Kinlaw, William B. and Turkington, David: Inflation Hedging: A Dynamic Approach Using Online Prices
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4214655
Abstract:
A vast literature, spanning back more than four decades, explores the relationship between inflation and asset prices. Most studies focus on the inflation hedging properties of stocks, bonds, or commodities assuming they are held in a static, buy-and-hold portfolio. Few have examined the inflation hedging properties of actively managed strategies. In this paper, we use high-frequency inflation indices derived from millions of product prices scraped from the websites of multi-channel retailers in 21 countries. We first show that these series contain forward-looking information with respect to official government inflation releases, and find that online inflation indices can predict changes in the breakeven inflation spread between nominal and inflation-linked Treasury bond yields in the United States. We then test an investment strategy to exploit this market inefficiency by allocating dynamically between Treasury Inflation Protected Securities (TIPS) and nominal Treasury bonds. A dynamic strategy offers investors the potential to capture the price appreciation of nominal bonds when realized inflation is below market expectations and the price appreciation of TIPS when realized inflation is above market expectations.
#797 – Improved Post-Earnings Announcement Drift with NLP Analysis
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2014-2022
Indicative performance: 5.89%
Estimated volatility: 7.75%
Source paper:
Dujava, Cyril and Kalús, Filip and Vojtko, Radovan: How to Improve Post-Earnings Announcement Drift with NLP Analysis
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4251574
Post–earnings-announcement drift (abbr. PEAD) is a well-researched phenomenon that describes the tendency for a stock’s cumulative abnormal returns to drift in the direction of an earnings surprise for some time (several weeks or even several months) following an earnings announcement. There have been many explanations for the existence of this phenomenon. One of the most widely accepted explanations for the effect is that investors under-react to the earnings announcements. Although we already addressed such an effect in some of our previous articles and strategies, we now present a handy method of improving the PEAD by using linguistic analysis of earnings call transcripts.
#798 – Cash Holdings Effect and Net Operating Assets
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1963-2014
Indicative performance: 9.25%
Estimated volatility: 17.26%
Source paper:
Ang, Lam, Ma, Wang & Wei: What is the Real Relationship between Cash Holdings and Stock Returns?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4131932
Abstract:
The literature has provided mixed evidence on the relationship between cash holdings and average stock returns. We empirically verify that the relationship is positive and robust to the adjustment of risk, the construction of cash holdings portfolios, and the weighting scheme of portfolio returns. We further examine a battery of potential channels that can explain the positive relationship. We find that the cash holding effect can be subsumed by accruals-related anomalies and it mainly comes from stocks with low net operating assets. It is stronger among stocks with high limits to arbitrage. Overall, our results indicate that the cash holding effect does not present a new asset-pricing regularity, but that it is a manifestation of existing anomalies closely related to mispricing.
#799 – Machine Learning Volatility Targeting of Equity Indices
Period of rebalancing: Weekly
Markets traded: equities, bonds
Instruments used for trading: ETFs, futures, bonds
Complexity: Very complex strategy
Backtest period: 2007-2020
Indicative performance: 14.49%
Estimated volatility: 23.04%
Source paper:
Chun, Dohyun and Cho, Hoon and Ryu, Doojin: Forecasting Stock Market Volatility and Application to Volatility Timing Portfolios
https://ssrn.com/abstract=4167561
Abstract:
This study predicts stock market volatility and applies them to the standard problem in finance, namely, asset allocation. Based on machine learning and model averaging approaches, we integrate the drivers’ predictive information to forecast market volatilities. Using various evaluation methods, we verify that those high-dimensional models have better predictive performance relative to the standard volatility models. Furthermore, we construct volatility timing portfolios and discover that portfolios based on high-dimensional models mostly yield higher Sharpe ratios compared with the market. Among others, the least absolute shrinkage and selection operator (LASSO) generates the most accurate forecasts, leading to outstanding investment performance, regardless of the forecasting horizon.
