New strategies:
#959 – Volume Weighted Average Price (VWAP) as Precise Trend-Following Indicator for Day-Traders
Period of rebalancing: Intraday
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2018-2023
Indicative performance: 43%
Estimated volatility: 18%
Source paper:
Zarattini, Carlo and Aziz, Andrew: Volume Weighted Average Price (VWAP) The Holy Grail for Day Trading Systems
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4631351
Abstract:
This paper explores the application of the Volume Weighted Average Price (VWAP) in detecting market imbalances and enhancing trading decisions across diverse market conditions. We introduce a straightforward VWAP-based day trading strategy, which initiates long positions when price is above the VWAP and short positions when it falls below the VWAP. Our analysis employs QQQ and TQQQ as primary trading instruments, covering the period from January 2, 2018, to September 28, 2023. This timeframe includes two bear markets and multiple high-volatility events, providing a comprehensive test of market variations. Our findings reveal that an initial investment of $25,000 in the VWAP Trend Trading strategy with QQQ would have grown to $192,656, net of commissions, yielding a 671% return. This performance is marked by a maximum drawdown of just 9.4% and a Sharpe Ratio of 2.1. In contrast, a passive buy-and-hold strategy in QQQ during the same period would have returned 126%, with a significantly higher maximum drawdown of 37% and a lower Sharpe Ratio of 0.7. Further enhancing our strategy with TQQQ (3x leveraged ETFs of QQQ), we observed extraordinary outcomes: a $25,000 investment surged to $2,085,417, net of commissions. This equates to an 8,242% total return, or an average annual return of 116%, maintaining a maximum drawdown comparable to the passive QQQ strategy. Although we do not regard it as a fully developed trading system, our findings highlight VWAP’s potential as a powerful tool for active traders and investors, emphasizing its superiority over standard buy-and-hold approaches in terms of profitability, risk-adjusted returns, and resilience during market fluctuations.
#960 – Anchoring Bias Factor in Chinese Stocks
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2005-2021
Indicative performance: 13.89%
Estimated volatility: 17.37%
Source paper:
Xie, Jun and Feng, Jinyu and Gao, Bin: Anchoring bias of individual stock
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4222079
Abstract:
Different from studies that only explore the existence of anchoring biases using anchor points, this paper quantifies the anchoring bias of individual stocks. Specifically, the paper utilizes the impact of the 52-week high ratio on the relative net purchase ratio as a measure of the anchoring bias, and discovers a significantly negative relationship between the anchoring bias and one-month ahead stock returns, which holds both in-sample and out-of-sample. Moreover, the paper identifies that retail investors demonstrate a stronger anchoring bias than institutional investors. However, only the anchoring bias of institutional investors has favorable predictive power that remains significant even after controlling for firm-specific characteristics and adjusting for various asset pricing factors. These findings highlight the importance of the anchoring bias in asset pricing and contribute to a better understanding of investor behavior.
#961 – Salient Theory Predicts US Stocks in the Cross Section
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1963 – 2018
Indicative performance: 8.18%
Estimated volatility: 25.57%
Source paper:
Guo, Jiaqi and Li, Youwei: Salience Theory and Risk Anomalies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4603171
Abstract:
Investor demand for stocks is driven by their salient returns (Bordalo, Gennaioli, and Shleifer, 2012). We hypothesize that high-risk stocks are more likely to yield salient returns than low-risk stocks. Using various risk measures, we show that the risk-return relation is significantly positive for stocks with salient downsides but negative for stocks with salient upsides. We provide evidence that retail investors pay strong attention to salient stock payoffs and demand more (less) high-risk stocks in the presence of salient upsides (downsides). Our findings hold after controlling for alternative explanations including lottery preference, arbitrage risk, reference-dependent preference, disagreement and coskewness risk.
#962 – Delta Hedged Short Straddle BTC Strategy
Period of rebalancing: Intraday
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Complex strategy
Backtest period: 2019-2023
Indicative performance: 39%
Estimated volatility: 30%
Source paper:
Sepp, Artur and Lucic, Vladimir: Valuation and Hedging of Cryptocurrency Inverse Options: With Backtest Simulations using Deribit Options Data
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4606748
Abstract:
Currently the most liquidly traded options on the crypto underlying are the so-called inverse options. An inverse option contract is quoted and traded in the units of the underlying cryptocurrency. The main economic reason for popularity of inverse contracts in the crypto exchanges (such as Deribit) is that inverse contracts enable to operate without maintaining fiat cash accounts. For the theoretical part, we show that inverse options are just regular vanilla options considered under the martingale measure using the underlying as the numeraire. This measure requires an adjustment to option delta. For the empirical part, we use Deribit options data of past four years to simulate delta-hedged option strategies. We introduce USD and Coin accounting of trading P&L which is important for designing strategies in crypto options. We show empirically that USD and Coin accounting rules are equivalent when performance is measured is Coin and USD units, respectively. We establish that the risk-premia observed in options on Deribit is negative and significant so that strategies selling volatility are expected to generate positive risk-adjusted performance in the long-term.
