New strategies:
#914 – Bitcoin Leads Altcoins on Intraday Basis
Period of rebalancing: Intraday
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Complex strategy
Backtest period: 2021 – 2022
Indicative performance: 10.21%
Estimated volatility: 3.36%
Source paper:
Tian, Jingyuan and Mikhelson, Ilya, The Pricing Myth of Altcoins: Statistical and Empirical Evidence of Market Consolidation Driven by Short-Term Bitcoin Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4545134
Abstract:
The cryptocurrency market has demonstrated rapid growth with the total market capitalization hitting $3 trillion in November 2021 and over 10,000 altcoins in existence as of June 2022. Bitcoin (BTC) accounts for over 40% of the market share as of early 2022. The pricing of altcoins remains a myth. We demonstrate the high correlation between BTC and 40 large-cap altcoins listed on Binance despite that most altcoins claim to serve different functionalities in the blockchain ecosystem. Besides long-term correlation, this paper also investigates the short-term impact of Bitcoin momentum. Statistical evidence suggests a temporary market consolidation following BTC upticks, as indicated by shifts in altcoin price distribution. We designed an empirical trading framework, Bitcoin Lead Universal Trading Algorithm (BLUTA), which extracts consistent alpha from market consolidation and Bitcoin dominance. BLUTA significantly outperforms a wide range of macro benchmarks and alternative trading strategies.
#915 – On-Chain Cashflows and the Cross-Section of Cryptocurrency Returns
Period of rebalancing: Daily
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Very complex strategy
Backtest period: 2019-2022
Indicative performance: 10.95%
Estimated volatility: 16.99%
Source paper:
To, Ainsley: Magical Internet Money? On-Chain Cashflows and the Cross-Section of Cryptocurrency Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4540433
Abstract:
I find that crypto valuation measures derived from on-chain fundamental cashflow characteristics, analogous to valuation metrics used in equity markets, are priced in the cross-section of token returns. A cashflow-based value factor constructed from these measures is not spanned by crypto factor models in the literature. I test different measures of cashflow and find that revenues retained by protocols show the strongest results, whilst token incentives as a cost of revenue measure have little pricing power. I also find evidence that different characteristics are significant for native tokens of a blockchain compared to tokens issued by decentralised applications. Lastly, I test a set of novel crypto native characteristics, unique to public blockchains, that proxy for capital gains overhang, insider ownership, and investor sophistication.
#916 – Intangibles Intensity in 10-K Reports
Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1995-2020
Indicative performance: 3.61%
Estimated volatility: 12.38%
Source paper:
David, Alexander and Hosseini, Amir and Srivastava, Anup: Is Intangibles Talk Informative about Future Returns? Evidence from 10-K Filings
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4507157
Abstract:
We construct a measure of intangible intensity — intangibles talk — based on textual analysis of discussions on intangibles in a firm’s 10-K filings. This measure is based on firms’ discussion of ex-post outcomes of intangibles investments. Our measure is correlated with, but carries orthogonal information to, prior measures of intangibles that are largely based on capitalization of initial investments. This distinction between expected outcomes of initial investments, as measured in prior studies, and ex-post outcomes, we examine, is important because intangible investments often have uncertain lottery-type payoffs. Thus the value of outcomes could differ from capitalized historical costs, if it was permitted in accounting. Managers are likely to discuss those positive outcomes in the 10-K documents. We test the informativeness of our measure about future returns. Returns from long and short portfolios based on high and low values of intangibles talk, respectively, outperform traditional book-to-market value strategy. Our strategy generates an average annual alpha of 3.26% from 1995 to 2020 in the four-factor (three Fama and French factors plus momentum) model. Our alphas are higher than those generated from portfolios sorted on other indicators of intangible intensity shown in the literature. We also generate positive returns by taking a long positions on our measure and taking a short position on intangibles-enhanced value portfolio studied in prior literature. Positive alphas are concentrated in stocks with higher arbitrage risk, proxied by idiosyncratic volatility, suggesting that investors misprice stocks with higher intangible intensity as described in 10-K documents.