#800 – Conservative Formula in India
Period of rebalancing: Quarterly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 2006-2022
Indicative performance: 14.49%
Estimated volatility: 23.04%
Source paper:
Raju, Rajan and Teli: Anish, The Conservative Formula: Evidence from India
https://ssrn.com/abstract=4163613
Abstract:
We implement the Conservative Formula outlined by Van Vliet and Blitz (2018) on data from Indian stock markets. It selects 100 liquid stocks based on three criteria: low realised volatility, high net payout yield and strong price momentum. We demonstrate that this simple yet robust formula exposes investors to key factors like low volatility, quality (through operating profitability and investment factors) and momentum in India. The quarterly rebalanced portfolio of 100 stocks significantly outperforms the S&P BSE 100 in absolute returns (by 12.6% pa compound) and risk-adjusted returns. We show the Conservative portfolio’s performance outperforms the S&P BSE 100 and the Speculative portfolio over different business cycles. The formula has been shown to work over long periods: in US markets since 1929 and in other markets like Europe, Japan and Emerging Markets. Our paper extends this evidence to India. The conservative formula uses three simple criteria that do not require accounting data and, therefore, should appeal to a broad base of asset owners and managers in India.
#801 – High Disagreement Predicts Hedge Fund Returns
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: funds
Complexity: Simple strategy
Backtest period: 1997-2020
Indicative performance: 10.49%
Estimated volatility: 22.68%
Source paper:
Jacoby, Gady and Li, Shi and Lin, Nanying: Disagreement Exploitation and the Cross-Section of Hedge Funds Performance
https://ssrn.com/abstract=4082461
Abstract:
This study examines the role of market disagreement in explaining the cross-section of hedge fund performance. In a market where disagreement fluctuates, skilled arbitrageurs may employ different trading strategies to exploit the mispricing caused by disagreement and short-sale constraints. Skilled hedge funds with high sensitivity to disagreement can take advantage of mispricing in high-disagreement periods to improve their performance. We show that hedge funds with a high disagreement beta tend to have a disagreement exploitation skill and thus earn higher cross-sectional returns relative to other hedge funds that do not have this skill. Experienced hedge funds (proxied with size and age) and hedge funds that charge a high incentive fee are likely to have high disagreement betas.
New research papers related to existing strategies:
#585 – Trend Factor in China
#612 – Abnormal Turnover in China
Ma, Yao and Yang, Baochen and Li, Jinyong and Shen, Yue: Trend Information and Cross-Sectional Returns: The Role of Analysts
https://ssrn.com/abstract=4249817
Abstract:
This study investigates the role of analysts on the relationship between trend information and cross-sectional returns. Our results show that the trend information about past price and trading volume significantly predicts future stock returns in the Chinese stock market. The trend effect is more pronounced among stocks without analyst coverage or low analyst coverage, which indicates that analysts play an important role in producing and disseminating information and helping information to be incorporated into stock price more quickly, thus reducing mispricing and weakening the trend effect. Considering the information’s updates from analysts, the findings suggest that the trend effect is stronger for stocks with no revision, while the trend effect becomes weaker or even disappears for stocks with upgrade or downgrade revision. Moreover, our results show that analyst recommendations fail to take full advantage of trend information or even contradict trend signals although analyst recommendations do contain forecast information related to future stock returns. Furthermore, we find that analyst recommendations provide additional useful information that can enhance the predictability of trend information, but the value of information reflected in their recommendations cannot offset the value of trend information.
