Return Predictability Research Articles

Binary response models for heterogeneous panel data with interactive fixed effects

In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross-sectional dimension and the temporal dimension to diverge. From a practical point of view, the proposed framework can be applied to predict the probability of corporate failure, conduct credit rating analysis, etc. Theoretically and methodologically, we build a link between a maximum likelihood estimation and a least squares approach, provide a simple information criterion to detect the number of factors, and establish the corresponding asymptotic theory. In addition, we conduct intensive simulations to examine the theoretical findings. In an empirical study, we focus on the sign prediction of stock returns, and then use the results of sign forecast to conduct portfolio analysis.

Adverse selection in cryptocurrency markets

AbstractIn this article we investigate the influence that information asymmetry may have on future volatility, liquidity, market toxicity, and returns within cryptocurrency markets. We use the adverse‐selection component of the effective spread as a proxy for overall information asymmetry. Using order and trade data from the Bitfinex exchange, we first document statistically significant adverse‐selection costs for major cryptocurrencies. Also, our results suggest that adverse‐selection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverse‐selection risk on transaction costs in cryptocurrency markets. Finally, we document that adverse‐selection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.

Cryptocurrency return predictability: What is the role of the environment?

This paper investigates the lead-lag relationship across the returns of four cryptocurrencies - Bitcoin, Ethereum, Tether and TrueUSD. It identifies the important role of the environment in forecasting the cryptocurrency returns. The study compares the predictive ability of environmental variables such as temperature anomalies and CO2 emissions with that of commodities i.e. Gold and WTI returns. To the best of our knowledge, our study is the first attempt to investigate the leading role of the environment in predicting cryptocurrency returns. Most studies in the literature examine the environmental cost of Bitcoin, but none has considered the possible causal effect of the environment on Bitcoin returns. Our results show evidence of limited predictive power of lagged commodity returns, while lagged CO2 emissions significantly predict the returns of volatile cryptocurrencies, i.e. Bitcoin and Ethereum. On the other hand, environmental variables do not show any predictive power in the returns of stable coins (Tether and TrueUSD). Our findings highlight the increased awareness regarding the degradation of the environment and rapid climate change. Our results have important policy implications.

Proposing a new loan recommendation framework for loan allocation strategies in online P2P lending

PurposeLenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns with risk limitations or lowering risks with expected returns for P2P lenders.Design/methodology/approachThis paper used data from a leading online P2P lending platform in America. First, the authors constructed a logistic regression-based credit scoring model and a linear regression-based profit scoring model to predict the default probabilities and profitability of loans. Second, based on the predictions of loan risk and loan return, the authors constructed linear programming model to form the optimal loan portfolio for lenders.FindingsThe research results show that compared to a logistic regression-based credit scoring method, the proposed new framework could make more returns for lenders with risks unchanged. Furthermore, compared to a linear regression-based profit scoring method, the proposed new framework could lower risks for lenders without lowering returns. In addition, comparisons with advanced machine learning techniques further validate its superiority.Originality/valueUnlike previous studies that focus solely on predicting the default probability or profitability of loans, this study considers loan allocation in online P2P lending as an optimization research problem using a new framework based upon modern portfolio theory (MPT). This study may contribute theoretically to the extension of MPT in the specific context of online P2P lending and benefit lenders and platforms to develop more efficient investment tools.

EX ANTE PREDICTABILITY OF STOCK RETURNS IN A FRONTIER MARKET

This study reports results on the ex ante predictability of stock returns using real-time stock market data in Vietnam, a frontier market, from June 2008 to June 2021. Countries classified as a frontier market are often known for currency manipulation, financial market illiquidity, and political instability. Despite the enormous risk usually posed by these inefficiencies, potential profits are large and achievable for many investors. This study provides evidence on existing a strategy to form out-of-sample long portfolios that generate statistically significant and positive mean monthly returns even in the presence of transaction costs. I also justify the magnitude of these returns by showing that they exceed those of VnIndex and MSCI Vietnam Index. The results reject the hypothesis that the stock prices in Vietnamese market follow random walks, thus oppose the stock market efficiency hypothesis. Evidence found in this study provides a better understanding of informational efficiency in a frontier equity market setting. Specifically, there are several implications on portfolio selection strategies, stock price patterns, and trading behavior bias related to Vietnamese stock market can be drawn from this study.

A moving-window bayesian network model for assessing systemic risk in financial markets.