#917 – Low-Risk Anomaly in India
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2007-2023
Indicative performance: 42.63%
Estimated volatility: 33.56%
Source paper:
Raju, Rajan, Low-Risk Anomaly: Evidence from India
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4398656
Abstract:
The low-risk anomaly in the Indian equity market is explored using a broad universe of 4,400 companies over 19 years in India. The anomaly is characterised by a strong convex relationship between returns and volatility. We describe the methodology used to construct five low-risk factors using realised volatility, ex-ante beta, CAPM beta, and realised IVOL as measures of risk. Our findings demonstrate that lower-risk portfolios constructed using these measures exhibit higher risk-adjusted returns than high-risk portfolios. The strength of the anomaly does not appear to diminish over time in Indian equities. There is clear evidence of the low-risk anomaly in the Indian equities market even after controlling for the standard academic factors.
#918 – Seasonality in Equity Long-Short Factor Strategies
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1931-2022
Indicative performance: 2.92%
Estimated volatility: 5.51%
Source paper:
Mercik, Aleksander R. and Cupriak, Daniel and Zaremba, Adam: Factor Seasonalities: International and Further Evidence
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4490643
Abstract:
We study factor return seasonalities in international markets. Using up to 143 characteristic-sorted portfolios from 39 countries, we document a pervasive cross-sectional pattern: anomalies with a high average same-calendar month return outperform those with low average returns. The effect persists across individual markets and global samples and cannot be attributed to common risk factors. Neither factor momentum nor cross-sectional variation in unconditional premia explains the phenomenon. Instead, the effect originates from price seasonalities, which transmit to factor portfolios, engendering seasonality in their returns. Consequently—rather than manifesting an independent asset pricing phenomenon—factor seasonality merely reflects its stock-level equivalent.
#919 – Improved Dispersion Trading
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: options, stocks
Complexity: Very complex strategy
Backtest period: 2005-2023
Indicative performance: 8.3%
Estimated volatility: 10%
Source paper:
Oliveira, Rogerio and Wasserstein, Gustavo: The Quest for Alpha in Equity Gamma
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4474815
Abstract:
This paper examines the relative value in and opportunities of gamma trades in equity options markets. It has been well documented in the literature that shorting index options straddles, with or without delta hedging, tends to produce positive expected returns, albeit at the expense of very high risks. Buying all the single name straddles against the short index straddle, a popular strategy known as “dispersion trade”, has been shown to significantly improve the risk return profile in other applied work. This strategy is well known to be related to the fact that implied correlation is often priced excessively high. Most econometric work highlighting these options strategies were conducted in the late 90´s and early 2000´s, before the 2008 financial crises and 20220 Covid crisis, and since then their attractiveness have been substantially mitigated. Dispersion trades usually assign market index weight to each single name straddle, without considering the relative value opportunities among them, ignoring the fact that some single name implied volatilities might be overpriced, and others underpriced. We suggest a measure for the relative price of gamma trades and hedge ratios to optimally allocate among single name straddles. We use this relative pricing to build a dynamic long/short gamma strategy which favors the “cheaper” straddles and avoid the “expensive” ones at any point in time. This significantly improves the risk return profile when compared to dispersion trades in the S&P500 market. Finally, we conduct a broad screening of all the worldwide equities indices and subindices where historical implied volatility is available to indicate where opportunities for our dynamic long/short gamma strategy appear very promising.
New research papers related to existing strategies:
#5 – FX Carry Trade
Granziera, Eleonora and Sihvonen, Markus Bonds: Currencies and Expectational Errors
https://ssrn.com/abstract=4513500
Abstract:
We propose a model in which sticky expectations concerning short-term interest rates generate joint predictability patterns in bond and currency markets. Using our calibrated model, we quantify the effect of this channel and find that it largely explains why short rates and yield spreads predict bond and currency returns. The model also creates a downward sloping term structure of carry trade returns, difficult to replicate in a rational expectations framework. Including a sticky short rate expectations channel into a standard affine term structure model improves its fit and allows the model to better capture the drift patterns in the data.
#5 – FX Carry Trade
#184 – Timing Carry Trade
#221 – Timing Carry Trade v2
Mokanov, Denis: Deviations from Rational Expectations and the Uncovered Interest Rate Parity Puzzle
https://ssrn.com/abstract=4490738
Abstract:
This paper documents a novel result regarding the uncovered interest rate parity (UIP) puzzle: investing in high interest rate currencies does not yield positive excess returns during recessions. That is, the UIP holds in bad times. This new finding is a challenge to existing rational expectations models that address the UIP puzzle. A model featuring investors whose interest rate expectations are distorted by extrapolation bias and time-varying stickiness is able to quantitatively account for this evidence when calibrated to available survey data. The model also generates predictions for bond return predictability, the profitability of time-series momentum in the foreign exchange and fixed income markets, and foreign exchange predictability during the post-2007 period, which are borne out in the data.