#245 – Post-Split Drift Combined with PEAD Anomaly
#315 – Stock Splits Strategy Based on Earnings Management
Blau, Benjamin M. and Cox, Justin and Griffith, Todd and Voges, Ryan: Daily Short Selling Around Reverse Stock Splits
https://ssrn.com/abstract=4162062
Abstract:
We examine daily short-selling activity and prices around reverse stock splits. Using a difference-in-difference approach with a matched sample of reverse splitting and non-reverse splitting stocks, we show that short selling increases in stocks that reverse split, relative to those that do not. Consistent with prior literature, we also document negative cumulative abnormal returns for stocks around reverse splits. Perhaps most interestingly, we find that in stocks that reverse split, short sellers appear to exacerbate the negative returns. Together, our findings indicate that limits to arbitrage do not restrict short sellers from trading on anticipated price declines.
#49 – S&P 500 Index Addition Effect
Eksi, Asli and Roy, Saurabh: Non-fundamental Shocks and Implied Volatility Skew: Evidence From S&P 500 Index Inclusions
https://ssrn.com/abstract=4192142
Abstract:
When stock prices deviate from their fundamental values due to excess demand, investors anticipate reversals and trade in the options market to exploit the temporary misvaluation. This leads to options’ predictability of stock returns beyond the well-known informed trading channel. Using S&P 500 index inclusions, we examine how option prices predict the reversal of a non-fundamental demand shock to the stock price. We find that the implied volatility skew of stocks added to the index becomes steeper in the months following index inclusion. This effect is not caused by an increase in systematic risk or the pre-inclusion momentum of added stocks. It exists only for stocks that experience a high index addition announcement return and fades after the announcement return reverses. Moreover, the implied volatility skew predicts next month’s return for added stocks but this predictability is mainly driven by return reversals.
#647 – Equity Duration
Walter, Dominik and Weber, Rüdiger: Is There An Equity Duration Premium?
https://ssrn.com/abstract=4157585
Abstract:
Equity duration is a measure of discount-rate sensitivity that is driven by both, stock-specific cash-flow timing and stock-specific discount-rate levels. Established measures of equity duration using market-price information derive their predictive power for returns from using market-implied discount rates. We introduce new measures of pure cash-flow timing which disentangle discount-rate level from cash-flow timing information. Our results indicate an unconditionally flat relationship between timing and average returns. However, it turns out that in recessions (expansion episodes), there is a negative (positive) relation between cash-flow timing and average stock returns.
And several interesting free blog posts have been published during last 2 weeks:
Stock-Bond Correlation, an In-Depth Look
The recent surge in global inflation sent shock waves across financial markets and affected the complicated relationship between stocks and bonds. Today, we would like to present you with a review of two interesting papers, which provide both a deep and easy-to-understand examination of the correlation structure of those two main asset classes. The first paper reviews specifics in various parts of the world, and the second one summarizes known information about the macroeconomic drivers of the US stock-bond correlation.
The Role of Interest Rates in Factor Discovery
Over the past several decades, economists and quantitative scientists found a very large number of asset pricing anomalies and published numerous research papers about their findings, and this is known in the financial jargon as “factor zoo.” However, one strong underlying force might drive the performance of many of those anomalies. What’s that force? The level and trend in the interest rates, as in almost all parts of the developed world, there was a long-term steady decline in rates and inflation for nearly 40 years. We use the past tense as it seems that the situation changed at the beginning of this year…
Van Binsbergen, Jules H. and Ma, Liang and Schwert, Michael (Sep 2022) touched on this subject and made a careful examination of both past factor research and found that a significant part of published papers and developed models are sometimes unknowingly exposed to fitting to low or even zero interest rates.
How to Replicate Any Portfolio
Would you like to see the performance of your portfolio 100 years back in history? Do you want to analyze the risk of your strategy under 100 years of real historical scenarios? All of these, and much more, will be soon (in a few days) available for Quantpedia Pro subscribers. How? We will explain today how we can model a 100-year history of your portfolio.
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
#483 – Forecasting Index Changes in the German DAX Family
#779 – Factor Momentum in the Chinese Stock Market
#791 – Institutional Equity Momentum in China
#792 – Retail Equity Reversal in China
#793 – Sovereign CDS Currency Factor
