Long-short Strategy Research Articles

Cross-sectional interactions in cryptocurrency returns

We investigate interaction effects in cryptocurrency markets by constructing and evaluating double-sorted portfolios based on 40 different characteristics. Using a dataset of over 500 major coins and tokens from 2017 to 2023, we identify numerous significant interactions. The most pronounced effects arise from the interplay of liquidity, risk, and past return measures. An out-of-sample long-short strategy that selects the top and bottom interactions achieves a Sharpe ratio exceeding 1. However, network graph analysis and additional tests reveal that low liquidity, which raises transaction costs, can dampen trading activity and contribute to the persistence of these anomalies.

The return predictability of technological links: evidence from the Chinese STAR Market

ABSTRACT This paper examines the predictive power of technology-linked returns in the Chinese Science and Technology Innovation Market (STAR Market). Using a sample from August 2019 to June 2023, we construct technology-linked returns based on patent portfolio similarities among firms. Our findings indicate that these returns significantly predict future stock returns, with a long-short strategy yielding weekly excess returns between 0.50% and 0.53%. Analysis of trade sizes reveals that institutional investors are more likely to drive this predictability, as the direction of large trades aligns with technology-linked returns. This study explores how technology-peer information affects stock returns in an emerging, technology-intensive market, highlighting the significant role of traders with large capital in leveraging technology-peer information.

Synchronous social media and the stock market

I examine stock discussions from real-time (synchronous) group chats on Discord and compare them with forum-style (asynchronous) postings on Reddit’s WallStreetBets. Findings suggest that popular stocks on Discord include fewer ”meme” stocks and are more diverse and profitable. A Discord-based long-short strategy outperforms several comparable strategies. Discord-based popularity predicts future trading volume, volatility, and returns, and this effect is stronger for smaller stocks. By contrast, popularity on WallStreetBets or on both platforms correlates with lower returns. Results suggest that academicians and practitioners should consider social interaction settings when evaluating the impact of social media on investment decisions.

Profitability trend prediction in crypto financial markets using Fibonacci technical indicator and hybrid CNN model

Cryptocurrency has become a popular trading asset due to its security, anonymity, and decentralization. However, predicting the direction of the financial market can be challenging, leading to difficult financial decisions and potential losses. The purpose of this study is to gain insights into the impact of Fibonacci technical indicator (TI) and multi-class classification based on trend direction and price-strength (trend-strength) to improve the performance and profitability of artificial intelligence (AI) models, particularly hybrid convolutional neural network (CNN) incorporating long short-term memory (LSTM), and to modify it to reduce its complexity. The main contribution of this paper lies in its introduction of Fibonacci TI, demonstrating its impact on financial prediction, and incorporation of a multi-classification technique focusing on trend strength, thereby enhancing the depth and accuracy of predictions. Lastly, profitability analysis sheds light on the tangible benefits of utilizing Fibonacci and multi-classification. The research methodology employed to carry out profitability analysis is based on a hybrid investment strategy—direction and strength by employing a six-stage predictive system: data collection, preprocessing, sampling, training and prediction, investment simulation, and evaluation. Empirical findings show that the Fibonacci TI has improved its performance (44% configurations) and profitability (68% configurations) of AI models. Hybrid CNNs showed most performance improvements particularly the C-LSTM model for trend (binary-0.0023) and trend-strength (4 class-0.0020) and 6 class-0.0099). Hybrid CNNs showed improved profitability, particularly in CLSTM, and performance in CLSTM mod. Trend-strength prediction showed max improvements in long strategy ROI (6.89%) and average ROIs for long-short strategy. Regarding the choice between hybrid CNNs, the C-LSTM mod is a viable option for trend-strength prediction at 4-class and 6-class due to better performance and profitability.

Combining low-volatility and momentum: recent evidence from the Nordic equities

ABSTRACT This paper investigates the profitability of combined low-volatility and momentum investment strategies in the Nordic stock markets from January 1999 to September 2022. Confirming earlier studies, our results first indicate that both the volatility and momentum effects persist as pure-play strategies. Further, we explore combined strategies using 50/50, double screening, and ranking strategies. Among the long-only portfolios, the momentum-first strategy generates the best Sharpe ratio using the double screening method−slightly outperforming the ranking method. Additionally, all long-only combination portfolios outperform the market in terms of risk-adjusted returns. Combination long-short strategies produce significantly higher risk-adjusted returns than pure-play strategies. Surprisingly, novel evidence suggests that none of the combination long-short strategies outperforms the pure momentum strategy after risk-adjusting the returns using the Fama and French five-factor model, implying that while momentum may enhance the returns from the low-volatility strategy, the reverse is not true for the Nordic stock markets.

Portfolio Sorting by Leverage Ratio and Validation in American Stock Market

The stock market has high risks. The purpose of this project is to sort five different portfolios from all stocks in S&P 500 by the leverage ratio, then perform both time-series and cross-sectional regression on each of the portfolios to find anomalies of pricing, also test whether to choose risk-premium or hedging strategy, and finally build both an out-performing strategy and a long-short strategy. Putting more stocks into the portfolio can help analysts carry out comprehensive analysis on different situations, periods, and types of investment portfolio, so as to disperse risks, in order to ensure the diversity of portfolio and a lower risk.