#224 – Profitability Factor Combined with Value Factor
Jiang, Xiaoquan and Lu, Xiaomeng: Modified Quality Investing and Value Investing
https://ssrn.com/abstract=4531618
Abstract:
We propose and identify two novel investment strategies, the modified quality (MQ) investing and the modified value (cheapness) (MC) investing. We show that the MQ (MC) investing notably outperforms the traditional quality (value) investing. Our MQ strategy tends to avoid overpriced quality firms while MC strategy aims to circumvent inferior cheap firms. We attribute the superior performance of our strategies to that MQ (MC) captures quality (value) using both fundamentals and market information. Furthermore, macroeconomic growth, liquidity risk, uncertainty, downside risk, and sentiment partially explain the return spreads in the MQ strategy but little in the MC strategy.
#890 – Customer Momentum
Wang, Xin: The Cross-predictability of Industry Returns in International Financial Markets
https://ssrn.com/abstract=4505004
Abstract:
This paper finds evidence of return cross-predictability among trading partners in international financial markets. We show that the predictability of international customers dominates the predictability of domestic customers, and the predictability of international intra-industry customers dominates the predictability of international inter-industry customers. This return cross-predictability decreases with two country characteristics: financial sophistication and size.
#460 – ESG Level Factor Investing Strategy
Karolyi, George Andrew and Wu, Ying and Xiong, Wei (William): Understanding the Global Equity Greenium
https://ssrn.com/abstract=4391189
Abstract:
We offer new evidence on how the application of environmental, social, and governance (ESG) criteria has affected international stock returns. We estimate the market-based equity greenium in a cross-section of 21,902 firms from 96 countries. We find reliable evidence that green stocks earned higher returns than brown stocks around the world. This outperformance is associated with lower stock returns of energy firms but not higher returns of technology stocks. Decomposing this outperformance further into five regions, including North America, Europe, Japan, Asia Pacific, and Emerging Markets, demonstrates that the equity greenium effect mostly occurs in North America and during the period before 2016. Most of the equity greenium performance cannot be explained by exposures to return factors prominent in the asset pricing literature.
And several interesting free blog posts have been published during last 2 weeks:
Seasonality effects, one of the most fascinating phenomena in the world of finance, have captured the attention of investors and researchers worldwide. Since these anomalies are often driven by factors other than general market trends, they usually don’t correlate strongly with market movements, which can help reduce the portfolio’s overall risk. Following the theme of our previous article Are There Seasonal Intraday or Overnight Anomalies in Bitcoin?, we decided to extend the data and conduct a more in-depth analysis of our earlier findings. This article explores potential seasonal patterns related to Bitcoin, focusing on whether these patterns are influenced by factors such as current market trends or the level of volatility in the market.
Language Analysis of Federal Open Market Committee Minutes
If there were a Superbowl of Finance for equities, it’d definitely be FOMC (Federal Open Market Committee) meetings. Investors and traders from around the world gather and make their decisions on the brink of releasing a statement and following the press conference. Shah, Paturi, and Chava (May 2023) contribute with a new cleaned, tokenized, and labeled open-source dataset for FOMC text analysis of various data categories (meeting minutes, speeches, and press conferences). They also propose a new sequence classification task to classify sentences into different monetary policy stances (hawkish, dovish, and neutral) and show the application of this task by generating a hawkish-dovish classification measure from the trained model that they later use in an interesting trading strategy.
Analysis of Price-Based Quantitative Strategies for Country Valuation
The motivation for this study comes from the idea of simplifying the concept of relative valuation among the countries. There exist several ideas for relative value approaches that compare the “visible price” (or market capitalization) of the stock market to some unseen “intrinsic value” of the market. The ideas of what we can use to measure the unseen “intrinsic value” of each individual country/market are numerous – it may be a number derived from GDP (like in a Buffet Indicator), total earnings of listed companies in the selected country (Shiller’s CAPE ratio), or ratios derived from yields, demographic, etc., etc. We asked ourselves – can we create a relative valuation model and use just the price data?
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
#437 – Optimalized Subportfolio Momentum
#879 – Implied Put-Call Volatility Spread in US Equities
#907 – Estimating Hedge Funds’ Returns Out of Sample