Systemic risk refers to the uncertainty that arises due to the breakdown of a financial system. The concept of "too connected to fail" suggests that network connectedness plays an important role in measuring systemic risk. In this paper, we first recover a time series of Bayesian networks for stock returns, which allow the direction of links among stock returns to be formed with Markov properties in directed graphs. We rank the stocks in the time series of Bayesian networks based on the topological orders of the stocks in the learned Bayesian networks and develop an order distance, a new measure with which to assess the changes in the topological orders of the stocks. In an empirical study using stock data from the Hang Seng Index in Hong Kong and the Dow Jones Industrial Average, we use the order distance to predict the extreme absolute return, which is a proxy of extreme market risks, or a signal of systemic risks, using the LASSO regression model. Our results indicate that the network statistics of the time series of Bayesian networks and the order distance substantially improve the predictability of extreme absolute returns and provide insights into the assessment of systemic risk.

Long-term adjusted volatility: Powerful capability in forecasting stock market returns

We develop the long-term adjusted volatility (LV_ADJ) by removing the interference information of short-term volatility from the simple long-term volatility and examine the role of LV_ADJ in the predictability of stock market returns. Using a sample from January 2000 to December 2019 and considering 19 popular predictors, LV_ADJ positively predicts the next-month returns of S&P 500 and the univariate model with LV_ADJ has the best forecasting performance with adjusted in-sample r-squared of 3.825%, out-of-sample r-squared of 3.356%, return gains of 5.976%, CER gains of 4.708 and Sharpe ratio gains of 0.394. The predictive information of LV_ADJ is independent of that obtained from the 19 popular predictors. Furthermore, we find that LV_ADJ also has predictive power for long-term (3–12 months) stock returns, and can forecast returns of industry portfolios and characteristic portfolios.

On the right jump tail inferred from the VIX market

This paper addresses the role of the right jump tail under the risk-neutral measure, as a proxy for fear-of-fear, in the return predictability implicit in the VIX market. A simulation establishes that the right jump tail dominates the left jump tail in explaining various risk measures and their associated term structures. Using VIX futures and options from 2006 until 2020, the superior predictive power for futures returns afforded by the variance-of-variance risk premium (VVRP) is shown to arise predominantly from the right jump tail risk. A separate consideration of the continuous and jump tail components of the VVRP outperforms the alternative models in an out-of-sample forecasting exercise and generates non-trivial economic value, especially over short horizons. However, the impact of right jump tail is weak on option returns and only evident for short maturities, suggesting that the fear component cannot be the sole factor explaining the observed losses incurred on the delta-hedged VIX options.

Cross-Section of Returns, Predictors Credibility, and Method Issues

The paper focuses on the relationship between firms’ characteristics and cross-section returns. The author reviews and critically assesses the most recent contributions in the literature. After comparing the abnormal returns (Alpha) and t statistics of the original works with those of replication works, the author concludes that 94 characteristics are robust. The limitation of the paper is that measurement errors in the COMPUSTAT could affect the predictability of cross-section returns. The practical implication of the paper is that the author validates the practice of fundamental analysis. Investors could benefit from those discovered characteristics. The author validates the policy consequence and connects the theoretical frameworks with empirical results. The author evaluates the empirical methodology and proposes several methods to improve future research.

Impact of Change in Promoters’ Shareholding Pattern on the Performance of Small-Cap-Value Equity Stocks in the National Stock Exchange of India

This paper studies the impact of a change in promoter shareholding on small-cap-value stocks. NIFTY Small Cap 250 index stocks within the top 20th percentile of the book-to-market (B/M) ratio of the same universe have been considered for this study. The paper uses regression analysis for understanding the impact of independent variables on returns. The universe is further narrowed down to stocks with a positive change in promoter shareholding, which is found to be negatively related to stock returns. In addition, although the book-to-market ratio does not play any role in the prediction of returns while within this narrowed-down universe, the size effect is present. The results are discussed with reference to some relevant past research literature, and the scope for further research is also discussed.

Where prices are not lazy: Evidence from REITs and the financial sector

This study investigates information processing and return predictability from reporting textual uniqueness. Latest research describes a positive link between changes in financial reports and future returns for corporates in general. In this context, we exploit the unique homogeneity of U.S. Real Estate Investment Trusts (REITs) and find no significant impact of textual uniqueness on future returns, suggesting efficient processing of financial information in this environment. The effect of reporting textual uniqueness for firms from the financial sector is, albeit small, apparent. We argue that the documented strong effects cannot be readily generalized and may be dependent on the observed industry.