Sentiment trading with large language models

We analyse the performance of the large language models (LLMs) OPT, BERT, and FinBERT, alongside the traditional Loughran-McDonald dictionary, in the sentiment analysis of 965,375 U.S. financial news articles from 2010 to 2023. Our findings reveal that the GPT-3-based OPT model significantly outperforms the others, predicting stock market returns with an accuracy of 74.4%. A long-short strategy based on OPT, accounting for 10 basis points (bps) in transaction costs, yields an exceptional Sharpe ratio of 3.05. From August 2021 to July 2023, this strategy produces an impressive 355% gain, outperforming other strategies and traditional market portfolios. This underscores the transformative potential of LLMs in financial market prediction and portfolio management and the necessity of employing sophisticated language models to develop effective investment strategies based on news sentiment.

Production complementarity and information transmission across industries

Economic theory suggests that production complementarity is an important driver of sectoral co-movements and business cycle fluctuations. We operationalize this concept using a measure of production complementarity proximity (COMPL) between any two companies. We show firms from different industries but are closely aligned in COMPL exhibit strong co-movement in their operating, investing, and financing activities, as well as quarterly earnings revisions and monthly returns. We further document a lead-lag effect in their returns, such that a long-short strategy based on recent COMPL peer returns yields a monthly 6-factor alpha of 122 basis points. This inter-industry momentum spillover effect is not explained by other network-based mechanisms, such as shared analyst coverage. We conclude information transmission takes place along complementarity networks, but stock prices do not update instantaneously.

Global de-diversification and stock returns

Using international firm-level data, we show that when a multinational corporation reported zero total sales made by its foreign operations, it constitutes a salient signal of global de-diversification. A simple long-short strategy portfolio that buys stocks of multinational firms and sells stocks of ex-multinational firms that report zero foreign sales earns up to 85 basis points per month (over 10% per year). Further examination suggests that investors’ inattention and limits to arbitrage could explain this return predictability. This paper is among the first to document evidence of the impact of global de-diversification on stock returns.

A self-attention based cross-sectional return forecasting model with evidence from the Chinese market

This study introduces a novel model based on self-attention mechanisms to generate out-of-sample forecasts of cross-sectional returns. This model is designed to capture the non-linearity, heterogeneity, and interaction between stocks inherent in cross-sectional pricing problems. The empirical results from the Chinese stock market reveal compelling findings, surpassing other benchmarks in terms of out-of-sample R2. Moreover, this model demonstrates both practical applicability and robustness. These results provide valuable evidence supporting the existence of the three aforementioned properties in cross-sectional pricing problems from a theoretical standpoint, and this model offers a powerful tool for implementing profitable long-short strategies.

Loss Harvesting or Gain Deferral? A Surprising Source of Tax Benefits of Tax-Aware Long-Short Strategies

Loss Harvesting or Gain Deferral? A Surprising Source of Tax Benefits of Tax-Aware Long-Short Strategies

VIX constant maturity futures trading strategy: A walk-forward machine learning study.

This study employs seven advanced machine learning approaches to conduct numerical predictions of the next-day returns of VIX constant-maturity futures (VIX CMFs) using the term structure information derived from VIX CMFs. Based on precise numerical predictions, this study proposes a new Constrained-Mean-Variance Portfolio Optimization (C-MVO) trading strategy and tests it against a benchmark long-short trading strategy to evaluate the profitability of the machine learning numerical predictions. This study applies three unique feature sets, each incrementally incorporating the VIX CMFs' term structure features, to individually examine the predictive ability of the seven machine learning models and their backtesting performance. Over a comprehensive 11-year period, the experiment adheres to a strict walk-forward expanding-window methodology for both training and backtesting. The predictive and backtesting results show that four of the seven machine learning models attain a prediction information ratio greater than 0.02, with an average prediction information ratio of 0.037. This result suggests that the VIX CMFs term structure features have predictive power for the next-day returns of VIX CMFs. Moreover, the average C-MVO information ratio is 0.623, and the long-short strategy information ratio is 0.404. This increase in the information ratio under the C-MVO strategy validates the effectiveness of the machine learning models and the C-MVO strategy.

Business aspects in focus, investor underreaction and return predictability

Overlap in business aspects serves as a proxy for firm relatedness. Employing an unsupervised topic modelling methodology from machine learning, we characterize the attention allocations of earnings conference call participants (corporate executives, financial analysts, and investors) over the topics discussed. We construct a novel topic similarity measure that captures incremental, difficult-to-observe, and time-varying firm relatedness. However, valuable information from topic peers is not incorporated into stock price in a timely fashion. A long-short strategy based on the returns of topic peers yields a monthly alpha of approximately 69 basis points. Furthermore, return predictability stems primarily from similar business models, customer management, and influential macroeconomic situations. Return predictability is more pronounced among focal firms with higher information complexities and arbitrage costs. Overall, this study provides a novel approach to automatically summarise firms' business aspects in focus and highlights the asset pricing implications of investors' underreactions to non-obvious and dynamic firm relatedness hidden in earnings conference calls.