New research papers related to existing strategies:
#614 – Climate sentiment, carbon prices and Emission minus Clean Portfolio
#679 – Carbon Emmision Intensity in Stocks
#707 – Benchmarks Portfolios with Decreasing Carbon Footprints
Cheema-Fox, Alexander and LaPerla, Bridget Realmuto and Serafeim, George and Turkington, David and Wang, Hui: Decarbonizing Everything
https://ssrn.com/abstract=3693941
Abstract:
We analyze how the use of different climate risk measures leads to different portfolio carbon outcomes and risk-adjusted returns. Our findings are synthesized in a rules-based investment framework, which selects a different type of climate metric across industries and weighs industries in the portfolio based on the variability of carbon outcomes among firms within each industry. We conclude that analyzing the merits and applicability of various climate data can help investors manage climate risk while increasing risk-adjusted returns.
#575 – Momentum and Low Risk Effects in India
#620 – Long Term Time-series Momentum in India
Raju, Rajan: Watch Your Weight: Navigating Portfolio Weighting Schemes in Indian Momentum Strategies
https://ssrn.com/abstract=4607933
Abstract:
This working paper is the second in a trilogy investigating the complexities of constructing long-only momentum portfolios in the Indian equity markets. The study rigorously evaluates six popular weighting schemes across various portfolio sizes and stock universe dimensions. Our findings reveal that the weighting scheme choice significantly influences the risk-return profile and factor exposures. Notably, all portfolios show robust exposure to the momentum factor, while non market-cap weighted schemes offer heightened exposure to the size factor. The study provides asset managers and investors with a disciplined framework for evaluating strategies, considering the unique microstructure of the Indian market.
#365 – Timing S&P500 Using a Large Set of Forecasting Variables
Gomez Cram, Roberto: Late to Recessions: Stocks and the Business Cycle
https://ssrn.com/abstract=3671346
Abstract:
I find that returns are predictably negative for several months after the onset of recessions, becoming high only thereafter. I identify business cycle turning points by estimating a state-space model using macroeconomic data. Conditioning on the business cycle further reveals that returns exhibit momentum in recessions, whereas in expansions, they display the mild reversals expected from discount rate changes. A strategy exploiting this pattern produces positive alphas. Using analyst forecast data, I show my findings are consistent with investors’ slow reaction to recessions. When expected returns are negative, analysts are too optimistic and their downward expectations revisions are exceptionally high.
#233 – Using Straddles to Trade on Earnings Announcements
Neururer, Thaddeus and Papadakis, George: Disagreements, private information, and the demand for options before earnings announcements
https://ssrn.com/abstract=3736127
Abstract:
We test whether investor uncertainty, disagreements, and private information about future equity price and variance outcomes impact the demand for, and the pricing of, options before earnings announcements. We proxy for excess option demand using straddle returns and use the option-to-stock trading volume ratio as a proxy for disagreement and private information. We find lower straddle returns around earnings announcements for firms with greater past option-to-stock trading ratios. This relationship holds conditional to other factors suggested to predict straddle returns, and the association appears specific to the announcement period. The association is driven by the prior trading of at-the-money options which have the greatest sensitivity to future volatility outcomes and shifts in post-earnings announcements volatilities. Finally, we find evidence that a higher option-to-stock trading volume ratio predicts a larger implied volatility term structure drop after the release of earnings information. Our results suggest that uncertainties and disagreements that drive variance trading lead to excess demand for hedging or speculative purposes around earnings announcements, and generate trading profits to investors willing to sell variance protection around these disclosure events.
#551 – Momentum Commodity Premia in China
Cho, Hoon and Ham, Hyuna and Kim, Hyeongjun and Ryu, Doojin: Time-Series Momentum in the Chinese Commodity Futures Market
https://ssrn.com/abstract=3311479
Abstract:
This study examines time-series momentum in the Chinese commodity futures market. The findings show that a time-series momentum strategy performs best with a one-month look-back period and a one-month holding period. Furthermore, this strategy outperforms passive long and cross-sectional momentum strategies in the Chinese futures market based on Sharpe ratios, risk-adjusted excess returns, and cumulative returns. But highly volatile market characteristic with many speculative investors limits the period in which time-series momentum is maintained. Our findings suggest that the anomaly is observed in international asset markets, including Chinese commodity futures, and support the implication that speculators profit from time-series momentum strategy is the expense of hedgers.