Estimation of a Structural Break Point in Linear Regression Models

This study proposes a point estimator of the break location for a one-time structural break in linear regression models. If the break magnitude is small, the least-squares estimator of the break date has two modes at the ends of the finite sample period, regardless of the true break location. To solve this problem, I suggest an alternative estimator based on a modification of the least-squares objective function. The modified objective function incorporates estimation uncertainty that varies across potential break dates. The new break point estimator is consistent and has a unimodal finite sample distribution under small break magnitudes. A limit distribution is provided under an in-fill asymptotic framework. Monte Carlo simulation results suggest that the new estimator outperforms the least-squares estimator. I apply the method to estimate the break date in U.S. and U.K. stock return prediction models.

Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models

We examine the potential of ChatGPT, and other large language models, in predicting stock market returns using sentiment analysis of news headlines. We use ChatGPT to indicate whether a given headline is good, bad, or irrelevant news for firms' stock prices. We then compute a numerical score and document a positive correlation between these ChatGPT scores and subsequent daily stock market returns. Further, ChatGPT outperforms traditional sentiment analysis methods. We find that more basic models such as GPT-1, GPT-2, and BERT cannot accurately forecast returns, indicating return predictability is an emerging capacity of complex models. Our results suggest that incorporating advanced language models into the investment decision-making process can yield more accurate predictions and enhance the performance of quantitative trading strategies.

Comment on 'The Virtue of Complexity in Return Prediction'

Comment on 'The Virtue of Complexity in Return Prediction'

The adaptive market hypothesis and the return predictability in the cryptocurrency markets

This study employs robust martingale difference hypothesis tests to examine return predictability in a broad sample of the 40 most capitalized cryptocurrency markets in the context of the adaptive market hypothesis. The tests were applied to daily returns using the rolling window method in the research period from May 1, 2013 to September 30, 2022. The results of this study suggest that the returns of the majority of the examined cryptocurrencies were unpredictable most of the time. However, a great part of them also suffered some short periods of weak-form inefficiency. The results obtained validate the adaptive market hypothesis. Additionally, this study allowed the observation of some differences in return predictability between the examined cryptocurrencies. Also some historical trends in weak-form efficiency were identified. The results suggest that the predictability of cryptocurrency returns might have decreased in recent years also no significant relationship between market cap and predictability was observed.

Efeito da atenção do investidor na eficiência do mercado brasileiro de ações

ABSTRACT The objective of this article was to evaluate the relationship between investor attention, both professional and non-professional, and the efficiency of the Brazilian stock market, as measured by the predictability of daily returns. The role of investor attention in the capital market is a controversial issue, since some studies show that it is capable of inducing efficiency, while others point out that it essentially contributes to greater volatility due to behavioral biases. This study contributes to the behavioral finance literature by pioneering the analysis of the effect of investor attention on Brazilian stock prices. Moreover, we found no previous studies that compare the effect of investor attention on market efficiency across investor classes, as measured by access to different financial information providers. The analysis of investor attention, as measured by the volume of searches on the internet, is relevant given the growing abundance of data and digital inclusion. The evidence of an effect of attention on market efficiency contributes to the critique of this classical hypothesis. The methodology consisted of estimating autoregressive models using daily data from 2018 to 2021 on search volume, returns, and transaction volume for Brazilian stocks, including the interaction between returns and search volume, to assess the influence of attention on price dynamics. In this work, we find that in the Brazilian stock market, investor attention contributes to greater market efficiency, as measured by lower predictability of returns, but the most pronounced effect comes from the attention of non-professional investors. Understanding how attention affects the incorporation of information into prices contributes to a critique of the market efficiency hypothesis, as well as allowing for profits to be made by exploiting opportunities based on the level of attention of a particular class of investors.

Volatility Dynamics, Return Predictability, and Leverage in the Campbell-Cochrane Model

Volatility Dynamics, Return Predictability, and Leverage in the Campbell-Cochrane Model

ETF Shorting Activity, Return Predictability and Information Efficiency of Underlying Securities

ETF Shorting Activity, Return Predictability and Information Efficiency of Underlying Securities

AI Democratization, Return Predictability, and Trading Inequality

AI Democratization, Return Predictability, and Trading Inequality

From Risk Parity to Outcome Risk Parity: A Review and Extension of the Risk Parity Portfolio with Return Predictability

From Risk Parity to Outcome Risk Parity: A Review and Extension of the Risk Parity Portfolio with Return Predictability