Can a machine learn from behavioral biases? Evidence from stock return predictability of deep learning models

We examine how the return predictability of deep learning models varies with stocks’ vulnerability to investors’ behavioral biases. Using an extensive list of anomaly variables, we find that the long-short strategy of buying (shorting) stocks with high (low) deep learning signals generates greater returns for stocks more vulnerable to behavioral biases, i.e., small, young, unprofitable, volatile, non-dividend-paying, close-to-default, and lottery-like stocks. This performance of deep learning models for speculative stocks becomes pronounced when investor sentiment is high, and when new information is delivered through earnings announcements. Moreover, our nonlinear deep learning signals are negatively associated with analysts’ earnings forecast error especially for speculative stocks, implying that analysts’ forecasts are too low for speculative stocks with high deep learning signals. These results suggest that deep learning models with nonlinear structures are useful for capturing mispricing induced by behavioral biases.

Expert aggregation for financial forecasting

Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest. But choosing between several algorithms can be challenging, as their estimation accuracy may be unstable over time. Online aggregation of experts combine the forecasts of a finite set of models in a single approach without making any assumption about the models. In this paper, a Bernstein Online Aggregation (BOA) procedure is applied to the construction of long-short strategies built from individual stock return forecasts coming from different machine learning models. The online mixture of experts leads to attractive portfolio performances even in non-stationary environments. The inclusion of neural networks experts in the aggregation contributes to a better average return, while Ordinary Least Squares with Huber Loss experts contribute to lower risk. The aggregation outperforms individual algorithms, offering a higher portfolio Sharpe ratio, lower shortfall, with a similar turnover. Extensions to expert and aggregation specialisations are also proposed to improve the overall mixture on a family of portfolio evaluation metrics.

Portfolio allocation and optimization with carbon offsets: Is it worth the while?

We explore whether the integration of carbon offsets into investment portfolios improves performance. Using compliance and voluntary carbon offsets from around the world, our results show that investment strategies that include such offsets broadly achieve higher Sharpe Ratios than the diversified benchmark, with the long-short strategy performing best. We find that compliance and voluntary carbon offsets are mostly net volatility and return spillover recipients, consistent from a macro-perspective. These results, which are documented for the first time in the literature provide new empirical evidence on integrating carbon offsets into portfolios and may attract new financial and retail investors to carbon markets.

Digesting FOREXS: Information Transmission Across Asset Classes and Return Predictability

We provide novel evidence that equity investors react to currency shocks with a delay. Using the cross-section of currency returns and the relative presence of U.S. firms in foreign economies, we compute a foreign operations-related exchange shock (FOREXS) measure. We find FOREXS to predict firms’ future cash flows and stock returns, driving much of the previously documented underreaction to foreign information. An FOREXS-based long-short strategy yields a 6.74% annualized abnormal return. FOREXS predictive power comes from firms’ incomplete hedging and investors’ limited attention, highlighting the challenges involved when processing information from a different asset class. This paper was accepted by David Sraer, finance. Funding: Z. Da acknowledges financial support from the Beijing Outstanding Young Scientist Program [Grant BJJWZYJH01201910034034] and the 111 Project [Grant B20094]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4778 .

Industry return lead-lag relationships between the US and other major countries

In this study, we analyze the lead-lag relationships between the US industry index and those of six other major countries from January 1973 to May 2021. We identify the leading role played by the US internationally by showing that the weekly returns of US industries, especially the US basic materials and energy industries, significantly Granger cause the returns of most other countries’ industries, suggesting that non-US industries react with some delay to new information. This delayed reaction is even more noticeable during periods of recession in the US when cross-country correlations are higher. This implies that the ability of the lagged returns of US industries to predict industries’ returns from other countries is even more pronounced when the US experienced an economic recession. A similar asymmetric relationship is found between the volatility of US industries and that of industries in other markets. The analysis of causality in the distribution of returns and volatility shows that causality runs mainly from the US to other countries, particularly in the presence of extreme negative shocks. Finally, we demonstrate that our predictions are valuable to real-world investors. Long-short strategies generate sizable and statistically significant alphas, and a constant relative risk-averse investor obtains certainty equivalent returns well above the risk-free rate.

Loss Harvesting or Gain Deferral? A Surprising Source of Tax Benefits of Tax-Aware Long-Short Strategies

Loss Harvesting or Gain Deferral? A Surprising Source of Tax Benefits of Tax-Aware Long-Short Strategies

Portfolio: A command for conducting portfolio analysis in Stata

Portfolio analysis is widely used in empirical asset pricing to explore the cross-sectional relation between two or more variables. In this article, we introduce the methodology of portfolio analysis and describe a new command, portfolio, that provides a one-step solution for portfolio analysis. portfolio calculates the equal- or value-weighted returns with a t statistic for the portfolio and tests the significance of a long-short strategy in portfolios. portfolio also provides the Newey–West standard-error adjustment option for alleviating the impact of potential autocorrelation and heteroskedasticity in financial time series.