#24 – Merger Arbitrage
#225 – Improved Merger Arbitrage
#273 – Overreaction to Merger and Acquisition Announcements
Davis, Frederick James and Khadivar, Hamed and Walker, Thomas John: Institutional Trading in Firms Rumored to be Takeover Targets
https://ssrn.com/abstract=3707240
Abstract:
In this paper we examine institutional trading in proximity to takeover rumors by combining the ANcerno dataset of transaction-level institutional trades with a unique sample of takeover rumor ‘scoops’. We find that institutions are net buyers in firms which subsequently become subject to takeover speculation and that institutional trading predicts which rumored firms will eventually receive takeover bids. Segregating funds according to their propensity to trade, we show that those less likely to purchase rumored targets by chance over the pre-rumor period are more likely to identify firms which will receive bid proposals and that they trade more profitably over both the pre- and post-rumor periods. We test for the presence of informed trading in a variety of ways and conclude that institutional investors appear to trade on material private information which identifies the firms soon to be the target of takeover speculation.
#180 – Simple NAV Arbitrage within Country ETFs
#354 – ETF Creation/Redemption Activity and Return Predictability
Gorbatikov, Evgenii and Sikorskaya, Taisiya: Two APs Are Better Than One: ETF Mispricing and Primary Market Participation
https://ssrn.com/abstract=3923503
Abstract:
Exchange-traded funds (ETFs) depend on arbitrageurs to correct deviations between a fund’s price and its fair value. ETFs have designated brokers, or authorized participants (APs), who have a unique right to create and redeem ETF shares, and who can thus trade on ETF mispricing without risk. Using novel regulatory filings, we provide the first description of the US ETF-AP network. It has a dense core and a sparse periphery, and the observed creation/redemption volumes are highly concentrated. The level of mispricing in a US equity ETF is negatively related to the fund’s network diversity, especially during times of high market volatility. Funds that share more APs exhibit stronger mispricing comovement. We theoretically show that diverse networks help mitigate the effect of shocks to AP-specific arbitrage costs. We highlight the importance of AP balance sheet usage costs in ETF markets by exploiting the Federal Reserve’s purchases of bond ETFs in 2020.
And several interesting free blog posts that have been published during the last 2 weeks:
Pragmatic Asset Allocation Model for Semi-Active Investors
The primary motivation behind our study stems from an observation of the Global Tactical Asset Allocation (GTAA) strategies throughout the existing papers – the majority of them require relatively frequent rebalancing from the point of view of the ordinary investor. Portfolio rebalancing is usually done on a weekly or monthly basis, and while this period may seem overly boring and slow for the majority of traders (who like to trade on intraday or daily basis), fans of GTAA strategies are not traders; they are investors. Of course, some like to follow the ebbs and flows of the market. But a lot of investors just want to have a life. The financial market is not their hobby. However, on the other hand, they also do not want to hold just the passive buy & hold portfolio. Recognizing the demand for the semi-active strategy, we introduce our novel Pragmatic Asset Allocation.
Machine Learning Execution Time in Asset Pricing
Machine Learning will quite certainly continue to be a hot topic in 2024, and we are committed to bringing you new developments and keeping you in the loop. Today, we will review original research from Demirbaga and Xu (2023) that highlights the critical role of machine learning model execution time (combination of time for ML training and prediction) in empirical asset pricing. The temporal efficiency of machine learning algorithms becomes more pivotal, given the necessity for swift investment decision-making based on the predictions generated from a lot of real-time data. Their study comprehensively evaluates execution time across various models and introduces two time-saving strategies: feature reduction and a reduction in time observations. Notably, XGBoost emerges as a top-performing model, combining high accuracy with relatively low execution time compared to other nonlinear models.
Exploration of CTA Momentum Strategies Using ETFs
Commodity Trading Advisor (CTA) funds are commonly associated with managed futures investing; however, beyond commodities, they have the flexibility to venture into other assets, including interest rates, currencies, fixed income, and equity indices. Most of the CTA strategies are trend-following, taking long positions in markets experiencing upward trends and short positions in markets undergoing downward trends, with the expectation that these trends will persist. CTA funds demonstrate a negative correlation with traditional assets, especially evident during periods of pronounced downturns in equity markets, and this characteristic positions them as an appealing alternative investment option, serving as a protective measure against extreme events in financial markets. We aim to explore these trend-following strategies by creating a “CTA proxy” using ETFs across all asset classes. Using ETFs allows for maintaining the diversification of CTA funds and represents an alternative with easier data availability compared to futures contracts. Additionally, we are very interested in seeing the contribution of the short leg of CTA sub-strategies to performance, as we have a hypothesis that we can significantly improve the risk-return profile of the CTA strategies by removing a short leg portion of the strategy from some assets.
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
#282 – Trading based on Higher Moments in Currency Markets
#953 – Stock Momentum Volatility Switching Strategy
#954 – Directional Momentum
